Oops! This page doesn't exist. Try going back to our home page.
You can learn how to make a 404 page like this in Custom 404 Pages.
Oops! This page doesn't exist. Try going back to our home page.
You can learn how to make a 404 page like this in Custom 404 Pages.
Goldydocs is a sample site using the Docsy Hugo theme that shows what it can do and provides you with a template site structure. It’s designed for you to clone and edit as much as you like. See the different sections of the documentation and site for more ideas.
Goldydocs is a sample site using the Docsy Hugo theme that shows what it can do and provides you with a template site structure. It’s designed for you to clone and edit as much as you like. See the different sections of the documentation and site for more ideas.
Goldydocs is a sample site using the Docsy Hugo theme that shows what it can do and provides you with a template site structure. It’s designed for you to clone and edit as much as you like. See the different sections of the documentation and site for more ideas.
Goldydocs is a sample site using the Docsy Hugo theme that shows what it can do and provides you with a template site structure. It’s designed for you to clone and edit as much as you like. See the different sections of the documentation and site for more ideas.
Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
Inline code inside table cells should still be distinguishable.
| Language | Code |
|---|---|
| Javascript | var foo = "bar"; |
| Ruby | foo = "bar"{ |
Small images should be shown at their actual size.
Large images should always scale down and fit in the content container.
Add some sections here to see how the ToC looks like. Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
This is the final element on the page and there should be no margin below this.
-The image will be rendered at the size and byline specified in the front matter.
+The image will be rendered at the size and byline specified in the front matter.
diff --git a/blog/2018/10/06/the-second-blog-post/index.html b/blog/2018/10/06/the-second-blog-post/index.html index 7d89aafc3..6cea86c4c 100644 --- a/blog/2018/10/06/the-second-blog-post/index.html +++ b/blog/2018/10/06/the-second-blog-post/index.html @@ -29,7 +29,7 @@ }Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
Inline code inside table cells should still be distinguishable.
| Language | Code |
|---|---|
| Javascript | var foo = "bar"; |
| Ruby | foo = "bar"{ |
Small images should be shown at their actual size.
Large images should always scale down and fit in the content container.
Add some sections here to see how the ToC looks like. Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
This is the final element on the page and there should be no margin below this.
-Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
Inline code inside table cells should still be distinguishable.
| Language | Code |
|---|---|
| Javascript | var foo = "bar"; |
| Ruby | foo = "bar"{ |
Small images should be shown at their actual size.
Large images should always scale down and fit in the content container.
Add some sections here to see how the ToC looks like. Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
This is the final element on the page and there should be no margin below this.
-Saturday, October 06, 2018 in News
This is a typical blog post that includes images. The front matter specifies the date of the blog post, its title, a short description that will be displayed on the blog landing page, and its author. Including images Here’s an image …
Thursday, January 04, 2018 in Releases
Text can be bold, italic, or strikethrough. Links should be blue with no underlines (unless hovered over). -There should be whitespace between paragraphs. There should be whitespace between paragraphs. There should be whitespace between paragraphs. …
Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
Inline code inside table cells should still be distinguishable.
| Language | Code |
|---|---|
| Javascript | var foo = "bar"; |
| Ruby | foo = "bar"{ |
Small images should be shown at their actual size.
Large images should always scale down and fit in the content container.
Add some sections here to see how the ToC looks like. Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
This is the final element on the page and there should be no margin below this.
-Saturday, October 06, 2018 in News
Text can be bold, italic, or strikethrough. Links should be blue with no underlines (unless hovered over). There should be whitespace between paragraphs. There should be whitespace between paragraphs. There should be whitespace between paragraphs. …
Saturday, October 06, 2018 in News
This is a typical blog post that includes images. The front matter specifies the date of the blog post, its title, a short description that will be displayed on the blog landing page, and its author. -Including images Here’s an image …
Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
Inline code inside table cells should still be distinguishable.
| Language | Code |
|---|---|
| Javascript | var foo = "bar"; |
| Ruby | foo = "bar"{ |
Small images should be shown at their actual size.
Large images should always scale down and fit in the content container.
Add some sections here to see how the ToC looks like. Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
This is the final element on the page and there should be no margin below this.
-Thursday, January 04, 2018 in Releases
Text can be bold, italic, or strikethrough. Links should be blue with no underlines (unless hovered over). -There should be whitespace between paragraphs. There should be whitespace between paragraphs. There should be whitespace between paragraphs. …
Oops! This page doesn't exist. Try going back to our home page.
You can learn how to make a 404 page like this in Custom 404 Pages.
Oops! This page doesn't exist. Try going back to our home page.
You can learn how to make a 404 page like this in Custom 404 Pages.
Goldydocs is a sample site using the Docsy Hugo theme that shows what it can do and provides you with a template site structure. It’s designed for you to clone and edit as much as you like. See the different sections of the documentation and site for more ideas.
Goldydocs is a sample site using the Docsy Hugo theme that shows what it can do and provides you with a template site structure. It’s designed for you to clone and edit as much as you like. See the different sections of the documentation and site for more ideas.
Goldydocs is a sample site using the Docsy Hugo theme that shows what it can do and provides you with a template site structure. It’s designed for you to clone and edit as much as you like. See the different sections of the documentation and site for more ideas.
Goldydocs is a sample site using the Docsy Hugo theme that shows what it can do and provides you with a template site structure. It’s designed for you to clone and edit as much as you like. See the different sections of the documentation and site for more ideas.
Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
Inline code inside table cells should still be distinguishable.
| Language | Code |
|---|---|
| Javascript | var foo = "bar"; |
| Ruby | foo = "bar"{ |
Small images should be shown at their actual size.
Large images should always scale down and fit in the content container.
Add some sections here to see how the ToC looks like. Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
This is the final element on the page and there should be no margin below this.
-The image will be rendered at the size and byline specified in the front matter.
+The image will be rendered at the size and byline specified in the front matter.
diff --git a/cn/blog/2018/10/06/the-second-blog-post/index.html b/cn/blog/2018/10/06/the-second-blog-post/index.html index 79f53e223..293a533e9 100644 --- a/cn/blog/2018/10/06/the-second-blog-post/index.html +++ b/cn/blog/2018/10/06/the-second-blog-post/index.html @@ -29,7 +29,7 @@ }Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
Inline code inside table cells should still be distinguishable.
| Language | Code |
|---|---|
| Javascript | var foo = "bar"; |
| Ruby | foo = "bar"{ |
Small images should be shown at their actual size.
Large images should always scale down and fit in the content container.
Add some sections here to see how the ToC looks like. Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
This is the final element on the page and there should be no margin below this.
-Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
Inline code inside table cells should still be distinguishable.
| Language | Code |
|---|---|
| Javascript | var foo = "bar"; |
| Ruby | foo = "bar"{ |
Small images should be shown at their actual size.
Large images should always scale down and fit in the content container.
Add some sections here to see how the ToC looks like. Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
This is the final element on the page and there should be no margin below this.
-Saturday, October 06, 2018 in News
This is a typical blog post that includes images. The front matter specifies the date of the blog post, its title, a short description that will be displayed on the blog landing page, and its author. Including images Here’s an image …
Thursday, January 04, 2018 in Releases
Text can be bold, italic, or strikethrough. Links should be blue with no underlines (unless hovered over). -There should be whitespace between paragraphs. There should be whitespace between paragraphs. There should be whitespace between paragraphs. …
Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
Inline code inside table cells should still be distinguishable.
| Language | Code |
|---|---|
| Javascript | var foo = "bar"; |
| Ruby | foo = "bar"{ |
Small images should be shown at their actual size.
Large images should always scale down and fit in the content container.
Add some sections here to see how the ToC looks like. Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
This is the final element on the page and there should be no margin below this.
-Saturday, October 06, 2018 in News
Text can be bold, italic, or strikethrough. Links should be blue with no underlines (unless hovered over). There should be whitespace between paragraphs. There should be whitespace between paragraphs. There should be whitespace between paragraphs. …
Saturday, October 06, 2018 in News
This is a typical blog post that includes images. The front matter specifies the date of the blog post, its title, a short description that will be displayed on the blog landing page, and its author. -Including images Here’s an image …
Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
Inline code inside table cells should still be distinguishable.
| Language | Code |
|---|---|
| Javascript | var foo = "bar"; |
| Ruby | foo = "bar"{ |
Small images should be shown at their actual size.
Large images should always scale down and fit in the content container.
Add some sections here to see how the ToC looks like. Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
Bacon ipsum dolor sit amet t-bone doner shank drumstick, pork belly porchetta chuck sausage brisket ham hock rump pig. Chuck kielbasa leberkas, pork bresaola ham hock filet mignon cow shoulder short ribs biltong.
This is the final element on the page and there should be no margin below this.
-Thursday, January 04, 2018 in Releases
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HugeGraph is an open source project that anyone in the community can use, improve, and enjoy. We'd love you to join us! Here's a few ways to find out what's happening and get involved.
Using or want to use HugeGraph? Find out more here:
If you want to get more involved by contributing to HugeGraph, join us here:
You can find out how to contribute to these docs in our Contribution Guidelines.
HugeGraph is an open source project that anyone in the community can use, improve, and enjoy. We'd love you to join us! Here's a few ways to find out what's happening and get involved.
Using or want to use HugeGraph? Find out more here:
If you want to get more involved by contributing to HugeGraph, join us here:
You can find out how to contribute to these docs in our Contribution Guidelines.
HugeGraph is an open source project that anyone in the community can use, improve, and enjoy. We'd love you to join us! Here's a few ways to find out what's happening and get involved.
Using or want to use HugeGraph? Find out more here:
If you want to get more involved by contributing to HugeGraph, join us here:
You can find out how to contribute to these docs in our Contribution Guidelines.
HugeGraph is an open source project that anyone in the community can use, improve, and enjoy. We'd love you to join us! Here's a few ways to find out what's happening and get involved.
Using or want to use HugeGraph? Find out more here:
If you want to get more involved by contributing to HugeGraph, join us here:
You can find out how to contribute to these docs in our Contribution Guidelines.
推荐使用HugeGraph-Studio 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。
HugeGraph 目前支持 Gremlin 的语法，用户可以通过 Gremlin / REST-API 实现各种查询需求。
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
| com-lj.ungraph.txt | 3997961 | 34681189 | 479MB |
HugeGraph版本：0.5.6，RestServer和Gremlin Server和backends都在同一台服务器上
Titan版本：0.5.4, 使用thrift+Cassandra模式
Neo4j版本：2.0.1
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.629 | 5.711 | 5.243 | 67.033 |
| Titan | 10.15 | 108.569 | 150.266 | 1217.944 |
| Neo4j | 3.884 | 18.938 | 24.890 | 281.537 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) | com-lj.ungraph(400w) |
|---|---|---|---|---|
| HugeGraph | 4.072 | 45.118 | 66.006 | 609.083 |
| Titan | 8.084 | 92.507 | 184.543 | 1099.371 |
| Neo4j | 2.424 | 10.537 | 11.609 | 106.919 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 1.540 | 10.764 | 11.243 | 151.271 |
| Titan | 7.361 | 93.344 | 169.218 | 1085.235 |
| Neo4j | 1.673 | 4.775 | 4.284 | 40.507 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.494 | 0.103 | 3.364 | 8.155 |
| Titan | 11.818 | 0.239 | 377.709 | 575.678 |
| Neo4j | 1.719 | 1.800 | 1.956 | 8.530 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| HugeGraph(core) | 20.804 | 242.099 | 744.780 | 1700.547 |
| Titan | 45.790 | 820.633 | 2652.235 | 9568.623 |
| Neo4j | 5.913 | 50.267 | 142.354 | 460.880 |
说明
HugeGraph API性能测试主要测试HugeGraph-Server对RESTful API请求的并发处理能力，包括：
HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考：
之前的版本只提供HugeGraph所支持的后端种类中性能最好的API性能测试，从0.5.6版本开始，分别提供了单机和集群的性能情况
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| 机器编号 | CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|---|
| 1 | 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps | 1.4T HDD |
| 2 | 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
1. CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）
2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）
3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）
不断提升并发量，测试server仍能正常提供服务的压力上限
起压和被压机器配置相同，基本参数如下：
| CPU | Memory | 网卡 |
|---|---|---|
| 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps |
测试工具：apache-Jmeter-2.5.1
batch_size_warn_threshold_in_kb: 1000
batch_size_fail_threshold_in_kb: 1000
-注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用HugeGraph-Loader工具持续、高速导入图数据
测试均采用网址数据的边数据
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 9.516 | 88.123 | 111.586 |
| RocksDB | 2.345 | 14.076 | 16.636 |
| Cassandra | 11.930 | 108.709 | 101.959 |
| Memory | 3.077 | 15.204 | 13.841 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) |
|---|---|---|---|
| Titan | 7.724 | 70.935 | 128.884 |
| RocksDB | 8.876 | 65.852 | 63.388 |
| Cassandra | 13.125 | 126.959 | 102.580 |
| Memory | 22.309 | 207.411 | 165.609 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 7.119 | 63.353 | 115.633 |
| RocksDB | 6.032 | 64.526 | 52.721 |
| Cassandra | 9.410 | 102.766 | 94.197 |
| Memory | 12.340 | 195.444 | 140.89 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 11.333 | 0.313 | 376.06 |
| RocksDB | 44.391 | 2.221 | 268.792 |
| Cassandra | 39.845 | 3.337 | 331.113 |
| Memory | 35.638 | 2.059 | 388.987 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| Titan | 45.943 | 849.168 | 2737.117 | 9791.46 |
| Memory(core) | 41.077 | 1825.905 | * | * |
| Cassandra（core） | 39.783 | 862.744 | 2423.136 | 6564.191 |
| RocksDB（core） | 33.383 | 199.894 | 763.869 | 1677.813 |
说明
无
0.2版实现了图数据库基本功能，提供如下功能：
增加顶点
删除顶点
增加顶点属性
删除顶点属性（必须为非索引列）
批量插入顶点
查询
批量查询
顶点ID策略
属性类型支持
支持单值属性
支持多值属性：List、Set（注意：非嵌套属性）
列出所有元数据、图数据（支持Limit，不支持分页）
根据ID查询元数据、图数据
根据指定属性的值查询图数据
根据指定属性的值范围查询图数据（属性必须为数字类型）
根据指定顶点/边类型、指定属性的值查询顶点/边
根据指定顶点/边类型、指定属性的值范围查询顶点（属性必须为数字类型）
根据顶点类型（Vertex Label）查询顶点
根据边类型（Edge Label）查询边
根据顶点查询边
标准Gremlin遍历
更多细节详见API文档
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注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用HugeGraph-Loader工具持续、高速导入图数据
测试均采用网址数据的边数据
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 9.516 | 88.123 | 111.586 |
| RocksDB | 2.345 | 14.076 | 16.636 |
| Cassandra | 11.930 | 108.709 | 101.959 |
| Memory | 3.077 | 15.204 | 13.841 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) |
|---|---|---|---|
| Titan | 7.724 | 70.935 | 128.884 |
| RocksDB | 8.876 | 65.852 | 63.388 |
| Cassandra | 13.125 | 126.959 | 102.580 |
| Memory | 22.309 | 207.411 | 165.609 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 7.119 | 63.353 | 115.633 |
| RocksDB | 6.032 | 64.526 | 52.721 |
| Cassandra | 9.410 | 102.766 | 94.197 |
| Memory | 12.340 | 195.444 | 140.89 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 11.333 | 0.313 | 376.06 |
| RocksDB | 44.391 | 2.221 | 268.792 |
| Cassandra | 39.845 | 3.337 | 331.113 |
| Memory | 35.638 | 2.059 | 388.987 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| Titan | 45.943 | 849.168 | 2737.117 | 9791.46 |
| Memory(core) | 41.077 | 1825.905 | * | * |
| Cassandra（core） | 39.783 | 862.744 | 2423.136 | 6564.191 |
| RocksDB（core） | 33.383 | 199.894 | 763.869 | 1677.813 |
说明
无
0.2版实现了图数据库基本功能，提供如下功能：
增加顶点
删除顶点
增加顶点属性
删除顶点属性（必须为非索引列）
批量插入顶点
查询
批量查询
顶点ID策略
属性类型支持
支持单值属性
支持多值属性：List、Set（注意：非嵌套属性）
列出所有元数据、图数据（支持Limit，不支持分页）
根据ID查询元数据、图数据
根据指定属性的值查询图数据
根据指定属性的值范围查询图数据（属性必须为数字类型）
根据指定顶点/边类型、指定属性的值查询顶点/边
根据指定顶点/边类型、指定属性的值范围查询顶点（属性必须为数字类型）
根据顶点类型（Vertex Label）查询顶点
根据边类型（Edge Label）查询边
根据顶点查询边
标准Gremlin遍历
更多细节详见API文档
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Upon such grant of rights to Us, We immediately grant to You a worldwide, royalty-free, non-exclusive, perpetual and irrevocable license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:
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THE CONTRIBUTION IS PROVIDED “AS IS”. MORE PARTICULARLY, ALL EXPRESS OR IMPLIED WARRANTIES INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTY OF SATISFACTORY QUALITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT ARE EXPRESSLY DISCLAIMED BY YOU TO US AND BY US TO YOU. TO THE EXTENT THAT ANY SUCH WARRANTIES CANNOT BE DISCLAIMED, SUCH WARRANTY IS LIMITED IN DURATION AND EXTENT TO THE MINIMUM PERIOD AND EXTENT PERMITTED BY LAW.
TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL YOU OR WE BE LIABLE FOR ANY LOSS OF PROFITS, LOSS OF ANTICIPATED SAVINGS, LOSS OF DATA, INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL AND EXEMPLARY DAMAGES ARISING OUT OF THIS AGREEMENT REGARDLESS OF THE LEGAL OR EQUITABLE THEORY (CONTRACT, TORT OR OTHERWISE) UPON WHICH THE CLAIM IS BASED.
IF THE DISCLAIMER AND DAMAGE WAIVER MENTIONED IN SECTION 5. AND SECTION 6. CANNOT BE GIVEN LEGAL EFFECT UNDER APPLICABLE LOCAL LAW, REVIEWING COURTS SHALL APPLY LOCAL LAW THAT MOST CLOSELY APPROXIMATES AN ABSOLUTE WAIVER OF ALL CIVIL OR CONTRACTUAL LIABILITY IN CONNECTION WITH THE CONTRIBUTION.
8.1 This Agreement shall come into effect upon Your acceptance of the terms and conditions.
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9.1 This Agreement and all disputes, claims, actions, suits or other proceedings arising out of this agreement or relating in any way to it shall be governed by the laws of China excluding its private international law provisions.
9.2 This Agreement sets out the entire agreement between You and Us for Your Contributions to Us and overrides all other agreements or understandings.
9.3 In case of Your death, this agreement shall continue with Your heirs. In case of more than one heir, all heirs must exercise their rights through a commonly authorized person.
9.4 If any provision of this Agreement is found void and unenforceable, such provision will be replaced to the extent possible with a provision that comes closest to the meaning of the original provision and that is enforceable. The terms and conditions set forth in this Agreement shall apply notwithstanding any failure of essential purpose of this Agreement or any limited remedy to the maximum extent possible under law.
9.5 You agree to notify Us of any facts or circumstances of which you become aware that would make this Agreement inaccurate in any respect.
This is the multi-page printable view of this section. -Click here to print.
无
0.2版实现了图数据库基本功能，提供如下功能：
增加顶点
删除顶点
增加顶点属性
删除顶点属性（必须为非索引列）
批量插入顶点
查询
批量查询
顶点ID策略
属性类型支持
支持单值属性
支持多值属性：List、Set（注意：非嵌套属性）
列出所有元数据、图数据（支持Limit，不支持分页）
根据ID查询元数据、图数据
根据指定属性的值查询图数据
根据指定属性的值范围查询图数据（属性必须为数字类型）
根据指定顶点/边类型、指定属性的值查询顶点/边
根据指定顶点/边类型、指定属性的值范围查询顶点（属性必须为数字类型）
根据顶点类型（Vertex Label）查询顶点
根据边类型（Edge Label）查询边
根据顶点查询边
标准Gremlin遍历
更多细节详见API文档
Return to the regular view of this page.
无
0.2版实现了图数据库基本功能，提供如下功能：
增加顶点
删除顶点
增加顶点属性
删除顶点属性（必须为非索引列）
批量插入顶点
查询
批量查询
顶点ID策略
属性类型支持
支持单值属性
支持多值属性：List、Set（注意：非嵌套属性）
列出所有元数据、图数据（支持Limit，不支持分页）
根据ID查询元数据、图数据
根据指定属性的值查询图数据
根据指定属性的值范围查询图数据（属性必须为数字类型）
根据指定顶点/边类型、指定属性的值查询顶点/边
根据指定顶点/边类型、指定属性的值范围查询顶点（属性必须为数字类型）
根据顶点类型（Vertex Label）查询顶点
根据边类型（Edge Label）查询边
根据顶点查询边
标准Gremlin遍历
更多细节详见API文档
0.2版实现了图数据库基本功能，提供如下功能：
增加顶点
删除顶点
增加顶点属性
删除顶点属性（必须为非索引列）
批量插入顶点
查询
批量查询
顶点ID策略
属性类型支持
支持单值属性
支持多值属性：List、Set（注意：非嵌套属性）
列出所有元数据、图数据（支持Limit，不支持分页）
根据ID查询元数据、图数据
根据指定属性的值查询图数据
根据指定属性的值范围查询图数据（属性必须为数字类型）
根据指定顶点/边类型、指定属性的值查询顶点/边
根据指定顶点/边类型、指定属性的值范围查询顶点（属性必须为数字类型）
根据顶点类型（Vertex Label）查询顶点
根据边类型（Edge Label）查询边
根据顶点查询边
标准Gremlin遍历
更多细节详见API文档
0.2版实现了图数据库基本功能，提供如下功能：
增加顶点
删除顶点
增加顶点属性
删除顶点属性（必须为非索引列）
批量插入顶点
查询
批量查询
顶点ID策略
属性类型支持
支持单值属性
支持多值属性：List、Set（注意：非嵌套属性）
列出所有元数据、图数据（支持Limit，不支持分页）
根据ID查询元数据、图数据
根据指定属性的值查询图数据
根据指定属性的值范围查询图数据（属性必须为数字类型）
根据指定顶点/边类型、指定属性的值查询顶点/边
根据指定顶点/边类型、指定属性的值范围查询顶点（属性必须为数字类型）
根据顶点类型（Vertex Label）查询顶点
根据边类型（Edge Label）查询边
根据顶点查询边
标准Gremlin遍历
更多细节详见API文档
无
无
Thank you for your interest in contributing to HugeGraph’s all projects (“We” or “Us”).
The purpose of this contributor agreement (“Agreement”) is to clarify and document the rights granted by contributors to Us. To make this document effective, please follow the comment of GitHub CLA-Assistant when submitting a new pull request.
If You are an employee and have created the Contribution as part of your employment, You need to have Your employer approve this Agreement or sign the Entity version of this document. If You do not own the Copyright in the entire work of authorship, any other author of the Contribution should also sign this – in any event, please contact Us at hugegraph@googlegroups.com
“You” means the individual Copyright owner who Submits a Contribution to Us.
“Contribution” means any original work of authorship, including any original modifications or additions to an existing work of authorship, Submitted by You to Us, in which You own the Copyright.
“Copyright” means all rights protecting works of authorship, including copyright, moral and neighboring rights, as appropriate, for the full term of their existence.
“Material” means the software or documentation made available by Us to third parties. When this Agreement covers more than one software project, the Material means the software or documentation to which the Contribution was Submitted. After You Submit the Contribution, it may be included in the Material.
“Submit” means any act by which a Contribution is transferred to Us by You by means of tangible or intangible media, including but not limited to electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, Us, but excluding any transfer that is conspicuously marked or otherwise designated in writing by You as “Not a Contribution.”
“Documentation” means any non-software portion of a Contribution.
Subject to the terms and conditions of this Agreement, You hereby grant to Us a worldwide, royalty-free, Exclusive, perpetual and irrevocable (except as stated in Section 8.2) license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:
Moral Rights remain unaffected to the extent they are recognized and not waivable by applicable law. Notwithstanding, You may add your name to the attribution mechanism customary used in the Materials you Contribute to, such as the header of the source code files of Your Contribution, and We will respect this attribution when using Your Contribution.
Upon such grant of rights to Us, We immediately grant to You a worldwide, royalty-free, non-exclusive, perpetual and irrevocable license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:
This license back is limited to the Contribution and does not provide any rights to the Material.
Subject to the terms and conditions of this Agreement You hereby grant to Us and to recipients of Materials distributed by Us a worldwide, royalty-free, non-exclusive, perpetual and irrevocable (except as stated in Section 3.2) patent license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, to make, have made, use, sell, offer for sale, import and otherwise transfer the Contribution and the Contribution in combination with any Material (and portions of such combination). This license applies to all patents owned or controlled by You, whether already acquired or hereafter acquired, that would be infringed by making, having made, using, selling, offering for sale, importing or otherwise transferring of Your Contribution(s) alone or by combination of Your Contribution(s) with any Material.
You reserve the right to revoke the patent license stated in section 3.1 if We make any infringement claim that is targeted at your Contribution and not asserted for a Defensive Purpose. An assertion of claims of the Patents shall be considered for a “Defensive Purpose” if the claims are asserted against an entity that has filed, maintained, threatened, or voluntarily participated in a patent infringement lawsuit against Us or any of Our licensees.
We agree to (sub)license the Contribution or any Materials containing, based on or derived from your Contribution under the terms of any licenses the Free Software Foundation classifies as Free Software License and which are approved by the Open Source Initiative as Open Source licenses.
More specifically and in strict accordance with the above paragraph, we agree to (sub)license the Contribution or any Materials containing, based on or derived from the Contribution only in accordance with our licensing policy available at: http://www.apache.org/licenses/LICENSE-2.0.
In addition, We may use the following licenses for Documentation in the Contribution: GFDL-1.2 (including any right to adopt any future version of a license).
We agree to license patents owned or controlled by You only to the extent necessary to (sub)license Your Contribution(s) and the combination of Your Contribution(s) with the Material under the terms of any licenses the Free Software Foundation classifies as Free Software licenses and which are approved by the Open Source Initiative as Open Source licenses..
THE CONTRIBUTION IS PROVIDED “AS IS”. MORE PARTICULARLY, ALL EXPRESS OR IMPLIED WARRANTIES INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTY OF SATISFACTORY QUALITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT ARE EXPRESSLY DISCLAIMED BY YOU TO US AND BY US TO YOU. TO THE EXTENT THAT ANY SUCH WARRANTIES CANNOT BE DISCLAIMED, SUCH WARRANTY IS LIMITED IN DURATION AND EXTENT TO THE MINIMUM PERIOD AND EXTENT PERMITTED BY LAW.
TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL YOU OR WE BE LIABLE FOR ANY LOSS OF PROFITS, LOSS OF ANTICIPATED SAVINGS, LOSS OF DATA, INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL AND EXEMPLARY DAMAGES ARISING OUT OF THIS AGREEMENT REGARDLESS OF THE LEGAL OR EQUITABLE THEORY (CONTRACT, TORT OR OTHERWISE) UPON WHICH THE CLAIM IS BASED.
IF THE DISCLAIMER AND DAMAGE WAIVER MENTIONED IN SECTION 5. AND SECTION 6. CANNOT BE GIVEN LEGAL EFFECT UNDER APPLICABLE LOCAL LAW, REVIEWING COURTS SHALL APPLY LOCAL LAW THAT MOST CLOSELY APPROXIMATES AN ABSOLUTE WAIVER OF ALL CIVIL OR CONTRACTUAL LIABILITY IN CONNECTION WITH THE CONTRIBUTION.
8.1 This Agreement shall come into effect upon Your acceptance of the terms and conditions.
8.2 This Agreement shall apply for the term of the copyright and patents licensed here. However, You shall have the right to terminate the Agreement if We do not fulfill the obligations as set forth in Section 4. Such termination must be made in writing.
8.3 In the event of a termination of this Agreement Sections 5, 6, 7, 8 and 9 shall survive such termination and shall remain in full force thereafter. For the avoidance of doubt, Free and Open Source Software (sub)licenses that have already been granted for Contributions at the date of the termination shall remain in full force after the termination of this Agreement.
9.1 This Agreement and all disputes, claims, actions, suits or other proceedings arising out of this agreement or relating in any way to it shall be governed by the laws of China excluding its private international law provisions.
9.2 This Agreement sets out the entire agreement between You and Us for Your Contributions to Us and overrides all other agreements or understandings.
9.3 In case of Your death, this agreement shall continue with Your heirs. In case of more than one heir, all heirs must exercise their rights through a commonly authorized person.
9.4 If any provision of this Agreement is found void and unenforceable, such provision will be replaced to the extent possible with a provision that comes closest to the meaning of the original provision and that is enforceable. The terms and conditions set forth in this Agreement shall apply notwithstanding any failure of essential purpose of this Agreement or any limited remedy to the maximum extent possible under law.
9.5 You agree to notify Us of any facts or circumstances of which you become aware that would make this Agreement inaccurate in any respect.
Thank you for your interest in contributing to HugeGraph’s all projects (“We” or “Us”).
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更多关于gremlin-console的使用，请参考Tinkerpop官网
+更多关于gremlin-console的使用，请参考Tinkerpop官网
diff --git a/cn/docs/clients/gremlin-console/index.html b/cn/docs/clients/gremlin-console/index.html index 993a7a892..d47f6132c 100644 --- a/cn/docs/clients/gremlin-console/index.html +++ b/cn/docs/clients/gremlin-console/index.html @@ -248,7 +248,7 @@ gremlin> :> @script ==>6 -更多关于gremlin-console的使用，请参考Tinkerpop官网
更多关于gremlin-console的使用，请参考Tinkerpop官网
顶点是构成图的最基本元素，一个图中可以有非常多的顶点。下面给出一个添加顶点的例子：
Vertex marko = graph.addVertex(T.label, "person", "name", "marko", "age", 29);
Vertex lop = graph.addVertex(T.label, "software", "name", "lop", "lang", "java", "price", 328);
key1 -> val1, key2 -> val2 ···的顺序排列，键值对之间的顺序是自由的。T.label -> "val"，用来定义该顶点的类别，以便于程序从缓存或后端获取到该VertexLabel的schema定义，然后做后续的约束检查。例子中的label定义为person。AUTOMATIC，则不允许用户传入 id 键值对。CUSTOMIZE_STRING，则用户需要自己传入 String 类型 id 的值，键值对形如："T.id", "123456"。CUSTOMIZE_NUMBER，则用户需要自己传入 Number 类型 id 的值，键值对形如："T.id", 123456。PRIMARY_KEY，参数还必须全部包含该primaryKeys对应属性的名和值，如果不设置会抛出异常。比如之前person的primaryKeys是name，例子中就设置了name的值为marko。addVertex方法后，顶点会立刻被插入到后端存储系统中。有了点，还需要边才能构成完整的图。下面给出一个添加边的例子：
Edge knows1 = marko.addEdge("knows", vadas, "city", "Beijing");
-key1 -> val1, key2 -> val2 ···的顺序排列，设置边的属性，键值对顺序自由。注意：当frequency为multiple时必须要设置sortKeys对应属性类型的值。
简单示例见HugeGraph-Client
key1 -> val1, key2 -> val2 ···的顺序排列，设置边的属性，键值对顺序自由。注意：当frequency为multiple时必须要设置sortKeys对应属性类型的值。
简单示例见HugeGraph-Client
204
根据Label+Id删除边
通过指定Label参数和Id来删除边时，一般来说其性能比仅根据Id删除会更好。
DELETE http://localhost:8080/graphs/hugegraph/graph/edges/S1:peter>1>>S2:lop?label=person
204
-{
"task_id": 1
}
-注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
{
"task_id": 2
}
-注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/2（其中"2"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/2（其中"2"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
HugeGraph-Server通过HugeGraph-API基于HTTP协议为Client提供操作图的接口，主要包括元数据和 -图数据的增删改查，遍历算法，变量，图操作及其他操作。
{
"task_id": 1
}
-注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
{
"task_id" : 0
}
-为给定的起点在不同的层中找到最应该推荐的顶点。
为给定的起点在不同的层中找到最应该推荐的顶点。
{
"task_id": 3
}
-注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/3（其中"3"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/3（其中"3"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
{
"cancelled": true
}
-此时查询 label 为 man 的顶点数目，一定是小于 10 的。
此时查询 label 为 man 的顶点数目，一定是小于 10 的。
DELETE http://localhost:8080/graphs/hugegraph/variables/name
204
-204
根据Label+Id删除顶点
通过指定Label参数和Id来删除顶点时，一般来说其性能比仅根据Id删除会更好。
DELETE http://localhost:8080/graphs/hugegraph/graph/vertices/"1:marko"?label=person
204
-{
"task_id": 1
}
-注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
keytool -export -alias serverkey -keystore server.keystore -file server.crt
server.crt 就是服务端的证书
keytool -import -alias serverkey -file server.crt -keystore client.truststore
-client.truststore 是给客户端⽤的，其中保存着受信任的证书
| config option | default value | description |
|---|---|---|
| algorithm.message_class | org.apache.hugegraph.computer.core.config.Null | The class of message passed when compute vertex. |
| algorithm.params_class | org.apache.hugegraph.computer.core.config.Null | The class used to transfer algorithms’ parameters before algorithm been run. |
| algorithm.result_class | org.apache.hugegraph.computer.core.config.Null | The class of vertex’s value, the instance is used to store computation result for the vertex. |
| allocator.max_vertices_per_thread | 10000 | Maximum number of vertices per thread processed in each memory allocator |
| bsp.etcd_endpoints | http://localhost:2379 | The end points to access etcd. |
| bsp.log_interval | 30000 | The log interval(in ms) to print the log while waiting bsp event. |
| bsp.max_super_step | 10 | The max super step of the algorithm. |
| bsp.register_timeout | 300000 | The max timeout to wait for master and works to register. |
| bsp.wait_master_timeout | 86400000 | The max timeout(in ms) to wait for master bsp event. |
| bsp.wait_workers_timeout | 86400000 | The max timeout to wait for workers bsp event. |
| hgkv.max_data_block_size | 65536 | The max byte size of hgkv-file data block. |
| hgkv.max_file_size | 2147483648 | The max number of bytes in each hgkv-file. |
| hgkv.max_merge_files | 10 | The max number of files to merge at one time. |
| hgkv.temp_file_dir | /tmp/hgkv | This folder is used to store temporary files, temporary files will be generated during the file merging process. |
| hugegraph.name | hugegraph | The graph name to load data and write results back. |
| hugegraph.url | http://127.0.0.1:8080 | The hugegraph url to load data and write results back. |
| input.edge_direction | OUT | The data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded. |
| input.edge_freq | MULTIPLE | The frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it. |
| input.filter_class | org.apache.hugegraph.computer.core.input.filter.DefaultInputFilter | The class to create input-filter object, input-filter is used to Filter vertex edges according to user needs. |
| input.loader_schema_path | The schema path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.loader_struct_path | The struct path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.max_edges_in_one_vertex | 200 | The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit. |
| input.source_type | hugegraph-server | The source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’. |
| input.split_fetch_timeout | 300 | The timeout in seconds to fetch input splits |
| input.split_max_splits | 10000000 | The maximum number of input splits |
| input.split_page_size | 500 | The page size for streamed load input split data |
| input.split_size | 1048576 | The input split size in bytes |
| job.id | local_0001 | The job id on Yarn cluster or K8s cluster. |
| job.partitions_count | 1 | The partitions count for computing one graph algorithm job. |
| job.partitions_thread_nums | 4 | The number of threads for partition parallel compute. |
| job.workers_count | 1 | The workers count for computing one graph algorithm job. |
| master.computation_class | org.apache.hugegraph.computer.core.master.DefaultMasterComputation | Master-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master. |
| output.batch_size | 500 | The batch size of output |
| output.batch_threads | 1 | The threads number used to batch output |
| output.hdfs_core_site_path | The hdfs core site path. | |
| output.hdfs_delimiter | , | The delimiter of hdfs output. |
| output.hdfs_kerberos_enable | false | Is Kerberos authentication enabled for Hdfs. |
| output.hdfs_kerberos_keytab | The Hdfs’s key tab file for kerberos authentication. | |
| output.hdfs_kerberos_principal | The Hdfs’s principal for kerberos authentication. | |
| output.hdfs_krb5_conf | /etc/krb5.conf | Kerberos configuration file. |
| output.hdfs_merge_partitions | true | Whether merge output files of multiple partitions. |
| output.hdfs_path_prefix | /hugegraph-computer/results | The directory of hdfs output result. |
| output.hdfs_replication | 3 | The replication number of hdfs. |
| output.hdfs_site_path | The hdfs site path. | |
| output.hdfs_url | hdfs://127.0.0.1:9000 | The hdfs url of output. |
| output.hdfs_user | hadoop | The hdfs user of output. |
| output.output_class | org.apache.hugegraph.computer.core.output.LogOutput | The class to output the computation result of each vertex. Be called after iteration computation. |
| output.result_name | value | The value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS. |
| output.result_write_type | OLAP_COMMON | The result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE]. |
| output.retry_interval | 10 | The retry interval when output failed |
| output.retry_times | 3 | The retry times when output failed |
| output.single_threads | 1 | The threads number used to single output |
| output.thread_pool_shutdown_timeout | 60 | The timeout seconds of output threads pool shutdown |
| output.with_adjacent_edges | false | Output the adjacent edges of the vertex or not |
| output.with_edge_properties | false | Output the properties of the edge or not |
| output.with_vertex_properties | false | Output the properties of the vertex or not |
| sort.thread_nums | 4 | The number of threads performing internal sorting. |
| transport.client_connect_timeout | 3000 | The timeout(in ms) of client connect to server. |
| transport.client_threads | 4 | The number of transport threads for client. |
| transport.close_timeout | 10000 | The timeout(in ms) of close server or close client. |
| transport.finish_session_timeout | 0 | The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests). |
| transport.heartbeat_interval | 20000 | The minimum interval(in ms) between heartbeats on client side. |
| transport.io_mode | AUTO | The network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically. |
| transport.max_pending_requests | 8 | The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests. |
| transport.max_syn_backlog | 511 | The capacity of SYN queue on server side, 0 means using system default value. |
| transport.max_timeout_heartbeat_count | 120 | The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side. |
| transport.min_ack_interval | 200 | The minimum interval(in ms) of server reply ack. |
| transport.min_pending_requests | 6 | The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests. |
| transport.network_retries | 3 | The number of retry attempts for network communication,if network unstable. |
| transport.provider_class | org.apache.hugegraph.computer.core.network.netty.NettyTransportProvider | The transport provider, currently only supports Netty. |
| transport.receive_buffer_size | 0 | The size of socket receive-buffer in bytes, 0 means using system default value. |
| transport.recv_file_mode | true | Whether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable. |
| transport.send_buffer_size | 0 | The size of socket send-buffer in bytes, 0 means using system default value. |
| transport.server_host | 127.0.0.1 | The server hostname or ip to listen on to transfer data. |
| transport.server_idle_timeout | 360000 | The max timeout(in ms) of server idle. |
| transport.server_port | 0 | The server port to listen on to transfer data. The system will assign a random port if it’s set to 0. |
| transport.server_threads | 4 | The number of transport threads for server. |
| transport.sync_request_timeout | 10000 | The timeout(in ms) to wait response after sending sync-request. |
| transport.tcp_keep_alive | true | Whether enable TCP keep-alive. |
| transport.transport_epoll_lt | false | Whether enable EPOLL level-trigger. |
| transport.write_buffer_high_mark | 67108864 | The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark. |
| transport.write_buffer_low_mark | 33554432 | The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b |
| transport.write_socket_timeout | 3000 | The timeout(in ms) to write data to socket buffer. |
| valuefile.max_segment_size | 1073741824 | The max number of bytes in each segment of value-file. |
| worker.combiner_class | org.apache.hugegraph.computer.core.config.Null | Combiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value. |
| worker.computation_class | org.apache.hugegraph.computer.core.config.Null | The class to create worker-computation object, worker-computation is used to compute each vertex in each superstep. |
| worker.data_dirs | [jobs] | The directories separated by ‘,’ that received vertices and messages can persist into. |
| worker.edge_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same edge into one properties at inputstep. |
| worker.partitioner | org.apache.hugegraph.computer.core.graph.partition.HashPartitioner | The partitioner that decides which partition a vertex should be in, and which worker a partition should be in. |
| worker.received_buffers_bytes_limit | 104857600 | The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file. |
| worker.vertex_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same vertex into one properties at inputstep. |
| worker.wait_finish_messages_timeout | 86400000 | The max timeout(in ms) message-handler wait for finish-message of all workers. |
| worker.wait_sort_timeout | 600000 | The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers. |
| worker.write_buffer_capacity | 52428800 | The initial size of write buffer that used to store vertex or message. |
| worker.write_buffer_threshold | 52428800 | The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message. |
NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL
| config option | default value | description |
|---|---|---|
| k8s.auto_destroy_pod | true | Whether to automatically destroy all pods when the job is completed or failed. |
| k8s.close_reconciler_timeout | 120 | The max timeout(in ms) to close reconciler. |
| k8s.internal_etcd_url | http://127.0.0.1:2379 | The internal etcd url for operator system. |
| k8s.max_reconcile_retry | 3 | The max retry times of reconcile. |
| k8s.probe_backlog | 50 | The maximum backlog for serving health probes. |
| k8s.probe_port | 9892 | The value is the port that the controller bind to for serving health probes. |
| k8s.ready_check_internal | 1000 | The time interval(ms) of check ready. |
| k8s.ready_timeout | 30000 | The max timeout(in ms) of check ready. |
| k8s.reconciler_count | 10 | The max number of reconciler thread. |
| k8s.resync_period | 600000 | The minimum frequency at which watched resources are reconciled. |
| k8s.timezone | Asia/Shanghai | The timezone of computer job and operator. |
| k8s.watch_namespace | hugegraph-computer-system | The value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched. |
| spec | default value | description | required |
|---|---|---|---|
| algorithmName | The name of algorithm. | true | |
| jobId | The job id. | true | |
| image | The image of algorithm. | true | |
| computerConf | The map of computer config options. | true | |
| workerInstances | The number of worker instances, it will instead the ‘job.workers_count’ option. | true | |
| pullPolicy | Always | The pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy | false |
| pullSecrets | The pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod | false | |
| masterCpu | The cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| workerCpu | The cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| masterMemory | The memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| workerMemory | The memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| log4jXml | The content of log4j.xml for computer job. | false | |
| jarFile | The jar path of computer algorithm. | false | |
| remoteJarUri | The remote jar uri of computer algorithm, it will overlay algorithm image. | false | |
| jvmOptions | The java startup parameters of computer job. | false | |
| envVars | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/ | false | |
| envFrom | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/ | false | |
| masterCommand | bin/start-computer.sh | The run command of master, equivalent to ‘Entrypoint’ field of Docker. | false |
| masterArgs | ["-r master", “-d k8s”] | The run args of master, equivalent to ‘Cmd’ field of Docker. | false |
| workerCommand | bin/start-computer.sh | The run command of worker, equivalent to ‘Entrypoint’ field of Docker. | false |
| workerArgs | ["-r worker", “-d k8s”] | The run args of worker, equivalent to ‘Cmd’ field of Docker. | false |
| volumes | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| volumeMounts | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| secretPaths | The map of k8s-secret name and mount path. | false | |
| configMapPaths | The map of k8s-configmap name and mount path. | false | |
| podTemplateSpec | Please refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpec | false | |
| securityContext | Please refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/ | false |
| config option | default value | description |
|---|---|---|
| k8s.build_image_bash_path | The path of command used to build image. | |
| k8s.enable_internal_algorithm | true | Whether enable internal algorithm. |
| k8s.framework_image_url | hugegraph/hugegraph-computer:latest | The image url of computer framework. |
| k8s.image_repository_password | The password for login image repository. | |
| k8s.image_repository_registry | The address for login image repository. | |
| k8s.image_repository_url | hugegraph/hugegraph-computer | The url of image repository. |
| k8s.image_repository_username | The username for login image repository. | |
| k8s.internal_algorithm | [pageRank] | The name list of all internal algorithm. |
| k8s.internal_algorithm_image_url | hugegraph/hugegraph-computer:latest | The image url of internal algorithm. |
| k8s.jar_file_dir | /cache/jars/ | The directory where the algorithm jar to upload location. |
| k8s.kube_config | ~/.kube/config | The path of k8s config file. |
| k8s.log4j_xml_path | The log4j.xml path for computer job. | |
| k8s.namespace | hugegraph-computer-system | The namespace of hugegraph-computer system. |
| k8s.pull_secret_names | [] | The names of pull-secret for pulling image. |
client.truststore 是给客户端⽤的，其中保存着受信任的证书
| config option | default value | description |
|---|---|---|
| algorithm.message_class | org.apache.hugegraph.computer.core.config.Null | The class of message passed when compute vertex. |
| algorithm.params_class | org.apache.hugegraph.computer.core.config.Null | The class used to transfer algorithms’ parameters before algorithm been run. |
| algorithm.result_class | org.apache.hugegraph.computer.core.config.Null | The class of vertex’s value, the instance is used to store computation result for the vertex. |
| allocator.max_vertices_per_thread | 10000 | Maximum number of vertices per thread processed in each memory allocator |
| bsp.etcd_endpoints | http://localhost:2379 | The end points to access etcd. |
| bsp.log_interval | 30000 | The log interval(in ms) to print the log while waiting bsp event. |
| bsp.max_super_step | 10 | The max super step of the algorithm. |
| bsp.register_timeout | 300000 | The max timeout to wait for master and works to register. |
| bsp.wait_master_timeout | 86400000 | The max timeout(in ms) to wait for master bsp event. |
| bsp.wait_workers_timeout | 86400000 | The max timeout to wait for workers bsp event. |
| hgkv.max_data_block_size | 65536 | The max byte size of hgkv-file data block. |
| hgkv.max_file_size | 2147483648 | The max number of bytes in each hgkv-file. |
| hgkv.max_merge_files | 10 | The max number of files to merge at one time. |
| hgkv.temp_file_dir | /tmp/hgkv | This folder is used to store temporary files, temporary files will be generated during the file merging process. |
| hugegraph.name | hugegraph | The graph name to load data and write results back. |
| hugegraph.url | http://127.0.0.1:8080 | The hugegraph url to load data and write results back. |
| input.edge_direction | OUT | The data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded. |
| input.edge_freq | MULTIPLE | The frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it. |
| input.filter_class | org.apache.hugegraph.computer.core.input.filter.DefaultInputFilter | The class to create input-filter object, input-filter is used to Filter vertex edges according to user needs. |
| input.loader_schema_path | The schema path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.loader_struct_path | The struct path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.max_edges_in_one_vertex | 200 | The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit. |
| input.source_type | hugegraph-server | The source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’. |
| input.split_fetch_timeout | 300 | The timeout in seconds to fetch input splits |
| input.split_max_splits | 10000000 | The maximum number of input splits |
| input.split_page_size | 500 | The page size for streamed load input split data |
| input.split_size | 1048576 | The input split size in bytes |
| job.id | local_0001 | The job id on Yarn cluster or K8s cluster. |
| job.partitions_count | 1 | The partitions count for computing one graph algorithm job. |
| job.partitions_thread_nums | 4 | The number of threads for partition parallel compute. |
| job.workers_count | 1 | The workers count for computing one graph algorithm job. |
| master.computation_class | org.apache.hugegraph.computer.core.master.DefaultMasterComputation | Master-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master. |
| output.batch_size | 500 | The batch size of output |
| output.batch_threads | 1 | The threads number used to batch output |
| output.hdfs_core_site_path | The hdfs core site path. | |
| output.hdfs_delimiter | , | The delimiter of hdfs output. |
| output.hdfs_kerberos_enable | false | Is Kerberos authentication enabled for Hdfs. |
| output.hdfs_kerberos_keytab | The Hdfs’s key tab file for kerberos authentication. | |
| output.hdfs_kerberos_principal | The Hdfs’s principal for kerberos authentication. | |
| output.hdfs_krb5_conf | /etc/krb5.conf | Kerberos configuration file. |
| output.hdfs_merge_partitions | true | Whether merge output files of multiple partitions. |
| output.hdfs_path_prefix | /hugegraph-computer/results | The directory of hdfs output result. |
| output.hdfs_replication | 3 | The replication number of hdfs. |
| output.hdfs_site_path | The hdfs site path. | |
| output.hdfs_url | hdfs://127.0.0.1:9000 | The hdfs url of output. |
| output.hdfs_user | hadoop | The hdfs user of output. |
| output.output_class | org.apache.hugegraph.computer.core.output.LogOutput | The class to output the computation result of each vertex. Be called after iteration computation. |
| output.result_name | value | The value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS. |
| output.result_write_type | OLAP_COMMON | The result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE]. |
| output.retry_interval | 10 | The retry interval when output failed |
| output.retry_times | 3 | The retry times when output failed |
| output.single_threads | 1 | The threads number used to single output |
| output.thread_pool_shutdown_timeout | 60 | The timeout seconds of output threads pool shutdown |
| output.with_adjacent_edges | false | Output the adjacent edges of the vertex or not |
| output.with_edge_properties | false | Output the properties of the edge or not |
| output.with_vertex_properties | false | Output the properties of the vertex or not |
| sort.thread_nums | 4 | The number of threads performing internal sorting. |
| transport.client_connect_timeout | 3000 | The timeout(in ms) of client connect to server. |
| transport.client_threads | 4 | The number of transport threads for client. |
| transport.close_timeout | 10000 | The timeout(in ms) of close server or close client. |
| transport.finish_session_timeout | 0 | The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests). |
| transport.heartbeat_interval | 20000 | The minimum interval(in ms) between heartbeats on client side. |
| transport.io_mode | AUTO | The network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically. |
| transport.max_pending_requests | 8 | The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests. |
| transport.max_syn_backlog | 511 | The capacity of SYN queue on server side, 0 means using system default value. |
| transport.max_timeout_heartbeat_count | 120 | The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side. |
| transport.min_ack_interval | 200 | The minimum interval(in ms) of server reply ack. |
| transport.min_pending_requests | 6 | The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests. |
| transport.network_retries | 3 | The number of retry attempts for network communication,if network unstable. |
| transport.provider_class | org.apache.hugegraph.computer.core.network.netty.NettyTransportProvider | The transport provider, currently only supports Netty. |
| transport.receive_buffer_size | 0 | The size of socket receive-buffer in bytes, 0 means using system default value. |
| transport.recv_file_mode | true | Whether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable. |
| transport.send_buffer_size | 0 | The size of socket send-buffer in bytes, 0 means using system default value. |
| transport.server_host | 127.0.0.1 | The server hostname or ip to listen on to transfer data. |
| transport.server_idle_timeout | 360000 | The max timeout(in ms) of server idle. |
| transport.server_port | 0 | The server port to listen on to transfer data. The system will assign a random port if it’s set to 0. |
| transport.server_threads | 4 | The number of transport threads for server. |
| transport.sync_request_timeout | 10000 | The timeout(in ms) to wait response after sending sync-request. |
| transport.tcp_keep_alive | true | Whether enable TCP keep-alive. |
| transport.transport_epoll_lt | false | Whether enable EPOLL level-trigger. |
| transport.write_buffer_high_mark | 67108864 | The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark. |
| transport.write_buffer_low_mark | 33554432 | The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b |
| transport.write_socket_timeout | 3000 | The timeout(in ms) to write data to socket buffer. |
| valuefile.max_segment_size | 1073741824 | The max number of bytes in each segment of value-file. |
| worker.combiner_class | org.apache.hugegraph.computer.core.config.Null | Combiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value. |
| worker.computation_class | org.apache.hugegraph.computer.core.config.Null | The class to create worker-computation object, worker-computation is used to compute each vertex in each superstep. |
| worker.data_dirs | [jobs] | The directories separated by ‘,’ that received vertices and messages can persist into. |
| worker.edge_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same edge into one properties at inputstep. |
| worker.partitioner | org.apache.hugegraph.computer.core.graph.partition.HashPartitioner | The partitioner that decides which partition a vertex should be in, and which worker a partition should be in. |
| worker.received_buffers_bytes_limit | 104857600 | The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file. |
| worker.vertex_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same vertex into one properties at inputstep. |
| worker.wait_finish_messages_timeout | 86400000 | The max timeout(in ms) message-handler wait for finish-message of all workers. |
| worker.wait_sort_timeout | 600000 | The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers. |
| worker.write_buffer_capacity | 52428800 | The initial size of write buffer that used to store vertex or message. |
| worker.write_buffer_threshold | 52428800 | The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message. |
NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL
| config option | default value | description |
|---|---|---|
| k8s.auto_destroy_pod | true | Whether to automatically destroy all pods when the job is completed or failed. |
| k8s.close_reconciler_timeout | 120 | The max timeout(in ms) to close reconciler. |
| k8s.internal_etcd_url | http://127.0.0.1:2379 | The internal etcd url for operator system. |
| k8s.max_reconcile_retry | 3 | The max retry times of reconcile. |
| k8s.probe_backlog | 50 | The maximum backlog for serving health probes. |
| k8s.probe_port | 9892 | The value is the port that the controller bind to for serving health probes. |
| k8s.ready_check_internal | 1000 | The time interval(ms) of check ready. |
| k8s.ready_timeout | 30000 | The max timeout(in ms) of check ready. |
| k8s.reconciler_count | 10 | The max number of reconciler thread. |
| k8s.resync_period | 600000 | The minimum frequency at which watched resources are reconciled. |
| k8s.timezone | Asia/Shanghai | The timezone of computer job and operator. |
| k8s.watch_namespace | hugegraph-computer-system | The value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched. |
| spec | default value | description | required |
|---|---|---|---|
| algorithmName | The name of algorithm. | true | |
| jobId | The job id. | true | |
| image | The image of algorithm. | true | |
| computerConf | The map of computer config options. | true | |
| workerInstances | The number of worker instances, it will instead the ‘job.workers_count’ option. | true | |
| pullPolicy | Always | The pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy | false |
| pullSecrets | The pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod | false | |
| masterCpu | The cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| workerCpu | The cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| masterMemory | The memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| workerMemory | The memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| log4jXml | The content of log4j.xml for computer job. | false | |
| jarFile | The jar path of computer algorithm. | false | |
| remoteJarUri | The remote jar uri of computer algorithm, it will overlay algorithm image. | false | |
| jvmOptions | The java startup parameters of computer job. | false | |
| envVars | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/ | false | |
| envFrom | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/ | false | |
| masterCommand | bin/start-computer.sh | The run command of master, equivalent to ‘Entrypoint’ field of Docker. | false |
| masterArgs | ["-r master", “-d k8s”] | The run args of master, equivalent to ‘Cmd’ field of Docker. | false |
| workerCommand | bin/start-computer.sh | The run command of worker, equivalent to ‘Entrypoint’ field of Docker. | false |
| workerArgs | ["-r worker", “-d k8s”] | The run args of worker, equivalent to ‘Cmd’ field of Docker. | false |
| volumes | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| volumeMounts | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| secretPaths | The map of k8s-secret name and mount path. | false | |
| configMapPaths | The map of k8s-configmap name and mount path. | false | |
| podTemplateSpec | Please refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpec | false | |
| securityContext | Please refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/ | false |
| config option | default value | description |
|---|---|---|
| k8s.build_image_bash_path | The path of command used to build image. | |
| k8s.enable_internal_algorithm | true | Whether enable internal algorithm. |
| k8s.framework_image_url | hugegraph/hugegraph-computer:latest | The image url of computer framework. |
| k8s.image_repository_password | The password for login image repository. | |
| k8s.image_repository_registry | The address for login image repository. | |
| k8s.image_repository_url | hugegraph/hugegraph-computer | The url of image repository. |
| k8s.image_repository_username | The username for login image repository. | |
| k8s.internal_algorithm | [pageRank] | The name list of all internal algorithm. |
| k8s.internal_algorithm_image_url | hugegraph/hugegraph-computer:latest | The image url of internal algorithm. |
| k8s.jar_file_dir | /cache/jars/ | The directory where the algorithm jar to upload location. |
| k8s.kube_config | ~/.kube/config | The path of k8s config file. |
| k8s.log4j_xml_path | The log4j.xml path for computer job. | |
| k8s.namespace | hugegraph-computer-system | The namespace of hugegraph-computer system. |
| k8s.pull_secret_names | [] | The names of pull-secret for pulling image. |
在配置文件hugegraph{n}.properties中配置gremlin.graph信息：
gremlin.graph=com.baidu.hugegraph.auth.HugeFactoryAuthProxy
-如果需要支持更加灵活的用户系统，可自定义authenticator进行扩展，自定义authenticator实现接口com.baidu.hugegraph.auth.HugeAuthenticator即可，然后修改配置文件中authenticator配置项指向该实现。
如果需要支持更加灵活的用户系统，可自定义authenticator进行扩展，自定义authenticator实现接口com.baidu.hugegraph.auth.HugeAuthenticator即可，然后修改配置文件中authenticator配置项指向该实现。
| config option | default value | description |
|---|---|---|
| algorithm.message_class | org.apache.hugegraph.computer.core.config.Null | The class of message passed when compute vertex. |
| algorithm.params_class | org.apache.hugegraph.computer.core.config.Null | The class used to transfer algorithms’ parameters before algorithm been run. |
| algorithm.result_class | org.apache.hugegraph.computer.core.config.Null | The class of vertex’s value, the instance is used to store computation result for the vertex. |
| allocator.max_vertices_per_thread | 10000 | Maximum number of vertices per thread processed in each memory allocator |
| bsp.etcd_endpoints | http://localhost:2379 | The end points to access etcd. |
| bsp.log_interval | 30000 | The log interval(in ms) to print the log while waiting bsp event. |
| bsp.max_super_step | 10 | The max super step of the algorithm. |
| bsp.register_timeout | 300000 | The max timeout to wait for master and works to register. |
| bsp.wait_master_timeout | 86400000 | The max timeout(in ms) to wait for master bsp event. |
| bsp.wait_workers_timeout | 86400000 | The max timeout to wait for workers bsp event. |
| hgkv.max_data_block_size | 65536 | The max byte size of hgkv-file data block. |
| hgkv.max_file_size | 2147483648 | The max number of bytes in each hgkv-file. |
| hgkv.max_merge_files | 10 | The max number of files to merge at one time. |
| hgkv.temp_file_dir | /tmp/hgkv | This folder is used to store temporary files, temporary files will be generated during the file merging process. |
| hugegraph.name | hugegraph | The graph name to load data and write results back. |
| hugegraph.url | http://127.0.0.1:8080 | The hugegraph url to load data and write results back. |
| input.edge_direction | OUT | The data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded. |
| input.edge_freq | MULTIPLE | The frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it. |
| input.filter_class | org.apache.hugegraph.computer.core.input.filter.DefaultInputFilter | The class to create input-filter object, input-filter is used to Filter vertex edges according to user needs. |
| input.loader_schema_path | The schema path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.loader_struct_path | The struct path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.max_edges_in_one_vertex | 200 | The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit. |
| input.source_type | hugegraph-server | The source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’. |
| input.split_fetch_timeout | 300 | The timeout in seconds to fetch input splits |
| input.split_max_splits | 10000000 | The maximum number of input splits |
| input.split_page_size | 500 | The page size for streamed load input split data |
| input.split_size | 1048576 | The input split size in bytes |
| job.id | local_0001 | The job id on Yarn cluster or K8s cluster. |
| job.partitions_count | 1 | The partitions count for computing one graph algorithm job. |
| job.partitions_thread_nums | 4 | The number of threads for partition parallel compute. |
| job.workers_count | 1 | The workers count for computing one graph algorithm job. |
| master.computation_class | org.apache.hugegraph.computer.core.master.DefaultMasterComputation | Master-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master. |
| output.batch_size | 500 | The batch size of output |
| output.batch_threads | 1 | The threads number used to batch output |
| output.hdfs_core_site_path | The hdfs core site path. | |
| output.hdfs_delimiter | , | The delimiter of hdfs output. |
| output.hdfs_kerberos_enable | false | Is Kerberos authentication enabled for Hdfs. |
| output.hdfs_kerberos_keytab | The Hdfs’s key tab file for kerberos authentication. | |
| output.hdfs_kerberos_principal | The Hdfs’s principal for kerberos authentication. | |
| output.hdfs_krb5_conf | /etc/krb5.conf | Kerberos configuration file. |
| output.hdfs_merge_partitions | true | Whether merge output files of multiple partitions. |
| output.hdfs_path_prefix | /hugegraph-computer/results | The directory of hdfs output result. |
| output.hdfs_replication | 3 | The replication number of hdfs. |
| output.hdfs_site_path | The hdfs site path. | |
| output.hdfs_url | hdfs://127.0.0.1:9000 | The hdfs url of output. |
| output.hdfs_user | hadoop | The hdfs user of output. |
| output.output_class | org.apache.hugegraph.computer.core.output.LogOutput | The class to output the computation result of each vertex. Be called after iteration computation. |
| output.result_name | value | The value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS. |
| output.result_write_type | OLAP_COMMON | The result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE]. |
| output.retry_interval | 10 | The retry interval when output failed |
| output.retry_times | 3 | The retry times when output failed |
| output.single_threads | 1 | The threads number used to single output |
| output.thread_pool_shutdown_timeout | 60 | The timeout seconds of output threads pool shutdown |
| output.with_adjacent_edges | false | Output the adjacent edges of the vertex or not |
| output.with_edge_properties | false | Output the properties of the edge or not |
| output.with_vertex_properties | false | Output the properties of the vertex or not |
| sort.thread_nums | 4 | The number of threads performing internal sorting. |
| transport.client_connect_timeout | 3000 | The timeout(in ms) of client connect to server. |
| transport.client_threads | 4 | The number of transport threads for client. |
| transport.close_timeout | 10000 | The timeout(in ms) of close server or close client. |
| transport.finish_session_timeout | 0 | The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests). |
| transport.heartbeat_interval | 20000 | The minimum interval(in ms) between heartbeats on client side. |
| transport.io_mode | AUTO | The network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically. |
| transport.max_pending_requests | 8 | The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests. |
| transport.max_syn_backlog | 511 | The capacity of SYN queue on server side, 0 means using system default value. |
| transport.max_timeout_heartbeat_count | 120 | The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side. |
| transport.min_ack_interval | 200 | The minimum interval(in ms) of server reply ack. |
| transport.min_pending_requests | 6 | The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests. |
| transport.network_retries | 3 | The number of retry attempts for network communication,if network unstable. |
| transport.provider_class | org.apache.hugegraph.computer.core.network.netty.NettyTransportProvider | The transport provider, currently only supports Netty. |
| transport.receive_buffer_size | 0 | The size of socket receive-buffer in bytes, 0 means using system default value. |
| transport.recv_file_mode | true | Whether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable. |
| transport.send_buffer_size | 0 | The size of socket send-buffer in bytes, 0 means using system default value. |
| transport.server_host | 127.0.0.1 | The server hostname or ip to listen on to transfer data. |
| transport.server_idle_timeout | 360000 | The max timeout(in ms) of server idle. |
| transport.server_port | 0 | The server port to listen on to transfer data. The system will assign a random port if it’s set to 0. |
| transport.server_threads | 4 | The number of transport threads for server. |
| transport.sync_request_timeout | 10000 | The timeout(in ms) to wait response after sending sync-request. |
| transport.tcp_keep_alive | true | Whether enable TCP keep-alive. |
| transport.transport_epoll_lt | false | Whether enable EPOLL level-trigger. |
| transport.write_buffer_high_mark | 67108864 | The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark. |
| transport.write_buffer_low_mark | 33554432 | The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b |
| transport.write_socket_timeout | 3000 | The timeout(in ms) to write data to socket buffer. |
| valuefile.max_segment_size | 1073741824 | The max number of bytes in each segment of value-file. |
| worker.combiner_class | org.apache.hugegraph.computer.core.config.Null | Combiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value. |
| worker.computation_class | org.apache.hugegraph.computer.core.config.Null | The class to create worker-computation object, worker-computation is used to compute each vertex in each superstep. |
| worker.data_dirs | [jobs] | The directories separated by ‘,’ that received vertices and messages can persist into. |
| worker.edge_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same edge into one properties at inputstep. |
| worker.partitioner | org.apache.hugegraph.computer.core.graph.partition.HashPartitioner | The partitioner that decides which partition a vertex should be in, and which worker a partition should be in. |
| worker.received_buffers_bytes_limit | 104857600 | The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file. |
| worker.vertex_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same vertex into one properties at inputstep. |
| worker.wait_finish_messages_timeout | 86400000 | The max timeout(in ms) message-handler wait for finish-message of all workers. |
| worker.wait_sort_timeout | 600000 | The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers. |
| worker.write_buffer_capacity | 52428800 | The initial size of write buffer that used to store vertex or message. |
| worker.write_buffer_threshold | 52428800 | The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message. |
NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL
| config option | default value | description |
|---|---|---|
| k8s.auto_destroy_pod | true | Whether to automatically destroy all pods when the job is completed or failed. |
| k8s.close_reconciler_timeout | 120 | The max timeout(in ms) to close reconciler. |
| k8s.internal_etcd_url | http://127.0.0.1:2379 | The internal etcd url for operator system. |
| k8s.max_reconcile_retry | 3 | The max retry times of reconcile. |
| k8s.probe_backlog | 50 | The maximum backlog for serving health probes. |
| k8s.probe_port | 9892 | The value is the port that the controller bind to for serving health probes. |
| k8s.ready_check_internal | 1000 | The time interval(ms) of check ready. |
| k8s.ready_timeout | 30000 | The max timeout(in ms) of check ready. |
| k8s.reconciler_count | 10 | The max number of reconciler thread. |
| k8s.resync_period | 600000 | The minimum frequency at which watched resources are reconciled. |
| k8s.timezone | Asia/Shanghai | The timezone of computer job and operator. |
| k8s.watch_namespace | hugegraph-computer-system | The value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched. |
| spec | default value | description | required |
|---|---|---|---|
| algorithmName | The name of algorithm. | true | |
| jobId | The job id. | true | |
| image | The image of algorithm. | true | |
| computerConf | The map of computer config options. | true | |
| workerInstances | The number of worker instances, it will instead the ‘job.workers_count’ option. | true | |
| pullPolicy | Always | The pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy | false |
| pullSecrets | The pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod | false | |
| masterCpu | The cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| workerCpu | The cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| masterMemory | The memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| workerMemory | The memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| log4jXml | The content of log4j.xml for computer job. | false | |
| jarFile | The jar path of computer algorithm. | false | |
| remoteJarUri | The remote jar uri of computer algorithm, it will overlay algorithm image. | false | |
| jvmOptions | The java startup parameters of computer job. | false | |
| envVars | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/ | false | |
| envFrom | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/ | false | |
| masterCommand | bin/start-computer.sh | The run command of master, equivalent to ‘Entrypoint’ field of Docker. | false |
| masterArgs | ["-r master", “-d k8s”] | The run args of master, equivalent to ‘Cmd’ field of Docker. | false |
| workerCommand | bin/start-computer.sh | The run command of worker, equivalent to ‘Entrypoint’ field of Docker. | false |
| workerArgs | ["-r worker", “-d k8s”] | The run args of worker, equivalent to ‘Cmd’ field of Docker. | false |
| volumes | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| volumeMounts | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| secretPaths | The map of k8s-secret name and mount path. | false | |
| configMapPaths | The map of k8s-configmap name and mount path. | false | |
| podTemplateSpec | Please refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpec | false | |
| securityContext | Please refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/ | false |
| config option | default value | description |
|---|---|---|
| k8s.build_image_bash_path | The path of command used to build image. | |
| k8s.enable_internal_algorithm | true | Whether enable internal algorithm. |
| k8s.framework_image_url | hugegraph/hugegraph-computer:latest | The image url of computer framework. |
| k8s.image_repository_password | The password for login image repository. | |
| k8s.image_repository_registry | The address for login image repository. | |
| k8s.image_repository_url | hugegraph/hugegraph-computer | The url of image repository. |
| k8s.image_repository_username | The username for login image repository. | |
| k8s.internal_algorithm | [pageRank] | The name list of all internal algorithm. |
| k8s.internal_algorithm_image_url | hugegraph/hugegraph-computer:latest | The image url of internal algorithm. |
| k8s.jar_file_dir | /cache/jars/ | The directory where the algorithm jar to upload location. |
| k8s.kube_config | ~/.kube/config | The path of k8s config file. |
| k8s.log4j_xml_path | The log4j.xml path for computer job. | |
| k8s.namespace | hugegraph-computer-system | The namespace of hugegraph-computer system. |
| k8s.pull_secret_names | [] | The names of pull-secret for pulling image. |
| config option | default value | description |
|---|---|---|
| algorithm.message_class | org.apache.hugegraph.computer.core.config.Null | The class of message passed when compute vertex. |
| algorithm.params_class | org.apache.hugegraph.computer.core.config.Null | The class used to transfer algorithms’ parameters before algorithm been run. |
| algorithm.result_class | org.apache.hugegraph.computer.core.config.Null | The class of vertex’s value, the instance is used to store computation result for the vertex. |
| allocator.max_vertices_per_thread | 10000 | Maximum number of vertices per thread processed in each memory allocator |
| bsp.etcd_endpoints | http://localhost:2379 | The end points to access etcd. |
| bsp.log_interval | 30000 | The log interval(in ms) to print the log while waiting bsp event. |
| bsp.max_super_step | 10 | The max super step of the algorithm. |
| bsp.register_timeout | 300000 | The max timeout to wait for master and works to register. |
| bsp.wait_master_timeout | 86400000 | The max timeout(in ms) to wait for master bsp event. |
| bsp.wait_workers_timeout | 86400000 | The max timeout to wait for workers bsp event. |
| hgkv.max_data_block_size | 65536 | The max byte size of hgkv-file data block. |
| hgkv.max_file_size | 2147483648 | The max number of bytes in each hgkv-file. |
| hgkv.max_merge_files | 10 | The max number of files to merge at one time. |
| hgkv.temp_file_dir | /tmp/hgkv | This folder is used to store temporary files, temporary files will be generated during the file merging process. |
| hugegraph.name | hugegraph | The graph name to load data and write results back. |
| hugegraph.url | http://127.0.0.1:8080 | The hugegraph url to load data and write results back. |
| input.edge_direction | OUT | The data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded. |
| input.edge_freq | MULTIPLE | The frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it. |
| input.filter_class | org.apache.hugegraph.computer.core.input.filter.DefaultInputFilter | The class to create input-filter object, input-filter is used to Filter vertex edges according to user needs. |
| input.loader_schema_path | The schema path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.loader_struct_path | The struct path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.max_edges_in_one_vertex | 200 | The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit. |
| input.source_type | hugegraph-server | The source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’. |
| input.split_fetch_timeout | 300 | The timeout in seconds to fetch input splits |
| input.split_max_splits | 10000000 | The maximum number of input splits |
| input.split_page_size | 500 | The page size for streamed load input split data |
| input.split_size | 1048576 | The input split size in bytes |
| job.id | local_0001 | The job id on Yarn cluster or K8s cluster. |
| job.partitions_count | 1 | The partitions count for computing one graph algorithm job. |
| job.partitions_thread_nums | 4 | The number of threads for partition parallel compute. |
| job.workers_count | 1 | The workers count for computing one graph algorithm job. |
| master.computation_class | org.apache.hugegraph.computer.core.master.DefaultMasterComputation | Master-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master. |
| output.batch_size | 500 | The batch size of output |
| output.batch_threads | 1 | The threads number used to batch output |
| output.hdfs_core_site_path | The hdfs core site path. | |
| output.hdfs_delimiter | , | The delimiter of hdfs output. |
| output.hdfs_kerberos_enable | false | Is Kerberos authentication enabled for Hdfs. |
| output.hdfs_kerberos_keytab | The Hdfs’s key tab file for kerberos authentication. | |
| output.hdfs_kerberos_principal | The Hdfs’s principal for kerberos authentication. | |
| output.hdfs_krb5_conf | /etc/krb5.conf | Kerberos configuration file. |
| output.hdfs_merge_partitions | true | Whether merge output files of multiple partitions. |
| output.hdfs_path_prefix | /hugegraph-computer/results | The directory of hdfs output result. |
| output.hdfs_replication | 3 | The replication number of hdfs. |
| output.hdfs_site_path | The hdfs site path. | |
| output.hdfs_url | hdfs://127.0.0.1:9000 | The hdfs url of output. |
| output.hdfs_user | hadoop | The hdfs user of output. |
| output.output_class | org.apache.hugegraph.computer.core.output.LogOutput | The class to output the computation result of each vertex. Be called after iteration computation. |
| output.result_name | value | The value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS. |
| output.result_write_type | OLAP_COMMON | The result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE]. |
| output.retry_interval | 10 | The retry interval when output failed |
| output.retry_times | 3 | The retry times when output failed |
| output.single_threads | 1 | The threads number used to single output |
| output.thread_pool_shutdown_timeout | 60 | The timeout seconds of output threads pool shutdown |
| output.with_adjacent_edges | false | Output the adjacent edges of the vertex or not |
| output.with_edge_properties | false | Output the properties of the edge or not |
| output.with_vertex_properties | false | Output the properties of the vertex or not |
| sort.thread_nums | 4 | The number of threads performing internal sorting. |
| transport.client_connect_timeout | 3000 | The timeout(in ms) of client connect to server. |
| transport.client_threads | 4 | The number of transport threads for client. |
| transport.close_timeout | 10000 | The timeout(in ms) of close server or close client. |
| transport.finish_session_timeout | 0 | The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests). |
| transport.heartbeat_interval | 20000 | The minimum interval(in ms) between heartbeats on client side. |
| transport.io_mode | AUTO | The network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically. |
| transport.max_pending_requests | 8 | The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests. |
| transport.max_syn_backlog | 511 | The capacity of SYN queue on server side, 0 means using system default value. |
| transport.max_timeout_heartbeat_count | 120 | The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side. |
| transport.min_ack_interval | 200 | The minimum interval(in ms) of server reply ack. |
| transport.min_pending_requests | 6 | The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests. |
| transport.network_retries | 3 | The number of retry attempts for network communication,if network unstable. |
| transport.provider_class | org.apache.hugegraph.computer.core.network.netty.NettyTransportProvider | The transport provider, currently only supports Netty. |
| transport.receive_buffer_size | 0 | The size of socket receive-buffer in bytes, 0 means using system default value. |
| transport.recv_file_mode | true | Whether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable. |
| transport.send_buffer_size | 0 | The size of socket send-buffer in bytes, 0 means using system default value. |
| transport.server_host | 127.0.0.1 | The server hostname or ip to listen on to transfer data. |
| transport.server_idle_timeout | 360000 | The max timeout(in ms) of server idle. |
| transport.server_port | 0 | The server port to listen on to transfer data. The system will assign a random port if it’s set to 0. |
| transport.server_threads | 4 | The number of transport threads for server. |
| transport.sync_request_timeout | 10000 | The timeout(in ms) to wait response after sending sync-request. |
| transport.tcp_keep_alive | true | Whether enable TCP keep-alive. |
| transport.transport_epoll_lt | false | Whether enable EPOLL level-trigger. |
| transport.write_buffer_high_mark | 67108864 | The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark. |
| transport.write_buffer_low_mark | 33554432 | The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b |
| transport.write_socket_timeout | 3000 | The timeout(in ms) to write data to socket buffer. |
| valuefile.max_segment_size | 1073741824 | The max number of bytes in each segment of value-file. |
| worker.combiner_class | org.apache.hugegraph.computer.core.config.Null | Combiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value. |
| worker.computation_class | org.apache.hugegraph.computer.core.config.Null | The class to create worker-computation object, worker-computation is used to compute each vertex in each superstep. |
| worker.data_dirs | [jobs] | The directories separated by ‘,’ that received vertices and messages can persist into. |
| worker.edge_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same edge into one properties at inputstep. |
| worker.partitioner | org.apache.hugegraph.computer.core.graph.partition.HashPartitioner | The partitioner that decides which partition a vertex should be in, and which worker a partition should be in. |
| worker.received_buffers_bytes_limit | 104857600 | The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file. |
| worker.vertex_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same vertex into one properties at inputstep. |
| worker.wait_finish_messages_timeout | 86400000 | The max timeout(in ms) message-handler wait for finish-message of all workers. |
| worker.wait_sort_timeout | 600000 | The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers. |
| worker.write_buffer_capacity | 52428800 | The initial size of write buffer that used to store vertex or message. |
| worker.write_buffer_threshold | 52428800 | The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message. |
NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL
| config option | default value | description |
|---|---|---|
| k8s.auto_destroy_pod | true | Whether to automatically destroy all pods when the job is completed or failed. |
| k8s.close_reconciler_timeout | 120 | The max timeout(in ms) to close reconciler. |
| k8s.internal_etcd_url | http://127.0.0.1:2379 | The internal etcd url for operator system. |
| k8s.max_reconcile_retry | 3 | The max retry times of reconcile. |
| k8s.probe_backlog | 50 | The maximum backlog for serving health probes. |
| k8s.probe_port | 9892 | The value is the port that the controller bind to for serving health probes. |
| k8s.ready_check_internal | 1000 | The time interval(ms) of check ready. |
| k8s.ready_timeout | 30000 | The max timeout(in ms) of check ready. |
| k8s.reconciler_count | 10 | The max number of reconciler thread. |
| k8s.resync_period | 600000 | The minimum frequency at which watched resources are reconciled. |
| k8s.timezone | Asia/Shanghai | The timezone of computer job and operator. |
| k8s.watch_namespace | hugegraph-computer-system | The value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched. |
| spec | default value | description | required |
|---|---|---|---|
| algorithmName | The name of algorithm. | true | |
| jobId | The job id. | true | |
| image | The image of algorithm. | true | |
| computerConf | The map of computer config options. | true | |
| workerInstances | The number of worker instances, it will instead the ‘job.workers_count’ option. | true | |
| pullPolicy | Always | The pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy | false |
| pullSecrets | The pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod | false | |
| masterCpu | The cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| workerCpu | The cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| masterMemory | The memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| workerMemory | The memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| log4jXml | The content of log4j.xml for computer job. | false | |
| jarFile | The jar path of computer algorithm. | false | |
| remoteJarUri | The remote jar uri of computer algorithm, it will overlay algorithm image. | false | |
| jvmOptions | The java startup parameters of computer job. | false | |
| envVars | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/ | false | |
| envFrom | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/ | false | |
| masterCommand | bin/start-computer.sh | The run command of master, equivalent to ‘Entrypoint’ field of Docker. | false |
| masterArgs | ["-r master", “-d k8s”] | The run args of master, equivalent to ‘Cmd’ field of Docker. | false |
| workerCommand | bin/start-computer.sh | The run command of worker, equivalent to ‘Entrypoint’ field of Docker. | false |
| workerArgs | ["-r worker", “-d k8s”] | The run args of worker, equivalent to ‘Cmd’ field of Docker. | false |
| volumes | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| volumeMounts | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| secretPaths | The map of k8s-secret name and mount path. | false | |
| configMapPaths | The map of k8s-configmap name and mount path. | false | |
| podTemplateSpec | Please refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpec | false | |
| securityContext | Please refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/ | false |
| config option | default value | description |
|---|---|---|
| k8s.build_image_bash_path | The path of command used to build image. | |
| k8s.enable_internal_algorithm | true | Whether enable internal algorithm. |
| k8s.framework_image_url | hugegraph/hugegraph-computer:latest | The image url of computer framework. |
| k8s.image_repository_password | The password for login image repository. | |
| k8s.image_repository_registry | The address for login image repository. | |
| k8s.image_repository_url | hugegraph/hugegraph-computer | The url of image repository. |
| k8s.image_repository_username | The username for login image repository. | |
| k8s.internal_algorithm | [pageRank] | The name list of all internal algorithm. |
| k8s.internal_algorithm_image_url | hugegraph/hugegraph-computer:latest | The image url of internal algorithm. |
| k8s.jar_file_dir | /cache/jars/ | The directory where the algorithm jar to upload location. |
| k8s.kube_config | ~/.kube/config | The path of k8s config file. |
| k8s.log4j_xml_path | The log4j.xml path for computer job. | |
| k8s.namespace | hugegraph-computer-system | The namespace of hugegraph-computer system. |
| k8s.pull_secret_names | [] | The names of pull-secret for pulling image. |
停止 Server，初始化执行 init-store.sh（为新的图创建数据库），重新启动 Server
$ bin/stop-hugegraph.sh
$ bin/init-store.sh
$ bin/start-hugegraph.sh
-keytool -export -alias serverkey -keystore server.keystore -file server.crt
server.crt 就是服务端的证书
keytool -import -alias serverkey -file server.crt -keystore client.truststore
-client.truststore 是给客户端⽤的，其中保存着受信任的证书
client.truststore 是给客户端⽤的，其中保存着受信任的证书
对应配置文件gremlin-server.yaml
| config option | default value | description |
|---|---|---|
| host | 127.0.0.1 | The host or ip of Gremlin Server. |
| port | 8182 | The listening port of Gremlin Server. |
| graphs | hugegraph: conf/hugegraph.properties | The map of graphs with name and config file path. |
| scriptEvaluationTimeout | 30000 | The timeout for gremlin script execution(millisecond). |
| channelizer | org.apache.tinkerpop.gremlin.server.channel.HttpChannelizer | Indicates the protocol which the Gremlin Server provides service. |
| authentication | authenticator: com.baidu.hugegraph.auth.StandardAuthenticator, config: {tokens: conf/rest-server.properties} | The authenticator and config(contains tokens path) of authentication mechanism. |
对应配置文件rest-server.properties
| config option | default value | description |
|---|---|---|
| graphs | [hugegraph:conf/hugegraph.properties] | The map of graphs’ name and config file. |
| server.id | server-1 | The id of rest server, used for license verification. |
| server.role | master | The role of nodes in the cluster, available types are [master, worker, computer] |
| restserver.url | http://127.0.0.1:8080 | The url for listening of rest server. |
| ssl.keystore_file | server.keystore | The path of server keystore file used when https protocol is enabled. |
| ssl.keystore_password | The password of the path of the server keystore file used when the https protocol is enabled. | |
| restserver.max_worker_threads | 2 * CPUs | The maximum worker threads of rest server. |
| restserver.min_free_memory | 64 | The minimum free memory(MB) of rest server, requests will be rejected when the available memory of system is lower than this value. |
| restserver.request_timeout | 30 | The time in seconds within which a request must complete, -1 means no timeout. |
| restserver.connection_idle_timeout | 30 | The time in seconds to keep an inactive connection alive, -1 means no timeout. |
| restserver.connection_max_requests | 256 | The max number of HTTP requests allowed to be processed on one keep-alive connection, -1 means unlimited. |
| gremlinserver.url | http://127.0.0.1:8182 | The url of gremlin server. |
| gremlinserver.max_route | 8 | The max route number for gremlin server. |
| gremlinserver.timeout | 30 | The timeout in seconds of waiting for gremlin server. |
| batch.max_edges_per_batch | 500 | The maximum number of edges submitted per batch. |
| batch.max_vertices_per_batch | 500 | The maximum number of vertices submitted per batch. |
| batch.max_write_ratio | 50 | The maximum thread ratio for batch writing, only take effect if the batch.max_write_threads is 0. |
| batch.max_write_threads | 0 | The maximum threads for batch writing, if the value is 0, the actual value will be set to batch.max_write_ratio * restserver.max_worker_threads. |
| auth.authenticator | The class path of authenticator implementation. e.g., com.baidu.hugegraph.auth.StandardAuthenticator, or com.baidu.hugegraph.auth.ConfigAuthenticator. | |
| auth.admin_token | 162f7848-0b6d-4faf-b557-3a0797869c55 | Token for administrator operations, only for com.baidu.hugegraph.auth.ConfigAuthenticator. |
| auth.graph_store | hugegraph | The name of graph used to store authentication information, like users, only for com.baidu.hugegraph.auth.StandardAuthenticator. |
| auth.user_tokens | [hugegraph:9fd95c9c-711b-415b-b85f-d4df46ba5c31] | The map of user tokens with name and password, only for com.baidu.hugegraph.auth.ConfigAuthenticator. |
| auth.audit_log_rate | 1000.0 | The max rate of audit log output per user, default value is 1000 records per second. |
| auth.cache_capacity | 10240 | The max cache capacity of each auth cache item. |
| auth.cache_expire | 600 | The expiration time in seconds of vertex cache. |
| auth.remote_url | If the address is empty, it provide auth service, otherwise it is auth client and also provide auth service through rpc forwarding. The remote url can be set to multiple addresses, which are concat by ‘,’. | |
| auth.token_expire | 86400 | The expiration time in seconds after token created |
| auth.token_secret | FXQXbJtbCLxODc6tGci732pkH1cyf8Qg | Secret key of HS256 algorithm. |
| exception.allow_trace | false | Whether to allow exception trace stack. |
基本配置项及后端配置项对应配置文件：{graph-name}.properties，如hugegraph.properties
| config option | default value | description |
|---|---|---|
| gremlin.graph | com.baidu.hugegraph.HugeFactory | Gremlin entrance to create graph. |
| backend | rocksdb | The data store type, available values are [memory, rocksdb, cassandra, scylladb, hbase, mysql]. |
| serializer | binary | The serializer for backend store, available values are [text, binary, cassandra, hbase, mysql]. |
| store | hugegraph | The database name like Cassandra Keyspace. |
| store.connection_detect_interval | 600 | The interval in seconds for detecting connections, if the idle time of a connection exceeds this value, detect it and reconnect if needed before using, value 0 means detecting every time. |
| store.graph | g | The graph table name, which store vertex, edge and property. |
| store.schema | m | The schema table name, which store meta data. |
| store.system | s | The system table name, which store system data. |
| schema.illegal_name_regex | .\s+$|~. | The regex specified the illegal format for schema name. |
| schema.cache_capacity | 10000 | The max cache size(items) of schema cache. |
| vertex.cache_type | l2 | The type of vertex cache, allowed values are [l1, l2]. |
| vertex.cache_capacity | 10000000 | The max cache size(items) of vertex cache. |
| vertex.cache_expire | 600 | The expire time in seconds of vertex cache. |
| vertex.check_customized_id_exist | false | Whether to check the vertices exist for those using customized id strategy. |
| vertex.default_label | vertex | The default vertex label. |
| vertex.tx_capacity | 10000 | The max size(items) of vertices(uncommitted) in transaction. |
| vertex.check_adjacent_vertex_exist | false | Whether to check the adjacent vertices of edges exist. |
| vertex.lazy_load_adjacent_vertex | true | Whether to lazy load adjacent vertices of edges. |
| vertex.part_edge_commit_size | 5000 | Whether to enable the mode to commit part of edges of vertex, enabled if commit size > 0, 0 means disabled. |
| vertex.encode_primary_key_number | true | Whether to encode number value of primary key in vertex id. |
| vertex.remove_left_index_at_overwrite | false | Whether remove left index at overwrite. |
| edge.cache_type | l2 | The type of edge cache, allowed values are [l1, l2]. |
| edge.cache_capacity | 1000000 | The max cache size(items) of edge cache. |
| edge.cache_expire | 600 | The expiration time in seconds of edge cache. |
| edge.tx_capacity | 10000 | The max size(items) of edges(uncommitted) in transaction. |
| query.page_size | 500 | The size of each page when querying by paging. |
| query.batch_size | 1000 | The size of each batch when querying by batch. |
| query.ignore_invalid_data | true | Whether to ignore invalid data of vertex or edge. |
| query.index_intersect_threshold | 1000 | The maximum number of intermediate results to intersect indexes when querying by multiple single index properties. |
| query.ramtable_edges_capacity | 20000000 | The maximum number of edges in ramtable, include OUT and IN edges. |
| query.ramtable_enable | false | Whether to enable ramtable for query of adjacent edges. |
| query.ramtable_vertices_capacity | 10000000 | The maximum number of vertices in ramtable, generally the largest vertex id is used as capacity. |
| query.optimize_aggregate_by_index | false | Whether to optimize aggregate query(like count) by index. |
| oltp.concurrent_depth | 10 | The min depth to enable concurrent oltp algorithm. |
| oltp.concurrent_threads | 10 | Thread number to concurrently execute oltp algorithm. |
| oltp.collection_type | EC | The implementation type of collections used in oltp algorithm. |
| rate_limit.read | 0 | The max rate(times/s) to execute query of vertices/edges. |
| rate_limit.write | 0 | The max rate(items/s) to add/update/delete vertices/edges. |
| task.wait_timeout | 10 | Timeout in seconds for waiting for the task to complete,such as when truncating or clearing the backend. |
| task.input_size_limit | 16777216 | The job input size limit in bytes. |
| task.result_size_limit | 16777216 | The job result size limit in bytes. |
| task.sync_deletion | false | Whether to delete schema or expired data synchronously. |
| task.ttl_delete_batch | 1 | The batch size used to delete expired data. |
| computer.config | /conf/computer.yaml | The config file path of computer job. |
| search.text_analyzer | ikanalyzer | Choose a text analyzer for searching the vertex/edge properties, available type are [word, ansj, hanlp, smartcn, jieba, jcseg, mmseg4j, ikanalyzer]. |
| search.text_analyzer_mode | smart | Specify the mode for the text analyzer, the available mode of analyzer are {word: [MaximumMatching, ReverseMaximumMatching, MinimumMatching, ReverseMinimumMatching, BidirectionalMaximumMatching, BidirectionalMinimumMatching, BidirectionalMaximumMinimumMatching, FullSegmentation, MinimalWordCount, MaxNgramScore, PureEnglish], ansj: [BaseAnalysis, IndexAnalysis, ToAnalysis, NlpAnalysis], hanlp: [standard, nlp, index, nShort, shortest, speed], smartcn: [], jieba: [SEARCH, INDEX], jcseg: [Simple, Complex], mmseg4j: [Simple, Complex, MaxWord], ikanalyzer: [smart, max_word]}. |
| snowflake.datecenter_id | 0 | The datacenter id of snowflake id generator. |
| snowflake.force_string | false | Whether to force the snowflake long id to be a string. |
| snowflake.worker_id | 0 | The worker id of snowflake id generator. |
| raft.mode | false | Whether the backend storage works in raft mode. |
| raft.safe_read | false | Whether to use linearly consistent read. |
| raft.use_snapshot | false | Whether to use snapshot. |
| raft.endpoint | 127.0.0.1:8281 | The peerid of current raft node. |
| raft.group_peers | 127.0.0.1:8281,127.0.0.1:8282,127.0.0.1:8283 | The peers of current raft group. |
| raft.path | ./raft-log | The log path of current raft node. |
| raft.use_replicator_pipeline | true | Whether to use replicator line, when turned on it multiple logs can be sent in parallel, and the next log doesn’t have to wait for the ack message of the current log to be sent. |
| raft.election_timeout | 10000 | Timeout in milliseconds to launch a round of election. |
| raft.snapshot_interval | 3600 | The interval in seconds to trigger snapshot save. |
| raft.backend_threads | current CPU v-cores | The thread number used to apply task to backend. |
| raft.read_index_threads | 8 | The thread number used to execute reading index. |
| raft.apply_batch | 1 | The apply batch size to trigger disruptor event handler. |
| raft.queue_size | 16384 | The disruptor buffers size for jraft RaftNode, StateMachine and LogManager. |
| raft.queue_publish_timeout | 60 | The timeout in second when publish event into disruptor. |
| raft.rpc_threads | 80 | The rpc threads for jraft RPC layer. |
| raft.rpc_connect_timeout | 5000 | The rpc connect timeout for jraft rpc. |
| raft.rpc_timeout | 60000 | The rpc timeout for jraft rpc. |
| raft.rpc_buf_low_water_mark | 10485760 | The ChannelOutboundBuffer’s low water mark of netty, when buffer size less than this size, the method ChannelOutboundBuffer.isWritable() will return true, it means that low downstream pressure or good network. |
| raft.rpc_buf_high_water_mark | 20971520 | The ChannelOutboundBuffer’s high water mark of netty, only when buffer size exceed this size, the method ChannelOutboundBuffer.isWritable() will return false, it means that the downstream pressure is too great to process the request or network is very congestion, upstream needs to limit rate at this time. |
| raft.read_strategy | ReadOnlyLeaseBased | The linearizability of read strategy. |
| config option | default value | description |
|---|---|---|
| rpc.client_connect_timeout | 20 | The timeout(in seconds) of rpc client connect to rpc server. |
| rpc.client_load_balancer | consistentHash | The rpc client uses a load-balancing algorithm to access multiple rpc servers in one cluster. Default value is ‘consistentHash’, means forwarding by request parameters. |
| rpc.client_read_timeout | 40 | The timeout(in seconds) of rpc client read from rpc server. |
| rpc.client_reconnect_period | 10 | The period(in seconds) of rpc client reconnect to rpc server. |
| rpc.client_retries | 3 | Failed retry number of rpc client calls to rpc server. |
| rpc.config_order | 999 | Sofa rpc configuration file loading order, the larger the more later loading. |
| rpc.logger_impl | com.alipay.sofa.rpc.log.SLF4JLoggerImpl | Sofa rpc log implementation class. |
| rpc.protocol | bolt | Rpc communication protocol, client and server need to be specified the same value. |
| rpc.remote_url | The remote urls of rpc peers, it can be set to multiple addresses, which are concat by ‘,’, empty value means not enabled. | |
| rpc.server_adaptive_port | false | Whether the bound port is adaptive, if it’s enabled, when the port is in use, automatically +1 to detect the next available port. Note that this process is not atomic, so there may still be port conflicts. |
| rpc.server_host | The hosts/ips bound by rpc server to provide services, empty value means not enabled. | |
| rpc.server_port | 8090 | The port bound by rpc server to provide services. |
| rpc.server_timeout | 30 | The timeout(in seconds) of rpc server execution. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to cassandra. | |
| serializer | Must be set to cassandra. | |
| cassandra.host | localhost | The seeds hostname or ip address of cassandra cluster. |
| cassandra.port | 9042 | The seeds port address of cassandra cluster. |
| cassandra.connect_timeout | 5 | The cassandra driver connect server timeout(seconds). |
| cassandra.read_timeout | 20 | The cassandra driver read from server timeout(seconds). |
| cassandra.keyspace.strategy | SimpleStrategy | The replication strategy of keyspace, valid value is SimpleStrategy or NetworkTopologyStrategy. |
| cassandra.keyspace.replication | [3] | The keyspace replication factor of SimpleStrategy, like ‘[3]’.Or replicas in each datacenter of NetworkTopologyStrategy, like ‘[dc1:2,dc2:1]’. |
| cassandra.username | The username to use to login to cassandra cluster. | |
| cassandra.password | The password corresponding to cassandra.username. | |
| cassandra.compression_type | none | The compression algorithm of cassandra transport: none/snappy/lz4. |
| cassandra.jmx_port=7199 | 7199 | The port of JMX API service for cassandra. |
| cassandra.aggregation_timeout | 43200 | The timeout in seconds of waiting for aggregation. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to scylladb. | |
| serializer | Must be set to scylladb. |
其它与 Cassandra 后端一致。
| config option | default value | description |
|---|---|---|
| backend | Must be set to rocksdb. | |
| serializer | Must be set to binary. | |
| rocksdb.data_disks | [] | The optimized disks for storing data of RocksDB. The format of each element: STORE/TABLE: /path/disk.Allowed keys are [g/vertex, g/edge_out, g/edge_in, g/vertex_label_index, g/edge_label_index, g/range_int_index, g/range_float_index, g/range_long_index, g/range_double_index, g/secondary_index, g/search_index, g/shard_index, g/unique_index, g/olap] |
| rocksdb.data_path | rocksdb-data | The path for storing data of RocksDB. |
| rocksdb.wal_path | rocksdb-data | The path for storing WAL of RocksDB. |
| rocksdb.allow_mmap_reads | false | Allow the OS to mmap file for reading sst tables. |
| rocksdb.allow_mmap_writes | false | Allow the OS to mmap file for writing. |
| rocksdb.block_cache_capacity | 8388608 | The amount of block cache in bytes that will be used by RocksDB, 0 means no block cache. |
| rocksdb.bloom_filter_bits_per_key | -1 | The bits per key in bloom filter, a good value is 10, which yields a filter with ~ 1% false positive rate, -1 means no bloom filter. |
| rocksdb.bloom_filter_block_based_mode | false | Use block based filter rather than full filter. |
| rocksdb.bloom_filter_whole_key_filtering | true | True if place whole keys in the bloom filter, else place the prefix of keys. |
| rocksdb.bottommost_compression | NO_COMPRESSION | The compression algorithm for the bottommost level of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.bulkload_mode | false | Switch to the mode to bulk load data into RocksDB. |
| rocksdb.cache_index_and_filter_blocks | false | Indicating if we’d put index/filter blocks to the block cache. |
| rocksdb.compaction_style | LEVEL | Set compaction style for RocksDB: LEVEL/UNIVERSAL/FIFO. |
| rocksdb.compression | SNAPPY_COMPRESSION | The compression algorithm for compressing blocks of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.compression_per_level | [NO_COMPRESSION, NO_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION] | The compression algorithms for different levels of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.delayed_write_rate | 16777216 | The rate limit in bytes/s of user write requests when need to slow down if the compaction gets behind. |
| rocksdb.log_level | INFO | The info log level of RocksDB. |
| rocksdb.max_background_jobs | 8 | Maximum number of concurrent background jobs, including flushes and compactions. |
| rocksdb.level_compaction_dynamic_level_bytes | false | Whether to enable level_compaction_dynamic_level_bytes, if it’s enabled we give max_bytes_for_level_multiplier a priority against max_bytes_for_level_base, the bytes of base level is dynamic for a more predictable LSM tree, it is useful to limit worse case space amplification. Turning this feature on/off for an existing DB can cause unexpected LSM tree structure so it’s not recommended. |
| rocksdb.max_bytes_for_level_base | 536870912 | The upper-bound of the total size of level-1 files in bytes. |
| rocksdb.max_bytes_for_level_multiplier | 10.0 | The ratio between the total size of level (L+1) files and the total size of level L files for all L. |
| rocksdb.max_open_files | -1 | The maximum number of open files that can be cached by RocksDB, -1 means no limit. |
| rocksdb.max_subcompactions | 4 | The value represents the maximum number of threads per compaction job. |
| rocksdb.max_write_buffer_number | 6 | The maximum number of write buffers that are built up in memory. |
| rocksdb.max_write_buffer_number_to_maintain | 0 | The total maximum number of write buffers to maintain in memory. |
| rocksdb.min_write_buffer_number_to_merge | 2 | The minimum number of write buffers that will be merged together. |
| rocksdb.num_levels | 7 | Set the number of levels for this database. |
| rocksdb.optimize_filters_for_hits | false | This flag allows us to not store filters for the last level. |
| rocksdb.optimize_mode | true | Optimize for heavy workloads and big datasets. |
| rocksdb.pin_l0_filter_and_index_blocks_in_cache | false | Indicating if we’d put index/filter blocks to the block cache. |
| rocksdb.sst_path | The path for ingesting SST file into RocksDB. | |
| rocksdb.target_file_size_base | 67108864 | The target file size for compaction in bytes. |
| rocksdb.target_file_size_multiplier | 1 | The size ratio between a level L file and a level (L+1) file. |
| rocksdb.use_direct_io_for_flush_and_compaction | false | Enable the OS to use direct read/writes in flush and compaction. |
| rocksdb.use_direct_reads | false | Enable the OS to use direct I/O for reading sst tables. |
| rocksdb.write_buffer_size | 134217728 | Amount of data in bytes to build up in memory. |
| rocksdb.max_manifest_file_size | 104857600 | The max size of manifest file in bytes. |
| rocksdb.skip_stats_update_on_db_open | false | Whether to skip statistics update when opening the database, setting this flag true allows us to not update statistics. |
| rocksdb.max_file_opening_threads | 16 | The max number of threads used to open files. |
| rocksdb.max_total_wal_size | 0 | Total size of WAL files in bytes. Once WALs exceed this size, we will start forcing the flush of column families related, 0 means no limit. |
| rocksdb.db_write_buffer_size | 0 | Total size of write buffers in bytes across all column families, 0 means no limit. |
| rocksdb.delete_obsolete_files_period | 21600 | The periodicity in seconds when obsolete files get deleted, 0 means always do full purge. |
| rocksdb.hard_pending_compaction_bytes_limit | 274877906944 | The hard limit to impose on pending compaction in bytes. |
| rocksdb.level0_file_num_compaction_trigger | 2 | Number of files to trigger level-0 compaction. |
| rocksdb.level0_slowdown_writes_trigger | 20 | Soft limit on number of level-0 files for slowing down writes. |
| rocksdb.level0_stop_writes_trigger | 36 | Hard limit on number of level-0 files for stopping writes. |
| rocksdb.soft_pending_compaction_bytes_limit | 68719476736 | The soft limit to impose on pending compaction in bytes. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to hbase. | |
| serializer | Must be set to hbase. | |
| hbase.hosts | localhost | The hostnames or ip addresses of HBase zookeeper, separated with commas. |
| hbase.port | 2181 | The port address of HBase zookeeper. |
| hbase.threads_max | 64 | The max threads num of hbase connections. |
| hbase.znode_parent | /hbase | The znode parent path of HBase zookeeper. |
| hbase.zk_retry | 3 | The recovery retry times of HBase zookeeper. |
| hbase.aggregation_timeout | 43200 | The timeout in seconds of waiting for aggregation. |
| hbase.kerberos_enable | false | Is Kerberos authentication enabled for HBase. |
| hbase.kerberos_keytab | The HBase’s key tab file for kerberos authentication. | |
| hbase.kerberos_principal | The HBase’s principal for kerberos authentication. | |
| hbase.krb5_conf | etc/krb5.conf | Kerberos configuration file, including KDC IP, default realm, etc. |
| hbase.hbase_site | /etc/hbase/conf/hbase-site.xml | The HBase’s configuration file |
| hbase.enable_partition | true | Is pre-split partitions enabled for HBase. |
| hbase.vertex_partitions | 10 | The number of partitions of the HBase vertex table. |
| hbase.edge_partitions | 30 | The number of partitions of the HBase edge table. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to mysql. | |
| serializer | Must be set to mysql. | |
| jdbc.driver | com.mysql.jdbc.Driver | The JDBC driver class to connect database. |
| jdbc.url | jdbc:mysql://127.0.0.1:3306 | The url of database in JDBC format. |
| jdbc.username | root | The username to login database. |
| jdbc.password | ****** | The password corresponding to jdbc.username. |
| jdbc.ssl_mode | false | The SSL mode of connections with database. |
| jdbc.reconnect_interval | 3 | The interval(seconds) between reconnections when the database connection fails. |
| jdbc.reconnect_max_times | 3 | The reconnect times when the database connection fails. |
| jdbc.storage_engine | InnoDB | The storage engine of backend store database, like InnoDB/MyISAM/RocksDB for MySQL. |
| jdbc.postgresql.connect_database | template1 | The database used to connect when init store, drop store or check store exist. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to postgresql. | |
| serializer | Must be set to postgresql. |
其它与 MySQL 后端一致。
PostgreSQL 后端的 driver 和 url 应该设置为:
jdbc.driver=org.postgresql.Driverjdbc.url=jdbc:postgresql://localhost:5432/
对应配置文件gremlin-server.yaml
| config option | default value | description |
|---|---|---|
| host | 127.0.0.1 | The host or ip of Gremlin Server. |
| port | 8182 | The listening port of Gremlin Server. |
| graphs | hugegraph: conf/hugegraph.properties | The map of graphs with name and config file path. |
| scriptEvaluationTimeout | 30000 | The timeout for gremlin script execution(millisecond). |
| channelizer | org.apache.tinkerpop.gremlin.server.channel.HttpChannelizer | Indicates the protocol which the Gremlin Server provides service. |
| authentication | authenticator: com.baidu.hugegraph.auth.StandardAuthenticator, config: {tokens: conf/rest-server.properties} | The authenticator and config(contains tokens path) of authentication mechanism. |
对应配置文件rest-server.properties
| config option | default value | description |
|---|---|---|
| graphs | [hugegraph:conf/hugegraph.properties] | The map of graphs’ name and config file. |
| server.id | server-1 | The id of rest server, used for license verification. |
| server.role | master | The role of nodes in the cluster, available types are [master, worker, computer] |
| restserver.url | http://127.0.0.1:8080 | The url for listening of rest server. |
| ssl.keystore_file | server.keystore | The path of server keystore file used when https protocol is enabled. |
| ssl.keystore_password | The password of the path of the server keystore file used when the https protocol is enabled. | |
| restserver.max_worker_threads | 2 * CPUs | The maximum worker threads of rest server. |
| restserver.min_free_memory | 64 | The minimum free memory(MB) of rest server, requests will be rejected when the available memory of system is lower than this value. |
| restserver.request_timeout | 30 | The time in seconds within which a request must complete, -1 means no timeout. |
| restserver.connection_idle_timeout | 30 | The time in seconds to keep an inactive connection alive, -1 means no timeout. |
| restserver.connection_max_requests | 256 | The max number of HTTP requests allowed to be processed on one keep-alive connection, -1 means unlimited. |
| gremlinserver.url | http://127.0.0.1:8182 | The url of gremlin server. |
| gremlinserver.max_route | 8 | The max route number for gremlin server. |
| gremlinserver.timeout | 30 | The timeout in seconds of waiting for gremlin server. |
| batch.max_edges_per_batch | 500 | The maximum number of edges submitted per batch. |
| batch.max_vertices_per_batch | 500 | The maximum number of vertices submitted per batch. |
| batch.max_write_ratio | 50 | The maximum thread ratio for batch writing, only take effect if the batch.max_write_threads is 0. |
| batch.max_write_threads | 0 | The maximum threads for batch writing, if the value is 0, the actual value will be set to batch.max_write_ratio * restserver.max_worker_threads. |
| auth.authenticator | The class path of authenticator implementation. e.g., com.baidu.hugegraph.auth.StandardAuthenticator, or com.baidu.hugegraph.auth.ConfigAuthenticator. | |
| auth.admin_token | 162f7848-0b6d-4faf-b557-3a0797869c55 | Token for administrator operations, only for com.baidu.hugegraph.auth.ConfigAuthenticator. |
| auth.graph_store | hugegraph | The name of graph used to store authentication information, like users, only for com.baidu.hugegraph.auth.StandardAuthenticator. |
| auth.user_tokens | [hugegraph:9fd95c9c-711b-415b-b85f-d4df46ba5c31] | The map of user tokens with name and password, only for com.baidu.hugegraph.auth.ConfigAuthenticator. |
| auth.audit_log_rate | 1000.0 | The max rate of audit log output per user, default value is 1000 records per second. |
| auth.cache_capacity | 10240 | The max cache capacity of each auth cache item. |
| auth.cache_expire | 600 | The expiration time in seconds of vertex cache. |
| auth.remote_url | If the address is empty, it provide auth service, otherwise it is auth client and also provide auth service through rpc forwarding. The remote url can be set to multiple addresses, which are concat by ‘,’. | |
| auth.token_expire | 86400 | The expiration time in seconds after token created |
| auth.token_secret | FXQXbJtbCLxODc6tGci732pkH1cyf8Qg | Secret key of HS256 algorithm. |
| exception.allow_trace | false | Whether to allow exception trace stack. |
基本配置项及后端配置项对应配置文件：{graph-name}.properties，如hugegraph.properties
| config option | default value | description |
|---|---|---|
| gremlin.graph | com.baidu.hugegraph.HugeFactory | Gremlin entrance to create graph. |
| backend | rocksdb | The data store type, available values are [memory, rocksdb, cassandra, scylladb, hbase, mysql]. |
| serializer | binary | The serializer for backend store, available values are [text, binary, cassandra, hbase, mysql]. |
| store | hugegraph | The database name like Cassandra Keyspace. |
| store.connection_detect_interval | 600 | The interval in seconds for detecting connections, if the idle time of a connection exceeds this value, detect it and reconnect if needed before using, value 0 means detecting every time. |
| store.graph | g | The graph table name, which store vertex, edge and property. |
| store.schema | m | The schema table name, which store meta data. |
| store.system | s | The system table name, which store system data. |
| schema.illegal_name_regex | .\s+$|~. | The regex specified the illegal format for schema name. |
| schema.cache_capacity | 10000 | The max cache size(items) of schema cache. |
| vertex.cache_type | l2 | The type of vertex cache, allowed values are [l1, l2]. |
| vertex.cache_capacity | 10000000 | The max cache size(items) of vertex cache. |
| vertex.cache_expire | 600 | The expire time in seconds of vertex cache. |
| vertex.check_customized_id_exist | false | Whether to check the vertices exist for those using customized id strategy. |
| vertex.default_label | vertex | The default vertex label. |
| vertex.tx_capacity | 10000 | The max size(items) of vertices(uncommitted) in transaction. |
| vertex.check_adjacent_vertex_exist | false | Whether to check the adjacent vertices of edges exist. |
| vertex.lazy_load_adjacent_vertex | true | Whether to lazy load adjacent vertices of edges. |
| vertex.part_edge_commit_size | 5000 | Whether to enable the mode to commit part of edges of vertex, enabled if commit size > 0, 0 means disabled. |
| vertex.encode_primary_key_number | true | Whether to encode number value of primary key in vertex id. |
| vertex.remove_left_index_at_overwrite | false | Whether remove left index at overwrite. |
| edge.cache_type | l2 | The type of edge cache, allowed values are [l1, l2]. |
| edge.cache_capacity | 1000000 | The max cache size(items) of edge cache. |
| edge.cache_expire | 600 | The expiration time in seconds of edge cache. |
| edge.tx_capacity | 10000 | The max size(items) of edges(uncommitted) in transaction. |
| query.page_size | 500 | The size of each page when querying by paging. |
| query.batch_size | 1000 | The size of each batch when querying by batch. |
| query.ignore_invalid_data | true | Whether to ignore invalid data of vertex or edge. |
| query.index_intersect_threshold | 1000 | The maximum number of intermediate results to intersect indexes when querying by multiple single index properties. |
| query.ramtable_edges_capacity | 20000000 | The maximum number of edges in ramtable, include OUT and IN edges. |
| query.ramtable_enable | false | Whether to enable ramtable for query of adjacent edges. |
| query.ramtable_vertices_capacity | 10000000 | The maximum number of vertices in ramtable, generally the largest vertex id is used as capacity. |
| query.optimize_aggregate_by_index | false | Whether to optimize aggregate query(like count) by index. |
| oltp.concurrent_depth | 10 | The min depth to enable concurrent oltp algorithm. |
| oltp.concurrent_threads | 10 | Thread number to concurrently execute oltp algorithm. |
| oltp.collection_type | EC | The implementation type of collections used in oltp algorithm. |
| rate_limit.read | 0 | The max rate(times/s) to execute query of vertices/edges. |
| rate_limit.write | 0 | The max rate(items/s) to add/update/delete vertices/edges. |
| task.wait_timeout | 10 | Timeout in seconds for waiting for the task to complete,such as when truncating or clearing the backend. |
| task.input_size_limit | 16777216 | The job input size limit in bytes. |
| task.result_size_limit | 16777216 | The job result size limit in bytes. |
| task.sync_deletion | false | Whether to delete schema or expired data synchronously. |
| task.ttl_delete_batch | 1 | The batch size used to delete expired data. |
| computer.config | /conf/computer.yaml | The config file path of computer job. |
| search.text_analyzer | ikanalyzer | Choose a text analyzer for searching the vertex/edge properties, available type are [word, ansj, hanlp, smartcn, jieba, jcseg, mmseg4j, ikanalyzer]. |
| search.text_analyzer_mode | smart | Specify the mode for the text analyzer, the available mode of analyzer are {word: [MaximumMatching, ReverseMaximumMatching, MinimumMatching, ReverseMinimumMatching, BidirectionalMaximumMatching, BidirectionalMinimumMatching, BidirectionalMaximumMinimumMatching, FullSegmentation, MinimalWordCount, MaxNgramScore, PureEnglish], ansj: [BaseAnalysis, IndexAnalysis, ToAnalysis, NlpAnalysis], hanlp: [standard, nlp, index, nShort, shortest, speed], smartcn: [], jieba: [SEARCH, INDEX], jcseg: [Simple, Complex], mmseg4j: [Simple, Complex, MaxWord], ikanalyzer: [smart, max_word]}. |
| snowflake.datecenter_id | 0 | The datacenter id of snowflake id generator. |
| snowflake.force_string | false | Whether to force the snowflake long id to be a string. |
| snowflake.worker_id | 0 | The worker id of snowflake id generator. |
| raft.mode | false | Whether the backend storage works in raft mode. |
| raft.safe_read | false | Whether to use linearly consistent read. |
| raft.use_snapshot | false | Whether to use snapshot. |
| raft.endpoint | 127.0.0.1:8281 | The peerid of current raft node. |
| raft.group_peers | 127.0.0.1:8281,127.0.0.1:8282,127.0.0.1:8283 | The peers of current raft group. |
| raft.path | ./raft-log | The log path of current raft node. |
| raft.use_replicator_pipeline | true | Whether to use replicator line, when turned on it multiple logs can be sent in parallel, and the next log doesn’t have to wait for the ack message of the current log to be sent. |
| raft.election_timeout | 10000 | Timeout in milliseconds to launch a round of election. |
| raft.snapshot_interval | 3600 | The interval in seconds to trigger snapshot save. |
| raft.backend_threads | current CPU v-cores | The thread number used to apply task to backend. |
| raft.read_index_threads | 8 | The thread number used to execute reading index. |
| raft.apply_batch | 1 | The apply batch size to trigger disruptor event handler. |
| raft.queue_size | 16384 | The disruptor buffers size for jraft RaftNode, StateMachine and LogManager. |
| raft.queue_publish_timeout | 60 | The timeout in second when publish event into disruptor. |
| raft.rpc_threads | 80 | The rpc threads for jraft RPC layer. |
| raft.rpc_connect_timeout | 5000 | The rpc connect timeout for jraft rpc. |
| raft.rpc_timeout | 60000 | The rpc timeout for jraft rpc. |
| raft.rpc_buf_low_water_mark | 10485760 | The ChannelOutboundBuffer’s low water mark of netty, when buffer size less than this size, the method ChannelOutboundBuffer.isWritable() will return true, it means that low downstream pressure or good network. |
| raft.rpc_buf_high_water_mark | 20971520 | The ChannelOutboundBuffer’s high water mark of netty, only when buffer size exceed this size, the method ChannelOutboundBuffer.isWritable() will return false, it means that the downstream pressure is too great to process the request or network is very congestion, upstream needs to limit rate at this time. |
| raft.read_strategy | ReadOnlyLeaseBased | The linearizability of read strategy. |
| config option | default value | description |
|---|---|---|
| rpc.client_connect_timeout | 20 | The timeout(in seconds) of rpc client connect to rpc server. |
| rpc.client_load_balancer | consistentHash | The rpc client uses a load-balancing algorithm to access multiple rpc servers in one cluster. Default value is ‘consistentHash’, means forwarding by request parameters. |
| rpc.client_read_timeout | 40 | The timeout(in seconds) of rpc client read from rpc server. |
| rpc.client_reconnect_period | 10 | The period(in seconds) of rpc client reconnect to rpc server. |
| rpc.client_retries | 3 | Failed retry number of rpc client calls to rpc server. |
| rpc.config_order | 999 | Sofa rpc configuration file loading order, the larger the more later loading. |
| rpc.logger_impl | com.alipay.sofa.rpc.log.SLF4JLoggerImpl | Sofa rpc log implementation class. |
| rpc.protocol | bolt | Rpc communication protocol, client and server need to be specified the same value. |
| rpc.remote_url | The remote urls of rpc peers, it can be set to multiple addresses, which are concat by ‘,’, empty value means not enabled. | |
| rpc.server_adaptive_port | false | Whether the bound port is adaptive, if it’s enabled, when the port is in use, automatically +1 to detect the next available port. Note that this process is not atomic, so there may still be port conflicts. |
| rpc.server_host | The hosts/ips bound by rpc server to provide services, empty value means not enabled. | |
| rpc.server_port | 8090 | The port bound by rpc server to provide services. |
| rpc.server_timeout | 30 | The timeout(in seconds) of rpc server execution. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to cassandra. | |
| serializer | Must be set to cassandra. | |
| cassandra.host | localhost | The seeds hostname or ip address of cassandra cluster. |
| cassandra.port | 9042 | The seeds port address of cassandra cluster. |
| cassandra.connect_timeout | 5 | The cassandra driver connect server timeout(seconds). |
| cassandra.read_timeout | 20 | The cassandra driver read from server timeout(seconds). |
| cassandra.keyspace.strategy | SimpleStrategy | The replication strategy of keyspace, valid value is SimpleStrategy or NetworkTopologyStrategy. |
| cassandra.keyspace.replication | [3] | The keyspace replication factor of SimpleStrategy, like ‘[3]’.Or replicas in each datacenter of NetworkTopologyStrategy, like ‘[dc1:2,dc2:1]’. |
| cassandra.username | The username to use to login to cassandra cluster. | |
| cassandra.password | The password corresponding to cassandra.username. | |
| cassandra.compression_type | none | The compression algorithm of cassandra transport: none/snappy/lz4. |
| cassandra.jmx_port=7199 | 7199 | The port of JMX API service for cassandra. |
| cassandra.aggregation_timeout | 43200 | The timeout in seconds of waiting for aggregation. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to scylladb. | |
| serializer | Must be set to scylladb. |
其它与 Cassandra 后端一致。
| config option | default value | description |
|---|---|---|
| backend | Must be set to rocksdb. | |
| serializer | Must be set to binary. | |
| rocksdb.data_disks | [] | The optimized disks for storing data of RocksDB. The format of each element: STORE/TABLE: /path/disk.Allowed keys are [g/vertex, g/edge_out, g/edge_in, g/vertex_label_index, g/edge_label_index, g/range_int_index, g/range_float_index, g/range_long_index, g/range_double_index, g/secondary_index, g/search_index, g/shard_index, g/unique_index, g/olap] |
| rocksdb.data_path | rocksdb-data | The path for storing data of RocksDB. |
| rocksdb.wal_path | rocksdb-data | The path for storing WAL of RocksDB. |
| rocksdb.allow_mmap_reads | false | Allow the OS to mmap file for reading sst tables. |
| rocksdb.allow_mmap_writes | false | Allow the OS to mmap file for writing. |
| rocksdb.block_cache_capacity | 8388608 | The amount of block cache in bytes that will be used by RocksDB, 0 means no block cache. |
| rocksdb.bloom_filter_bits_per_key | -1 | The bits per key in bloom filter, a good value is 10, which yields a filter with ~ 1% false positive rate, -1 means no bloom filter. |
| rocksdb.bloom_filter_block_based_mode | false | Use block based filter rather than full filter. |
| rocksdb.bloom_filter_whole_key_filtering | true | True if place whole keys in the bloom filter, else place the prefix of keys. |
| rocksdb.bottommost_compression | NO_COMPRESSION | The compression algorithm for the bottommost level of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.bulkload_mode | false | Switch to the mode to bulk load data into RocksDB. |
| rocksdb.cache_index_and_filter_blocks | false | Indicating if we’d put index/filter blocks to the block cache. |
| rocksdb.compaction_style | LEVEL | Set compaction style for RocksDB: LEVEL/UNIVERSAL/FIFO. |
| rocksdb.compression | SNAPPY_COMPRESSION | The compression algorithm for compressing blocks of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.compression_per_level | [NO_COMPRESSION, NO_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION] | The compression algorithms for different levels of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.delayed_write_rate | 16777216 | The rate limit in bytes/s of user write requests when need to slow down if the compaction gets behind. |
| rocksdb.log_level | INFO | The info log level of RocksDB. |
| rocksdb.max_background_jobs | 8 | Maximum number of concurrent background jobs, including flushes and compactions. |
| rocksdb.level_compaction_dynamic_level_bytes | false | Whether to enable level_compaction_dynamic_level_bytes, if it’s enabled we give max_bytes_for_level_multiplier a priority against max_bytes_for_level_base, the bytes of base level is dynamic for a more predictable LSM tree, it is useful to limit worse case space amplification. Turning this feature on/off for an existing DB can cause unexpected LSM tree structure so it’s not recommended. |
| rocksdb.max_bytes_for_level_base | 536870912 | The upper-bound of the total size of level-1 files in bytes. |
| rocksdb.max_bytes_for_level_multiplier | 10.0 | The ratio between the total size of level (L+1) files and the total size of level L files for all L. |
| rocksdb.max_open_files | -1 | The maximum number of open files that can be cached by RocksDB, -1 means no limit. |
| rocksdb.max_subcompactions | 4 | The value represents the maximum number of threads per compaction job. |
| rocksdb.max_write_buffer_number | 6 | The maximum number of write buffers that are built up in memory. |
| rocksdb.max_write_buffer_number_to_maintain | 0 | The total maximum number of write buffers to maintain in memory. |
| rocksdb.min_write_buffer_number_to_merge | 2 | The minimum number of write buffers that will be merged together. |
| rocksdb.num_levels | 7 | Set the number of levels for this database. |
| rocksdb.optimize_filters_for_hits | false | This flag allows us to not store filters for the last level. |
| rocksdb.optimize_mode | true | Optimize for heavy workloads and big datasets. |
| rocksdb.pin_l0_filter_and_index_blocks_in_cache | false | Indicating if we’d put index/filter blocks to the block cache. |
| rocksdb.sst_path | The path for ingesting SST file into RocksDB. | |
| rocksdb.target_file_size_base | 67108864 | The target file size for compaction in bytes. |
| rocksdb.target_file_size_multiplier | 1 | The size ratio between a level L file and a level (L+1) file. |
| rocksdb.use_direct_io_for_flush_and_compaction | false | Enable the OS to use direct read/writes in flush and compaction. |
| rocksdb.use_direct_reads | false | Enable the OS to use direct I/O for reading sst tables. |
| rocksdb.write_buffer_size | 134217728 | Amount of data in bytes to build up in memory. |
| rocksdb.max_manifest_file_size | 104857600 | The max size of manifest file in bytes. |
| rocksdb.skip_stats_update_on_db_open | false | Whether to skip statistics update when opening the database, setting this flag true allows us to not update statistics. |
| rocksdb.max_file_opening_threads | 16 | The max number of threads used to open files. |
| rocksdb.max_total_wal_size | 0 | Total size of WAL files in bytes. Once WALs exceed this size, we will start forcing the flush of column families related, 0 means no limit. |
| rocksdb.db_write_buffer_size | 0 | Total size of write buffers in bytes across all column families, 0 means no limit. |
| rocksdb.delete_obsolete_files_period | 21600 | The periodicity in seconds when obsolete files get deleted, 0 means always do full purge. |
| rocksdb.hard_pending_compaction_bytes_limit | 274877906944 | The hard limit to impose on pending compaction in bytes. |
| rocksdb.level0_file_num_compaction_trigger | 2 | Number of files to trigger level-0 compaction. |
| rocksdb.level0_slowdown_writes_trigger | 20 | Soft limit on number of level-0 files for slowing down writes. |
| rocksdb.level0_stop_writes_trigger | 36 | Hard limit on number of level-0 files for stopping writes. |
| rocksdb.soft_pending_compaction_bytes_limit | 68719476736 | The soft limit to impose on pending compaction in bytes. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to hbase. | |
| serializer | Must be set to hbase. | |
| hbase.hosts | localhost | The hostnames or ip addresses of HBase zookeeper, separated with commas. |
| hbase.port | 2181 | The port address of HBase zookeeper. |
| hbase.threads_max | 64 | The max threads num of hbase connections. |
| hbase.znode_parent | /hbase | The znode parent path of HBase zookeeper. |
| hbase.zk_retry | 3 | The recovery retry times of HBase zookeeper. |
| hbase.aggregation_timeout | 43200 | The timeout in seconds of waiting for aggregation. |
| hbase.kerberos_enable | false | Is Kerberos authentication enabled for HBase. |
| hbase.kerberos_keytab | The HBase’s key tab file for kerberos authentication. | |
| hbase.kerberos_principal | The HBase’s principal for kerberos authentication. | |
| hbase.krb5_conf | etc/krb5.conf | Kerberos configuration file, including KDC IP, default realm, etc. |
| hbase.hbase_site | /etc/hbase/conf/hbase-site.xml | The HBase’s configuration file |
| hbase.enable_partition | true | Is pre-split partitions enabled for HBase. |
| hbase.vertex_partitions | 10 | The number of partitions of the HBase vertex table. |
| hbase.edge_partitions | 30 | The number of partitions of the HBase edge table. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to mysql. | |
| serializer | Must be set to mysql. | |
| jdbc.driver | com.mysql.jdbc.Driver | The JDBC driver class to connect database. |
| jdbc.url | jdbc:mysql://127.0.0.1:3306 | The url of database in JDBC format. |
| jdbc.username | root | The username to login database. |
| jdbc.password | ****** | The password corresponding to jdbc.username. |
| jdbc.ssl_mode | false | The SSL mode of connections with database. |
| jdbc.reconnect_interval | 3 | The interval(seconds) between reconnections when the database connection fails. |
| jdbc.reconnect_max_times | 3 | The reconnect times when the database connection fails. |
| jdbc.storage_engine | InnoDB | The storage engine of backend store database, like InnoDB/MyISAM/RocksDB for MySQL. |
| jdbc.postgresql.connect_database | template1 | The database used to connect when init store, drop store or check store exist. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to postgresql. | |
| serializer | Must be set to postgresql. |
其它与 MySQL 后端一致。
PostgreSQL 后端的 driver 和 url 应该设置为:
jdbc.driver=org.postgresql.Driverjdbc.url=jdbc:postgresql://localhost:5432/
The latest HugeGraph: 0.12.0, released on 2021-12-31.
| components | description | download |
|---|---|---|
| HugeGraph-Server | HugeGraph的主程序 | 0.12.0 |
| HugeGraph-Hubble | 基于Web的可视化图形界面 | 1.6.0 |
| HugeGraph-Loader | 数据导入工具 | 0.12.0 |
| HugeGraph-Tools | 命令行工具集 | 1.6.0 |
说明：最新的图分析和展示平台为 hubble，支持 0.10 及之后的 server 版本；studio 为 server 0.10.x 以及之前的版本的图分析和展示平台，其功能从 0.10 起不再更新。
The latest HugeGraph: 0.12.0, released on 2021-12-31.
| components | description | download |
|---|---|---|
| HugeGraph-Server | HugeGraph的主程序 | 0.12.0 |
| HugeGraph-Hubble | 基于Web的可视化图形界面 | 1.6.0 |
| HugeGraph-Loader | 数据导入工具 | 0.12.0 |
| HugeGraph-Tools | 命令行工具集 | 1.6.0 |
说明：最新的图分析和展示平台为 hubble，支持 0.10 及之后的 server 版本；studio 为 server 0.10.x 以及之前的版本的图分析和展示平台，其功能从 0.10 起不再更新。
查询某一类别的顶点或边（query by label）时提示超时
由于属于某一label的数据量可能比较多，请加上limit限制。
通过RESTful API操作图是可以的，但是发送Gremlin语句就报错：Request Failed(500)
可能是GremlinServer的配置有误，检查gremlin-server.yaml的host、port是否与rest-server.properties的gremlinserver.url匹配，如不匹配则修改，然后重启服务。
使用Loader导数据出现Socket Timeout异常，然后导致Loader中断
持续地导入数据会使Server的压力过大，然后导致有些请求超时。可以通过调整Loader的参数来适当缓解Server压力（如：重试次数，重试间隔，错误容忍数等），降低该问题出现频率。
如何删除全部的顶点和边，RESTful API中没有这样的接口，调用gremlin的g.V().drop()会报错Vertices in transaction have reached capacity xxx
目前确实没有好办法删除全部的数据，用户如果是自己部署的Server和后端，可以直接清空数据库，重启Server。可以使用paging API或scan API先获取所有数据，再逐条删除。
清空了数据库，并且执行了init-store，但是添加schema时提示"xxx has existed"
HugeGraphServer内是有缓存的，清空数据库的同时是需要重启Server的，否则残留的缓存会产生不一致。
插入顶点或边的过程中报错：Id max length is 128, but got xxx {yyy} 或 Big id max length is 32768, but got xxx
为了保证查询性能，目前的后端存储对id列的长度做了限制，顶点id不能超过128字节，边id长度不能超过32768字节，索引id不能超过128字节。
是否支持嵌套属性，如果不支持，是否有什么替代方案
嵌套属性目前暂不支持。替代方案：可以把嵌套属性作为单独的顶点拿出来，然后用边连接起来。
一个EdgeLabel是否可以连接多对VertexLabel，比如"投资"关系，可以是"个人"投资"企业"，也可以是"企业"投资"企业"
一个EdgeLabel不支持连接多对VertexLabel，需要用户将EdgeLabel拆分得更细一点，如：“个人投资”，“企业投资”。
通过RestAPI发送请求时提示HTTP 415 Unsupported Media Type
请求头中需要指定Content-Type:application/json
其他问题可以在对应项目的 issue 区搜索，例如 Server-Issues / Loader Issues
+查询某一类别的顶点或边（query by label）时提示超时
由于属于某一label的数据量可能比较多，请加上limit限制。
通过RESTful API操作图是可以的，但是发送Gremlin语句就报错：Request Failed(500)
可能是GremlinServer的配置有误，检查gremlin-server.yaml的host、port是否与rest-server.properties的gremlinserver.url匹配，如不匹配则修改，然后重启服务。
使用Loader导数据出现Socket Timeout异常，然后导致Loader中断
持续地导入数据会使Server的压力过大，然后导致有些请求超时。可以通过调整Loader的参数来适当缓解Server压力（如：重试次数，重试间隔，错误容忍数等），降低该问题出现频率。
如何删除全部的顶点和边，RESTful API中没有这样的接口，调用gremlin的g.V().drop()会报错Vertices in transaction have reached capacity xxx
目前确实没有好办法删除全部的数据，用户如果是自己部署的Server和后端，可以直接清空数据库，重启Server。可以使用paging API或scan API先获取所有数据，再逐条删除。
清空了数据库，并且执行了init-store，但是添加schema时提示"xxx has existed"
HugeGraphServer内是有缓存的，清空数据库的同时是需要重启Server的，否则残留的缓存会产生不一致。
插入顶点或边的过程中报错：Id max length is 128, but got xxx {yyy} 或 Big id max length is 32768, but got xxx
为了保证查询性能，目前的后端存储对id列的长度做了限制，顶点id不能超过128字节，边id长度不能超过32768字节，索引id不能超过128字节。
是否支持嵌套属性，如果不支持，是否有什么替代方案
嵌套属性目前暂不支持。替代方案：可以把嵌套属性作为单独的顶点拿出来，然后用边连接起来。
一个EdgeLabel是否可以连接多对VertexLabel，比如"投资"关系，可以是"个人"投资"企业"，也可以是"企业"投资"企业"
一个EdgeLabel不支持连接多对VertexLabel，需要用户将EdgeLabel拆分得更细一点，如：“个人投资”，“企业投资”。
通过RestAPI发送请求时提示HTTP 415 Unsupported Media Type
请求头中需要指定Content-Type:application/json
其他问题可以在对应项目的 issue 区搜索，例如 Server-Issues / Loader Issues
diff --git a/cn/docs/guides/architectural/index.html b/cn/docs/guides/architectural/index.html index 44dcd146d..f56e77f4b 100644 --- a/cn/docs/guides/architectural/index.html +++ b/cn/docs/guides/architectural/index.html @@ -11,7 +11,7 @@ Create child page Create documentation issue Create project issue - Print entire section作为一款通用的图数据库产品，HugeGraph需具备图数据的基本功能，如下图所示。HugeGraph包括三个层次的功能，分别是存储层、计算层和用户接口层。 HugeGraph支持OLTP和OLAP两种图计算类型，其中OLTP实现了Apache TinkerPop3框架，并支持Gremlin查询语言。 OLAP计算是基于SparkGraphX实现。
HugeGraph的主要功能分为HugeCore、ApiServer、HugeGraph-Client、HugeGraph-Loader和HugeGraph-Studio等组件构成，各组件之间的通信关系如下图所示。
作为一款通用的图数据库产品，HugeGraph需具备图数据的基本功能，如下图所示。HugeGraph包括三个层次的功能，分别是存储层、计算层和用户接口层。 HugeGraph支持OLTP和OLAP两种图计算类型，其中OLTP实现了Apache TinkerPop3框架，并支持Gremlin查询语言。 OLAP计算是基于SparkGraphX实现。
HugeGraph的主要功能分为HugeCore、ApiServer、HugeGraph-Client、HugeGraph-Loader和HugeGraph-Studio等组件构成，各组件之间的通信关系如下图所示。
{
"mode": "RESTORING"
}
-通过maven打包，在项目目录下执行命令mvn package，在target目录下会生成Jar包文件。
-使用时将该Jar包拷到plugins目录，重启服务即可生效。
plugins目录，重启服务即可生效。RESTful API暂时未暴露事务接口
TinkerPop API允许打开事务，请求完成时会自动关闭(Gremlin Server强制关闭)
RESTful API暂时未暴露事务接口
TinkerPop API允许打开事务，请求完成时会自动关闭(Gremlin Server强制关闭)
查询某一类别的顶点或边（query by label）时提示超时
由于属于某一label的数据量可能比较多，请加上limit限制。
通过RESTful API操作图是可以的，但是发送Gremlin语句就报错：Request Failed(500)
可能是GremlinServer的配置有误，检查gremlin-server.yaml的host、port是否与rest-server.properties的gremlinserver.url匹配，如不匹配则修改，然后重启服务。
使用Loader导数据出现Socket Timeout异常，然后导致Loader中断
持续地导入数据会使Server的压力过大，然后导致有些请求超时。可以通过调整Loader的参数来适当缓解Server压力（如：重试次数，重试间隔，错误容忍数等），降低该问题出现频率。
如何删除全部的顶点和边，RESTful API中没有这样的接口，调用gremlin的g.V().drop()会报错Vertices in transaction have reached capacity xxx
目前确实没有好办法删除全部的数据，用户如果是自己部署的Server和后端，可以直接清空数据库，重启Server。可以使用paging API或scan API先获取所有数据，再逐条删除。
清空了数据库，并且执行了init-store，但是添加schema时提示"xxx has existed"
HugeGraphServer内是有缓存的，清空数据库的同时是需要重启Server的，否则残留的缓存会产生不一致。
插入顶点或边的过程中报错：Id max length is 128, but got xxx {yyy} 或 Big id max length is 32768, but got xxx
为了保证查询性能，目前的后端存储对id列的长度做了限制，顶点id不能超过128字节，边id长度不能超过32768字节，索引id不能超过128字节。
是否支持嵌套属性，如果不支持，是否有什么替代方案
嵌套属性目前暂不支持。替代方案：可以把嵌套属性作为单独的顶点拿出来，然后用边连接起来。
一个EdgeLabel是否可以连接多对VertexLabel，比如"投资"关系，可以是"个人"投资"企业"，也可以是"企业"投资"企业"
一个EdgeLabel不支持连接多对VertexLabel，需要用户将EdgeLabel拆分得更细一点，如：“个人投资”，“企业投资”。
通过RestAPI发送请求时提示HTTP 415 Unsupported Media Type
请求头中需要指定Content-Type:application/json
其他问题可以在对应项目的 issue 区搜索，例如 Server-Issues / Loader Issues
查询某一类别的顶点或边（query by label）时提示超时
由于属于某一label的数据量可能比较多，请加上limit限制。
通过RESTful API操作图是可以的，但是发送Gremlin语句就报错：Request Failed(500)
可能是GremlinServer的配置有误，检查gremlin-server.yaml的host、port是否与rest-server.properties的gremlinserver.url匹配，如不匹配则修改，然后重启服务。
使用Loader导数据出现Socket Timeout异常，然后导致Loader中断
持续地导入数据会使Server的压力过大，然后导致有些请求超时。可以通过调整Loader的参数来适当缓解Server压力（如：重试次数，重试间隔，错误容忍数等），降低该问题出现频率。
如何删除全部的顶点和边，RESTful API中没有这样的接口，调用gremlin的g.V().drop()会报错Vertices in transaction have reached capacity xxx
目前确实没有好办法删除全部的数据，用户如果是自己部署的Server和后端，可以直接清空数据库，重启Server。可以使用paging API或scan API先获取所有数据，再逐条删除。
清空了数据库，并且执行了init-store，但是添加schema时提示"xxx has existed"
HugeGraphServer内是有缓存的，清空数据库的同时是需要重启Server的，否则残留的缓存会产生不一致。
插入顶点或边的过程中报错：Id max length is 128, but got xxx {yyy} 或 Big id max length is 32768, but got xxx
为了保证查询性能，目前的后端存储对id列的长度做了限制，顶点id不能超过128字节，边id长度不能超过32768字节，索引id不能超过128字节。
是否支持嵌套属性，如果不支持，是否有什么替代方案
嵌套属性目前暂不支持。替代方案：可以把嵌套属性作为单独的顶点拿出来，然后用边连接起来。
一个EdgeLabel是否可以连接多对VertexLabel，比如"投资"关系，可以是"个人"投资"企业"，也可以是"企业"投资"企业"
一个EdgeLabel不支持连接多对VertexLabel，需要用户将EdgeLabel拆分得更细一点，如：“个人投资”，“企业投资”。
通过RestAPI发送请求时提示HTTP 415 Unsupported Media Type
请求头中需要指定Content-Type:application/json
其他问题可以在对应项目的 issue 区搜索，例如 Server-Issues / Loader Issues
欢迎阅读HugeGraph文档
欢迎阅读HugeGraph文档
HugeGraph典型应用场景包括深度关系探索、关联分析、路径搜索、特征抽取、数据聚类、社区检测、 知识图谱等，适用业务领域有如网络安全、电信诈骗、金融风控、广告推荐、社交网络和智能机器人等。
本系统的主要应用场景是解决反欺诈、威胁情报、黑产打击等业务的图数据存储和建模分析需求，在此基础上逐步扩展及支持了更多的通用图应用。
HugeGraph支持在线及离线环境下的图操作，支持批量导入数据，支持高效的复杂关联关系分析，并且能够与大数据平台无缝集成。 -HugeGraph支持多用户并行操作，用户可输入Gremlin查询语句，并及时得到图查询结果，也可在用户程序中调用HugeGraph API进行图分析或查询。
本系统具备如下特点：
本系统的功能包括但不限于：
本系统具备如下特点：
本系统的功能包括但不限于：
推荐使用HugeGraph-Studio 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。
HugeGraph 目前支持 Gremlin 的语法，用户可以通过 Gremlin / REST-API 实现各种查询需求。
推荐使用HugeGraph-Studio 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。
HugeGraph 目前支持 Gremlin 的语法，用户可以通过 Gremlin / REST-API 实现各种查询需求。
推荐使用HugeGraph-Studio 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。
HugeGraph 目前支持 Gremlin 的语法，用户可以通过 Gremlin / REST-API 实现各种查询需求。
推荐使用HugeGraph-Studio 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。
HugeGraph 目前支持 Gremlin 的语法，用户可以通过 Gremlin / REST-API 实现各种查询需求。
HugeGraph支持Apache TinkerPop3的图形遍历查询语言Gremlin。 SQL是关系型数据库查询语言，而Gremlin是一种通用的图数据库查询语言，Gremlin可用于创建图的实体（Vertex和Edge）、修改实体内部属性、删除实体，也可执行图的查询操作。
Gremlin可用于创建图的实体（Vertex和Edge）、修改实体内部属性、删除实体，更主要的是可用于执行图的查询及分析操作。
HugeGraph实现了TinkerPop框架，但是并没有实现TinkerPop所有的特性。
下表列出HugeGraph对TinkerPop各种特性的支持情况：
| Name | Description | Support |
|---|---|---|
| Computer | Determines if the {@code Graph} implementation supports {@link GraphComputer} based processing | false |
| Transactions | Determines if the {@code Graph} implementations supports transactions. | true |
| Persistence | Determines if the {@code Graph} implementation supports persisting it’s contents natively to disk.This feature does not refer to every graph’s ability to write to disk via the Gremlin IO packages(.e.g. GraphML), unless the graph natively persists to disk via those options somehow. For example,TinkerGraph does not support this feature as it is a pure in-sideEffects graph. | true |
| ThreadedTransactions | Determines if the {@code Graph} implementation supports threaded transactions which allow a transaction be executed across multiple threads via {@link Transaction#createThreadedTx()}. | false |
| ConcurrentAccess | Determines if the {@code Graph} implementation supports more than one connection to the same instance at the same time. For example, Neo4j embedded does not support this feature because concurrent access to the same database files by multiple instances is not possible. However, Neo4j HA could support this feature as each new {@code Graph} instance coordinates with the Neo4j cluster allowing multiple instances to operate on the same database. | false |
| Name | Description | Support |
|---|---|---|
| UserSuppliedIds | Determines if an {@link Element} can have a user defined identifier. Implementation that do not support this feature will be expected to auto-generate unique identifiers. In other words, if the {@link Graph} allows {@code graph.addVertex(id,x)} to work and thus set the identifier of the newly added {@link Vertex} to the value of {@code x} then this feature should return true. In this case, {@code x} is assumed to be an identifier data type that the {@link Graph} will accept. | false |
| NumericIds | Determines if an {@link Element} has numeric identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a numeric value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| StringIds | Determines if an {@link Element} has string identifiers as their internal representation. In other words, if the value returned from {@link Element#id()} is a string value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| UuidIds | Determines if an {@link Element} has UUID identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a {@link UUID} value then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| CustomIds | Determines if an {@link Element} has a specific custom object as their internal representation.In other words, if the value returned from {@link Element#id()} is a type defined by the graph implementations, such as OrientDB’s {@code Rid}, then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| AnyIds | Determines if an {@link Element} any Java object is a suitable identifier. TinkerGraph is a good example of a {@link Graph} that can support this feature, as it can use any {@link Object} as a value for the identifier. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. This setting should only return {@code true} if {@link #supportsUserSuppliedIds()} is {@code true}. | false |
| AddProperty | Determines if an {@link Element} allows properties to be added. This feature is set independently from supporting “data types” and refers to support of calls to {@link Element#property(String, Object)}. | true |
| RemoveProperty | Determines if an {@link Element} allows properties to be removed. | true |
| AddVertices | Determines if a {@link Vertex} can be added to the {@code Graph}. | true |
| MultiProperties | Determines if a {@link Vertex} can support multiple properties with the same key. | false |
| DuplicateMultiProperties | Determines if a {@link Vertex} can support non-unique values on the same key. For this value to be {@code true}, then {@link #supportsMetaProperties()} must also return true. By default this method, just returns what {@link #supportsMultiProperties()} returns. | false |
| MetaProperties | Determines if a {@link Vertex} can support properties on vertex properties. It is assumed that a graph will support all the same data types for meta-properties that are supported for regular properties. | false |
| RemoveVertices | Determines if a {@link Vertex} can be removed from the {@code Graph}. | true |
| Name | Description | Support |
|---|---|---|
| UserSuppliedIds | Determines if an {@link Element} can have a user defined identifier. Implementation that do not support this feature will be expected to auto-generate unique identifiers. In other words, if the {@link Graph} allows {@code graph.addVertex(id,x)} to work and thus set the identifier of the newly added {@link Vertex} to the value of {@code x} then this feature should return true. In this case, {@code x} is assumed to be an identifier data type that the {@link Graph} will accept. | false |
| NumericIds | Determines if an {@link Element} has numeric identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a numeric value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| StringIds | Determines if an {@link Element} has string identifiers as their internal representation. In other words, if the value returned from {@link Element#id()} is a string value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| UuidIds | Determines if an {@link Element} has UUID identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a {@link UUID} value then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| CustomIds | Determines if an {@link Element} has a specific custom object as their internal representation.In other words, if the value returned from {@link Element#id()} is a type defined by the graph implementations, such as OrientDB’s {@code Rid}, then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| AnyIds | Determines if an {@link Element} any Java object is a suitable identifier. TinkerGraph is a good example of a {@link Graph} that can support this feature, as it can use any {@link Object} as a value for the identifier. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. This setting should only return {@code true} if {@link #supportsUserSuppliedIds()} is {@code true}. | false |
| AddProperty | Determines if an {@link Element} allows properties to be added. This feature is set independently from supporting “data types” and refers to support of calls to {@link Element#property(String, Object)}. | true |
| RemoveProperty | Determines if an {@link Element} allows properties to be removed. | true |
| AddEdges | Determines if an {@link Edge} can be added to a {@code Vertex}. | true |
| RemoveEdges | Determines if an {@link Edge} can be removed from a {@code Vertex}. | true |
| Name | Description | Support |
|---|---|---|
| BooleanValues | true | |
| ByteValues | true | |
| DoubleValues | true | |
| FloatValues | true | |
| IntegerValues | true | |
| LongValues | true | |
| MapValues | Supports setting of a {@code Map} value. The assumption is that the {@code Map} can contain arbitrary serializable values that may or may not be defined as a feature itself | false |
| MixedListValues | Supports setting of a {@code List} value. The assumption is that the {@code List} can contain arbitrary serializable values that may or may not be defined as a feature itself. As this{@code List} is “mixed” it does not need to contain objects of the same type. | false |
| BooleanArrayValues | false | |
| ByteArrayValues | true | |
| DoubleArrayValues | false | |
| FloatArrayValues | false | |
| IntegerArrayValues | false | |
| LongArrayValues | false | |
| SerializableValues | false | |
| StringArrayValues | false | |
| StringValues | true | |
| UniformListValues | Supports setting of a {@code List} value. The assumption is that the {@code List} can contain arbitrary serializable values that may or may not be defined as a feature itself. As this{@code List} is “uniform” it must contain objects of the same type. | false |
HugeGraph支持Gremlin的所有步骤。有关Gremlin的完整参考信息，请参与Gremlin官网。
| 步骤 | 说明 | 文档 |
|---|---|---|
| addE | 在两个顶点之间添加边 | addE step |
| addV | 将顶点添加到图形 | addV step |
| and | 确保所有遍历都返回值 | and step |
| as | 用于向步骤的输出分配变量的步骤调制器 | as step |
| by | 与group和order配合使用的步骤调制器 | by step |
| coalesce | 返回第一个返回结果的遍历 | coalesce step |
| constant | 返回常量值。 与coalesce配合使用 | constant step |
| count | 从遍历返回计数 | count step |
| dedup | 返回已删除重复内容的值 | dedup step |
| drop | 丢弃值（顶点/边缘） | drop step |
| fold | 充当用于计算结果聚合值的屏障 | fold step |
| group | 根据指定的标签将值分组 | group step |
| has | 用于筛选属性、顶点和边缘。 支持hasLabel、hasId、hasNot 和 has 变体 | has step |
| inject | 将值注入流中 | inject step |
| is | 用于通过布尔表达式执行筛选器 | is step |
| limit | 用于限制遍历中的项数 | limit step |
| local | 本地包装遍历的某个部分，类似于子查询 | local step |
| not | 用于生成筛选器的求反结果 | not step |
| optional | 如果生成了某个结果，则返回指定遍历的结果，否则返回调用元素 | optional step |
| or | 确保至少有一个遍历会返回值 | or step |
| order | 按指定的排序顺序返回结果 | order step |
| path | 返回遍历的完整路径 | path step |
| project | 将属性投影为映射 | project step |
| properties | 返回指定标签的属性 | properties step |
| range | 根据指定的值范围进行筛选 | range step |
| repeat | 将步骤重复指定的次数。 用于循环 | repeat step |
| sample | 用于对遍历返回的结果采样 | sample step |
| select | 用于投影遍历返回的结果 | select step |
| store | 用于遍历返回的非阻塞聚合 | store step |
| tree | 将顶点中的路径聚合到树中 | tree step |
| unfold | 将迭代器作为步骤展开 | unfold step |
| union | 合并多个遍历返回的结果 | union step |
| V | 包括顶点与边之间的遍历所需的步骤：V、E、out、in、both、outE、inE、bothE、outV、inV、bothV 和 otherV | order step |
| where | 用于筛选遍历返回的结果。 支持 eq、neq、lt、lte、gt、gte 和 between 运算符 | where step |
HugeGraph支持Apache TinkerPop3的图形遍历查询语言Gremlin。 SQL是关系型数据库查询语言，而Gremlin是一种通用的图数据库查询语言，Gremlin可用于创建图的实体（Vertex和Edge）、修改实体内部属性、删除实体，也可执行图的查询操作。
Gremlin可用于创建图的实体（Vertex和Edge）、修改实体内部属性、删除实体，更主要的是可用于执行图的查询及分析操作。
HugeGraph实现了TinkerPop框架，但是并没有实现TinkerPop所有的特性。
下表列出HugeGraph对TinkerPop各种特性的支持情况：
| Name | Description | Support |
|---|---|---|
| Computer | Determines if the {@code Graph} implementation supports {@link GraphComputer} based processing | false |
| Transactions | Determines if the {@code Graph} implementations supports transactions. | true |
| Persistence | Determines if the {@code Graph} implementation supports persisting it’s contents natively to disk.This feature does not refer to every graph’s ability to write to disk via the Gremlin IO packages(.e.g. GraphML), unless the graph natively persists to disk via those options somehow. For example,TinkerGraph does not support this feature as it is a pure in-sideEffects graph. | true |
| ThreadedTransactions | Determines if the {@code Graph} implementation supports threaded transactions which allow a transaction be executed across multiple threads via {@link Transaction#createThreadedTx()}. | false |
| ConcurrentAccess | Determines if the {@code Graph} implementation supports more than one connection to the same instance at the same time. For example, Neo4j embedded does not support this feature because concurrent access to the same database files by multiple instances is not possible. However, Neo4j HA could support this feature as each new {@code Graph} instance coordinates with the Neo4j cluster allowing multiple instances to operate on the same database. | false |
| Name | Description | Support |
|---|---|---|
| UserSuppliedIds | Determines if an {@link Element} can have a user defined identifier. Implementation that do not support this feature will be expected to auto-generate unique identifiers. In other words, if the {@link Graph} allows {@code graph.addVertex(id,x)} to work and thus set the identifier of the newly added {@link Vertex} to the value of {@code x} then this feature should return true. In this case, {@code x} is assumed to be an identifier data type that the {@link Graph} will accept. | false |
| NumericIds | Determines if an {@link Element} has numeric identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a numeric value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| StringIds | Determines if an {@link Element} has string identifiers as their internal representation. In other words, if the value returned from {@link Element#id()} is a string value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| UuidIds | Determines if an {@link Element} has UUID identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a {@link UUID} value then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| CustomIds | Determines if an {@link Element} has a specific custom object as their internal representation.In other words, if the value returned from {@link Element#id()} is a type defined by the graph implementations, such as OrientDB’s {@code Rid}, then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| AnyIds | Determines if an {@link Element} any Java object is a suitable identifier. TinkerGraph is a good example of a {@link Graph} that can support this feature, as it can use any {@link Object} as a value for the identifier. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. This setting should only return {@code true} if {@link #supportsUserSuppliedIds()} is {@code true}. | false |
| AddProperty | Determines if an {@link Element} allows properties to be added. This feature is set independently from supporting “data types” and refers to support of calls to {@link Element#property(String, Object)}. | true |
| RemoveProperty | Determines if an {@link Element} allows properties to be removed. | true |
| AddVertices | Determines if a {@link Vertex} can be added to the {@code Graph}. | true |
| MultiProperties | Determines if a {@link Vertex} can support multiple properties with the same key. | false |
| DuplicateMultiProperties | Determines if a {@link Vertex} can support non-unique values on the same key. For this value to be {@code true}, then {@link #supportsMetaProperties()} must also return true. By default this method, just returns what {@link #supportsMultiProperties()} returns. | false |
| MetaProperties | Determines if a {@link Vertex} can support properties on vertex properties. It is assumed that a graph will support all the same data types for meta-properties that are supported for regular properties. | false |
| RemoveVertices | Determines if a {@link Vertex} can be removed from the {@code Graph}. | true |
| Name | Description | Support |
|---|---|---|
| UserSuppliedIds | Determines if an {@link Element} can have a user defined identifier. Implementation that do not support this feature will be expected to auto-generate unique identifiers. In other words, if the {@link Graph} allows {@code graph.addVertex(id,x)} to work and thus set the identifier of the newly added {@link Vertex} to the value of {@code x} then this feature should return true. In this case, {@code x} is assumed to be an identifier data type that the {@link Graph} will accept. | false |
| NumericIds | Determines if an {@link Element} has numeric identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a numeric value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| StringIds | Determines if an {@link Element} has string identifiers as their internal representation. In other words, if the value returned from {@link Element#id()} is a string value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| UuidIds | Determines if an {@link Element} has UUID identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a {@link UUID} value then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| CustomIds | Determines if an {@link Element} has a specific custom object as their internal representation.In other words, if the value returned from {@link Element#id()} is a type defined by the graph implementations, such as OrientDB’s {@code Rid}, then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| AnyIds | Determines if an {@link Element} any Java object is a suitable identifier. TinkerGraph is a good example of a {@link Graph} that can support this feature, as it can use any {@link Object} as a value for the identifier. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. This setting should only return {@code true} if {@link #supportsUserSuppliedIds()} is {@code true}. | false |
| AddProperty | Determines if an {@link Element} allows properties to be added. This feature is set independently from supporting “data types” and refers to support of calls to {@link Element#property(String, Object)}. | true |
| RemoveProperty | Determines if an {@link Element} allows properties to be removed. | true |
| AddEdges | Determines if an {@link Edge} can be added to a {@code Vertex}. | true |
| RemoveEdges | Determines if an {@link Edge} can be removed from a {@code Vertex}. | true |
| Name | Description | Support |
|---|---|---|
| BooleanValues | true | |
| ByteValues | true | |
| DoubleValues | true | |
| FloatValues | true | |
| IntegerValues | true | |
| LongValues | true | |
| MapValues | Supports setting of a {@code Map} value. The assumption is that the {@code Map} can contain arbitrary serializable values that may or may not be defined as a feature itself | false |
| MixedListValues | Supports setting of a {@code List} value. The assumption is that the {@code List} can contain arbitrary serializable values that may or may not be defined as a feature itself. As this{@code List} is “mixed” it does not need to contain objects of the same type. | false |
| BooleanArrayValues | false | |
| ByteArrayValues | true | |
| DoubleArrayValues | false | |
| FloatArrayValues | false | |
| IntegerArrayValues | false | |
| LongArrayValues | false | |
| SerializableValues | false | |
| StringArrayValues | false | |
| StringValues | true | |
| UniformListValues | Supports setting of a {@code List} value. The assumption is that the {@code List} can contain arbitrary serializable values that may or may not be defined as a feature itself. As this{@code List} is “uniform” it must contain objects of the same type. | false |
HugeGraph支持Gremlin的所有步骤。有关Gremlin的完整参考信息，请参与Gremlin官网。
| 步骤 | 说明 | 文档 |
|---|---|---|
| addE | 在两个顶点之间添加边 | addE step |
| addV | 将顶点添加到图形 | addV step |
| and | 确保所有遍历都返回值 | and step |
| as | 用于向步骤的输出分配变量的步骤调制器 | as step |
| by | 与group和order配合使用的步骤调制器 | by step |
| coalesce | 返回第一个返回结果的遍历 | coalesce step |
| constant | 返回常量值。 与coalesce配合使用 | constant step |
| count | 从遍历返回计数 | count step |
| dedup | 返回已删除重复内容的值 | dedup step |
| drop | 丢弃值（顶点/边缘） | drop step |
| fold | 充当用于计算结果聚合值的屏障 | fold step |
| group | 根据指定的标签将值分组 | group step |
| has | 用于筛选属性、顶点和边缘。 支持hasLabel、hasId、hasNot 和 has 变体 | has step |
| inject | 将值注入流中 | inject step |
| is | 用于通过布尔表达式执行筛选器 | is step |
| limit | 用于限制遍历中的项数 | limit step |
| local | 本地包装遍历的某个部分，类似于子查询 | local step |
| not | 用于生成筛选器的求反结果 | not step |
| optional | 如果生成了某个结果，则返回指定遍历的结果，否则返回调用元素 | optional step |
| or | 确保至少有一个遍历会返回值 | or step |
| order | 按指定的排序顺序返回结果 | order step |
| path | 返回遍历的完整路径 | path step |
| project | 将属性投影为映射 | project step |
| properties | 返回指定标签的属性 | properties step |
| range | 根据指定的值范围进行筛选 | range step |
| repeat | 将步骤重复指定的次数。 用于循环 | repeat step |
| sample | 用于对遍历返回的结果采样 | sample step |
| select | 用于投影遍历返回的结果 | select step |
| store | 用于遍历返回的非阻塞聚合 | store step |
| tree | 将顶点中的路径聚合到树中 | tree step |
| unfold | 将迭代器作为步骤展开 | unfold step |
| union | 合并多个遍历返回的结果 | union step |
| V | 包括顶点与边之间的遍历所需的步骤：V、E、out、in、both、outE、inE、bothE、outV、inV、bothV 和 otherV | order step |
| where | 用于筛选遍历返回的结果。 支持 eq、neq、lt、lte、gt、gte 和 between 运算符 | where step |
This is the multi-page printable view of this section. Click here to print.
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
| com-lj.ungraph.txt | 3997961 | 34681189 | 479MB |
HugeGraph版本：0.5.6，RestServer和Gremlin Server和backends都在同一台服务器上
Titan版本：0.5.4, 使用thrift+Cassandra模式
Neo4j版本：2.0.1
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.629 | 5.711 | 5.243 | 67.033 |
| Titan | 10.15 | 108.569 | 150.266 | 1217.944 |
| Neo4j | 3.884 | 18.938 | 24.890 | 281.537 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) | com-lj.ungraph(400w) |
|---|---|---|---|---|
| HugeGraph | 4.072 | 45.118 | 66.006 | 609.083 |
| Titan | 8.084 | 92.507 | 184.543 | 1099.371 |
| Neo4j | 2.424 | 10.537 | 11.609 | 106.919 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 1.540 | 10.764 | 11.243 | 151.271 |
| Titan | 7.361 | 93.344 | 169.218 | 1085.235 |
| Neo4j | 1.673 | 4.775 | 4.284 | 40.507 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.494 | 0.103 | 3.364 | 8.155 |
| Titan | 11.818 | 0.239 | 377.709 | 575.678 |
| Neo4j | 1.719 | 1.800 | 1.956 | 8.530 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| HugeGraph(core) | 20.804 | 242.099 | 744.780 | 1700.547 |
| Titan | 45.790 | 820.633 | 2652.235 | 9568.623 |
| Neo4j | 5.913 | 50.267 | 142.354 | 460.880 |
说明
HugeGraph API性能测试主要测试HugeGraph-Server对RESTful API请求的并发处理能力，包括：
HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考：
之前的版本只提供HugeGraph所支持的后端种类中性能最好的API性能测试，从0.5.6版本开始，分别提供了单机和集群的性能情况
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| 机器编号 | CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|---|
| 1 | 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps | 1.4T HDD |
| 2 | 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
1. CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）
2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）
3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）
不断提升并发量，测试server仍能正常提供服务的压力上限
起压和被压机器配置相同，基本参数如下：
| CPU | Memory | 网卡 |
|---|---|---|
| 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps |
测试工具：apache-Jmeter-2.5.1
batch_size_warn_threshold_in_kb: 1000
batch_size_fail_threshold_in_kb: 1000
-注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用HugeGraph-Loader工具持续、高速导入图数据
测试均采用网址数据的边数据
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 9.516 | 88.123 | 111.586 |
| RocksDB | 2.345 | 14.076 | 16.636 |
| Cassandra | 11.930 | 108.709 | 101.959 |
| Memory | 3.077 | 15.204 | 13.841 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) |
|---|---|---|---|
| Titan | 7.724 | 70.935 | 128.884 |
| RocksDB | 8.876 | 65.852 | 63.388 |
| Cassandra | 13.125 | 126.959 | 102.580 |
| Memory | 22.309 | 207.411 | 165.609 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 7.119 | 63.353 | 115.633 |
| RocksDB | 6.032 | 64.526 | 52.721 |
| Cassandra | 9.410 | 102.766 | 94.197 |
| Memory | 12.340 | 195.444 | 140.89 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 11.333 | 0.313 | 376.06 |
| RocksDB | 44.391 | 2.221 | 268.792 |
| Cassandra | 39.845 | 3.337 | 331.113 |
| Memory | 35.638 | 2.059 | 388.987 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| Titan | 45.943 | 849.168 | 2737.117 | 9791.46 |
| Memory(core) | 41.077 | 1825.905 | * | * |
| Cassandra（core） | 39.783 | 862.744 | 2423.136 | 6564.191 |
| RocksDB（core） | 33.383 | 199.894 | 763.869 | 1677.813 |
说明
注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用HugeGraph-Loader工具持续、高速导入图数据
测试均采用网址数据的边数据
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 9.516 | 88.123 | 111.586 |
| RocksDB | 2.345 | 14.076 | 16.636 |
| Cassandra | 11.930 | 108.709 | 101.959 |
| Memory | 3.077 | 15.204 | 13.841 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) |
|---|---|---|---|
| Titan | 7.724 | 70.935 | 128.884 |
| RocksDB | 8.876 | 65.852 | 63.388 |
| Cassandra | 13.125 | 126.959 | 102.580 |
| Memory | 22.309 | 207.411 | 165.609 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 7.119 | 63.353 | 115.633 |
| RocksDB | 6.032 | 64.526 | 52.721 |
| Cassandra | 9.410 | 102.766 | 94.197 |
| Memory | 12.340 | 195.444 | 140.89 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 11.333 | 0.313 | 376.06 |
| RocksDB | 44.391 | 2.221 | 268.792 |
| Cassandra | 39.845 | 3.337 | 331.113 |
| Memory | 35.638 | 2.059 | 388.987 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| Titan | 45.943 | 849.168 | 2737.117 | 9791.46 |
| Memory(core) | 41.077 | 1825.905 | * | * |
| Cassandra（core） | 39.783 | 862.744 | 2423.136 | 6564.191 |
| RocksDB（core） | 33.383 | 199.894 | 763.869 | 1677.813 |
说明
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HugeGraph API性能测试主要测试HugeGraph-Server对RESTful API请求的并发处理能力，包括：
HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考：
之前的版本只提供HugeGraph所支持的后端种类中性能最好的API性能测试，从0.5.6版本开始，分别提供了单机和集群的性能情况
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| 机器编号 | CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|---|
| 1 | 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps | 1.4T HDD |
| 2 | 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
1. CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）
2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）
3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）
不断提升并发量，测试server仍能正常提供服务的压力上限
起压和被压机器配置相同，基本参数如下：
| CPU | Memory | 网卡 |
|---|---|---|
| 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps |
测试工具：apache-Jmeter-2.5.1
batch_size_warn_threshold_in_kb: 1000
batch_size_fail_threshold_in_kb: 1000
-注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
起压和被压机器配置相同，基本参数如下：
| CPU | Memory | 网卡 |
|---|---|---|
| 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps |
测试工具：apache-Jmeter-2.5.1
batch_size_warn_threshold_in_kb: 1000
batch_size_fail_threshold_in_kb: 1000
-注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
被压机器信息
| 机器编号 | CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|---|
| 1 | 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps | 1.4T HDD |
| 2 | 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
1. CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）
2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）
3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）
不断提升并发量，测试server仍能正常提供服务的压力上限
被压机器信息
| 机器编号 | CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|---|
| 1 | 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps | 1.4T HDD |
| 2 | 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
1. CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）
2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）
3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）
不断提升并发量，测试server仍能正常提供服务的压力上限
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
HugeGraph API性能测试主要测试HugeGraph-Server对RESTful API请求的并发处理能力，包括：
HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考：
之前的版本只提供HugeGraph所支持的后端种类中性能最好的API性能测试，从0.5.6版本开始，分别提供了单机和集群的性能情况
HugeGraph API性能测试主要测试HugeGraph-Server对RESTful API请求的并发处理能力，包括：
HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考：
之前的版本只提供HugeGraph所支持的后端种类中性能最好的API性能测试，从0.5.6版本开始，分别提供了单机和集群的性能情况
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 9.516 | 88.123 | 111.586 |
| RocksDB | 2.345 | 14.076 | 16.636 |
| Cassandra | 11.930 | 108.709 | 101.959 |
| Memory | 3.077 | 15.204 | 13.841 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) |
|---|---|---|---|
| Titan | 7.724 | 70.935 | 128.884 |
| RocksDB | 8.876 | 65.852 | 63.388 |
| Cassandra | 13.125 | 126.959 | 102.580 |
| Memory | 22.309 | 207.411 | 165.609 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 7.119 | 63.353 | 115.633 |
| RocksDB | 6.032 | 64.526 | 52.721 |
| Cassandra | 9.410 | 102.766 | 94.197 |
| Memory | 12.340 | 195.444 | 140.89 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 11.333 | 0.313 | 376.06 |
| RocksDB | 44.391 | 2.221 | 268.792 |
| Cassandra | 39.845 | 3.337 | 331.113 |
| Memory | 35.638 | 2.059 | 388.987 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| Titan | 45.943 | 849.168 | 2737.117 | 9791.46 |
| Memory(core) | 41.077 | 1825.905 | * | * |
| Cassandra（core） | 39.783 | 862.744 | 2423.136 | 6564.191 |
| RocksDB（core） | 33.383 | 199.894 | 763.869 | 1677.813 |
说明
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 9.516 | 88.123 | 111.586 |
| RocksDB | 2.345 | 14.076 | 16.636 |
| Cassandra | 11.930 | 108.709 | 101.959 |
| Memory | 3.077 | 15.204 | 13.841 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) |
|---|---|---|---|
| Titan | 7.724 | 70.935 | 128.884 |
| RocksDB | 8.876 | 65.852 | 63.388 |
| Cassandra | 13.125 | 126.959 | 102.580 |
| Memory | 22.309 | 207.411 | 165.609 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 7.119 | 63.353 | 115.633 |
| RocksDB | 6.032 | 64.526 | 52.721 |
| Cassandra | 9.410 | 102.766 | 94.197 |
| Memory | 12.340 | 195.444 | 140.89 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 11.333 | 0.313 | 376.06 |
| RocksDB | 44.391 | 2.221 | 268.792 |
| Cassandra | 39.845 | 3.337 | 331.113 |
| Memory | 35.638 | 2.059 | 388.987 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| Titan | 45.943 | 849.168 | 2737.117 | 9791.46 |
| Memory(core) | 41.077 | 1825.905 | * | * |
| Cassandra（core） | 39.783 | 862.744 | 2423.136 | 6564.191 |
| RocksDB（core） | 33.383 | 199.894 | 763.869 | 1677.813 |
说明
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
| com-lj.ungraph.txt | 3997961 | 34681189 | 479MB |
HugeGraph版本：0.5.6，RestServer和Gremlin Server和backends都在同一台服务器上
Titan版本：0.5.4, 使用thrift+Cassandra模式
Neo4j版本：2.0.1
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.629 | 5.711 | 5.243 | 67.033 |
| Titan | 10.15 | 108.569 | 150.266 | 1217.944 |
| Neo4j | 3.884 | 18.938 | 24.890 | 281.537 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) | com-lj.ungraph(400w) |
|---|---|---|---|---|
| HugeGraph | 4.072 | 45.118 | 66.006 | 609.083 |
| Titan | 8.084 | 92.507 | 184.543 | 1099.371 |
| Neo4j | 2.424 | 10.537 | 11.609 | 106.919 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 1.540 | 10.764 | 11.243 | 151.271 |
| Titan | 7.361 | 93.344 | 169.218 | 1085.235 |
| Neo4j | 1.673 | 4.775 | 4.284 | 40.507 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.494 | 0.103 | 3.364 | 8.155 |
| Titan | 11.818 | 0.239 | 377.709 | 575.678 |
| Neo4j | 1.719 | 1.800 | 1.956 | 8.530 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| HugeGraph(core) | 20.804 | 242.099 | 744.780 | 1700.547 |
| Titan | 45.790 | 820.633 | 2652.235 | 9568.623 |
| Neo4j | 5.913 | 50.267 | 142.354 | 460.880 |
说明
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
| com-lj.ungraph.txt | 3997961 | 34681189 | 479MB |
HugeGraph版本：0.5.6，RestServer和Gremlin Server和backends都在同一台服务器上
Titan版本：0.5.4, 使用thrift+Cassandra模式
Neo4j版本：2.0.1
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.629 | 5.711 | 5.243 | 67.033 |
| Titan | 10.15 | 108.569 | 150.266 | 1217.944 |
| Neo4j | 3.884 | 18.938 | 24.890 | 281.537 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) | com-lj.ungraph(400w) |
|---|---|---|---|---|
| HugeGraph | 4.072 | 45.118 | 66.006 | 609.083 |
| Titan | 8.084 | 92.507 | 184.543 | 1099.371 |
| Neo4j | 2.424 | 10.537 | 11.609 | 106.919 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 1.540 | 10.764 | 11.243 | 151.271 |
| Titan | 7.361 | 93.344 | 169.218 | 1085.235 |
| Neo4j | 1.673 | 4.775 | 4.284 | 40.507 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.494 | 0.103 | 3.364 | 8.155 |
| Titan | 11.818 | 0.239 | 377.709 | 575.678 |
| Neo4j | 1.719 | 1.800 | 1.956 | 8.530 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| HugeGraph(core) | 20.804 | 242.099 | 744.780 | 1700.547 |
| Titan | 45.790 | 820.633 | 2652.235 | 9568.623 |
| Neo4j | 5.913 | 50.267 | 142.354 | 460.880 |
说明
当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用HugeGraph-Loader工具持续、高速导入图数据
测试均采用网址数据的边数据
当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用HugeGraph-Loader工具持续、高速导入图数据
测试均采用网址数据的边数据
NOTE: it will only be saved for one hour
kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system
-如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。
更多算法请看: Built-In algorithms
TODO
TODO
+如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。
更多算法请看: Built-In algorithms
TODO
TODO
diff --git a/cn/docs/quickstart/hugegraph-client/index.html b/cn/docs/quickstart/hugegraph-client/index.html index 1f7fcf078..dc922bc83 100644 --- a/cn/docs/quickstart/hugegraph-client/index.html +++ b/cn/docs/quickstart/hugegraph-client/index.html @@ -309,7 +309,7 @@ hugeClient.close(); } } -运行Example之前需要启动Server, 启动过程见HugeGraph-Server Quick Start
运行Example之前需要启动Server, 启动过程见HugeGraph-Server Quick Start
NOTE: it will only be saved for one hour
kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system
-如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。
更多算法请看: Built-In algorithms
TODO
TODO
如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。
更多算法请看: Built-In algorithms
TODO
TODO
HugeGraph是一款面向分析型，支持批量操作的图数据库系统，它由百度安全团队自主研发，全面支持Apache TinkerPop3框架和Gremlin图查询语言，提供导出、备份、恢复等完善的工具链生态，有效解决海量图数据的存储、查询和关联分析需求。HugeGraph广泛应用于银行券商的风控打击、保险理赔、推荐搜索、公安犯罪打击、知识图谱构建、网络安全、IT运维等领域，致力于让更多行业、组织及用户享受到更广泛的数据综合价值。
HugeGraph-Hubble 是HugeGraph的一站式可视化分析平台，平台涵盖了从数据建模，到数据快速导入，再到数据的在线、离线分析、以及图的统一管理的全过程，实现了图应用的全流程向导式操作，旨在提升用户的使用流畅度，降低用户的使用门槛，提供更为高效易用的使用体验。
平台主要包括以下模块：
图管理模块通过图的创建，连接平台与图数据，实现多图的统一管理，并实现图的访问、编辑、删除、查询操作。
元数据建模模块通过创建属性库，顶点类型，边类型，索引类型，实现图模型的构建与管理，平台提供两种模式，列表模式和图模式，可实时展示元数据模型，更加直观。同时还提供了跨图的元数据复用功能，省去相同元数据繁琐的重复创建过程，极大地提升建模效率，增强易用性。
数据导入是将用户的业务数据转化为图的顶点和边并插入图数据库中，平台提供了向导式的可视化导入模块，通过创建导入任务，实现导入任务的管理及多个导入任务的并行运行，提高导入效能。进入导入任务后，只需跟随平台步骤提示，按需上传文件，填写内容，就可轻松实现图数据的导入过程，同时支持断点续传，错误重试机制等，降低导入成本，提升效率。
通过输入图遍历语言Gremlin可实现图数据的高性能通用分析，并提供顶点的定制化多维路径查询等功能，提供3种图结果展示方式，包括：图形式、表格形式、Json形式，多维度展示数据形态，满足用户使用的多种场景需求。提供运行记录及常用语句收藏等功能，实现图操作的可追溯，以及查询输入的复用共享，快捷高效。支持图数据的导出，导出格式为Json格式。
对于需要遍历全图的Gremlin任务，索引的创建与重建等耗时较长的异步任务，平台提供相应的任务管理功能，实现异步任务的统一的管理与结果查看。
平台的模块使用流程如下：
图管理模块下，点击【创建图】，通过填写图ID、图名称、主机名、端口号、用户名、密码的信息，实现多图的连接。
创建图填写内容如下：
实现图空间的信息访问，进入后，可进行图的多维查询分析、元数据管理、数据导入、算法分析等操作。
左侧导航处：
列表模式：
图模式：
选择复用项：
校验复用项：
列表模式：
图模式：
可进行编辑操作，顶点样式、关联类型、顶点展示内容、属性索引可编辑，其余不可编辑。
可进行单条删除或批量删除操作。
列表模式：
图模式：
展示顶点类型和边类型的顶点索引和边索引。
数据导入的使用流程如下：
左侧导航处：
对上传的文件分别设置数据映射，包括文件设置和类型设置
文件设置：勾选或填写是否包含表头、分隔符、编码格式等文件本身的设置内容，均设置默认值，无需手动填写
类型设置：
顶点映射和边映射：
【顶点类型】 ：选择顶点类型，并为其ID映射上传文件中列数据；
【边类型】：选择边类型，为其起点类型和终点类型的ID列映射上传文件的列数据；
映射设置：为选定的顶点类型的属性映射上传文件中的列数据，此处，若属性名称与文件的表头名称一致，可自动匹配映射属性，无需手动填选
完成设置后，显示设置列表，方可进行下一步操作，支持映射的新增、编辑、删除操作
设置映射的填写内容：
映射列表：
导入前需要填写导入设置参数，填写完成后，可开始向图库中导入数据
左侧导航处：
通过左侧切换入口，灵活切换多图的操作空间
HugeGraph支持Apache TinkerPop3的图遍历查询语言Gremlin，Gremlin是一种通用的图数据库查询语言，通过输入Gremlin语句，点击执行，即可执行图数据的查询分析操作，并可实现顶点/边的创建及删除、顶点/边的属性修改等。
Gremlin查询后，下方为图结果展示区域，提供3种图结果展示方式，分别为：【图模式】、【表格模式】、【Json模式】。
支持缩放、居中、全屏、导出等操作。
【图模式】
【表格模式】
【Json模式】
点击顶点/边实体，可查看顶点/边的数据详情，包括：顶点/边类型，顶点ID，属性及对应值，拓展图的信息展示维度，提高易用性。
除了全局的查询外，可针对查询结果中的顶点进行深度定制化查询以及隐藏操作，实现图结果的定制化挖掘。
右击顶点，出现顶点的菜单入口，可进行展示、查询、隐藏等操作。
双击顶点，也可展示与选中点关联的顶点。
在图区可通过两个入口，动态新增顶点，如下：
通过选择或填写顶点类型、ID值、属性信息，完成顶点的增加。
入口如下：
添加顶点内容如下：
右击图结果中的顶点，可增加该点的出边或者入边。
左侧导航处：
1.创建任务
点击查看入口，跳转到任务管理列表，如下：
4.查看结果
Hubble上暂未提供可视化的OLAP算法执行，可调用RESTful API进行OLAP类算法任务，在任务管理中通过ID找到相应任务，查看进度与结果等。
1.创建任务
2.任务详情
点击查看入口，跳转到任务管理列表，如下：
4.查看结果
Hubble上暂未提供可视化的OLAP算法执行，可调用RESTful API进行OLAP类算法任务，在任务管理中通过ID找到相应任务，查看进度与结果等。
1.创建任务
2.任务详情
详细的API请参考RESTful-API文档
$cd hugegraph-${version}
$bin/stop-hugegraph.sh
-./bin/hugegraph --url http://127.0.0.1:8080 --graph hugegraph migrate --target-url http://127.0.0.1:8090 --target-graph hugegraph
-HugeGraph支持百亿以上的顶点(Vertex)和边(Edge)快速导入，毫秒级的关联查询能力，并可与Hadoop、Spark等
大数据平台集成以进行离线分析，主要应用场景包括关联分析、欺诈检测和知识图谱等。
支持Gremlin图查询语言与RESTful API，并提供图检索常用接口，具备齐全的周边工具，支持分布式存储、数据多副本及横向扩容，内置多种后端存储引擎，轻松实现各种查询、分析。
在图存储和图计算方面做了深度优化，提供支持多种数据源的批量导入工具，轻松完成百亿级数据快速导入，通过优化过的查询达到图检索的毫秒级响应，支持数千用户并发的在线实时操作。
支持Apache Gremlin标准图查询语言和Property Graph标准图建模方法，支持基于图的OLTP和OLAP方案。集成Apache Hadoop及Apache Spark大数据平台，也可插件式轻松扩展后端存储引擎。
HugeGraph是一款易用、高效、通用的图数据库
实现了Apache TinkerPop3框架、兼容Gremlin查询语言。
HugeGraph支持百亿以上的顶点(Vertex)和边(Edge)快速导入，毫秒级的关联查询能力，并可与Hadoop、Spark等
大数据平台集成以进行离线分析，主要应用场景包括关联分析、欺诈检测和知识图谱等。
支持Gremlin图查询语言与RESTful API，并提供图检索常用接口，具备齐全的周边工具，支持分布式存储、数据多副本及横向扩容，内置多种后端存储引擎，轻松实现各种查询、分析。
在图存储和图计算方面做了深度优化，提供支持多种数据源的批量导入工具，轻松完成百亿级数据快速导入，通过优化过的查询达到图检索的毫秒级响应，支持数千用户并发的在线实时操作。
支持Apache Gremlin标准图查询语言和Property Graph标准图建模方法，支持基于图的OLTP和OLAP方案。集成Apache Hadoop及Apache Spark大数据平台，也可插件式轻松扩展后端存储引擎。
Apache EventMesh is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
HugeGraph is an open source project that anyone in the community can use, improve, and enjoy. We'd love you to join us! Here's a few ways to find out what's happening and get involved.
Using or want to use HugeGraph? Find out more here:
If you want to get more involved by contributing to HugeGraph, join us here:
You can find out how to contribute to these docs in our Contribution Guidelines.
HugeGraph is an open source project that anyone in the community can use, improve, and enjoy. We'd love you to join us! Here's a few ways to find out what's happening and get involved.
Using or want to use HugeGraph? Find out more here:
If you want to get more involved by contributing to HugeGraph, join us here:
You can find out how to contribute to these docs in our Contribution Guidelines.
HugeGraph is an open source project that anyone in the community can use, improve, and enjoy. We'd love you to join us! Here's a few ways to find out what's happening and get involved.
Using or want to use HugeGraph? Find out more here:
If you want to get more involved by contributing to HugeGraph, join us here:
You can find out how to contribute to these docs in our Contribution Guidelines.
HugeGraph is an open source project that anyone in the community can use, improve, and enjoy. We'd love you to join us! Here's a few ways to find out what's happening and get involved.
Using or want to use HugeGraph? Find out more here:
If you want to get more involved by contributing to HugeGraph, join us here:
You can find out how to contribute to these docs in our Contribution Guidelines.
And push it to GitHub fork repo again:
# force push the local commit to fork repo
git push -f origin bugfix-branch:bugfix-branch
-GitHub will automatically update the Pull Request after we push it, just wait for code review.
It is highly recommended to subscribe to the development mailing list to keep up-to-date with the community.
In the process of using HugeGraph, if you have any questions or ideas, suggestions, you can participate in the HugeGraph community building through the Apache mailing list. Sending a subscription email is also very simple, the steps are as follows:
Email dev-subscribe@hugegraph.apache.org with your own email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
Receive a welcome email. After completing the above steps, you will receive a welcome email with the subject WELCOME to dev@hugegraph.apache.org, and you have successfully subscribed to the Apache HugeGraph mailing list.
If you do not need to know what’s going on with HugeGraph, you can unsubscribe from the mailing list.
Unsubscribe from the mailing list steps are as follows:
Email dev-unsubscribe@hugegraph.apache.org with your subscribed email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
Receive a goodbye email. After completing the above steps, you will receive a goodbye email with the subject GOODBYE from dev@hugegraph.apache.org, and you have successfully unsubscribed to the Apache HugeGraph mailing list, and you will not receive emails from dev@hugegraph.apache.org.
Thank you for your interest in contributing to HugeGraph’s all projects (“We” or “Us”).
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GitHub will automatically update the Pull Request after we push it, just wait for code review.
It is highly recommended to subscribe to the development mailing list to keep up-to-date with the community.
In the process of using HugeGraph, if you have any questions or ideas, suggestions, you can participate in the HugeGraph community building through the Apache mailing list. Sending a subscription email is also very simple, the steps are as follows:
Email dev-subscribe@hugegraph.apache.org with your own email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
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We agree to (sub)license the Contribution or any Materials containing, based on or derived from your Contribution under the terms of any licenses the Free Software Foundation classifies as Free Software License and which are approved by the Open Source Initiative as Open Source licenses.
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In addition, We may use the following licenses for Documentation in the Contribution: GFDL-1.2 (including any right to adopt any future version of a license).
We agree to license patents owned or controlled by You only to the extent necessary to (sub)license Your Contribution(s) and the combination of Your Contribution(s) with the Material under the terms of any licenses the Free Software Foundation classifies as Free Software licenses and which are approved by the Open Source Initiative as Open Source licenses..
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TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL YOU OR WE BE LIABLE FOR ANY LOSS OF PROFITS, LOSS OF ANTICIPATED SAVINGS, LOSS OF DATA, INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL AND EXEMPLARY DAMAGES ARISING OUT OF THIS AGREEMENT REGARDLESS OF THE LEGAL OR EQUITABLE THEORY (CONTRACT, TORT OR OTHERWISE) UPON WHICH THE CLAIM IS BASED.
IF THE DISCLAIMER AND DAMAGE WAIVER MENTIONED IN SECTION 5. AND SECTION 6. CANNOT BE GIVEN LEGAL EFFECT UNDER APPLICABLE LOCAL LAW, REVIEWING COURTS SHALL APPLY LOCAL LAW THAT MOST CLOSELY APPROXIMATES AN ABSOLUTE WAIVER OF ALL CIVIL OR CONTRACTUAL LIABILITY IN CONNECTION WITH THE CONTRIBUTION.
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9.3 In case of Your death, this agreement shall continue with Your heirs. In case of more than one heir, all heirs must exercise their rights through a commonly authorized person.
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Thank you for your interest in contributing to HugeGraph’s all projects (“We” or “Us”).
The purpose of this contributor agreement (“Agreement”) is to clarify and document the rights granted by contributors to Us. To make this document effective, please follow the comment of GitHub CLA-Assistant when submitting a new pull request.
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Moral Rights remain unaffected to the extent they are recognized and not waivable by applicable law. Notwithstanding, You may add your name to the attribution mechanism customary used in the Materials you Contribute to, such as the header of the source code files of Your Contribution, and We will respect this attribution when using Your Contribution.
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You reserve the right to revoke the patent license stated in section 3.1 if We make any infringement claim that is targeted at your Contribution and not asserted for a Defensive Purpose. An assertion of claims of the Patents shall be considered for a “Defensive Purpose” if the claims are asserted against an entity that has filed, maintained, threatened, or voluntarily participated in a patent infringement lawsuit against Us or any of Our licensees.
We agree to (sub)license the Contribution or any Materials containing, based on or derived from your Contribution under the terms of any licenses the Free Software Foundation classifies as Free Software License and which are approved by the Open Source Initiative as Open Source licenses.
More specifically and in strict accordance with the above paragraph, we agree to (sub)license the Contribution or any Materials containing, based on or derived from the Contribution only in accordance with our licensing policy available at: http://www.apache.org/licenses/LICENSE-2.0.
In addition, We may use the following licenses for Documentation in the Contribution: GFDL-1.2 (including any right to adopt any future version of a license).
We agree to license patents owned or controlled by You only to the extent necessary to (sub)license Your Contribution(s) and the combination of Your Contribution(s) with the Material under the terms of any licenses the Free Software Foundation classifies as Free Software licenses and which are approved by the Open Source Initiative as Open Source licenses..
THE CONTRIBUTION IS PROVIDED “AS IS”. MORE PARTICULARLY, ALL EXPRESS OR IMPLIED WARRANTIES INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTY OF SATISFACTORY QUALITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT ARE EXPRESSLY DISCLAIMED BY YOU TO US AND BY US TO YOU. TO THE EXTENT THAT ANY SUCH WARRANTIES CANNOT BE DISCLAIMED, SUCH WARRANTY IS LIMITED IN DURATION AND EXTENT TO THE MINIMUM PERIOD AND EXTENT PERMITTED BY LAW.
TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL YOU OR WE BE LIABLE FOR ANY LOSS OF PROFITS, LOSS OF ANTICIPATED SAVINGS, LOSS OF DATA, INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL AND EXEMPLARY DAMAGES ARISING OUT OF THIS AGREEMENT REGARDLESS OF THE LEGAL OR EQUITABLE THEORY (CONTRACT, TORT OR OTHERWISE) UPON WHICH THE CLAIM IS BASED.
IF THE DISCLAIMER AND DAMAGE WAIVER MENTIONED IN SECTION 5. AND SECTION 6. CANNOT BE GIVEN LEGAL EFFECT UNDER APPLICABLE LOCAL LAW, REVIEWING COURTS SHALL APPLY LOCAL LAW THAT MOST CLOSELY APPROXIMATES AN ABSOLUTE WAIVER OF ALL CIVIL OR CONTRACTUAL LIABILITY IN CONNECTION WITH THE CONTRIBUTION.
8.1 This Agreement shall come into effect upon Your acceptance of the terms and conditions.
8.2 This Agreement shall apply for the term of the copyright and patents licensed here. However, You shall have the right to terminate the Agreement if We do not fulfill the obligations as set forth in Section 4. Such termination must be made in writing.
8.3 In the event of a termination of this Agreement Sections 5, 6, 7, 8 and 9 shall survive such termination and shall remain in full force thereafter. For the avoidance of doubt, Free and Open Source Software (sub)licenses that have already been granted for Contributions at the date of the termination shall remain in full force after the termination of this Agreement.
9.1 This Agreement and all disputes, claims, actions, suits or other proceedings arising out of this agreement or relating in any way to it shall be governed by the laws of China excluding its private international law provisions.
9.2 This Agreement sets out the entire agreement between You and Us for Your Contributions to Us and overrides all other agreements or understandings.
9.3 In case of Your death, this agreement shall continue with Your heirs. In case of more than one heir, all heirs must exercise their rights through a commonly authorized person.
9.4 If any provision of this Agreement is found void and unenforceable, such provision will be replaced to the extent possible with a provision that comes closest to the meaning of the original provision and that is enforceable. The terms and conditions set forth in this Agreement shall apply notwithstanding any failure of essential purpose of this Agreement or any limited remedy to the maximum extent possible under law.
9.5 You agree to notify Us of any facts or circumstances of which you become aware that would make this Agreement inaccurate in any respect.
Thank you for your interest in contributing to HugeGraph’s all projects (“We” or “Us”).
The purpose of this contributor agreement (“Agreement”) is to clarify and document the rights granted by contributors to Us. To make this document effective, please follow the comment of GitHub CLA-Assistant when submitting a new pull request.
If You are an employee and have created the Contribution as part of your employment, You need to have Your employer approve this Agreement or sign the Entity version of this document. If You do not own the Copyright in the entire work of authorship, any other author of the Contribution should also sign this – in any event, please contact Us at hugegraph@googlegroups.com
“You” means the individual Copyright owner who Submits a Contribution to Us.
“Contribution” means any original work of authorship, including any original modifications or additions to an existing work of authorship, Submitted by You to Us, in which You own the Copyright.
“Copyright” means all rights protecting works of authorship, including copyright, moral and neighboring rights, as appropriate, for the full term of their existence.
“Material” means the software or documentation made available by Us to third parties. When this Agreement covers more than one software project, the Material means the software or documentation to which the Contribution was Submitted. After You Submit the Contribution, it may be included in the Material.
“Submit” means any act by which a Contribution is transferred to Us by You by means of tangible or intangible media, including but not limited to electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, Us, but excluding any transfer that is conspicuously marked or otherwise designated in writing by You as “Not a Contribution.”
“Documentation” means any non-software portion of a Contribution.
Subject to the terms and conditions of this Agreement, You hereby grant to Us a worldwide, royalty-free, Exclusive, perpetual and irrevocable (except as stated in Section 8.2) license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:
Moral Rights remain unaffected to the extent they are recognized and not waivable by applicable law. Notwithstanding, You may add your name to the attribution mechanism customary used in the Materials you Contribute to, such as the header of the source code files of Your Contribution, and We will respect this attribution when using Your Contribution.
Upon such grant of rights to Us, We immediately grant to You a worldwide, royalty-free, non-exclusive, perpetual and irrevocable license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, under the Copyright covering the Contribution to use the Contribution by all means, including, but not limited to:
This license back is limited to the Contribution and does not provide any rights to the Material.
Subject to the terms and conditions of this Agreement You hereby grant to Us and to recipients of Materials distributed by Us a worldwide, royalty-free, non-exclusive, perpetual and irrevocable (except as stated in Section 3.2) patent license, with the right to transfer an unlimited number of non-exclusive licenses or to grant sublicenses to third parties, to make, have made, use, sell, offer for sale, import and otherwise transfer the Contribution and the Contribution in combination with any Material (and portions of such combination). This license applies to all patents owned or controlled by You, whether already acquired or hereafter acquired, that would be infringed by making, having made, using, selling, offering for sale, importing or otherwise transferring of Your Contribution(s) alone or by combination of Your Contribution(s) with any Material.
You reserve the right to revoke the patent license stated in section 3.1 if We make any infringement claim that is targeted at your Contribution and not asserted for a Defensive Purpose. An assertion of claims of the Patents shall be considered for a “Defensive Purpose” if the claims are asserted against an entity that has filed, maintained, threatened, or voluntarily participated in a patent infringement lawsuit against Us or any of Our licensees.
We agree to (sub)license the Contribution or any Materials containing, based on or derived from your Contribution under the terms of any licenses the Free Software Foundation classifies as Free Software License and which are approved by the Open Source Initiative as Open Source licenses.
More specifically and in strict accordance with the above paragraph, we agree to (sub)license the Contribution or any Materials containing, based on or derived from the Contribution only in accordance with our licensing policy available at: http://www.apache.org/licenses/LICENSE-2.0.
In addition, We may use the following licenses for Documentation in the Contribution: GFDL-1.2 (including any right to adopt any future version of a license).
We agree to license patents owned or controlled by You only to the extent necessary to (sub)license Your Contribution(s) and the combination of Your Contribution(s) with the Material under the terms of any licenses the Free Software Foundation classifies as Free Software licenses and which are approved by the Open Source Initiative as Open Source licenses..
THE CONTRIBUTION IS PROVIDED “AS IS”. MORE PARTICULARLY, ALL EXPRESS OR IMPLIED WARRANTIES INCLUDING, WITHOUT LIMITATION, ANY IMPLIED WARRANTY OF SATISFACTORY QUALITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT ARE EXPRESSLY DISCLAIMED BY YOU TO US AND BY US TO YOU. TO THE EXTENT THAT ANY SUCH WARRANTIES CANNOT BE DISCLAIMED, SUCH WARRANTY IS LIMITED IN DURATION AND EXTENT TO THE MINIMUM PERIOD AND EXTENT PERMITTED BY LAW.
TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL YOU OR WE BE LIABLE FOR ANY LOSS OF PROFITS, LOSS OF ANTICIPATED SAVINGS, LOSS OF DATA, INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL AND EXEMPLARY DAMAGES ARISING OUT OF THIS AGREEMENT REGARDLESS OF THE LEGAL OR EQUITABLE THEORY (CONTRACT, TORT OR OTHERWISE) UPON WHICH THE CLAIM IS BASED.
IF THE DISCLAIMER AND DAMAGE WAIVER MENTIONED IN SECTION 5. AND SECTION 6. CANNOT BE GIVEN LEGAL EFFECT UNDER APPLICABLE LOCAL LAW, REVIEWING COURTS SHALL APPLY LOCAL LAW THAT MOST CLOSELY APPROXIMATES AN ABSOLUTE WAIVER OF ALL CIVIL OR CONTRACTUAL LIABILITY IN CONNECTION WITH THE CONTRIBUTION.
8.1 This Agreement shall come into effect upon Your acceptance of the terms and conditions.
8.2 This Agreement shall apply for the term of the copyright and patents licensed here. However, You shall have the right to terminate the Agreement if We do not fulfill the obligations as set forth in Section 4. Such termination must be made in writing.
8.3 In the event of a termination of this Agreement Sections 5, 6, 7, 8 and 9 shall survive such termination and shall remain in full force thereafter. For the avoidance of doubt, Free and Open Source Software (sub)licenses that have already been granted for Contributions at the date of the termination shall remain in full force after the termination of this Agreement.
9.1 This Agreement and all disputes, claims, actions, suits or other proceedings arising out of this agreement or relating in any way to it shall be governed by the laws of China excluding its private international law provisions.
9.2 This Agreement sets out the entire agreement between You and Us for Your Contributions to Us and overrides all other agreements or understandings.
9.3 In case of Your death, this agreement shall continue with Your heirs. In case of more than one heir, all heirs must exercise their rights through a commonly authorized person.
9.4 If any provision of this Agreement is found void and unenforceable, such provision will be replaced to the extent possible with a provision that comes closest to the meaning of the original provision and that is enforceable. The terms and conditions set forth in this Agreement shall apply notwithstanding any failure of essential purpose of this Agreement or any limited remedy to the maximum extent possible under law.
9.5 You agree to notify Us of any facts or circumstances of which you become aware that would make this Agreement inaccurate in any respect.
For more information on the use of gremlin-console, please refer to Tinkerpop Official Website
+For more information on the use of gremlin-console, please refer to Tinkerpop Official Website
diff --git a/docs/clients/gremlin-console/index.html b/docs/clients/gremlin-console/index.html index 7b7e066a1..d9beb682c 100644 --- a/docs/clients/gremlin-console/index.html +++ b/docs/clients/gremlin-console/index.html @@ -233,7 +233,7 @@ gremlin> :> @script ==>6 -For more information on the use of gremlin-console, please refer to Tinkerpop Official Website
For more information on the use of gremlin-console, please refer to Tinkerpop Official Website
Vertices are the most basic elements of a graph, and there can be many vertices in a graph. Here is an example of adding vertices:
Vertex marko = graph.addVertex(T.label, "person", "name", "marko", "age", 29);
Vertex lop = graph.addVertex(T.label, "software", "name", "lop", "lang", "java", "price", 328);
key1 -> val1, key2 -> val2 ..., and the order between key-value pairs is free .T.label -> "val", which is used to define the category of the vertex, so that the program can obtain the schema definition of the VertexLabel from the cache or backend, and then do subsequent constraint checks. The label in the example is defined as person.AUTOMATIC, users are not allowed to pass in id key-value pairs.CUSTOMIZE_STRING, the user needs to pass in the value of the id of the String type. The key-value pair is like: "T.id", "123456".CUSTOMIZE_NUMBER, the user needs to pass in the value of the id of the Number type. The key-value pair is like: "T.id", 123456.PRIMARY_KEY, the parameters must also contain the name and value of the properties corresponding to the primaryKeys, if not set an exception will be thrown. For example, the primaryKeys of person is name, in the example, the value of name is set to marko.addVertex method, the vertices are inserted into the backend storage system immediately.After added vertices, edges are also needed to form a complete graph. Here is an example of adding edges:
Edge knows1 = marko.addEdge("knows", vadas, "city", "Beijing");
-addEdge() of the (source) vertex is to add an edge(relationship) between itself and another vertex. The first parameter of the function is the label of the edge, and the second parameter is the target vertex. The position and order of these two parameters are fixed. The subsequent parameters are the order of key1 -> val1, key2 -> val2 ..., set the properties of the edge, and the key-value pair order is free.Note: When frequency is multiple, the value of the property type corresponding to sortKeys must be set.
Simple examples can reference HugeGraph-Client
addEdge() of the (source) vertex is to add an edge(relationship) between itself and another vertex. The first parameter of the function is the label of the edge, and the second parameter is the target vertex. The position and order of these two parameters are fixed. The subsequent parameters are the order of key1 -> val1, key2 -> val2 ..., set the properties of the edge, and the key-value pair order is free.Note: When frequency is multiple, the value of the property type corresponding to sortKeys must be set.
Simple examples can reference HugeGraph-Client
204
根据Label+Id删除边
通过指定Label参数和Id来删除边时，一般来说其性能比仅根据Id删除会更好。
DELETE http://localhost:8080/graphs/hugegraph/graph/edges/S1:peter>1>>S2:lop?label=person
204
-{
"task_id": 1
}
-注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
{
"task_id": 2
}
-注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/2（其中"2"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/2（其中"2"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
HugeGraph-Server通过HugeGraph-API基于HTTP协议为Client提供操作图的接口，主要包括元数据和 -图数据的增删改查，遍历算法，变量，图操作及其他操作。
{
"task_id": 1
}
-注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
{
"task_id" : 0
}
-Find the vertices in different layers for a given start point that should be most recommended
Find the vertices in different layers for a given start point that should be most recommended
{
"task_id": 3
}
-Note:
You can get the asynchronous job status by
GET http://localhost:8080/graphs/hugegraph/tasks/${task_id}(the task_id here should be 3). See More AsyncJob RESTfull API
Note:
You can get the asynchronous job status by
GET http://localhost:8080/graphs/hugegraph/tasks/${task_id}(the task_id here should be 3). See More AsyncJob RESTfull API
{
"cancelled": true
}
-At this point, the number of vertices whose label is man must be less than 10.
At this point, the number of vertices whose label is man must be less than 10.
DELETE http://localhost:8080/graphs/hugegraph/variables/name
204
-204
根据Label+Id删除顶点
通过指定Label参数和Id来删除顶点时，一般来说其性能比仅根据Id删除会更好。
DELETE http://localhost:8080/graphs/hugegraph/graph/vertices/"1:marko"?label=person
204
-{
"task_id": 1
}
-注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
注：
可以通过
GET http://localhost:8080/graphs/hugegraph/tasks/1（其中"1"是task_id）来查询异步任务的执行状态，更多异步任务RESTful API
keytool -export -alias serverkey -keystore server.keystore -file server.crt
server.crt 就是服务端的证书
keytool -import -alias serverkey -file server.crt -keystore client.truststore
-client.truststore 是给客户端⽤的，其中保存着受信任的证书
| config option | default value | description |
|---|---|---|
| algorithm.message_class | org.apache.hugegraph.computer.core.config.Null | The class of message passed when compute vertex. |
| algorithm.params_class | org.apache.hugegraph.computer.core.config.Null | The class used to transfer algorithms’ parameters before algorithm been run. |
| algorithm.result_class | org.apache.hugegraph.computer.core.config.Null | The class of vertex’s value, the instance is used to store computation result for the vertex. |
| allocator.max_vertices_per_thread | 10000 | Maximum number of vertices per thread processed in each memory allocator |
| bsp.etcd_endpoints | http://localhost:2379 | The end points to access etcd. |
| bsp.log_interval | 30000 | The log interval(in ms) to print the log while waiting bsp event. |
| bsp.max_super_step | 10 | The max super step of the algorithm. |
| bsp.register_timeout | 300000 | The max timeout to wait for master and works to register. |
| bsp.wait_master_timeout | 86400000 | The max timeout(in ms) to wait for master bsp event. |
| bsp.wait_workers_timeout | 86400000 | The max timeout to wait for workers bsp event. |
| hgkv.max_data_block_size | 65536 | The max byte size of hgkv-file data block. |
| hgkv.max_file_size | 2147483648 | The max number of bytes in each hgkv-file. |
| hgkv.max_merge_files | 10 | The max number of files to merge at one time. |
| hgkv.temp_file_dir | /tmp/hgkv | This folder is used to store temporary files, temporary files will be generated during the file merging process. |
| hugegraph.name | hugegraph | The graph name to load data and write results back. |
| hugegraph.url | http://127.0.0.1:8080 | The hugegraph url to load data and write results back. |
| input.edge_direction | OUT | The data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded. |
| input.edge_freq | MULTIPLE | The frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it. |
| input.filter_class | org.apache.hugegraph.computer.core.input.filter.DefaultInputFilter | The class to create input-filter object, input-filter is used to Filter vertex edges according to user needs. |
| input.loader_schema_path | The schema path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.loader_struct_path | The struct path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.max_edges_in_one_vertex | 200 | The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit. |
| input.source_type | hugegraph-server | The source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’. |
| input.split_fetch_timeout | 300 | The timeout in seconds to fetch input splits |
| input.split_max_splits | 10000000 | The maximum number of input splits |
| input.split_page_size | 500 | The page size for streamed load input split data |
| input.split_size | 1048576 | The input split size in bytes |
| job.id | local_0001 | The job id on Yarn cluster or K8s cluster. |
| job.partitions_count | 1 | The partitions count for computing one graph algorithm job. |
| job.partitions_thread_nums | 4 | The number of threads for partition parallel compute. |
| job.workers_count | 1 | The workers count for computing one graph algorithm job. |
| master.computation_class | org.apache.hugegraph.computer.core.master.DefaultMasterComputation | Master-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master. |
| output.batch_size | 500 | The batch size of output |
| output.batch_threads | 1 | The threads number used to batch output |
| output.hdfs_core_site_path | The hdfs core site path. | |
| output.hdfs_delimiter | , | The delimiter of hdfs output. |
| output.hdfs_kerberos_enable | false | Is Kerberos authentication enabled for Hdfs. |
| output.hdfs_kerberos_keytab | The Hdfs’s key tab file for kerberos authentication. | |
| output.hdfs_kerberos_principal | The Hdfs’s principal for kerberos authentication. | |
| output.hdfs_krb5_conf | /etc/krb5.conf | Kerberos configuration file. |
| output.hdfs_merge_partitions | true | Whether merge output files of multiple partitions. |
| output.hdfs_path_prefix | /hugegraph-computer/results | The directory of hdfs output result. |
| output.hdfs_replication | 3 | The replication number of hdfs. |
| output.hdfs_site_path | The hdfs site path. | |
| output.hdfs_url | hdfs://127.0.0.1:9000 | The hdfs url of output. |
| output.hdfs_user | hadoop | The hdfs user of output. |
| output.output_class | org.apache.hugegraph.computer.core.output.LogOutput | The class to output the computation result of each vertex. Be called after iteration computation. |
| output.result_name | value | The value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS. |
| output.result_write_type | OLAP_COMMON | The result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE]. |
| output.retry_interval | 10 | The retry interval when output failed |
| output.retry_times | 3 | The retry times when output failed |
| output.single_threads | 1 | The threads number used to single output |
| output.thread_pool_shutdown_timeout | 60 | The timeout seconds of output threads pool shutdown |
| output.with_adjacent_edges | false | Output the adjacent edges of the vertex or not |
| output.with_edge_properties | false | Output the properties of the edge or not |
| output.with_vertex_properties | false | Output the properties of the vertex or not |
| sort.thread_nums | 4 | The number of threads performing internal sorting. |
| transport.client_connect_timeout | 3000 | The timeout(in ms) of client connect to server. |
| transport.client_threads | 4 | The number of transport threads for client. |
| transport.close_timeout | 10000 | The timeout(in ms) of close server or close client. |
| transport.finish_session_timeout | 0 | The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests). |
| transport.heartbeat_interval | 20000 | The minimum interval(in ms) between heartbeats on client side. |
| transport.io_mode | AUTO | The network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically. |
| transport.max_pending_requests | 8 | The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests. |
| transport.max_syn_backlog | 511 | The capacity of SYN queue on server side, 0 means using system default value. |
| transport.max_timeout_heartbeat_count | 120 | The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side. |
| transport.min_ack_interval | 200 | The minimum interval(in ms) of server reply ack. |
| transport.min_pending_requests | 6 | The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests. |
| transport.network_retries | 3 | The number of retry attempts for network communication,if network unstable. |
| transport.provider_class | org.apache.hugegraph.computer.core.network.netty.NettyTransportProvider | The transport provider, currently only supports Netty. |
| transport.receive_buffer_size | 0 | The size of socket receive-buffer in bytes, 0 means using system default value. |
| transport.recv_file_mode | true | Whether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable. |
| transport.send_buffer_size | 0 | The size of socket send-buffer in bytes, 0 means using system default value. |
| transport.server_host | 127.0.0.1 | The server hostname or ip to listen on to transfer data. |
| transport.server_idle_timeout | 360000 | The max timeout(in ms) of server idle. |
| transport.server_port | 0 | The server port to listen on to transfer data. The system will assign a random port if it’s set to 0. |
| transport.server_threads | 4 | The number of transport threads for server. |
| transport.sync_request_timeout | 10000 | The timeout(in ms) to wait response after sending sync-request. |
| transport.tcp_keep_alive | true | Whether enable TCP keep-alive. |
| transport.transport_epoll_lt | false | Whether enable EPOLL level-trigger. |
| transport.write_buffer_high_mark | 67108864 | The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark. |
| transport.write_buffer_low_mark | 33554432 | The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b |
| transport.write_socket_timeout | 3000 | The timeout(in ms) to write data to socket buffer. |
| valuefile.max_segment_size | 1073741824 | The max number of bytes in each segment of value-file. |
| worker.combiner_class | org.apache.hugegraph.computer.core.config.Null | Combiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value. |
| worker.computation_class | org.apache.hugegraph.computer.core.config.Null | The class to create worker-computation object, worker-computation is used to compute each vertex in each superstep. |
| worker.data_dirs | [jobs] | The directories separated by ‘,’ that received vertices and messages can persist into. |
| worker.edge_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same edge into one properties at inputstep. |
| worker.partitioner | org.apache.hugegraph.computer.core.graph.partition.HashPartitioner | The partitioner that decides which partition a vertex should be in, and which worker a partition should be in. |
| worker.received_buffers_bytes_limit | 104857600 | The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file. |
| worker.vertex_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same vertex into one properties at inputstep. |
| worker.wait_finish_messages_timeout | 86400000 | The max timeout(in ms) message-handler wait for finish-message of all workers. |
| worker.wait_sort_timeout | 600000 | The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers. |
| worker.write_buffer_capacity | 52428800 | The initial size of write buffer that used to store vertex or message. |
| worker.write_buffer_threshold | 52428800 | The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message. |
NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL
| config option | default value | description |
|---|---|---|
| k8s.auto_destroy_pod | true | Whether to automatically destroy all pods when the job is completed or failed. |
| k8s.close_reconciler_timeout | 120 | The max timeout(in ms) to close reconciler. |
| k8s.internal_etcd_url | http://127.0.0.1:2379 | The internal etcd url for operator system. |
| k8s.max_reconcile_retry | 3 | The max retry times of reconcile. |
| k8s.probe_backlog | 50 | The maximum backlog for serving health probes. |
| k8s.probe_port | 9892 | The value is the port that the controller bind to for serving health probes. |
| k8s.ready_check_internal | 1000 | The time interval(ms) of check ready. |
| k8s.ready_timeout | 30000 | The max timeout(in ms) of check ready. |
| k8s.reconciler_count | 10 | The max number of reconciler thread. |
| k8s.resync_period | 600000 | The minimum frequency at which watched resources are reconciled. |
| k8s.timezone | Asia/Shanghai | The timezone of computer job and operator. |
| k8s.watch_namespace | hugegraph-computer-system | The value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched. |
| spec | default value | description | required |
|---|---|---|---|
| algorithmName | The name of algorithm. | true | |
| jobId | The job id. | true | |
| image | The image of algorithm. | true | |
| computerConf | The map of computer config options. | true | |
| workerInstances | The number of worker instances, it will instead the ‘job.workers_count’ option. | true | |
| pullPolicy | Always | The pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy | false |
| pullSecrets | The pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod | false | |
| masterCpu | The cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| workerCpu | The cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| masterMemory | The memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| workerMemory | The memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| log4jXml | The content of log4j.xml for computer job. | false | |
| jarFile | The jar path of computer algorithm. | false | |
| remoteJarUri | The remote jar uri of computer algorithm, it will overlay algorithm image. | false | |
| jvmOptions | The java startup parameters of computer job. | false | |
| envVars | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/ | false | |
| envFrom | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/ | false | |
| masterCommand | bin/start-computer.sh | The run command of master, equivalent to ‘Entrypoint’ field of Docker. | false |
| masterArgs | ["-r master", “-d k8s”] | The run args of master, equivalent to ‘Cmd’ field of Docker. | false |
| workerCommand | bin/start-computer.sh | The run command of worker, equivalent to ‘Entrypoint’ field of Docker. | false |
| workerArgs | ["-r worker", “-d k8s”] | The run args of worker, equivalent to ‘Cmd’ field of Docker. | false |
| volumes | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| volumeMounts | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| secretPaths | The map of k8s-secret name and mount path. | false | |
| configMapPaths | The map of k8s-configmap name and mount path. | false | |
| podTemplateSpec | Please refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpec | false | |
| securityContext | Please refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/ | false |
| config option | default value | description |
|---|---|---|
| k8s.build_image_bash_path | The path of command used to build image. | |
| k8s.enable_internal_algorithm | true | Whether enable internal algorithm. |
| k8s.framework_image_url | hugegraph/hugegraph-computer:latest | The image url of computer framework. |
| k8s.image_repository_password | The password for login image repository. | |
| k8s.image_repository_registry | The address for login image repository. | |
| k8s.image_repository_url | hugegraph/hugegraph-computer | The url of image repository. |
| k8s.image_repository_username | The username for login image repository. | |
| k8s.internal_algorithm | [pageRank] | The name list of all internal algorithm. |
| k8s.internal_algorithm_image_url | hugegraph/hugegraph-computer:latest | The image url of internal algorithm. |
| k8s.jar_file_dir | /cache/jars/ | The directory where the algorithm jar to upload location. |
| k8s.kube_config | ~/.kube/config | The path of k8s config file. |
| k8s.log4j_xml_path | The log4j.xml path for computer job. | |
| k8s.namespace | hugegraph-computer-system | The namespace of hugegraph-computer system. |
| k8s.pull_secret_names | [] | The names of pull-secret for pulling image. |
client.truststore 是给客户端⽤的，其中保存着受信任的证书
| config option | default value | description |
|---|---|---|
| algorithm.message_class | org.apache.hugegraph.computer.core.config.Null | The class of message passed when compute vertex. |
| algorithm.params_class | org.apache.hugegraph.computer.core.config.Null | The class used to transfer algorithms’ parameters before algorithm been run. |
| algorithm.result_class | org.apache.hugegraph.computer.core.config.Null | The class of vertex’s value, the instance is used to store computation result for the vertex. |
| allocator.max_vertices_per_thread | 10000 | Maximum number of vertices per thread processed in each memory allocator |
| bsp.etcd_endpoints | http://localhost:2379 | The end points to access etcd. |
| bsp.log_interval | 30000 | The log interval(in ms) to print the log while waiting bsp event. |
| bsp.max_super_step | 10 | The max super step of the algorithm. |
| bsp.register_timeout | 300000 | The max timeout to wait for master and works to register. |
| bsp.wait_master_timeout | 86400000 | The max timeout(in ms) to wait for master bsp event. |
| bsp.wait_workers_timeout | 86400000 | The max timeout to wait for workers bsp event. |
| hgkv.max_data_block_size | 65536 | The max byte size of hgkv-file data block. |
| hgkv.max_file_size | 2147483648 | The max number of bytes in each hgkv-file. |
| hgkv.max_merge_files | 10 | The max number of files to merge at one time. |
| hgkv.temp_file_dir | /tmp/hgkv | This folder is used to store temporary files, temporary files will be generated during the file merging process. |
| hugegraph.name | hugegraph | The graph name to load data and write results back. |
| hugegraph.url | http://127.0.0.1:8080 | The hugegraph url to load data and write results back. |
| input.edge_direction | OUT | The data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded. |
| input.edge_freq | MULTIPLE | The frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it. |
| input.filter_class | org.apache.hugegraph.computer.core.input.filter.DefaultInputFilter | The class to create input-filter object, input-filter is used to Filter vertex edges according to user needs. |
| input.loader_schema_path | The schema path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.loader_struct_path | The struct path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.max_edges_in_one_vertex | 200 | The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit. |
| input.source_type | hugegraph-server | The source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’. |
| input.split_fetch_timeout | 300 | The timeout in seconds to fetch input splits |
| input.split_max_splits | 10000000 | The maximum number of input splits |
| input.split_page_size | 500 | The page size for streamed load input split data |
| input.split_size | 1048576 | The input split size in bytes |
| job.id | local_0001 | The job id on Yarn cluster or K8s cluster. |
| job.partitions_count | 1 | The partitions count for computing one graph algorithm job. |
| job.partitions_thread_nums | 4 | The number of threads for partition parallel compute. |
| job.workers_count | 1 | The workers count for computing one graph algorithm job. |
| master.computation_class | org.apache.hugegraph.computer.core.master.DefaultMasterComputation | Master-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master. |
| output.batch_size | 500 | The batch size of output |
| output.batch_threads | 1 | The threads number used to batch output |
| output.hdfs_core_site_path | The hdfs core site path. | |
| output.hdfs_delimiter | , | The delimiter of hdfs output. |
| output.hdfs_kerberos_enable | false | Is Kerberos authentication enabled for Hdfs. |
| output.hdfs_kerberos_keytab | The Hdfs’s key tab file for kerberos authentication. | |
| output.hdfs_kerberos_principal | The Hdfs’s principal for kerberos authentication. | |
| output.hdfs_krb5_conf | /etc/krb5.conf | Kerberos configuration file. |
| output.hdfs_merge_partitions | true | Whether merge output files of multiple partitions. |
| output.hdfs_path_prefix | /hugegraph-computer/results | The directory of hdfs output result. |
| output.hdfs_replication | 3 | The replication number of hdfs. |
| output.hdfs_site_path | The hdfs site path. | |
| output.hdfs_url | hdfs://127.0.0.1:9000 | The hdfs url of output. |
| output.hdfs_user | hadoop | The hdfs user of output. |
| output.output_class | org.apache.hugegraph.computer.core.output.LogOutput | The class to output the computation result of each vertex. Be called after iteration computation. |
| output.result_name | value | The value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS. |
| output.result_write_type | OLAP_COMMON | The result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE]. |
| output.retry_interval | 10 | The retry interval when output failed |
| output.retry_times | 3 | The retry times when output failed |
| output.single_threads | 1 | The threads number used to single output |
| output.thread_pool_shutdown_timeout | 60 | The timeout seconds of output threads pool shutdown |
| output.with_adjacent_edges | false | Output the adjacent edges of the vertex or not |
| output.with_edge_properties | false | Output the properties of the edge or not |
| output.with_vertex_properties | false | Output the properties of the vertex or not |
| sort.thread_nums | 4 | The number of threads performing internal sorting. |
| transport.client_connect_timeout | 3000 | The timeout(in ms) of client connect to server. |
| transport.client_threads | 4 | The number of transport threads for client. |
| transport.close_timeout | 10000 | The timeout(in ms) of close server or close client. |
| transport.finish_session_timeout | 0 | The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests). |
| transport.heartbeat_interval | 20000 | The minimum interval(in ms) between heartbeats on client side. |
| transport.io_mode | AUTO | The network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically. |
| transport.max_pending_requests | 8 | The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests. |
| transport.max_syn_backlog | 511 | The capacity of SYN queue on server side, 0 means using system default value. |
| transport.max_timeout_heartbeat_count | 120 | The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side. |
| transport.min_ack_interval | 200 | The minimum interval(in ms) of server reply ack. |
| transport.min_pending_requests | 6 | The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests. |
| transport.network_retries | 3 | The number of retry attempts for network communication,if network unstable. |
| transport.provider_class | org.apache.hugegraph.computer.core.network.netty.NettyTransportProvider | The transport provider, currently only supports Netty. |
| transport.receive_buffer_size | 0 | The size of socket receive-buffer in bytes, 0 means using system default value. |
| transport.recv_file_mode | true | Whether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable. |
| transport.send_buffer_size | 0 | The size of socket send-buffer in bytes, 0 means using system default value. |
| transport.server_host | 127.0.0.1 | The server hostname or ip to listen on to transfer data. |
| transport.server_idle_timeout | 360000 | The max timeout(in ms) of server idle. |
| transport.server_port | 0 | The server port to listen on to transfer data. The system will assign a random port if it’s set to 0. |
| transport.server_threads | 4 | The number of transport threads for server. |
| transport.sync_request_timeout | 10000 | The timeout(in ms) to wait response after sending sync-request. |
| transport.tcp_keep_alive | true | Whether enable TCP keep-alive. |
| transport.transport_epoll_lt | false | Whether enable EPOLL level-trigger. |
| transport.write_buffer_high_mark | 67108864 | The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark. |
| transport.write_buffer_low_mark | 33554432 | The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b |
| transport.write_socket_timeout | 3000 | The timeout(in ms) to write data to socket buffer. |
| valuefile.max_segment_size | 1073741824 | The max number of bytes in each segment of value-file. |
| worker.combiner_class | org.apache.hugegraph.computer.core.config.Null | Combiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value. |
| worker.computation_class | org.apache.hugegraph.computer.core.config.Null | The class to create worker-computation object, worker-computation is used to compute each vertex in each superstep. |
| worker.data_dirs | [jobs] | The directories separated by ‘,’ that received vertices and messages can persist into. |
| worker.edge_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same edge into one properties at inputstep. |
| worker.partitioner | org.apache.hugegraph.computer.core.graph.partition.HashPartitioner | The partitioner that decides which partition a vertex should be in, and which worker a partition should be in. |
| worker.received_buffers_bytes_limit | 104857600 | The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file. |
| worker.vertex_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same vertex into one properties at inputstep. |
| worker.wait_finish_messages_timeout | 86400000 | The max timeout(in ms) message-handler wait for finish-message of all workers. |
| worker.wait_sort_timeout | 600000 | The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers. |
| worker.write_buffer_capacity | 52428800 | The initial size of write buffer that used to store vertex or message. |
| worker.write_buffer_threshold | 52428800 | The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message. |
NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL
| config option | default value | description |
|---|---|---|
| k8s.auto_destroy_pod | true | Whether to automatically destroy all pods when the job is completed or failed. |
| k8s.close_reconciler_timeout | 120 | The max timeout(in ms) to close reconciler. |
| k8s.internal_etcd_url | http://127.0.0.1:2379 | The internal etcd url for operator system. |
| k8s.max_reconcile_retry | 3 | The max retry times of reconcile. |
| k8s.probe_backlog | 50 | The maximum backlog for serving health probes. |
| k8s.probe_port | 9892 | The value is the port that the controller bind to for serving health probes. |
| k8s.ready_check_internal | 1000 | The time interval(ms) of check ready. |
| k8s.ready_timeout | 30000 | The max timeout(in ms) of check ready. |
| k8s.reconciler_count | 10 | The max number of reconciler thread. |
| k8s.resync_period | 600000 | The minimum frequency at which watched resources are reconciled. |
| k8s.timezone | Asia/Shanghai | The timezone of computer job and operator. |
| k8s.watch_namespace | hugegraph-computer-system | The value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched. |
| spec | default value | description | required |
|---|---|---|---|
| algorithmName | The name of algorithm. | true | |
| jobId | The job id. | true | |
| image | The image of algorithm. | true | |
| computerConf | The map of computer config options. | true | |
| workerInstances | The number of worker instances, it will instead the ‘job.workers_count’ option. | true | |
| pullPolicy | Always | The pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy | false |
| pullSecrets | The pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod | false | |
| masterCpu | The cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| workerCpu | The cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| masterMemory | The memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| workerMemory | The memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| log4jXml | The content of log4j.xml for computer job. | false | |
| jarFile | The jar path of computer algorithm. | false | |
| remoteJarUri | The remote jar uri of computer algorithm, it will overlay algorithm image. | false | |
| jvmOptions | The java startup parameters of computer job. | false | |
| envVars | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/ | false | |
| envFrom | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/ | false | |
| masterCommand | bin/start-computer.sh | The run command of master, equivalent to ‘Entrypoint’ field of Docker. | false |
| masterArgs | ["-r master", “-d k8s”] | The run args of master, equivalent to ‘Cmd’ field of Docker. | false |
| workerCommand | bin/start-computer.sh | The run command of worker, equivalent to ‘Entrypoint’ field of Docker. | false |
| workerArgs | ["-r worker", “-d k8s”] | The run args of worker, equivalent to ‘Cmd’ field of Docker. | false |
| volumes | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| volumeMounts | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| secretPaths | The map of k8s-secret name and mount path. | false | |
| configMapPaths | The map of k8s-configmap name and mount path. | false | |
| podTemplateSpec | Please refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpec | false | |
| securityContext | Please refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/ | false |
| config option | default value | description |
|---|---|---|
| k8s.build_image_bash_path | The path of command used to build image. | |
| k8s.enable_internal_algorithm | true | Whether enable internal algorithm. |
| k8s.framework_image_url | hugegraph/hugegraph-computer:latest | The image url of computer framework. |
| k8s.image_repository_password | The password for login image repository. | |
| k8s.image_repository_registry | The address for login image repository. | |
| k8s.image_repository_url | hugegraph/hugegraph-computer | The url of image repository. |
| k8s.image_repository_username | The username for login image repository. | |
| k8s.internal_algorithm | [pageRank] | The name list of all internal algorithm. |
| k8s.internal_algorithm_image_url | hugegraph/hugegraph-computer:latest | The image url of internal algorithm. |
| k8s.jar_file_dir | /cache/jars/ | The directory where the algorithm jar to upload location. |
| k8s.kube_config | ~/.kube/config | The path of k8s config file. |
| k8s.log4j_xml_path | The log4j.xml path for computer job. | |
| k8s.namespace | hugegraph-computer-system | The namespace of hugegraph-computer system. |
| k8s.pull_secret_names | [] | The names of pull-secret for pulling image. |
在配置文件hugegraph{n}.properties中配置gremlin.graph信息：
gremlin.graph=com.baidu.hugegraph.auth.HugeFactoryAuthProxy
-如果需要支持更加灵活的用户系统，可自定义authenticator进行扩展，自定义authenticator实现接口com.baidu.hugegraph.auth.HugeAuthenticator即可，然后修改配置文件中authenticator配置项指向该实现。
如果需要支持更加灵活的用户系统，可自定义authenticator进行扩展，自定义authenticator实现接口com.baidu.hugegraph.auth.HugeAuthenticator即可，然后修改配置文件中authenticator配置项指向该实现。
| config option | default value | description |
|---|---|---|
| algorithm.message_class | org.apache.hugegraph.computer.core.config.Null | The class of message passed when compute vertex. |
| algorithm.params_class | org.apache.hugegraph.computer.core.config.Null | The class used to transfer algorithms’ parameters before algorithm been run. |
| algorithm.result_class | org.apache.hugegraph.computer.core.config.Null | The class of vertex’s value, the instance is used to store computation result for the vertex. |
| allocator.max_vertices_per_thread | 10000 | Maximum number of vertices per thread processed in each memory allocator |
| bsp.etcd_endpoints | http://localhost:2379 | The end points to access etcd. |
| bsp.log_interval | 30000 | The log interval(in ms) to print the log while waiting bsp event. |
| bsp.max_super_step | 10 | The max super step of the algorithm. |
| bsp.register_timeout | 300000 | The max timeout to wait for master and works to register. |
| bsp.wait_master_timeout | 86400000 | The max timeout(in ms) to wait for master bsp event. |
| bsp.wait_workers_timeout | 86400000 | The max timeout to wait for workers bsp event. |
| hgkv.max_data_block_size | 65536 | The max byte size of hgkv-file data block. |
| hgkv.max_file_size | 2147483648 | The max number of bytes in each hgkv-file. |
| hgkv.max_merge_files | 10 | The max number of files to merge at one time. |
| hgkv.temp_file_dir | /tmp/hgkv | This folder is used to store temporary files, temporary files will be generated during the file merging process. |
| hugegraph.name | hugegraph | The graph name to load data and write results back. |
| hugegraph.url | http://127.0.0.1:8080 | The hugegraph url to load data and write results back. |
| input.edge_direction | OUT | The data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded. |
| input.edge_freq | MULTIPLE | The frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it. |
| input.filter_class | org.apache.hugegraph.computer.core.input.filter.DefaultInputFilter | The class to create input-filter object, input-filter is used to Filter vertex edges according to user needs. |
| input.loader_schema_path | The schema path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.loader_struct_path | The struct path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.max_edges_in_one_vertex | 200 | The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit. |
| input.source_type | hugegraph-server | The source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’. |
| input.split_fetch_timeout | 300 | The timeout in seconds to fetch input splits |
| input.split_max_splits | 10000000 | The maximum number of input splits |
| input.split_page_size | 500 | The page size for streamed load input split data |
| input.split_size | 1048576 | The input split size in bytes |
| job.id | local_0001 | The job id on Yarn cluster or K8s cluster. |
| job.partitions_count | 1 | The partitions count for computing one graph algorithm job. |
| job.partitions_thread_nums | 4 | The number of threads for partition parallel compute. |
| job.workers_count | 1 | The workers count for computing one graph algorithm job. |
| master.computation_class | org.apache.hugegraph.computer.core.master.DefaultMasterComputation | Master-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master. |
| output.batch_size | 500 | The batch size of output |
| output.batch_threads | 1 | The threads number used to batch output |
| output.hdfs_core_site_path | The hdfs core site path. | |
| output.hdfs_delimiter | , | The delimiter of hdfs output. |
| output.hdfs_kerberos_enable | false | Is Kerberos authentication enabled for Hdfs. |
| output.hdfs_kerberos_keytab | The Hdfs’s key tab file for kerberos authentication. | |
| output.hdfs_kerberos_principal | The Hdfs’s principal for kerberos authentication. | |
| output.hdfs_krb5_conf | /etc/krb5.conf | Kerberos configuration file. |
| output.hdfs_merge_partitions | true | Whether merge output files of multiple partitions. |
| output.hdfs_path_prefix | /hugegraph-computer/results | The directory of hdfs output result. |
| output.hdfs_replication | 3 | The replication number of hdfs. |
| output.hdfs_site_path | The hdfs site path. | |
| output.hdfs_url | hdfs://127.0.0.1:9000 | The hdfs url of output. |
| output.hdfs_user | hadoop | The hdfs user of output. |
| output.output_class | org.apache.hugegraph.computer.core.output.LogOutput | The class to output the computation result of each vertex. Be called after iteration computation. |
| output.result_name | value | The value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS. |
| output.result_write_type | OLAP_COMMON | The result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE]. |
| output.retry_interval | 10 | The retry interval when output failed |
| output.retry_times | 3 | The retry times when output failed |
| output.single_threads | 1 | The threads number used to single output |
| output.thread_pool_shutdown_timeout | 60 | The timeout seconds of output threads pool shutdown |
| output.with_adjacent_edges | false | Output the adjacent edges of the vertex or not |
| output.with_edge_properties | false | Output the properties of the edge or not |
| output.with_vertex_properties | false | Output the properties of the vertex or not |
| sort.thread_nums | 4 | The number of threads performing internal sorting. |
| transport.client_connect_timeout | 3000 | The timeout(in ms) of client connect to server. |
| transport.client_threads | 4 | The number of transport threads for client. |
| transport.close_timeout | 10000 | The timeout(in ms) of close server or close client. |
| transport.finish_session_timeout | 0 | The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests). |
| transport.heartbeat_interval | 20000 | The minimum interval(in ms) between heartbeats on client side. |
| transport.io_mode | AUTO | The network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically. |
| transport.max_pending_requests | 8 | The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests. |
| transport.max_syn_backlog | 511 | The capacity of SYN queue on server side, 0 means using system default value. |
| transport.max_timeout_heartbeat_count | 120 | The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side. |
| transport.min_ack_interval | 200 | The minimum interval(in ms) of server reply ack. |
| transport.min_pending_requests | 6 | The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests. |
| transport.network_retries | 3 | The number of retry attempts for network communication,if network unstable. |
| transport.provider_class | org.apache.hugegraph.computer.core.network.netty.NettyTransportProvider | The transport provider, currently only supports Netty. |
| transport.receive_buffer_size | 0 | The size of socket receive-buffer in bytes, 0 means using system default value. |
| transport.recv_file_mode | true | Whether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable. |
| transport.send_buffer_size | 0 | The size of socket send-buffer in bytes, 0 means using system default value. |
| transport.server_host | 127.0.0.1 | The server hostname or ip to listen on to transfer data. |
| transport.server_idle_timeout | 360000 | The max timeout(in ms) of server idle. |
| transport.server_port | 0 | The server port to listen on to transfer data. The system will assign a random port if it’s set to 0. |
| transport.server_threads | 4 | The number of transport threads for server. |
| transport.sync_request_timeout | 10000 | The timeout(in ms) to wait response after sending sync-request. |
| transport.tcp_keep_alive | true | Whether enable TCP keep-alive. |
| transport.transport_epoll_lt | false | Whether enable EPOLL level-trigger. |
| transport.write_buffer_high_mark | 67108864 | The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark. |
| transport.write_buffer_low_mark | 33554432 | The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b |
| transport.write_socket_timeout | 3000 | The timeout(in ms) to write data to socket buffer. |
| valuefile.max_segment_size | 1073741824 | The max number of bytes in each segment of value-file. |
| worker.combiner_class | org.apache.hugegraph.computer.core.config.Null | Combiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value. |
| worker.computation_class | org.apache.hugegraph.computer.core.config.Null | The class to create worker-computation object, worker-computation is used to compute each vertex in each superstep. |
| worker.data_dirs | [jobs] | The directories separated by ‘,’ that received vertices and messages can persist into. |
| worker.edge_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same edge into one properties at inputstep. |
| worker.partitioner | org.apache.hugegraph.computer.core.graph.partition.HashPartitioner | The partitioner that decides which partition a vertex should be in, and which worker a partition should be in. |
| worker.received_buffers_bytes_limit | 104857600 | The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file. |
| worker.vertex_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same vertex into one properties at inputstep. |
| worker.wait_finish_messages_timeout | 86400000 | The max timeout(in ms) message-handler wait for finish-message of all workers. |
| worker.wait_sort_timeout | 600000 | The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers. |
| worker.write_buffer_capacity | 52428800 | The initial size of write buffer that used to store vertex or message. |
| worker.write_buffer_threshold | 52428800 | The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message. |
NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL
| config option | default value | description |
|---|---|---|
| k8s.auto_destroy_pod | true | Whether to automatically destroy all pods when the job is completed or failed. |
| k8s.close_reconciler_timeout | 120 | The max timeout(in ms) to close reconciler. |
| k8s.internal_etcd_url | http://127.0.0.1:2379 | The internal etcd url for operator system. |
| k8s.max_reconcile_retry | 3 | The max retry times of reconcile. |
| k8s.probe_backlog | 50 | The maximum backlog for serving health probes. |
| k8s.probe_port | 9892 | The value is the port that the controller bind to for serving health probes. |
| k8s.ready_check_internal | 1000 | The time interval(ms) of check ready. |
| k8s.ready_timeout | 30000 | The max timeout(in ms) of check ready. |
| k8s.reconciler_count | 10 | The max number of reconciler thread. |
| k8s.resync_period | 600000 | The minimum frequency at which watched resources are reconciled. |
| k8s.timezone | Asia/Shanghai | The timezone of computer job and operator. |
| k8s.watch_namespace | hugegraph-computer-system | The value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched. |
| spec | default value | description | required |
|---|---|---|---|
| algorithmName | The name of algorithm. | true | |
| jobId | The job id. | true | |
| image | The image of algorithm. | true | |
| computerConf | The map of computer config options. | true | |
| workerInstances | The number of worker instances, it will instead the ‘job.workers_count’ option. | true | |
| pullPolicy | Always | The pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy | false |
| pullSecrets | The pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod | false | |
| masterCpu | The cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| workerCpu | The cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| masterMemory | The memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| workerMemory | The memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| log4jXml | The content of log4j.xml for computer job. | false | |
| jarFile | The jar path of computer algorithm. | false | |
| remoteJarUri | The remote jar uri of computer algorithm, it will overlay algorithm image. | false | |
| jvmOptions | The java startup parameters of computer job. | false | |
| envVars | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/ | false | |
| envFrom | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/ | false | |
| masterCommand | bin/start-computer.sh | The run command of master, equivalent to ‘Entrypoint’ field of Docker. | false |
| masterArgs | ["-r master", “-d k8s”] | The run args of master, equivalent to ‘Cmd’ field of Docker. | false |
| workerCommand | bin/start-computer.sh | The run command of worker, equivalent to ‘Entrypoint’ field of Docker. | false |
| workerArgs | ["-r worker", “-d k8s”] | The run args of worker, equivalent to ‘Cmd’ field of Docker. | false |
| volumes | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| volumeMounts | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| secretPaths | The map of k8s-secret name and mount path. | false | |
| configMapPaths | The map of k8s-configmap name and mount path. | false | |
| podTemplateSpec | Please refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpec | false | |
| securityContext | Please refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/ | false |
| config option | default value | description |
|---|---|---|
| k8s.build_image_bash_path | The path of command used to build image. | |
| k8s.enable_internal_algorithm | true | Whether enable internal algorithm. |
| k8s.framework_image_url | hugegraph/hugegraph-computer:latest | The image url of computer framework. |
| k8s.image_repository_password | The password for login image repository. | |
| k8s.image_repository_registry | The address for login image repository. | |
| k8s.image_repository_url | hugegraph/hugegraph-computer | The url of image repository. |
| k8s.image_repository_username | The username for login image repository. | |
| k8s.internal_algorithm | [pageRank] | The name list of all internal algorithm. |
| k8s.internal_algorithm_image_url | hugegraph/hugegraph-computer:latest | The image url of internal algorithm. |
| k8s.jar_file_dir | /cache/jars/ | The directory where the algorithm jar to upload location. |
| k8s.kube_config | ~/.kube/config | The path of k8s config file. |
| k8s.log4j_xml_path | The log4j.xml path for computer job. | |
| k8s.namespace | hugegraph-computer-system | The namespace of hugegraph-computer system. |
| k8s.pull_secret_names | [] | The names of pull-secret for pulling image. |
| config option | default value | description |
|---|---|---|
| algorithm.message_class | org.apache.hugegraph.computer.core.config.Null | The class of message passed when compute vertex. |
| algorithm.params_class | org.apache.hugegraph.computer.core.config.Null | The class used to transfer algorithms’ parameters before algorithm been run. |
| algorithm.result_class | org.apache.hugegraph.computer.core.config.Null | The class of vertex’s value, the instance is used to store computation result for the vertex. |
| allocator.max_vertices_per_thread | 10000 | Maximum number of vertices per thread processed in each memory allocator |
| bsp.etcd_endpoints | http://localhost:2379 | The end points to access etcd. |
| bsp.log_interval | 30000 | The log interval(in ms) to print the log while waiting bsp event. |
| bsp.max_super_step | 10 | The max super step of the algorithm. |
| bsp.register_timeout | 300000 | The max timeout to wait for master and works to register. |
| bsp.wait_master_timeout | 86400000 | The max timeout(in ms) to wait for master bsp event. |
| bsp.wait_workers_timeout | 86400000 | The max timeout to wait for workers bsp event. |
| hgkv.max_data_block_size | 65536 | The max byte size of hgkv-file data block. |
| hgkv.max_file_size | 2147483648 | The max number of bytes in each hgkv-file. |
| hgkv.max_merge_files | 10 | The max number of files to merge at one time. |
| hgkv.temp_file_dir | /tmp/hgkv | This folder is used to store temporary files, temporary files will be generated during the file merging process. |
| hugegraph.name | hugegraph | The graph name to load data and write results back. |
| hugegraph.url | http://127.0.0.1:8080 | The hugegraph url to load data and write results back. |
| input.edge_direction | OUT | The data of the edge in which direction is loaded, when the value is BOTH, the edges in both OUT and IN direction will be loaded. |
| input.edge_freq | MULTIPLE | The frequency of edges can exist between a pair of vertices, allowed values: [SINGLE, SINGLE_PER_LABEL, MULTIPLE]. SINGLE means that only one edge can exist between a pair of vertices, use sourceId + targetId to identify it; SINGLE_PER_LABEL means that each edge label can exist one edge between a pair of vertices, use sourceId + edgelabel + targetId to identify it; MULTIPLE means that many edge can exist between a pair of vertices, use sourceId + edgelabel + sortValues + targetId to identify it. |
| input.filter_class | org.apache.hugegraph.computer.core.input.filter.DefaultInputFilter | The class to create input-filter object, input-filter is used to Filter vertex edges according to user needs. |
| input.loader_schema_path | The schema path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.loader_struct_path | The struct path of loader input, only takes effect when the input.source_type=loader is enabled | |
| input.max_edges_in_one_vertex | 200 | The maximum number of adjacent edges allowed to be attached to a vertex, the adjacent edges will be stored and transferred together as a batch unit. |
| input.source_type | hugegraph-server | The source type to load input data, allowed values: [‘hugegraph-server’, ‘hugegraph-loader’], the ‘hugegraph-loader’ means use hugegraph-loader load data from HDFS or file, if use ‘hugegraph-loader’ load data then please config ‘input.loader_struct_path’ and ‘input.loader_schema_path’. |
| input.split_fetch_timeout | 300 | The timeout in seconds to fetch input splits |
| input.split_max_splits | 10000000 | The maximum number of input splits |
| input.split_page_size | 500 | The page size for streamed load input split data |
| input.split_size | 1048576 | The input split size in bytes |
| job.id | local_0001 | The job id on Yarn cluster or K8s cluster. |
| job.partitions_count | 1 | The partitions count for computing one graph algorithm job. |
| job.partitions_thread_nums | 4 | The number of threads for partition parallel compute. |
| job.workers_count | 1 | The workers count for computing one graph algorithm job. |
| master.computation_class | org.apache.hugegraph.computer.core.master.DefaultMasterComputation | Master-computation is computation that can determine whether to continue next superstep. It runs at the end of each superstep on master. |
| output.batch_size | 500 | The batch size of output |
| output.batch_threads | 1 | The threads number used to batch output |
| output.hdfs_core_site_path | The hdfs core site path. | |
| output.hdfs_delimiter | , | The delimiter of hdfs output. |
| output.hdfs_kerberos_enable | false | Is Kerberos authentication enabled for Hdfs. |
| output.hdfs_kerberos_keytab | The Hdfs’s key tab file for kerberos authentication. | |
| output.hdfs_kerberos_principal | The Hdfs’s principal for kerberos authentication. | |
| output.hdfs_krb5_conf | /etc/krb5.conf | Kerberos configuration file. |
| output.hdfs_merge_partitions | true | Whether merge output files of multiple partitions. |
| output.hdfs_path_prefix | /hugegraph-computer/results | The directory of hdfs output result. |
| output.hdfs_replication | 3 | The replication number of hdfs. |
| output.hdfs_site_path | The hdfs site path. | |
| output.hdfs_url | hdfs://127.0.0.1:9000 | The hdfs url of output. |
| output.hdfs_user | hadoop | The hdfs user of output. |
| output.output_class | org.apache.hugegraph.computer.core.output.LogOutput | The class to output the computation result of each vertex. Be called after iteration computation. |
| output.result_name | value | The value is assigned dynamically by #name() of instance created by WORKER_COMPUTATION_CLASS. |
| output.result_write_type | OLAP_COMMON | The result write-type to output to hugegraph, allowed values are: [OLAP_COMMON, OLAP_SECONDARY, OLAP_RANGE]. |
| output.retry_interval | 10 | The retry interval when output failed |
| output.retry_times | 3 | The retry times when output failed |
| output.single_threads | 1 | The threads number used to single output |
| output.thread_pool_shutdown_timeout | 60 | The timeout seconds of output threads pool shutdown |
| output.with_adjacent_edges | false | Output the adjacent edges of the vertex or not |
| output.with_edge_properties | false | Output the properties of the edge or not |
| output.with_vertex_properties | false | Output the properties of the vertex or not |
| sort.thread_nums | 4 | The number of threads performing internal sorting. |
| transport.client_connect_timeout | 3000 | The timeout(in ms) of client connect to server. |
| transport.client_threads | 4 | The number of transport threads for client. |
| transport.close_timeout | 10000 | The timeout(in ms) of close server or close client. |
| transport.finish_session_timeout | 0 | The timeout(in ms) to finish session, 0 means using (transport.sync_request_timeout * transport.max_pending_requests). |
| transport.heartbeat_interval | 20000 | The minimum interval(in ms) between heartbeats on client side. |
| transport.io_mode | AUTO | The network IO Mode, either ‘NIO’, ‘EPOLL’, ‘AUTO’, the ‘AUTO’ means selecting the property mode automatically. |
| transport.max_pending_requests | 8 | The max number of client unreceived ack, it will trigger the sending unavailable if the number of unreceived ack >= max_pending_requests. |
| transport.max_syn_backlog | 511 | The capacity of SYN queue on server side, 0 means using system default value. |
| transport.max_timeout_heartbeat_count | 120 | The maximum times of timeout heartbeat on client side, if the number of timeouts waiting for heartbeat response continuously > max_heartbeat_timeouts the channel will be closed from client side. |
| transport.min_ack_interval | 200 | The minimum interval(in ms) of server reply ack. |
| transport.min_pending_requests | 6 | The minimum number of client unreceived ack, it will trigger the sending available if the number of unreceived ack < min_pending_requests. |
| transport.network_retries | 3 | The number of retry attempts for network communication,if network unstable. |
| transport.provider_class | org.apache.hugegraph.computer.core.network.netty.NettyTransportProvider | The transport provider, currently only supports Netty. |
| transport.receive_buffer_size | 0 | The size of socket receive-buffer in bytes, 0 means using system default value. |
| transport.recv_file_mode | true | Whether enable receive buffer-file mode, it will receive buffer write file from socket by zero-copy if enable. |
| transport.send_buffer_size | 0 | The size of socket send-buffer in bytes, 0 means using system default value. |
| transport.server_host | 127.0.0.1 | The server hostname or ip to listen on to transfer data. |
| transport.server_idle_timeout | 360000 | The max timeout(in ms) of server idle. |
| transport.server_port | 0 | The server port to listen on to transfer data. The system will assign a random port if it’s set to 0. |
| transport.server_threads | 4 | The number of transport threads for server. |
| transport.sync_request_timeout | 10000 | The timeout(in ms) to wait response after sending sync-request. |
| transport.tcp_keep_alive | true | Whether enable TCP keep-alive. |
| transport.transport_epoll_lt | false | Whether enable EPOLL level-trigger. |
| transport.write_buffer_high_mark | 67108864 | The high water mark for write buffer in bytes, it will trigger the sending unavailable if the number of queued bytes > write_buffer_high_mark. |
| transport.write_buffer_low_mark | 33554432 | The low water mark for write buffer in bytes, it will trigger the sending available if the number of queued bytes < write_buffer_low_mark.org.apache.hugegraph.config.OptionChecker$$Lambda$97/0x00000008001c8440@776a6d9b |
| transport.write_socket_timeout | 3000 | The timeout(in ms) to write data to socket buffer. |
| valuefile.max_segment_size | 1073741824 | The max number of bytes in each segment of value-file. |
| worker.combiner_class | org.apache.hugegraph.computer.core.config.Null | Combiner can combine messages into one value for a vertex, for example page-rank algorithm can combine messages of a vertex to a sum value. |
| worker.computation_class | org.apache.hugegraph.computer.core.config.Null | The class to create worker-computation object, worker-computation is used to compute each vertex in each superstep. |
| worker.data_dirs | [jobs] | The directories separated by ‘,’ that received vertices and messages can persist into. |
| worker.edge_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same edge into one properties at inputstep. |
| worker.partitioner | org.apache.hugegraph.computer.core.graph.partition.HashPartitioner | The partitioner that decides which partition a vertex should be in, and which worker a partition should be in. |
| worker.received_buffers_bytes_limit | 104857600 | The limit bytes of buffers of received data, the total size of all buffers can’t excess this limit. If received buffers reach this limit, they will be merged into a file. |
| worker.vertex_properties_combiner_class | org.apache.hugegraph.computer.core.combiner.OverwritePropertiesCombiner | The combiner can combine several properties of the same vertex into one properties at inputstep. |
| worker.wait_finish_messages_timeout | 86400000 | The max timeout(in ms) message-handler wait for finish-message of all workers. |
| worker.wait_sort_timeout | 600000 | The max timeout(in ms) message-handler wait for sort-thread to sort one batch of buffers. |
| worker.write_buffer_capacity | 52428800 | The initial size of write buffer that used to store vertex or message. |
| worker.write_buffer_threshold | 52428800 | The threshold of write buffer, exceeding it will trigger sorting, the write buffer is used to store vertex or message. |
NOTE: Option needs to be converted through environment variable settings, e.g k8s.internal_etcd_url => INTERNAL_ETCD_URL
| config option | default value | description |
|---|---|---|
| k8s.auto_destroy_pod | true | Whether to automatically destroy all pods when the job is completed or failed. |
| k8s.close_reconciler_timeout | 120 | The max timeout(in ms) to close reconciler. |
| k8s.internal_etcd_url | http://127.0.0.1:2379 | The internal etcd url for operator system. |
| k8s.max_reconcile_retry | 3 | The max retry times of reconcile. |
| k8s.probe_backlog | 50 | The maximum backlog for serving health probes. |
| k8s.probe_port | 9892 | The value is the port that the controller bind to for serving health probes. |
| k8s.ready_check_internal | 1000 | The time interval(ms) of check ready. |
| k8s.ready_timeout | 30000 | The max timeout(in ms) of check ready. |
| k8s.reconciler_count | 10 | The max number of reconciler thread. |
| k8s.resync_period | 600000 | The minimum frequency at which watched resources are reconciled. |
| k8s.timezone | Asia/Shanghai | The timezone of computer job and operator. |
| k8s.watch_namespace | hugegraph-computer-system | The value is watch custom resources in the namespace, ignore other namespaces, the ‘*’ means is all namespaces will be watched. |
| spec | default value | description | required |
|---|---|---|---|
| algorithmName | The name of algorithm. | true | |
| jobId | The job id. | true | |
| image | The image of algorithm. | true | |
| computerConf | The map of computer config options. | true | |
| workerInstances | The number of worker instances, it will instead the ‘job.workers_count’ option. | true | |
| pullPolicy | Always | The pull-policy of image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#image-pull-policy | false |
| pullSecrets | The pull-secrets of Image, detail please refer to: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod | false | |
| masterCpu | The cpu limit of master, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| workerCpu | The cpu limit of worker, the unit can be ’m’ or without unit detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu | false | |
| masterMemory | The memory limit of master, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| workerMemory | The memory limit of worker, the unit can be one of Ei、Pi、Ti、Gi、Mi、Ki detail please refer to：https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory | false | |
| log4jXml | The content of log4j.xml for computer job. | false | |
| jarFile | The jar path of computer algorithm. | false | |
| remoteJarUri | The remote jar uri of computer algorithm, it will overlay algorithm image. | false | |
| jvmOptions | The java startup parameters of computer job. | false | |
| envVars | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-interdependent-environment-variables/ | false | |
| envFrom | please refer to: https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/ | false | |
| masterCommand | bin/start-computer.sh | The run command of master, equivalent to ‘Entrypoint’ field of Docker. | false |
| masterArgs | ["-r master", “-d k8s”] | The run args of master, equivalent to ‘Cmd’ field of Docker. | false |
| workerCommand | bin/start-computer.sh | The run command of worker, equivalent to ‘Entrypoint’ field of Docker. | false |
| workerArgs | ["-r worker", “-d k8s”] | The run args of worker, equivalent to ‘Cmd’ field of Docker. | false |
| volumes | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| volumeMounts | Please refer to: https://kubernetes.io/docs/concepts/storage/volumes/ | false | |
| secretPaths | The map of k8s-secret name and mount path. | false | |
| configMapPaths | The map of k8s-configmap name and mount path. | false | |
| podTemplateSpec | Please refer to: https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-template-v1/#PodTemplateSpec | false | |
| securityContext | Please refer to: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/ | false |
| config option | default value | description |
|---|---|---|
| k8s.build_image_bash_path | The path of command used to build image. | |
| k8s.enable_internal_algorithm | true | Whether enable internal algorithm. |
| k8s.framework_image_url | hugegraph/hugegraph-computer:latest | The image url of computer framework. |
| k8s.image_repository_password | The password for login image repository. | |
| k8s.image_repository_registry | The address for login image repository. | |
| k8s.image_repository_url | hugegraph/hugegraph-computer | The url of image repository. |
| k8s.image_repository_username | The username for login image repository. | |
| k8s.internal_algorithm | [pageRank] | The name list of all internal algorithm. |
| k8s.internal_algorithm_image_url | hugegraph/hugegraph-computer:latest | The image url of internal algorithm. |
| k8s.jar_file_dir | /cache/jars/ | The directory where the algorithm jar to upload location. |
| k8s.kube_config | ~/.kube/config | The path of k8s config file. |
| k8s.log4j_xml_path | The log4j.xml path for computer job. | |
| k8s.namespace | hugegraph-computer-system | The namespace of hugegraph-computer system. |
| k8s.pull_secret_names | [] | The names of pull-secret for pulling image. |
停止 Server，初始化执行 init-store.sh（为新的图创建数据库），重新启动 Server
$ bin/stop-hugegraph.sh
$ bin/init-store.sh
$ bin/start-hugegraph.sh
-keytool -export -alias serverkey -keystore server.keystore -file server.crt
server.crt 就是服务端的证书
keytool -import -alias serverkey -file server.crt -keystore client.truststore
-client.truststore 是给客户端⽤的，其中保存着受信任的证书
client.truststore 是给客户端⽤的，其中保存着受信任的证书
Corresponding configuration file gremlin-server.yaml
| config option | default value | description |
|---|---|---|
| host | 127.0.0.1 | The host or ip of Gremlin Server. |
| port | 8182 | The listening port of Gremlin Server. |
| graphs | hugegraph: conf/hugegraph.properties | The map of graphs with name and config file path. |
| scriptEvaluationTimeout | 30000 | The timeout for gremlin script execution(millisecond). |
| channelizer | org.apache.tinkerpop.gremlin.server.channel.HttpChannelizer | Indicates the protocol which the Gremlin Server provides service. |
| authentication | authenticator: com.baidu.hugegraph.auth.StandardAuthenticator, config: {tokens: conf/rest-server.properties} | The authenticator and config(contains tokens path) of authentication mechanism. |
Corresponding configuration file rest-server.properties
| config option | default value | description |
|---|---|---|
| graphs | [hugegraph:conf/hugegraph.properties] | The map of graphs’ name and config file. |
| server.id | server-1 | The id of rest server, used for license verification. |
| server.role | master | The role of nodes in the cluster, available types are [master, worker, computer] |
| restserver.url | http://127.0.0.1:8080 | The url for listening of rest server. |
| ssl.keystore_file | server.keystore | The path of server keystore file used when https protocol is enabled. |
| ssl.keystore_password | The password of the path of the server keystore file used when the https protocol is enabled. | |
| restserver.max_worker_threads | 2 * CPUs | The maximum worker threads of rest server. |
| restserver.min_free_memory | 64 | The minimum free memory(MB) of rest server, requests will be rejected when the available memory of system is lower than this value. |
| restserver.request_timeout | 30 | The time in seconds within which a request must complete, -1 means no timeout. |
| restserver.connection_idle_timeout | 30 | The time in seconds to keep an inactive connection alive, -1 means no timeout. |
| restserver.connection_max_requests | 256 | The max number of HTTP requests allowed to be processed on one keep-alive connection, -1 means unlimited. |
| gremlinserver.url | http://127.0.0.1:8182 | The url of gremlin server. |
| gremlinserver.max_route | 8 | The max route number for gremlin server. |
| gremlinserver.timeout | 30 | The timeout in seconds of waiting for gremlin server. |
| batch.max_edges_per_batch | 500 | The maximum number of edges submitted per batch. |
| batch.max_vertices_per_batch | 500 | The maximum number of vertices submitted per batch. |
| batch.max_write_ratio | 50 | The maximum thread ratio for batch writing, only take effect if the batch.max_write_threads is 0. |
| batch.max_write_threads | 0 | The maximum threads for batch writing, if the value is 0, the actual value will be set to batch.max_write_ratio * restserver.max_worker_threads. |
| auth.authenticator | The class path of authenticator implementation. e.g., com.baidu.hugegraph.auth.StandardAuthenticator, or com.baidu.hugegraph.auth.ConfigAuthenticator. | |
| auth.admin_token | 162f7848-0b6d-4faf-b557-3a0797869c55 | Token for administrator operations, only for com.baidu.hugegraph.auth.ConfigAuthenticator. |
| auth.graph_store | hugegraph | The name of graph used to store authentication information, like users, only for com.baidu.hugegraph.auth.StandardAuthenticator. |
| auth.user_tokens | [hugegraph:9fd95c9c-711b-415b-b85f-d4df46ba5c31] | The map of user tokens with name and password, only for com.baidu.hugegraph.auth.ConfigAuthenticator. |
| auth.audit_log_rate | 1000.0 | The max rate of audit log output per user, default value is 1000 records per second. |
| auth.cache_capacity | 10240 | The max cache capacity of each auth cache item. |
| auth.cache_expire | 600 | The expiration time in seconds of vertex cache. |
| auth.remote_url | If the address is empty, it provide auth service, otherwise it is auth client and also provide auth service through rpc forwarding. The remote url can be set to multiple addresses, which are concat by ‘,’. | |
| auth.token_expire | 86400 | The expiration time in seconds after token created |
| auth.token_secret | FXQXbJtbCLxODc6tGci732pkH1cyf8Qg | Secret key of HS256 algorithm. |
| exception.allow_trace | false | Whether to allow exception trace stack. |
Basic Config Options and Backend Config Options correspond to configuration files：{graph-name}.properties，such as hugegraph.properties
| config option | default value | description |
|---|---|---|
| gremlin.graph | com.baidu.hugegraph.HugeFactory | Gremlin entrance to create graph. |
| backend | rocksdb | The data store type, available values are [memory, rocksdb, cassandra, scylladb, hbase, mysql]. |
| serializer | binary | The serializer for backend store, available values are [text, binary, cassandra, hbase, mysql]. |
| store | hugegraph | The database name like Cassandra Keyspace. |
| store.connection_detect_interval | 600 | The interval in seconds for detecting connections, if the idle time of a connection exceeds this value, detect it and reconnect if needed before using, value 0 means detecting every time. |
| store.graph | g | The graph table name, which store vertex, edge and property. |
| store.schema | m | The schema table name, which store meta data. |
| store.system | s | The system table name, which store system data. |
| schema.illegal_name_regex | .\s+$|~. | The regex specified the illegal format for schema name. |
| schema.cache_capacity | 10000 | The max cache size(items) of schema cache. |
| vertex.cache_type | l2 | The type of vertex cache, allowed values are [l1, l2]. |
| vertex.cache_capacity | 10000000 | The max cache size(items) of vertex cache. |
| vertex.cache_expire | 600 | The expire time in seconds of vertex cache. |
| vertex.check_customized_id_exist | false | Whether to check the vertices exist for those using customized id strategy. |
| vertex.default_label | vertex | The default vertex label. |
| vertex.tx_capacity | 10000 | The max size(items) of vertices(uncommitted) in transaction. |
| vertex.check_adjacent_vertex_exist | false | Whether to check the adjacent vertices of edges exist. |
| vertex.lazy_load_adjacent_vertex | true | Whether to lazy load adjacent vertices of edges. |
| vertex.part_edge_commit_size | 5000 | Whether to enable the mode to commit part of edges of vertex, enabled if commit size > 0, 0 means disabled. |
| vertex.encode_primary_key_number | true | Whether to encode number value of primary key in vertex id. |
| vertex.remove_left_index_at_overwrite | false | Whether remove left index at overwrite. |
| edge.cache_type | l2 | The type of edge cache, allowed values are [l1, l2]. |
| edge.cache_capacity | 1000000 | The max cache size(items) of edge cache. |
| edge.cache_expire | 600 | The expiration time in seconds of edge cache. |
| edge.tx_capacity | 10000 | The max size(items) of edges(uncommitted) in transaction. |
| query.page_size | 500 | The size of each page when querying by paging. |
| query.batch_size | 1000 | The size of each batch when querying by batch. |
| query.ignore_invalid_data | true | Whether to ignore invalid data of vertex or edge. |
| query.index_intersect_threshold | 1000 | The maximum number of intermediate results to intersect indexes when querying by multiple single index properties. |
| query.ramtable_edges_capacity | 20000000 | The maximum number of edges in ramtable, include OUT and IN edges. |
| query.ramtable_enable | false | Whether to enable ramtable for query of adjacent edges. |
| query.ramtable_vertices_capacity | 10000000 | The maximum number of vertices in ramtable, generally the largest vertex id is used as capacity. |
| query.optimize_aggregate_by_index | false | Whether to optimize aggregate query(like count) by index. |
| oltp.concurrent_depth | 10 | The min depth to enable concurrent oltp algorithm. |
| oltp.concurrent_threads | 10 | Thread number to concurrently execute oltp algorithm. |
| oltp.collection_type | EC | The implementation type of collections used in oltp algorithm. |
| rate_limit.read | 0 | The max rate(times/s) to execute query of vertices/edges. |
| rate_limit.write | 0 | The max rate(items/s) to add/update/delete vertices/edges. |
| task.wait_timeout | 10 | Timeout in seconds for waiting for the task to complete,such as when truncating or clearing the backend. |
| task.input_size_limit | 16777216 | The job input size limit in bytes. |
| task.result_size_limit | 16777216 | The job result size limit in bytes. |
| task.sync_deletion | false | Whether to delete schema or expired data synchronously. |
| task.ttl_delete_batch | 1 | The batch size used to delete expired data. |
| computer.config | /conf/computer.yaml | The config file path of computer job. |
| search.text_analyzer | ikanalyzer | Choose a text analyzer for searching the vertex/edge properties, available type are [word, ansj, hanlp, smartcn, jieba, jcseg, mmseg4j, ikanalyzer]. |
| search.text_analyzer_mode | smart | Specify the mode for the text analyzer, the available mode of analyzer are {word: [MaximumMatching, ReverseMaximumMatching, MinimumMatching, ReverseMinimumMatching, BidirectionalMaximumMatching, BidirectionalMinimumMatching, BidirectionalMaximumMinimumMatching, FullSegmentation, MinimalWordCount, MaxNgramScore, PureEnglish], ansj: [BaseAnalysis, IndexAnalysis, ToAnalysis, NlpAnalysis], hanlp: [standard, nlp, index, nShort, shortest, speed], smartcn: [], jieba: [SEARCH, INDEX], jcseg: [Simple, Complex], mmseg4j: [Simple, Complex, MaxWord], ikanalyzer: [smart, max_word]}. |
| snowflake.datacenter_id | 0 | The datacenter id of snowflake id generator. |
| snowflake.force_string | false | Whether to force the snowflake long id to be a string. |
| snowflake.worker_id | 0 | The worker id of snowflake id generator. |
| raft.mode | false | Whether the backend storage works in raft mode. |
| raft.safe_read | false | Whether to use linearly consistent read. |
| raft.use_snapshot | false | Whether to use snapshot. |
| raft.endpoint | 127.0.0.1:8281 | The peerid of current raft node. |
| raft.group_peers | 127.0.0.1:8281,127.0.0.1:8282,127.0.0.1:8283 | The peers of current raft group. |
| raft.path | ./raft-log | The log path of current raft node. |
| raft.use_replicator_pipeline | true | Whether to use replicator line, when turned on it multiple logs can be sent in parallel, and the next log doesn’t have to wait for the ack message of the current log to be sent. |
| raft.election_timeout | 10000 | Timeout in milliseconds to launch a round of election. |
| raft.snapshot_interval | 3600 | The interval in seconds to trigger snapshot save. |
| raft.backend_threads | current CPU v-cores | The thread number used to apply task to backend. |
| raft.read_index_threads | 8 | The thread number used to execute reading index. |
| raft.apply_batch | 1 | The apply batch size to trigger disruptor event handler. |
| raft.queue_size | 16384 | The disruptor buffers size for jraft RaftNode, StateMachine and LogManager. |
| raft.queue_publish_timeout | 60 | The timeout in second when publish event into disruptor. |
| raft.rpc_threads | 80 | The rpc threads for jraft RPC layer. |
| raft.rpc_connect_timeout | 5000 | The rpc connect timeout for jraft rpc. |
| raft.rpc_timeout | 60000 | The rpc timeout for jraft rpc. |
| raft.rpc_buf_low_water_mark | 10485760 | The ChannelOutboundBuffer’s low water mark of netty, when buffer size less than this size, the method ChannelOutboundBuffer.isWritable() will return true, it means that low downstream pressure or good network. |
| raft.rpc_buf_high_water_mark | 20971520 | The ChannelOutboundBuffer’s high water mark of netty, only when buffer size exceed this size, the method ChannelOutboundBuffer.isWritable() will return false, it means that the downstream pressure is too great to process the request or network is very congestion, upstream needs to limit rate at this time. |
| raft.read_strategy | ReadOnlyLeaseBased | The linearizability of read strategy. |
| config option | default value | description |
|---|---|---|
| rpc.client_connect_timeout | 20 | The timeout(in seconds) of rpc client connect to rpc server. |
| rpc.client_load_balancer | consistentHash | The rpc client uses a load-balancing algorithm to access multiple rpc servers in one cluster. Default value is ‘consistentHash’, means forwarding by request parameters. |
| rpc.client_read_timeout | 40 | The timeout(in seconds) of rpc client read from rpc server. |
| rpc.client_reconnect_period | 10 | The period(in seconds) of rpc client reconnect to rpc server. |
| rpc.client_retries | 3 | Failed retry number of rpc client calls to rpc server. |
| rpc.config_order | 999 | Sofa rpc configuration file loading order, the larger the more later loading. |
| rpc.logger_impl | com.alipay.sofa.rpc.log.SLF4JLoggerImpl | Sofa rpc log implementation class. |
| rpc.protocol | bolt | Rpc communication protocol, client and server need to be specified the same value. |
| rpc.remote_url | The remote urls of rpc peers, it can be set to multiple addresses, which are concat by ‘,’, empty value means not enabled. | |
| rpc.server_adaptive_port | false | Whether the bound port is adaptive, if it’s enabled, when the port is in use, automatically +1 to detect the next available port. Note that this process is not atomic, so there may still be port conflicts. |
| rpc.server_host | The hosts/ips bound by rpc server to provide services, empty value means not enabled. | |
| rpc.server_port | 8090 | The port bound by rpc server to provide services. |
| rpc.server_timeout | 30 | The timeout(in seconds) of rpc server execution. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to cassandra. | |
| serializer | Must be set to cassandra. | |
| cassandra.host | localhost | The seeds hostname or ip address of cassandra cluster. |
| cassandra.port | 9042 | The seeds port address of cassandra cluster. |
| cassandra.connect_timeout | 5 | The cassandra driver connect server timeout(seconds). |
| cassandra.read_timeout | 20 | The cassandra driver read from server timeout(seconds). |
| cassandra.keyspace.strategy | SimpleStrategy | The replication strategy of keyspace, valid value is SimpleStrategy or NetworkTopologyStrategy. |
| cassandra.keyspace.replication | [3] | The keyspace replication factor of SimpleStrategy, like ‘[3]’.Or replicas in each datacenter of NetworkTopologyStrategy, like ‘[dc1:2,dc2:1]’. |
| cassandra.username | The username to use to login to cassandra cluster. | |
| cassandra.password | The password corresponding to cassandra.username. | |
| cassandra.compression_type | none | The compression algorithm of cassandra transport: none/snappy/lz4. |
| cassandra.jmx_port=7199 | 7199 | The port of JMX API service for cassandra. |
| cassandra.aggregation_timeout | 43200 | The timeout in seconds of waiting for aggregation. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to scylladb. | |
| serializer | Must be set to scylladb. |
Other options are consistent with the Cassandra backend.
| config option | default value | description |
|---|---|---|
| backend | Must be set to rocksdb. | |
| serializer | Must be set to binary. | |
| rocksdb.data_disks | [] | The optimized disks for storing data of RocksDB. The format of each element: STORE/TABLE: /path/disk.Allowed keys are [g/vertex, g/edge_out, g/edge_in, g/vertex_label_index, g/edge_label_index, g/range_int_index, g/range_float_index, g/range_long_index, g/range_double_index, g/secondary_index, g/search_index, g/shard_index, g/unique_index, g/olap] |
| rocksdb.data_path | rocksdb-data | The path for storing data of RocksDB. |
| rocksdb.wal_path | rocksdb-data | The path for storing WAL of RocksDB. |
| rocksdb.allow_mmap_reads | false | Allow the OS to mmap file for reading sst tables. |
| rocksdb.allow_mmap_writes | false | Allow the OS to mmap file for writing. |
| rocksdb.block_cache_capacity | 8388608 | The amount of block cache in bytes that will be used by RocksDB, 0 means no block cache. |
| rocksdb.bloom_filter_bits_per_key | -1 | The bits per key in bloom filter, a good value is 10, which yields a filter with ~ 1% false positive rate, -1 means no bloom filter. |
| rocksdb.bloom_filter_block_based_mode | false | Use block based filter rather than full filter. |
| rocksdb.bloom_filter_whole_key_filtering | true | True if place whole keys in the bloom filter, else place the prefix of keys. |
| rocksdb.bottommost_compression | NO_COMPRESSION | The compression algorithm for the bottommost level of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.bulkload_mode | false | Switch to the mode to bulk load data into RocksDB. |
| rocksdb.cache_index_and_filter_blocks | false | Indicating if we’d put index/filter blocks to the block cache. |
| rocksdb.compaction_style | LEVEL | Set compaction style for RocksDB: LEVEL/UNIVERSAL/FIFO. |
| rocksdb.compression | SNAPPY_COMPRESSION | The compression algorithm for compressing blocks of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.compression_per_level | [NO_COMPRESSION, NO_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION] | The compression algorithms for different levels of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.delayed_write_rate | 16777216 | The rate limit in bytes/s of user write requests when need to slow down if the compaction gets behind. |
| rocksdb.log_level | INFO | The info log level of RocksDB. |
| rocksdb.max_background_jobs | 8 | Maximum number of concurrent background jobs, including flushes and compactions. |
| rocksdb.level_compaction_dynamic_level_bytes | false | Whether to enable level_compaction_dynamic_level_bytes, if it’s enabled we give max_bytes_for_level_multiplier a priority against max_bytes_for_level_base, the bytes of base level is dynamic for a more predictable LSM tree, it is useful to limit worse case space amplification. Turning this feature on/off for an existing DB can cause unexpected LSM tree structure so it’s not recommended. |
| rocksdb.max_bytes_for_level_base | 536870912 | The upper-bound of the total size of level-1 files in bytes. |
| rocksdb.max_bytes_for_level_multiplier | 10.0 | The ratio between the total size of level (L+1) files and the total size of level L files for all L. |
| rocksdb.max_open_files | -1 | The maximum number of open files that can be cached by RocksDB, -1 means no limit. |
| rocksdb.max_subcompactions | 4 | The value represents the maximum number of threads per compaction job. |
| rocksdb.max_write_buffer_number | 6 | The maximum number of write buffers that are built up in memory. |
| rocksdb.max_write_buffer_number_to_maintain | 0 | The total maximum number of write buffers to maintain in memory. |
| rocksdb.min_write_buffer_number_to_merge | 2 | The minimum number of write buffers that will be merged together. |
| rocksdb.num_levels | 7 | Set the number of levels for this database. |
| rocksdb.optimize_filters_for_hits | false | This flag allows us to not store filters for the last level. |
| rocksdb.optimize_mode | true | Optimize for heavy workloads and big datasets. |
| rocksdb.pin_l0_filter_and_index_blocks_in_cache | false | Indicating if we’d put index/filter blocks to the block cache. |
| rocksdb.sst_path | The path for ingesting SST file into RocksDB. | |
| rocksdb.target_file_size_base | 67108864 | The target file size for compaction in bytes. |
| rocksdb.target_file_size_multiplier | 1 | The size ratio between a level L file and a level (L+1) file. |
| rocksdb.use_direct_io_for_flush_and_compaction | false | Enable the OS to use direct read/writes in flush and compaction. |
| rocksdb.use_direct_reads | false | Enable the OS to use direct I/O for reading sst tables. |
| rocksdb.write_buffer_size | 134217728 | Amount of data in bytes to build up in memory. |
| rocksdb.max_manifest_file_size | 104857600 | The max size of manifest file in bytes. |
| rocksdb.skip_stats_update_on_db_open | false | Whether to skip statistics update when opening the database, setting this flag true allows us to not update statistics. |
| rocksdb.max_file_opening_threads | 16 | The max number of threads used to open files. |
| rocksdb.max_total_wal_size | 0 | Total size of WAL files in bytes. Once WALs exceed this size, we will start forcing the flush of column families related, 0 means no limit. |
| rocksdb.db_write_buffer_size | 0 | Total size of write buffers in bytes across all column families, 0 means no limit. |
| rocksdb.delete_obsolete_files_period | 21600 | The periodicity in seconds when obsolete files get deleted, 0 means always do full purge. |
| rocksdb.hard_pending_compaction_bytes_limit | 274877906944 | The hard limit to impose on pending compaction in bytes. |
| rocksdb.level0_file_num_compaction_trigger | 2 | Number of files to trigger level-0 compaction. |
| rocksdb.level0_slowdown_writes_trigger | 20 | Soft limit on number of level-0 files for slowing down writes. |
| rocksdb.level0_stop_writes_trigger | 36 | Hard limit on number of level-0 files for stopping writes. |
| rocksdb.soft_pending_compaction_bytes_limit | 68719476736 | The soft limit to impose on pending compaction in bytes. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to hbase. | |
| serializer | Must be set to hbase. | |
| hbase.hosts | localhost | The hostnames or ip addresses of HBase zookeeper, separated with commas. |
| hbase.port | 2181 | The port address of HBase zookeeper. |
| hbase.threads_max | 64 | The max threads num of hbase connections. |
| hbase.znode_parent | /hbase | The znode parent path of HBase zookeeper. |
| hbase.zk_retry | 3 | The recovery retry times of HBase zookeeper. |
| hbase.aggregation_timeout | 43200 | The timeout in seconds of waiting for aggregation. |
| hbase.kerberos_enable | false | Is Kerberos authentication enabled for HBase. |
| hbase.kerberos_keytab | The HBase’s key tab file for kerberos authentication. | |
| hbase.kerberos_principal | The HBase’s principal for kerberos authentication. | |
| hbase.krb5_conf | etc/krb5.conf | Kerberos configuration file, including KDC IP, default realm, etc. |
| hbase.hbase_site | /etc/hbase/conf/hbase-site.xml | The HBase’s configuration file |
| hbase.enable_partition | true | Is pre-split partitions enabled for HBase. |
| hbase.vertex_partitions | 10 | The number of partitions of the HBase vertex table. |
| hbase.edge_partitions | 30 | The number of partitions of the HBase edge table. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to mysql. | |
| serializer | Must be set to mysql. | |
| jdbc.driver | com.mysql.jdbc.Driver | The JDBC driver class to connect database. |
| jdbc.url | jdbc:mysql://127.0.0.1:3306 | The url of database in JDBC format. |
| jdbc.username | root | The username to login database. |
| jdbc.password | ****** | The password corresponding to jdbc.username. |
| jdbc.ssl_mode | false | The SSL mode of connections with database. |
| jdbc.reconnect_interval | 3 | The interval(seconds) between reconnections when the database connection fails. |
| jdbc.reconnect_max_times | 3 | The reconnect times when the database connection fails. |
| jdbc.storage_engine | InnoDB | The storage engine of backend store database, like InnoDB/MyISAM/RocksDB for MySQL. |
| jdbc.postgresql.connect_database | template1 | The database used to connect when init store, drop store or check store exist. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to postgresql. | |
| serializer | Must be set to postgresql. |
Other options are consistent with the MySQL backend.
The driver and url of the PostgreSQL backend should be set to:
jdbc.driver=org.postgresql.Driverjdbc.url=jdbc:postgresql://localhost:5432/
Corresponding configuration file gremlin-server.yaml
| config option | default value | description |
|---|---|---|
| host | 127.0.0.1 | The host or ip of Gremlin Server. |
| port | 8182 | The listening port of Gremlin Server. |
| graphs | hugegraph: conf/hugegraph.properties | The map of graphs with name and config file path. |
| scriptEvaluationTimeout | 30000 | The timeout for gremlin script execution(millisecond). |
| channelizer | org.apache.tinkerpop.gremlin.server.channel.HttpChannelizer | Indicates the protocol which the Gremlin Server provides service. |
| authentication | authenticator: com.baidu.hugegraph.auth.StandardAuthenticator, config: {tokens: conf/rest-server.properties} | The authenticator and config(contains tokens path) of authentication mechanism. |
Corresponding configuration file rest-server.properties
| config option | default value | description |
|---|---|---|
| graphs | [hugegraph:conf/hugegraph.properties] | The map of graphs’ name and config file. |
| server.id | server-1 | The id of rest server, used for license verification. |
| server.role | master | The role of nodes in the cluster, available types are [master, worker, computer] |
| restserver.url | http://127.0.0.1:8080 | The url for listening of rest server. |
| ssl.keystore_file | server.keystore | The path of server keystore file used when https protocol is enabled. |
| ssl.keystore_password | The password of the path of the server keystore file used when the https protocol is enabled. | |
| restserver.max_worker_threads | 2 * CPUs | The maximum worker threads of rest server. |
| restserver.min_free_memory | 64 | The minimum free memory(MB) of rest server, requests will be rejected when the available memory of system is lower than this value. |
| restserver.request_timeout | 30 | The time in seconds within which a request must complete, -1 means no timeout. |
| restserver.connection_idle_timeout | 30 | The time in seconds to keep an inactive connection alive, -1 means no timeout. |
| restserver.connection_max_requests | 256 | The max number of HTTP requests allowed to be processed on one keep-alive connection, -1 means unlimited. |
| gremlinserver.url | http://127.0.0.1:8182 | The url of gremlin server. |
| gremlinserver.max_route | 8 | The max route number for gremlin server. |
| gremlinserver.timeout | 30 | The timeout in seconds of waiting for gremlin server. |
| batch.max_edges_per_batch | 500 | The maximum number of edges submitted per batch. |
| batch.max_vertices_per_batch | 500 | The maximum number of vertices submitted per batch. |
| batch.max_write_ratio | 50 | The maximum thread ratio for batch writing, only take effect if the batch.max_write_threads is 0. |
| batch.max_write_threads | 0 | The maximum threads for batch writing, if the value is 0, the actual value will be set to batch.max_write_ratio * restserver.max_worker_threads. |
| auth.authenticator | The class path of authenticator implementation. e.g., com.baidu.hugegraph.auth.StandardAuthenticator, or com.baidu.hugegraph.auth.ConfigAuthenticator. | |
| auth.admin_token | 162f7848-0b6d-4faf-b557-3a0797869c55 | Token for administrator operations, only for com.baidu.hugegraph.auth.ConfigAuthenticator. |
| auth.graph_store | hugegraph | The name of graph used to store authentication information, like users, only for com.baidu.hugegraph.auth.StandardAuthenticator. |
| auth.user_tokens | [hugegraph:9fd95c9c-711b-415b-b85f-d4df46ba5c31] | The map of user tokens with name and password, only for com.baidu.hugegraph.auth.ConfigAuthenticator. |
| auth.audit_log_rate | 1000.0 | The max rate of audit log output per user, default value is 1000 records per second. |
| auth.cache_capacity | 10240 | The max cache capacity of each auth cache item. |
| auth.cache_expire | 600 | The expiration time in seconds of vertex cache. |
| auth.remote_url | If the address is empty, it provide auth service, otherwise it is auth client and also provide auth service through rpc forwarding. The remote url can be set to multiple addresses, which are concat by ‘,’. | |
| auth.token_expire | 86400 | The expiration time in seconds after token created |
| auth.token_secret | FXQXbJtbCLxODc6tGci732pkH1cyf8Qg | Secret key of HS256 algorithm. |
| exception.allow_trace | false | Whether to allow exception trace stack. |
Basic Config Options and Backend Config Options correspond to configuration files：{graph-name}.properties，such as hugegraph.properties
| config option | default value | description |
|---|---|---|
| gremlin.graph | com.baidu.hugegraph.HugeFactory | Gremlin entrance to create graph. |
| backend | rocksdb | The data store type, available values are [memory, rocksdb, cassandra, scylladb, hbase, mysql]. |
| serializer | binary | The serializer for backend store, available values are [text, binary, cassandra, hbase, mysql]. |
| store | hugegraph | The database name like Cassandra Keyspace. |
| store.connection_detect_interval | 600 | The interval in seconds for detecting connections, if the idle time of a connection exceeds this value, detect it and reconnect if needed before using, value 0 means detecting every time. |
| store.graph | g | The graph table name, which store vertex, edge and property. |
| store.schema | m | The schema table name, which store meta data. |
| store.system | s | The system table name, which store system data. |
| schema.illegal_name_regex | .\s+$|~. | The regex specified the illegal format for schema name. |
| schema.cache_capacity | 10000 | The max cache size(items) of schema cache. |
| vertex.cache_type | l2 | The type of vertex cache, allowed values are [l1, l2]. |
| vertex.cache_capacity | 10000000 | The max cache size(items) of vertex cache. |
| vertex.cache_expire | 600 | The expire time in seconds of vertex cache. |
| vertex.check_customized_id_exist | false | Whether to check the vertices exist for those using customized id strategy. |
| vertex.default_label | vertex | The default vertex label. |
| vertex.tx_capacity | 10000 | The max size(items) of vertices(uncommitted) in transaction. |
| vertex.check_adjacent_vertex_exist | false | Whether to check the adjacent vertices of edges exist. |
| vertex.lazy_load_adjacent_vertex | true | Whether to lazy load adjacent vertices of edges. |
| vertex.part_edge_commit_size | 5000 | Whether to enable the mode to commit part of edges of vertex, enabled if commit size > 0, 0 means disabled. |
| vertex.encode_primary_key_number | true | Whether to encode number value of primary key in vertex id. |
| vertex.remove_left_index_at_overwrite | false | Whether remove left index at overwrite. |
| edge.cache_type | l2 | The type of edge cache, allowed values are [l1, l2]. |
| edge.cache_capacity | 1000000 | The max cache size(items) of edge cache. |
| edge.cache_expire | 600 | The expiration time in seconds of edge cache. |
| edge.tx_capacity | 10000 | The max size(items) of edges(uncommitted) in transaction. |
| query.page_size | 500 | The size of each page when querying by paging. |
| query.batch_size | 1000 | The size of each batch when querying by batch. |
| query.ignore_invalid_data | true | Whether to ignore invalid data of vertex or edge. |
| query.index_intersect_threshold | 1000 | The maximum number of intermediate results to intersect indexes when querying by multiple single index properties. |
| query.ramtable_edges_capacity | 20000000 | The maximum number of edges in ramtable, include OUT and IN edges. |
| query.ramtable_enable | false | Whether to enable ramtable for query of adjacent edges. |
| query.ramtable_vertices_capacity | 10000000 | The maximum number of vertices in ramtable, generally the largest vertex id is used as capacity. |
| query.optimize_aggregate_by_index | false | Whether to optimize aggregate query(like count) by index. |
| oltp.concurrent_depth | 10 | The min depth to enable concurrent oltp algorithm. |
| oltp.concurrent_threads | 10 | Thread number to concurrently execute oltp algorithm. |
| oltp.collection_type | EC | The implementation type of collections used in oltp algorithm. |
| rate_limit.read | 0 | The max rate(times/s) to execute query of vertices/edges. |
| rate_limit.write | 0 | The max rate(items/s) to add/update/delete vertices/edges. |
| task.wait_timeout | 10 | Timeout in seconds for waiting for the task to complete,such as when truncating or clearing the backend. |
| task.input_size_limit | 16777216 | The job input size limit in bytes. |
| task.result_size_limit | 16777216 | The job result size limit in bytes. |
| task.sync_deletion | false | Whether to delete schema or expired data synchronously. |
| task.ttl_delete_batch | 1 | The batch size used to delete expired data. |
| computer.config | /conf/computer.yaml | The config file path of computer job. |
| search.text_analyzer | ikanalyzer | Choose a text analyzer for searching the vertex/edge properties, available type are [word, ansj, hanlp, smartcn, jieba, jcseg, mmseg4j, ikanalyzer]. |
| search.text_analyzer_mode | smart | Specify the mode for the text analyzer, the available mode of analyzer are {word: [MaximumMatching, ReverseMaximumMatching, MinimumMatching, ReverseMinimumMatching, BidirectionalMaximumMatching, BidirectionalMinimumMatching, BidirectionalMaximumMinimumMatching, FullSegmentation, MinimalWordCount, MaxNgramScore, PureEnglish], ansj: [BaseAnalysis, IndexAnalysis, ToAnalysis, NlpAnalysis], hanlp: [standard, nlp, index, nShort, shortest, speed], smartcn: [], jieba: [SEARCH, INDEX], jcseg: [Simple, Complex], mmseg4j: [Simple, Complex, MaxWord], ikanalyzer: [smart, max_word]}. |
| snowflake.datacenter_id | 0 | The datacenter id of snowflake id generator. |
| snowflake.force_string | false | Whether to force the snowflake long id to be a string. |
| snowflake.worker_id | 0 | The worker id of snowflake id generator. |
| raft.mode | false | Whether the backend storage works in raft mode. |
| raft.safe_read | false | Whether to use linearly consistent read. |
| raft.use_snapshot | false | Whether to use snapshot. |
| raft.endpoint | 127.0.0.1:8281 | The peerid of current raft node. |
| raft.group_peers | 127.0.0.1:8281,127.0.0.1:8282,127.0.0.1:8283 | The peers of current raft group. |
| raft.path | ./raft-log | The log path of current raft node. |
| raft.use_replicator_pipeline | true | Whether to use replicator line, when turned on it multiple logs can be sent in parallel, and the next log doesn’t have to wait for the ack message of the current log to be sent. |
| raft.election_timeout | 10000 | Timeout in milliseconds to launch a round of election. |
| raft.snapshot_interval | 3600 | The interval in seconds to trigger snapshot save. |
| raft.backend_threads | current CPU v-cores | The thread number used to apply task to backend. |
| raft.read_index_threads | 8 | The thread number used to execute reading index. |
| raft.apply_batch | 1 | The apply batch size to trigger disruptor event handler. |
| raft.queue_size | 16384 | The disruptor buffers size for jraft RaftNode, StateMachine and LogManager. |
| raft.queue_publish_timeout | 60 | The timeout in second when publish event into disruptor. |
| raft.rpc_threads | 80 | The rpc threads for jraft RPC layer. |
| raft.rpc_connect_timeout | 5000 | The rpc connect timeout for jraft rpc. |
| raft.rpc_timeout | 60000 | The rpc timeout for jraft rpc. |
| raft.rpc_buf_low_water_mark | 10485760 | The ChannelOutboundBuffer’s low water mark of netty, when buffer size less than this size, the method ChannelOutboundBuffer.isWritable() will return true, it means that low downstream pressure or good network. |
| raft.rpc_buf_high_water_mark | 20971520 | The ChannelOutboundBuffer’s high water mark of netty, only when buffer size exceed this size, the method ChannelOutboundBuffer.isWritable() will return false, it means that the downstream pressure is too great to process the request or network is very congestion, upstream needs to limit rate at this time. |
| raft.read_strategy | ReadOnlyLeaseBased | The linearizability of read strategy. |
| config option | default value | description |
|---|---|---|
| rpc.client_connect_timeout | 20 | The timeout(in seconds) of rpc client connect to rpc server. |
| rpc.client_load_balancer | consistentHash | The rpc client uses a load-balancing algorithm to access multiple rpc servers in one cluster. Default value is ‘consistentHash’, means forwarding by request parameters. |
| rpc.client_read_timeout | 40 | The timeout(in seconds) of rpc client read from rpc server. |
| rpc.client_reconnect_period | 10 | The period(in seconds) of rpc client reconnect to rpc server. |
| rpc.client_retries | 3 | Failed retry number of rpc client calls to rpc server. |
| rpc.config_order | 999 | Sofa rpc configuration file loading order, the larger the more later loading. |
| rpc.logger_impl | com.alipay.sofa.rpc.log.SLF4JLoggerImpl | Sofa rpc log implementation class. |
| rpc.protocol | bolt | Rpc communication protocol, client and server need to be specified the same value. |
| rpc.remote_url | The remote urls of rpc peers, it can be set to multiple addresses, which are concat by ‘,’, empty value means not enabled. | |
| rpc.server_adaptive_port | false | Whether the bound port is adaptive, if it’s enabled, when the port is in use, automatically +1 to detect the next available port. Note that this process is not atomic, so there may still be port conflicts. |
| rpc.server_host | The hosts/ips bound by rpc server to provide services, empty value means not enabled. | |
| rpc.server_port | 8090 | The port bound by rpc server to provide services. |
| rpc.server_timeout | 30 | The timeout(in seconds) of rpc server execution. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to cassandra. | |
| serializer | Must be set to cassandra. | |
| cassandra.host | localhost | The seeds hostname or ip address of cassandra cluster. |
| cassandra.port | 9042 | The seeds port address of cassandra cluster. |
| cassandra.connect_timeout | 5 | The cassandra driver connect server timeout(seconds). |
| cassandra.read_timeout | 20 | The cassandra driver read from server timeout(seconds). |
| cassandra.keyspace.strategy | SimpleStrategy | The replication strategy of keyspace, valid value is SimpleStrategy or NetworkTopologyStrategy. |
| cassandra.keyspace.replication | [3] | The keyspace replication factor of SimpleStrategy, like ‘[3]’.Or replicas in each datacenter of NetworkTopologyStrategy, like ‘[dc1:2,dc2:1]’. |
| cassandra.username | The username to use to login to cassandra cluster. | |
| cassandra.password | The password corresponding to cassandra.username. | |
| cassandra.compression_type | none | The compression algorithm of cassandra transport: none/snappy/lz4. |
| cassandra.jmx_port=7199 | 7199 | The port of JMX API service for cassandra. |
| cassandra.aggregation_timeout | 43200 | The timeout in seconds of waiting for aggregation. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to scylladb. | |
| serializer | Must be set to scylladb. |
Other options are consistent with the Cassandra backend.
| config option | default value | description |
|---|---|---|
| backend | Must be set to rocksdb. | |
| serializer | Must be set to binary. | |
| rocksdb.data_disks | [] | The optimized disks for storing data of RocksDB. The format of each element: STORE/TABLE: /path/disk.Allowed keys are [g/vertex, g/edge_out, g/edge_in, g/vertex_label_index, g/edge_label_index, g/range_int_index, g/range_float_index, g/range_long_index, g/range_double_index, g/secondary_index, g/search_index, g/shard_index, g/unique_index, g/olap] |
| rocksdb.data_path | rocksdb-data | The path for storing data of RocksDB. |
| rocksdb.wal_path | rocksdb-data | The path for storing WAL of RocksDB. |
| rocksdb.allow_mmap_reads | false | Allow the OS to mmap file for reading sst tables. |
| rocksdb.allow_mmap_writes | false | Allow the OS to mmap file for writing. |
| rocksdb.block_cache_capacity | 8388608 | The amount of block cache in bytes that will be used by RocksDB, 0 means no block cache. |
| rocksdb.bloom_filter_bits_per_key | -1 | The bits per key in bloom filter, a good value is 10, which yields a filter with ~ 1% false positive rate, -1 means no bloom filter. |
| rocksdb.bloom_filter_block_based_mode | false | Use block based filter rather than full filter. |
| rocksdb.bloom_filter_whole_key_filtering | true | True if place whole keys in the bloom filter, else place the prefix of keys. |
| rocksdb.bottommost_compression | NO_COMPRESSION | The compression algorithm for the bottommost level of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.bulkload_mode | false | Switch to the mode to bulk load data into RocksDB. |
| rocksdb.cache_index_and_filter_blocks | false | Indicating if we’d put index/filter blocks to the block cache. |
| rocksdb.compaction_style | LEVEL | Set compaction style for RocksDB: LEVEL/UNIVERSAL/FIFO. |
| rocksdb.compression | SNAPPY_COMPRESSION | The compression algorithm for compressing blocks of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.compression_per_level | [NO_COMPRESSION, NO_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION, SNAPPY_COMPRESSION] | The compression algorithms for different levels of RocksDB, allowed values are none/snappy/z/bzip2/lz4/lz4hc/xpress/zstd. |
| rocksdb.delayed_write_rate | 16777216 | The rate limit in bytes/s of user write requests when need to slow down if the compaction gets behind. |
| rocksdb.log_level | INFO | The info log level of RocksDB. |
| rocksdb.max_background_jobs | 8 | Maximum number of concurrent background jobs, including flushes and compactions. |
| rocksdb.level_compaction_dynamic_level_bytes | false | Whether to enable level_compaction_dynamic_level_bytes, if it’s enabled we give max_bytes_for_level_multiplier a priority against max_bytes_for_level_base, the bytes of base level is dynamic for a more predictable LSM tree, it is useful to limit worse case space amplification. Turning this feature on/off for an existing DB can cause unexpected LSM tree structure so it’s not recommended. |
| rocksdb.max_bytes_for_level_base | 536870912 | The upper-bound of the total size of level-1 files in bytes. |
| rocksdb.max_bytes_for_level_multiplier | 10.0 | The ratio between the total size of level (L+1) files and the total size of level L files for all L. |
| rocksdb.max_open_files | -1 | The maximum number of open files that can be cached by RocksDB, -1 means no limit. |
| rocksdb.max_subcompactions | 4 | The value represents the maximum number of threads per compaction job. |
| rocksdb.max_write_buffer_number | 6 | The maximum number of write buffers that are built up in memory. |
| rocksdb.max_write_buffer_number_to_maintain | 0 | The total maximum number of write buffers to maintain in memory. |
| rocksdb.min_write_buffer_number_to_merge | 2 | The minimum number of write buffers that will be merged together. |
| rocksdb.num_levels | 7 | Set the number of levels for this database. |
| rocksdb.optimize_filters_for_hits | false | This flag allows us to not store filters for the last level. |
| rocksdb.optimize_mode | true | Optimize for heavy workloads and big datasets. |
| rocksdb.pin_l0_filter_and_index_blocks_in_cache | false | Indicating if we’d put index/filter blocks to the block cache. |
| rocksdb.sst_path | The path for ingesting SST file into RocksDB. | |
| rocksdb.target_file_size_base | 67108864 | The target file size for compaction in bytes. |
| rocksdb.target_file_size_multiplier | 1 | The size ratio between a level L file and a level (L+1) file. |
| rocksdb.use_direct_io_for_flush_and_compaction | false | Enable the OS to use direct read/writes in flush and compaction. |
| rocksdb.use_direct_reads | false | Enable the OS to use direct I/O for reading sst tables. |
| rocksdb.write_buffer_size | 134217728 | Amount of data in bytes to build up in memory. |
| rocksdb.max_manifest_file_size | 104857600 | The max size of manifest file in bytes. |
| rocksdb.skip_stats_update_on_db_open | false | Whether to skip statistics update when opening the database, setting this flag true allows us to not update statistics. |
| rocksdb.max_file_opening_threads | 16 | The max number of threads used to open files. |
| rocksdb.max_total_wal_size | 0 | Total size of WAL files in bytes. Once WALs exceed this size, we will start forcing the flush of column families related, 0 means no limit. |
| rocksdb.db_write_buffer_size | 0 | Total size of write buffers in bytes across all column families, 0 means no limit. |
| rocksdb.delete_obsolete_files_period | 21600 | The periodicity in seconds when obsolete files get deleted, 0 means always do full purge. |
| rocksdb.hard_pending_compaction_bytes_limit | 274877906944 | The hard limit to impose on pending compaction in bytes. |
| rocksdb.level0_file_num_compaction_trigger | 2 | Number of files to trigger level-0 compaction. |
| rocksdb.level0_slowdown_writes_trigger | 20 | Soft limit on number of level-0 files for slowing down writes. |
| rocksdb.level0_stop_writes_trigger | 36 | Hard limit on number of level-0 files for stopping writes. |
| rocksdb.soft_pending_compaction_bytes_limit | 68719476736 | The soft limit to impose on pending compaction in bytes. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to hbase. | |
| serializer | Must be set to hbase. | |
| hbase.hosts | localhost | The hostnames or ip addresses of HBase zookeeper, separated with commas. |
| hbase.port | 2181 | The port address of HBase zookeeper. |
| hbase.threads_max | 64 | The max threads num of hbase connections. |
| hbase.znode_parent | /hbase | The znode parent path of HBase zookeeper. |
| hbase.zk_retry | 3 | The recovery retry times of HBase zookeeper. |
| hbase.aggregation_timeout | 43200 | The timeout in seconds of waiting for aggregation. |
| hbase.kerberos_enable | false | Is Kerberos authentication enabled for HBase. |
| hbase.kerberos_keytab | The HBase’s key tab file for kerberos authentication. | |
| hbase.kerberos_principal | The HBase’s principal for kerberos authentication. | |
| hbase.krb5_conf | etc/krb5.conf | Kerberos configuration file, including KDC IP, default realm, etc. |
| hbase.hbase_site | /etc/hbase/conf/hbase-site.xml | The HBase’s configuration file |
| hbase.enable_partition | true | Is pre-split partitions enabled for HBase. |
| hbase.vertex_partitions | 10 | The number of partitions of the HBase vertex table. |
| hbase.edge_partitions | 30 | The number of partitions of the HBase edge table. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to mysql. | |
| serializer | Must be set to mysql. | |
| jdbc.driver | com.mysql.jdbc.Driver | The JDBC driver class to connect database. |
| jdbc.url | jdbc:mysql://127.0.0.1:3306 | The url of database in JDBC format. |
| jdbc.username | root | The username to login database. |
| jdbc.password | ****** | The password corresponding to jdbc.username. |
| jdbc.ssl_mode | false | The SSL mode of connections with database. |
| jdbc.reconnect_interval | 3 | The interval(seconds) between reconnections when the database connection fails. |
| jdbc.reconnect_max_times | 3 | The reconnect times when the database connection fails. |
| jdbc.storage_engine | InnoDB | The storage engine of backend store database, like InnoDB/MyISAM/RocksDB for MySQL. |
| jdbc.postgresql.connect_database | template1 | The database used to connect when init store, drop store or check store exist. |
| config option | default value | description |
|---|---|---|
| backend | Must be set to postgresql. | |
| serializer | Must be set to postgresql. |
Other options are consistent with the MySQL backend.
The driver and url of the PostgreSQL backend should be set to:
jdbc.driver=org.postgresql.Driverjdbc.url=jdbc:postgresql://localhost:5432/
And push it to GitHub fork repo again:
# force push the local commit to fork repo
git push -f origin bugfix-branch:bugfix-branch
-GitHub will automatically update the Pull Request after we push it, just wait for code review.
It is highly recommended to subscribe to the development mailing list to keep up-to-date with the community.
In the process of using HugeGraph, if you have any questions or ideas, suggestions, you can participate in the HugeGraph community building through the Apache mailing list. Sending a subscription email is also very simple, the steps are as follows:
Email dev-subscribe@hugegraph.apache.org with your own email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
Receive a welcome email. After completing the above steps, you will receive a welcome email with the subject WELCOME to dev@hugegraph.apache.org, and you have successfully subscribed to the Apache HugeGraph mailing list.
If you do not need to know what’s going on with HugeGraph, you can unsubscribe from the mailing list.
Unsubscribe from the mailing list steps are as follows:
Email dev-unsubscribe@hugegraph.apache.org with your subscribed email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
Receive a goodbye email. After completing the above steps, you will receive a goodbye email with the subject GOODBYE from dev@hugegraph.apache.org, and you have successfully unsubscribed to the Apache HugeGraph mailing list, and you will not receive emails from dev@hugegraph.apache.org.
GitHub will automatically update the Pull Request after we push it, just wait for code review.
It is highly recommended to subscribe to the development mailing list to keep up-to-date with the community.
In the process of using HugeGraph, if you have any questions or ideas, suggestions, you can participate in the HugeGraph community building through the Apache mailing list. Sending a subscription email is also very simple, the steps are as follows:
Email dev-subscribe@hugegraph.apache.org with your own email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
Receive a welcome email. After completing the above steps, you will receive a welcome email with the subject WELCOME to dev@hugegraph.apache.org, and you have successfully subscribed to the Apache HugeGraph mailing list.
If you do not need to know what’s going on with HugeGraph, you can unsubscribe from the mailing list.
Unsubscribe from the mailing list steps are as follows:
Email dev-unsubscribe@hugegraph.apache.org with your subscribed email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
Receive a goodbye email. After completing the above steps, you will receive a goodbye email with the subject GOODBYE from dev@hugegraph.apache.org, and you have successfully unsubscribed to the Apache HugeGraph mailing list, and you will not receive emails from dev@hugegraph.apache.org.
And push it to GitHub fork repo again:
# force push the local commit to fork repo
git push -f origin bugfix-branch:bugfix-branch
-GitHub will automatically update the Pull Request after we push it, just wait for code review.
GitHub will automatically update the Pull Request after we push it, just wait for code review.
It is highly recommended to subscribe to the development mailing list to keep up-to-date with the community.
In the process of using HugeGraph, if you have any questions or ideas, suggestions, you can participate in the HugeGraph community building through the Apache mailing list. Sending a subscription email is also very simple, the steps are as follows:
Email dev-subscribe@hugegraph.apache.org with your own email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
Receive a welcome email. After completing the above steps, you will receive a welcome email with the subject WELCOME to dev@hugegraph.apache.org, and you have successfully subscribed to the Apache HugeGraph mailing list.
If you do not need to know what’s going on with HugeGraph, you can unsubscribe from the mailing list.
Unsubscribe from the mailing list steps are as follows:
Email dev-unsubscribe@hugegraph.apache.org with your subscribed email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
Receive a goodbye email. After completing the above steps, you will receive a goodbye email with the subject GOODBYE from dev@hugegraph.apache.org, and you have successfully unsubscribed to the Apache HugeGraph mailing list, and you will not receive emails from dev@hugegraph.apache.org.
It is highly recommended to subscribe to the development mailing list to keep up-to-date with the community.
In the process of using HugeGraph, if you have any questions or ideas, suggestions, you can participate in the HugeGraph community building through the Apache mailing list. Sending a subscription email is also very simple, the steps are as follows:
Email dev-subscribe@hugegraph.apache.org with your own email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
Receive a welcome email. After completing the above steps, you will receive a welcome email with the subject WELCOME to dev@hugegraph.apache.org, and you have successfully subscribed to the Apache HugeGraph mailing list.
If you do not need to know what’s going on with HugeGraph, you can unsubscribe from the mailing list.
Unsubscribe from the mailing list steps are as follows:
Email dev-unsubscribe@hugegraph.apache.org with your subscribed email address, subject and content are arbitrary.
Receive confirmation email and reply. After completing step 1, you will receive a confirmation email from dev-help@hugegraph.apache.org (if not received, please confirm whether the email is automatically classified as spam, promotion email, subscription email, etc.) . Then reply directly to the email, or click on the link in the email to reply quickly, the subject and content are arbitrary.
Receive a goodbye email. After completing the above steps, you will receive a goodbye email with the subject GOODBYE from dev@hugegraph.apache.org, and you have successfully unsubscribed to the Apache HugeGraph mailing list, and you will not receive emails from dev@hugegraph.apache.org.
The latest HugeGraph: 0.12.0, released on 2021-12-31.
| components | description | download |
|---|---|---|
| HugeGraph-Server | The main program of HugeGraph | 0.12.0 |
| HugeGraph-Hubble | Web-based Visual Graphical Interface | 1.6.0 |
| HugeGraph-Loader | Data import tool | 0.12.0 |
| HugeGraph-Tools | Command line toolset | 1.6.0 |
Note: The latest graph analysis and display platform is Hubble, which supports server v0.10 +.
The latest HugeGraph: 0.12.0, released on 2021-12-31.
| components | description | download |
|---|---|---|
| HugeGraph-Server | The main program of HugeGraph | 0.12.0 |
| HugeGraph-Hubble | Web-based Visual Graphical Interface | 1.6.0 |
| HugeGraph-Loader | Data import tool | 0.12.0 |
| HugeGraph-Tools | Command line toolset | 1.6.0 |
Note: The latest graph analysis and display platform is Hubble, which supports server v0.10 +.
查询某一类别的顶点或边（query by label）时提示超时
由于属于某一label的数据量可能比较多，请加上limit限制。
通过RESTful API操作图是可以的，但是发送Gremlin语句就报错：Request Failed(500)
可能是GremlinServer的配置有误，检查gremlin-server.yaml的host、port是否与rest-server.properties的gremlinserver.url匹配，如不匹配则修改，然后重启服务。
使用Loader导数据出现Socket Timeout异常，然后导致Loader中断
持续地导入数据会使Server的压力过大，然后导致有些请求超时。可以通过调整Loader的参数来适当缓解Server压力（如：重试次数，重试间隔，错误容忍数等），降低该问题出现频率。
如何删除全部的顶点和边，RESTful API中没有这样的接口，调用gremlin的g.V().drop()会报错Vertices in transaction have reached capacity xxx
目前确实没有好办法删除全部的数据，用户如果是自己部署的Server和后端，可以直接清空数据库，重启Server。可以使用paging API或scan API先获取所有数据，再逐条删除。
清空了数据库，并且执行了init-store，但是添加schema时提示"xxx has existed"
HugeGraphServer内是有缓存的，清空数据库的同时是需要重启Server的，否则残留的缓存会产生不一致。
插入顶点或边的过程中报错：Id max length is 128, but got xxx {yyy} 或 Big id max length is 32768, but got xxx
为了保证查询性能，目前的后端存储对id列的长度做了限制，顶点id不能超过128字节，边id长度不能超过32768字节，索引id不能超过128字节。
是否支持嵌套属性，如果不支持，是否有什么替代方案
嵌套属性目前暂不支持。替代方案：可以把嵌套属性作为单独的顶点拿出来，然后用边连接起来。
一个EdgeLabel是否可以连接多对VertexLabel，比如"投资"关系，可以是"个人"投资"企业"，也可以是"企业"投资"企业"
一个EdgeLabel不支持连接多对VertexLabel，需要用户将EdgeLabel拆分得更细一点，如：“个人投资”，“企业投资”。
通过RestAPI发送请求时提示HTTP 415 Unsupported Media Type
请求头中需要指定Content-Type:application/json
其他问题可以在对应项目的 issue 区搜索，例如 Server-Issues / Loader Issues
+查询某一类别的顶点或边（query by label）时提示超时
由于属于某一label的数据量可能比较多，请加上limit限制。
通过RESTful API操作图是可以的，但是发送Gremlin语句就报错：Request Failed(500)
可能是GremlinServer的配置有误，检查gremlin-server.yaml的host、port是否与rest-server.properties的gremlinserver.url匹配，如不匹配则修改，然后重启服务。
使用Loader导数据出现Socket Timeout异常，然后导致Loader中断
持续地导入数据会使Server的压力过大，然后导致有些请求超时。可以通过调整Loader的参数来适当缓解Server压力（如：重试次数，重试间隔，错误容忍数等），降低该问题出现频率。
如何删除全部的顶点和边，RESTful API中没有这样的接口，调用gremlin的g.V().drop()会报错Vertices in transaction have reached capacity xxx
目前确实没有好办法删除全部的数据，用户如果是自己部署的Server和后端，可以直接清空数据库，重启Server。可以使用paging API或scan API先获取所有数据，再逐条删除。
清空了数据库，并且执行了init-store，但是添加schema时提示"xxx has existed"
HugeGraphServer内是有缓存的，清空数据库的同时是需要重启Server的，否则残留的缓存会产生不一致。
插入顶点或边的过程中报错：Id max length is 128, but got xxx {yyy} 或 Big id max length is 32768, but got xxx
为了保证查询性能，目前的后端存储对id列的长度做了限制，顶点id不能超过128字节，边id长度不能超过32768字节，索引id不能超过128字节。
是否支持嵌套属性，如果不支持，是否有什么替代方案
嵌套属性目前暂不支持。替代方案：可以把嵌套属性作为单独的顶点拿出来，然后用边连接起来。
一个EdgeLabel是否可以连接多对VertexLabel，比如"投资"关系，可以是"个人"投资"企业"，也可以是"企业"投资"企业"
一个EdgeLabel不支持连接多对VertexLabel，需要用户将EdgeLabel拆分得更细一点，如：“个人投资”，“企业投资”。
通过RestAPI发送请求时提示HTTP 415 Unsupported Media Type
请求头中需要指定Content-Type:application/json
其他问题可以在对应项目的 issue 区搜索，例如 Server-Issues / Loader Issues
diff --git a/docs/guides/architectural/index.html b/docs/guides/architectural/index.html index 1b3561687..ec3cdb2b6 100644 --- a/docs/guides/architectural/index.html +++ b/docs/guides/architectural/index.html @@ -11,7 +11,7 @@ Create child page Create documentation issue Create project issue - Print entire section作为一款通用的图数据库产品，HugeGraph需具备图数据的基本功能，如下图所示。HugeGraph包括三个层次的功能，分别是存储层、计算层和用户接口层。 HugeGraph支持OLTP和OLAP两种图计算类型，其中OLTP实现了Apache TinkerPop3框架，并支持Gremlin查询语言。 OLAP计算是基于SparkGraphX实现。
HugeGraph的主要功能分为HugeCore、ApiServer、HugeGraph-Client、HugeGraph-Loader和HugeGraph-Studio等组件构成，各组件之间的通信关系如下图所示。
作为一款通用的图数据库产品，HugeGraph需具备图数据的基本功能，如下图所示。HugeGraph包括三个层次的功能，分别是存储层、计算层和用户接口层。 HugeGraph支持OLTP和OLAP两种图计算类型，其中OLTP实现了Apache TinkerPop3框架，并支持Gremlin查询语言。 OLAP计算是基于SparkGraphX实现。
HugeGraph的主要功能分为HugeCore、ApiServer、HugeGraph-Client、HugeGraph-Loader和HugeGraph-Studio等组件构成，各组件之间的通信关系如下图所示。
{
"mode": "RESTORING"
}
-通过maven打包，在项目目录下执行命令mvn package，在target目录下会生成Jar包文件。
-使用时将该Jar包拷到plugins目录，重启服务即可生效。
plugins目录，重启服务即可生效。RESTful API暂时未暴露事务接口
TinkerPop API允许打开事务，请求完成时会自动关闭(Gremlin Server强制关闭)
RESTful API暂时未暴露事务接口
TinkerPop API允许打开事务，请求完成时会自动关闭(Gremlin Server强制关闭)
查询某一类别的顶点或边（query by label）时提示超时
由于属于某一label的数据量可能比较多，请加上limit限制。
通过RESTful API操作图是可以的，但是发送Gremlin语句就报错：Request Failed(500)
可能是GremlinServer的配置有误，检查gremlin-server.yaml的host、port是否与rest-server.properties的gremlinserver.url匹配，如不匹配则修改，然后重启服务。
使用Loader导数据出现Socket Timeout异常，然后导致Loader中断
持续地导入数据会使Server的压力过大，然后导致有些请求超时。可以通过调整Loader的参数来适当缓解Server压力（如：重试次数，重试间隔，错误容忍数等），降低该问题出现频率。
如何删除全部的顶点和边，RESTful API中没有这样的接口，调用gremlin的g.V().drop()会报错Vertices in transaction have reached capacity xxx
目前确实没有好办法删除全部的数据，用户如果是自己部署的Server和后端，可以直接清空数据库，重启Server。可以使用paging API或scan API先获取所有数据，再逐条删除。
清空了数据库，并且执行了init-store，但是添加schema时提示"xxx has existed"
HugeGraphServer内是有缓存的，清空数据库的同时是需要重启Server的，否则残留的缓存会产生不一致。
插入顶点或边的过程中报错：Id max length is 128, but got xxx {yyy} 或 Big id max length is 32768, but got xxx
为了保证查询性能，目前的后端存储对id列的长度做了限制，顶点id不能超过128字节，边id长度不能超过32768字节，索引id不能超过128字节。
是否支持嵌套属性，如果不支持，是否有什么替代方案
嵌套属性目前暂不支持。替代方案：可以把嵌套属性作为单独的顶点拿出来，然后用边连接起来。
一个EdgeLabel是否可以连接多对VertexLabel，比如"投资"关系，可以是"个人"投资"企业"，也可以是"企业"投资"企业"
一个EdgeLabel不支持连接多对VertexLabel，需要用户将EdgeLabel拆分得更细一点，如：“个人投资”，“企业投资”。
通过RestAPI发送请求时提示HTTP 415 Unsupported Media Type
请求头中需要指定Content-Type:application/json
其他问题可以在对应项目的 issue 区搜索，例如 Server-Issues / Loader Issues
查询某一类别的顶点或边（query by label）时提示超时
由于属于某一label的数据量可能比较多，请加上limit限制。
通过RESTful API操作图是可以的，但是发送Gremlin语句就报错：Request Failed(500)
可能是GremlinServer的配置有误，检查gremlin-server.yaml的host、port是否与rest-server.properties的gremlinserver.url匹配，如不匹配则修改，然后重启服务。
使用Loader导数据出现Socket Timeout异常，然后导致Loader中断
持续地导入数据会使Server的压力过大，然后导致有些请求超时。可以通过调整Loader的参数来适当缓解Server压力（如：重试次数，重试间隔，错误容忍数等），降低该问题出现频率。
如何删除全部的顶点和边，RESTful API中没有这样的接口，调用gremlin的g.V().drop()会报错Vertices in transaction have reached capacity xxx
目前确实没有好办法删除全部的数据，用户如果是自己部署的Server和后端，可以直接清空数据库，重启Server。可以使用paging API或scan API先获取所有数据，再逐条删除。
清空了数据库，并且执行了init-store，但是添加schema时提示"xxx has existed"
HugeGraphServer内是有缓存的，清空数据库的同时是需要重启Server的，否则残留的缓存会产生不一致。
插入顶点或边的过程中报错：Id max length is 128, but got xxx {yyy} 或 Big id max length is 32768, but got xxx
为了保证查询性能，目前的后端存储对id列的长度做了限制，顶点id不能超过128字节，边id长度不能超过32768字节，索引id不能超过128字节。
是否支持嵌套属性，如果不支持，是否有什么替代方案
嵌套属性目前暂不支持。替代方案：可以把嵌套属性作为单独的顶点拿出来，然后用边连接起来。
一个EdgeLabel是否可以连接多对VertexLabel，比如"投资"关系，可以是"个人"投资"企业"，也可以是"企业"投资"企业"
一个EdgeLabel不支持连接多对VertexLabel，需要用户将EdgeLabel拆分得更细一点，如：“个人投资”，“企业投资”。
通过RestAPI发送请求时提示HTTP 415 Unsupported Media Type
请求头中需要指定Content-Type:application/json
其他问题可以在对应项目的 issue 区搜索，例如 Server-Issues / Loader Issues
Typical application scenarios of HugeGraph include deep relationship exploration, association analysis, path search, feature extraction, data clustering, community detection, knowledge graph, etc., and are applicable to business fields such as network security, telecommunication fraud, financial risk control, advertising recommendation, social network and intelligence Robots etc.
Typical application scenarios of HugeGraph include deep relationship exploration, association analysis, path search, feature extraction, data clustering, community detection, knowledge graph, etc., and are applicable to business fields such as network security, telecommunication fraud, financial risk control, advertising recommendation, social network and intelligence Robots etc.
HugeGraph supports graph operations in online and offline environments, supports batch import of data, supports efficient complex relationship analysis, and can be seamlessly integrated with big data platforms. -HugeGraph supports multi-user parallel operations. Users can enter Gremlin query statements and get graph query results in time. They can also call HugeGraph API in user programs for graph analysis or query.
This system has the following features:
The functions of this system include but are not limited to:
This system has the following features:
The functions of this system include but are not limited to:
推荐使用HugeGraph-Studio 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。
HugeGraph 目前支持 Gremlin 的语法，用户可以通过 Gremlin / REST-API 实现各种查询需求。
推荐使用HugeGraph-Studio 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。
HugeGraph 目前支持 Gremlin 的语法，用户可以通过 Gremlin / REST-API 实现各种查询需求。
推荐使用HugeGraph-Studio 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。
HugeGraph 目前支持 Gremlin 的语法，用户可以通过 Gremlin / REST-API 实现各种查询需求。
推荐使用HugeGraph-Studio 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。
HugeGraph 目前支持 Gremlin 的语法，用户可以通过 Gremlin / REST-API 实现各种查询需求。
HugeGraph支持Apache TinkerPop3的图形遍历查询语言Gremlin。 SQL是关系型数据库查询语言，而Gremlin是一种通用的图数据库查询语言，Gremlin可用于创建图的实体（Vertex和Edge）、修改实体内部属性、删除实体，也可执行图的查询操作。
Gremlin可用于创建图的实体（Vertex和Edge）、修改实体内部属性、删除实体，更主要的是可用于执行图的查询及分析操作。
HugeGraph实现了TinkerPop框架，但是并没有实现TinkerPop所有的特性。
下表列出HugeGraph对TinkerPop各种特性的支持情况：
| Name | Description | Support |
|---|---|---|
| Computer | Determines if the {@code Graph} implementation supports {@link GraphComputer} based processing | false |
| Transactions | Determines if the {@code Graph} implementations supports transactions. | true |
| Persistence | Determines if the {@code Graph} implementation supports persisting it’s contents natively to disk.This feature does not refer to every graph’s ability to write to disk via the Gremlin IO packages(.e.g. GraphML), unless the graph natively persists to disk via those options somehow. For example,TinkerGraph does not support this feature as it is a pure in-sideEffects graph. | true |
| ThreadedTransactions | Determines if the {@code Graph} implementation supports threaded transactions which allow a transaction be executed across multiple threads via {@link Transaction#createThreadedTx()}. | false |
| ConcurrentAccess | Determines if the {@code Graph} implementation supports more than one connection to the same instance at the same time. For example, Neo4j embedded does not support this feature because concurrent access to the same database files by multiple instances is not possible. However, Neo4j HA could support this feature as each new {@code Graph} instance coordinates with the Neo4j cluster allowing multiple instances to operate on the same database. | false |
| Name | Description | Support |
|---|---|---|
| UserSuppliedIds | Determines if an {@link Element} can have a user defined identifier. Implementation that do not support this feature will be expected to auto-generate unique identifiers. In other words, if the {@link Graph} allows {@code graph.addVertex(id,x)} to work and thus set the identifier of the newly added {@link Vertex} to the value of {@code x} then this feature should return true. In this case, {@code x} is assumed to be an identifier data type that the {@link Graph} will accept. | false |
| NumericIds | Determines if an {@link Element} has numeric identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a numeric value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| StringIds | Determines if an {@link Element} has string identifiers as their internal representation. In other words, if the value returned from {@link Element#id()} is a string value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| UuidIds | Determines if an {@link Element} has UUID identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a {@link UUID} value then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| CustomIds | Determines if an {@link Element} has a specific custom object as their internal representation.In other words, if the value returned from {@link Element#id()} is a type defined by the graph implementations, such as OrientDB’s {@code Rid}, then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| AnyIds | Determines if an {@link Element} any Java object is a suitable identifier. TinkerGraph is a good example of a {@link Graph} that can support this feature, as it can use any {@link Object} as a value for the identifier. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. This setting should only return {@code true} if {@link #supportsUserSuppliedIds()} is {@code true}. | false |
| AddProperty | Determines if an {@link Element} allows properties to be added. This feature is set independently from supporting “data types” and refers to support of calls to {@link Element#property(String, Object)}. | true |
| RemoveProperty | Determines if an {@link Element} allows properties to be removed. | true |
| AddVertices | Determines if a {@link Vertex} can be added to the {@code Graph}. | true |
| MultiProperties | Determines if a {@link Vertex} can support multiple properties with the same key. | false |
| DuplicateMultiProperties | Determines if a {@link Vertex} can support non-unique values on the same key. For this value to be {@code true}, then {@link #supportsMetaProperties()} must also return true. By default this method, just returns what {@link #supportsMultiProperties()} returns. | false |
| MetaProperties | Determines if a {@link Vertex} can support properties on vertex properties. It is assumed that a graph will support all the same data types for meta-properties that are supported for regular properties. | false |
| RemoveVertices | Determines if a {@link Vertex} can be removed from the {@code Graph}. | true |
| Name | Description | Support |
|---|---|---|
| UserSuppliedIds | Determines if an {@link Element} can have a user defined identifier. Implementation that do not support this feature will be expected to auto-generate unique identifiers. In other words, if the {@link Graph} allows {@code graph.addVertex(id,x)} to work and thus set the identifier of the newly added {@link Vertex} to the value of {@code x} then this feature should return true. In this case, {@code x} is assumed to be an identifier data type that the {@link Graph} will accept. | false |
| NumericIds | Determines if an {@link Element} has numeric identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a numeric value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| StringIds | Determines if an {@link Element} has string identifiers as their internal representation. In other words, if the value returned from {@link Element#id()} is a string value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| UuidIds | Determines if an {@link Element} has UUID identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a {@link UUID} value then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| CustomIds | Determines if an {@link Element} has a specific custom object as their internal representation.In other words, if the value returned from {@link Element#id()} is a type defined by the graph implementations, such as OrientDB’s {@code Rid}, then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| AnyIds | Determines if an {@link Element} any Java object is a suitable identifier. TinkerGraph is a good example of a {@link Graph} that can support this feature, as it can use any {@link Object} as a value for the identifier. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. This setting should only return {@code true} if {@link #supportsUserSuppliedIds()} is {@code true}. | false |
| AddProperty | Determines if an {@link Element} allows properties to be added. This feature is set independently from supporting “data types” and refers to support of calls to {@link Element#property(String, Object)}. | true |
| RemoveProperty | Determines if an {@link Element} allows properties to be removed. | true |
| AddEdges | Determines if an {@link Edge} can be added to a {@code Vertex}. | true |
| RemoveEdges | Determines if an {@link Edge} can be removed from a {@code Vertex}. | true |
| Name | Description | Support |
|---|---|---|
| BooleanValues | true | |
| ByteValues | true | |
| DoubleValues | true | |
| FloatValues | true | |
| IntegerValues | true | |
| LongValues | true | |
| MapValues | Supports setting of a {@code Map} value. The assumption is that the {@code Map} can contain arbitrary serializable values that may or may not be defined as a feature itself | false |
| MixedListValues | Supports setting of a {@code List} value. The assumption is that the {@code List} can contain arbitrary serializable values that may or may not be defined as a feature itself. As this{@code List} is “mixed” it does not need to contain objects of the same type. | false |
| BooleanArrayValues | false | |
| ByteArrayValues | true | |
| DoubleArrayValues | false | |
| FloatArrayValues | false | |
| IntegerArrayValues | false | |
| LongArrayValues | false | |
| SerializableValues | false | |
| StringArrayValues | false | |
| StringValues | true | |
| UniformListValues | Supports setting of a {@code List} value. The assumption is that the {@code List} can contain arbitrary serializable values that may or may not be defined as a feature itself. As this{@code List} is “uniform” it must contain objects of the same type. | false |
HugeGraph支持Gremlin的所有步骤。有关Gremlin的完整参考信息，请参与Gremlin官网。
| 步骤 | 说明 | 文档 |
|---|---|---|
| addE | 在两个顶点之间添加边 | addE step |
| addV | 将顶点添加到图形 | addV step |
| and | 确保所有遍历都返回值 | and step |
| as | 用于向步骤的输出分配变量的步骤调制器 | as step |
| by | 与group和order配合使用的步骤调制器 | by step |
| coalesce | 返回第一个返回结果的遍历 | coalesce step |
| constant | 返回常量值。 与coalesce配合使用 | constant step |
| count | 从遍历返回计数 | count step |
| dedup | 返回已删除重复内容的值 | dedup step |
| drop | 丢弃值（顶点/边缘） | drop step |
| fold | 充当用于计算结果聚合值的屏障 | fold step |
| group | 根据指定的标签将值分组 | group step |
| has | 用于筛选属性、顶点和边缘。 支持hasLabel、hasId、hasNot 和 has 变体 | has step |
| inject | 将值注入流中 | inject step |
| is | 用于通过布尔表达式执行筛选器 | is step |
| limit | 用于限制遍历中的项数 | limit step |
| local | 本地包装遍历的某个部分，类似于子查询 | local step |
| not | 用于生成筛选器的求反结果 | not step |
| optional | 如果生成了某个结果，则返回指定遍历的结果，否则返回调用元素 | optional step |
| or | 确保至少有一个遍历会返回值 | or step |
| order | 按指定的排序顺序返回结果 | order step |
| path | 返回遍历的完整路径 | path step |
| project | 将属性投影为映射 | project step |
| properties | 返回指定标签的属性 | properties step |
| range | 根据指定的值范围进行筛选 | range step |
| repeat | 将步骤重复指定的次数。 用于循环 | repeat step |
| sample | 用于对遍历返回的结果采样 | sample step |
| select | 用于投影遍历返回的结果 | select step |
| store | 用于遍历返回的非阻塞聚合 | store step |
| tree | 将顶点中的路径聚合到树中 | tree step |
| unfold | 将迭代器作为步骤展开 | unfold step |
| union | 合并多个遍历返回的结果 | union step |
| V | 包括顶点与边之间的遍历所需的步骤：V、E、out、in、both、outE、inE、bothE、outV、inV、bothV 和 otherV | order step |
| where | 用于筛选遍历返回的结果。 支持 eq、neq、lt、lte、gt、gte 和 between 运算符 | where step |
HugeGraph支持Apache TinkerPop3的图形遍历查询语言Gremlin。 SQL是关系型数据库查询语言，而Gremlin是一种通用的图数据库查询语言，Gremlin可用于创建图的实体（Vertex和Edge）、修改实体内部属性、删除实体，也可执行图的查询操作。
Gremlin可用于创建图的实体（Vertex和Edge）、修改实体内部属性、删除实体，更主要的是可用于执行图的查询及分析操作。
HugeGraph实现了TinkerPop框架，但是并没有实现TinkerPop所有的特性。
下表列出HugeGraph对TinkerPop各种特性的支持情况：
| Name | Description | Support |
|---|---|---|
| Computer | Determines if the {@code Graph} implementation supports {@link GraphComputer} based processing | false |
| Transactions | Determines if the {@code Graph} implementations supports transactions. | true |
| Persistence | Determines if the {@code Graph} implementation supports persisting it’s contents natively to disk.This feature does not refer to every graph’s ability to write to disk via the Gremlin IO packages(.e.g. GraphML), unless the graph natively persists to disk via those options somehow. For example,TinkerGraph does not support this feature as it is a pure in-sideEffects graph. | true |
| ThreadedTransactions | Determines if the {@code Graph} implementation supports threaded transactions which allow a transaction be executed across multiple threads via {@link Transaction#createThreadedTx()}. | false |
| ConcurrentAccess | Determines if the {@code Graph} implementation supports more than one connection to the same instance at the same time. For example, Neo4j embedded does not support this feature because concurrent access to the same database files by multiple instances is not possible. However, Neo4j HA could support this feature as each new {@code Graph} instance coordinates with the Neo4j cluster allowing multiple instances to operate on the same database. | false |
| Name | Description | Support |
|---|---|---|
| UserSuppliedIds | Determines if an {@link Element} can have a user defined identifier. Implementation that do not support this feature will be expected to auto-generate unique identifiers. In other words, if the {@link Graph} allows {@code graph.addVertex(id,x)} to work and thus set the identifier of the newly added {@link Vertex} to the value of {@code x} then this feature should return true. In this case, {@code x} is assumed to be an identifier data type that the {@link Graph} will accept. | false |
| NumericIds | Determines if an {@link Element} has numeric identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a numeric value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| StringIds | Determines if an {@link Element} has string identifiers as their internal representation. In other words, if the value returned from {@link Element#id()} is a string value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| UuidIds | Determines if an {@link Element} has UUID identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a {@link UUID} value then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| CustomIds | Determines if an {@link Element} has a specific custom object as their internal representation.In other words, if the value returned from {@link Element#id()} is a type defined by the graph implementations, such as OrientDB’s {@code Rid}, then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| AnyIds | Determines if an {@link Element} any Java object is a suitable identifier. TinkerGraph is a good example of a {@link Graph} that can support this feature, as it can use any {@link Object} as a value for the identifier. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. This setting should only return {@code true} if {@link #supportsUserSuppliedIds()} is {@code true}. | false |
| AddProperty | Determines if an {@link Element} allows properties to be added. This feature is set independently from supporting “data types” and refers to support of calls to {@link Element#property(String, Object)}. | true |
| RemoveProperty | Determines if an {@link Element} allows properties to be removed. | true |
| AddVertices | Determines if a {@link Vertex} can be added to the {@code Graph}. | true |
| MultiProperties | Determines if a {@link Vertex} can support multiple properties with the same key. | false |
| DuplicateMultiProperties | Determines if a {@link Vertex} can support non-unique values on the same key. For this value to be {@code true}, then {@link #supportsMetaProperties()} must also return true. By default this method, just returns what {@link #supportsMultiProperties()} returns. | false |
| MetaProperties | Determines if a {@link Vertex} can support properties on vertex properties. It is assumed that a graph will support all the same data types for meta-properties that are supported for regular properties. | false |
| RemoveVertices | Determines if a {@link Vertex} can be removed from the {@code Graph}. | true |
| Name | Description | Support |
|---|---|---|
| UserSuppliedIds | Determines if an {@link Element} can have a user defined identifier. Implementation that do not support this feature will be expected to auto-generate unique identifiers. In other words, if the {@link Graph} allows {@code graph.addVertex(id,x)} to work and thus set the identifier of the newly added {@link Vertex} to the value of {@code x} then this feature should return true. In this case, {@code x} is assumed to be an identifier data type that the {@link Graph} will accept. | false |
| NumericIds | Determines if an {@link Element} has numeric identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a numeric value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| StringIds | Determines if an {@link Element} has string identifiers as their internal representation. In other words, if the value returned from {@link Element#id()} is a string value then this method should be return {@code true}. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| UuidIds | Determines if an {@link Element} has UUID identifiers as their internal representation. In other words,if the value returned from {@link Element#id()} is a {@link UUID} value then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| CustomIds | Determines if an {@link Element} has a specific custom object as their internal representation.In other words, if the value returned from {@link Element#id()} is a type defined by the graph implementations, such as OrientDB’s {@code Rid}, then this method should be return {@code true}.Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. | false |
| AnyIds | Determines if an {@link Element} any Java object is a suitable identifier. TinkerGraph is a good example of a {@link Graph} that can support this feature, as it can use any {@link Object} as a value for the identifier. Note that this feature is most generally used for determining the appropriate tests to execute in the Gremlin Test Suite. This setting should only return {@code true} if {@link #supportsUserSuppliedIds()} is {@code true}. | false |
| AddProperty | Determines if an {@link Element} allows properties to be added. This feature is set independently from supporting “data types” and refers to support of calls to {@link Element#property(String, Object)}. | true |
| RemoveProperty | Determines if an {@link Element} allows properties to be removed. | true |
| AddEdges | Determines if an {@link Edge} can be added to a {@code Vertex}. | true |
| RemoveEdges | Determines if an {@link Edge} can be removed from a {@code Vertex}. | true |
| Name | Description | Support |
|---|---|---|
| BooleanValues | true | |
| ByteValues | true | |
| DoubleValues | true | |
| FloatValues | true | |
| IntegerValues | true | |
| LongValues | true | |
| MapValues | Supports setting of a {@code Map} value. The assumption is that the {@code Map} can contain arbitrary serializable values that may or may not be defined as a feature itself | false |
| MixedListValues | Supports setting of a {@code List} value. The assumption is that the {@code List} can contain arbitrary serializable values that may or may not be defined as a feature itself. As this{@code List} is “mixed” it does not need to contain objects of the same type. | false |
| BooleanArrayValues | false | |
| ByteArrayValues | true | |
| DoubleArrayValues | false | |
| FloatArrayValues | false | |
| IntegerArrayValues | false | |
| LongArrayValues | false | |
| SerializableValues | false | |
| StringArrayValues | false | |
| StringValues | true | |
| UniformListValues | Supports setting of a {@code List} value. The assumption is that the {@code List} can contain arbitrary serializable values that may or may not be defined as a feature itself. As this{@code List} is “uniform” it must contain objects of the same type. | false |
HugeGraph支持Gremlin的所有步骤。有关Gremlin的完整参考信息，请参与Gremlin官网。
| 步骤 | 说明 | 文档 |
|---|---|---|
| addE | 在两个顶点之间添加边 | addE step |
| addV | 将顶点添加到图形 | addV step |
| and | 确保所有遍历都返回值 | and step |
| as | 用于向步骤的输出分配变量的步骤调制器 | as step |
| by | 与group和order配合使用的步骤调制器 | by step |
| coalesce | 返回第一个返回结果的遍历 | coalesce step |
| constant | 返回常量值。 与coalesce配合使用 | constant step |
| count | 从遍历返回计数 | count step |
| dedup | 返回已删除重复内容的值 | dedup step |
| drop | 丢弃值（顶点/边缘） | drop step |
| fold | 充当用于计算结果聚合值的屏障 | fold step |
| group | 根据指定的标签将值分组 | group step |
| has | 用于筛选属性、顶点和边缘。 支持hasLabel、hasId、hasNot 和 has 变体 | has step |
| inject | 将值注入流中 | inject step |
| is | 用于通过布尔表达式执行筛选器 | is step |
| limit | 用于限制遍历中的项数 | limit step |
| local | 本地包装遍历的某个部分，类似于子查询 | local step |
| not | 用于生成筛选器的求反结果 | not step |
| optional | 如果生成了某个结果，则返回指定遍历的结果，否则返回调用元素 | optional step |
| or | 确保至少有一个遍历会返回值 | or step |
| order | 按指定的排序顺序返回结果 | order step |
| path | 返回遍历的完整路径 | path step |
| project | 将属性投影为映射 | project step |
| properties | 返回指定标签的属性 | properties step |
| range | 根据指定的值范围进行筛选 | range step |
| repeat | 将步骤重复指定的次数。 用于循环 | repeat step |
| sample | 用于对遍历返回的结果采样 | sample step |
| select | 用于投影遍历返回的结果 | select step |
| store | 用于遍历返回的非阻塞聚合 | store step |
| tree | 将顶点中的路径聚合到树中 | tree step |
| unfold | 将迭代器作为步骤展开 | unfold step |
| union | 合并多个遍历返回的结果 | union step |
| V | 包括顶点与边之间的遍历所需的步骤：V、E、out、in、both、outE、inE、bothE、outV、inV、bothV 和 otherV | order step |
| where | 用于筛选遍历返回的结果。 支持 eq、neq、lt、lte、gt、gte 和 between 运算符 | where step |
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| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
| com-lj.ungraph.txt | 3997961 | 34681189 | 479MB |
HugeGraph版本：0.5.6，RestServer和Gremlin Server和backends都在同一台服务器上
Titan版本：0.5.4, 使用thrift+Cassandra模式
Neo4j版本：2.0.1
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.629 | 5.711 | 5.243 | 67.033 |
| Titan | 10.15 | 108.569 | 150.266 | 1217.944 |
| Neo4j | 3.884 | 18.938 | 24.890 | 281.537 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) | com-lj.ungraph(400w) |
|---|---|---|---|---|
| HugeGraph | 4.072 | 45.118 | 66.006 | 609.083 |
| Titan | 8.084 | 92.507 | 184.543 | 1099.371 |
| Neo4j | 2.424 | 10.537 | 11.609 | 106.919 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 1.540 | 10.764 | 11.243 | 151.271 |
| Titan | 7.361 | 93.344 | 169.218 | 1085.235 |
| Neo4j | 1.673 | 4.775 | 4.284 | 40.507 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.494 | 0.103 | 3.364 | 8.155 |
| Titan | 11.818 | 0.239 | 377.709 | 575.678 |
| Neo4j | 1.719 | 1.800 | 1.956 | 8.530 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| HugeGraph(core) | 20.804 | 242.099 | 744.780 | 1700.547 |
| Titan | 45.790 | 820.633 | 2652.235 | 9568.623 |
| Neo4j | 5.913 | 50.267 | 142.354 | 460.880 |
说明
HugeGraph API性能测试主要测试HugeGraph-Server对RESTful API请求的并发处理能力，包括：
HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考：
之前的版本只提供HugeGraph所支持的后端种类中性能最好的API性能测试，从0.5.6版本开始，分别提供了单机和集群的性能情况
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| 机器编号 | CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|---|
| 1 | 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps | 1.4T HDD |
| 2 | 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
1. CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）
2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）
3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）
不断提升并发量，测试server仍能正常提供服务的压力上限
起压和被压机器配置相同，基本参数如下：
| CPU | Memory | 网卡 |
|---|---|---|
| 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps |
测试工具：apache-Jmeter-2.5.1
batch_size_warn_threshold_in_kb: 1000
batch_size_fail_threshold_in_kb: 1000
-注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用HugeGraph-Loader工具持续、高速导入图数据
测试均采用网址数据的边数据
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 9.516 | 88.123 | 111.586 |
| RocksDB | 2.345 | 14.076 | 16.636 |
| Cassandra | 11.930 | 108.709 | 101.959 |
| Memory | 3.077 | 15.204 | 13.841 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) |
|---|---|---|---|
| Titan | 7.724 | 70.935 | 128.884 |
| RocksDB | 8.876 | 65.852 | 63.388 |
| Cassandra | 13.125 | 126.959 | 102.580 |
| Memory | 22.309 | 207.411 | 165.609 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 7.119 | 63.353 | 115.633 |
| RocksDB | 6.032 | 64.526 | 52.721 |
| Cassandra | 9.410 | 102.766 | 94.197 |
| Memory | 12.340 | 195.444 | 140.89 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 11.333 | 0.313 | 376.06 |
| RocksDB | 44.391 | 2.221 | 268.792 |
| Cassandra | 39.845 | 3.337 | 331.113 |
| Memory | 35.638 | 2.059 | 388.987 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| Titan | 45.943 | 849.168 | 2737.117 | 9791.46 |
| Memory(core) | 41.077 | 1825.905 | * | * |
| Cassandra（core） | 39.783 | 862.744 | 2423.136 | 6564.191 |
| RocksDB（core） | 33.383 | 199.894 | 763.869 | 1677.813 |
说明
注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用HugeGraph-Loader工具持续、高速导入图数据
测试均采用网址数据的边数据
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 9.516 | 88.123 | 111.586 |
| RocksDB | 2.345 | 14.076 | 16.636 |
| Cassandra | 11.930 | 108.709 | 101.959 |
| Memory | 3.077 | 15.204 | 13.841 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) |
|---|---|---|---|
| Titan | 7.724 | 70.935 | 128.884 |
| RocksDB | 8.876 | 65.852 | 63.388 |
| Cassandra | 13.125 | 126.959 | 102.580 |
| Memory | 22.309 | 207.411 | 165.609 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 7.119 | 63.353 | 115.633 |
| RocksDB | 6.032 | 64.526 | 52.721 |
| Cassandra | 9.410 | 102.766 | 94.197 |
| Memory | 12.340 | 195.444 | 140.89 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 11.333 | 0.313 | 376.06 |
| RocksDB | 44.391 | 2.221 | 268.792 |
| Cassandra | 39.845 | 3.337 | 331.113 |
| Memory | 35.638 | 2.059 | 388.987 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| Titan | 45.943 | 849.168 | 2737.117 | 9791.46 |
| Memory(core) | 41.077 | 1825.905 | * | * |
| Cassandra（core） | 39.783 | 862.744 | 2423.136 | 6564.191 |
| RocksDB（core） | 33.383 | 199.894 | 763.869 | 1677.813 |
说明
This is the multi-page printable view of this section. Click here to print.
HugeGraph API性能测试主要测试HugeGraph-Server对RESTful API请求的并发处理能力，包括：
HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考：
之前的版本只提供HugeGraph所支持的后端种类中性能最好的API性能测试，从0.5.6版本开始，分别提供了单机和集群的性能情况
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| 机器编号 | CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|---|
| 1 | 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps | 1.4T HDD |
| 2 | 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
1. CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）
2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）
3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）
不断提升并发量，测试server仍能正常提供服务的压力上限
起压和被压机器配置相同，基本参数如下：
| CPU | Memory | 网卡 |
|---|---|---|
| 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps |
测试工具：apache-Jmeter-2.5.1
batch_size_warn_threshold_in_kb: 1000
batch_size_fail_threshold_in_kb: 1000
-注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
起压和被压机器配置相同，基本参数如下：
| CPU | Memory | 网卡 |
|---|---|---|
| 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps |
测试工具：apache-Jmeter-2.5.1
batch_size_warn_threshold_in_kb: 1000
batch_size_fail_threshold_in_kb: 1000
-注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
注：时间的单位均为ms
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| property_keys | 331000 | 1 | 1 | 2 | 0 | 172 | 0.00% | 920.7/sec | 178.1 |
| vertex_labels | 331000 | 1 | 2 | 2 | 1 | 126 | 0.00% | 920.7/sec | 193.4 |
| edge_labels | 331000 | 2 | 2 | 3 | 1 | 158 | 0.00% | 920.7/sec | 242.8 |
结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| single_insert_vertices | 331000 | 0 | 1 | 1 | 0 | 21 | 0.00% | 920.7/sec | 234.4 |
| single_insert_edges | 331000 | 2 | 2 | 3 | 1 | 53 | 0.00% | 920.7/sec | 309.1 |
测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限
| Concurrency | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| 2000(vertex) | 661916 | 1 | 1 | 1 | 0 | 3012 | 0.00% | 1842.9/sec | 469.1 |
| 4000(vertex) | 1316124 | 13 | 1 | 14 | 0 | 9023 | 0.00% | 3673.1/sec | 935.0 |
| 5000(vertex) | 1468121 | 1010 | 1135 | 1227 | 0 | 9223 | 0.06% | 4095.6/sec | 1046.0 |
| 7000(vertex) | 1378454 | 1617 | 1708 | 1886 | 0 | 9361 | 0.08% | 3860.3/sec | 987.1 |
| 2000(edge) | 629399 | 953 | 1043 | 1113 | 1 | 9001 | 0.00% | 1750.3/sec | 587.6 |
| 3000(edge) | 648364 | 2258 | 2404 | 2500 | 2 | 9001 | 0.00% | 1810.7/sec | 607.9 |
| 4000(edge) | 649904 | 1992 | 2112 | 2211 | 1 | 9001 | 0.06% | 1812.5/sec | 608.5 |
测试方法：固定并发量，测试server和后端的处理速率
| Label | Samples | Average | Median | 90%Line | Min | Max | Error% | Throughput | KB/sec |
|---|---|---|---|---|---|---|---|---|---|
| batch_insert_vertices | 37162 | 8959 | 9595 | 9704 | 17 | 9852 | 0.00% | 103.4/sec | 393.3 |
| batch_insert_edges | 10800 | 31849 | 34544 | 35132 | 435 | 35747 | 0.00% | 28.8/sec | 814.9 |
被压机器信息
| 机器编号 | CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|---|
| 1 | 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps | 1.4T HDD |
| 2 | 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
1. CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）
2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）
3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）
不断提升并发量，测试server仍能正常提供服务的压力上限
被压机器信息
| 机器编号 | CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|---|
| 1 | 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz | 61G | 1000Mbps | 1.4T HDD |
| 2 | 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
1. CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）
2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）
3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）
不断提升并发量，测试server仍能正常提供服务的压力上限
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
被压机器信息
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD,2.7T HDD |
注：起压机器和被压机器在同一机房
后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。
不断提升并发量，测试server仍能正常提供服务的压力上限
持续时间：5min
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
不断提升并发量，测试server仍能正常提供服务的压力上限
####### 结论：
####### 结论：
HugeGraph API性能测试主要测试HugeGraph-Server对RESTful API请求的并发处理能力，包括：
HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考：
之前的版本只提供HugeGraph所支持的后端种类中性能最好的API性能测试，从0.5.6版本开始，分别提供了单机和集群的性能情况
HugeGraph API性能测试主要测试HugeGraph-Server对RESTful API请求的并发处理能力，包括：
HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考：
之前的版本只提供HugeGraph所支持的后端种类中性能最好的API性能测试，从0.5.6版本开始，分别提供了单机和集群的性能情况
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 9.516 | 88.123 | 111.586 |
| RocksDB | 2.345 | 14.076 | 16.636 |
| Cassandra | 11.930 | 108.709 | 101.959 |
| Memory | 3.077 | 15.204 | 13.841 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) |
|---|---|---|---|
| Titan | 7.724 | 70.935 | 128.884 |
| RocksDB | 8.876 | 65.852 | 63.388 |
| Cassandra | 13.125 | 126.959 | 102.580 |
| Memory | 22.309 | 207.411 | 165.609 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 7.119 | 63.353 | 115.633 |
| RocksDB | 6.032 | 64.526 | 52.721 |
| Cassandra | 9.410 | 102.766 | 94.197 |
| Memory | 12.340 | 195.444 | 140.89 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 11.333 | 0.313 | 376.06 |
| RocksDB | 44.391 | 2.221 | 268.792 |
| Cassandra | 39.845 | 3.337 | 331.113 |
| Memory | 35.638 | 2.059 | 388.987 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| Titan | 45.943 | 849.168 | 2737.117 | 9791.46 |
| Memory(core) | 41.077 | 1825.905 | * | * |
| Cassandra（core） | 39.783 | 862.744 | 2423.136 | 6564.191 |
| RocksDB（core） | 33.383 | 199.894 | 763.869 | 1677.813 |
说明
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 9.516 | 88.123 | 111.586 |
| RocksDB | 2.345 | 14.076 | 16.636 |
| Cassandra | 11.930 | 108.709 | 101.959 |
| Memory | 3.077 | 15.204 | 13.841 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) |
|---|---|---|---|
| Titan | 7.724 | 70.935 | 128.884 |
| RocksDB | 8.876 | 65.852 | 63.388 |
| Cassandra | 13.125 | 126.959 | 102.580 |
| Memory | 22.309 | 207.411 | 165.609 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 7.119 | 63.353 | 115.633 |
| RocksDB | 6.032 | 64.526 | 52.721 |
| Cassandra | 9.410 | 102.766 | 94.197 |
| Memory | 12.340 | 195.444 | 140.89 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) |
|---|---|---|---|
| Titan | 11.333 | 0.313 | 376.06 |
| RocksDB | 44.391 | 2.221 | 268.792 |
| Cassandra | 39.845 | 3.337 | 331.113 |
| Memory | 35.638 | 2.059 | 388.987 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| Titan | 45.943 | 849.168 | 2737.117 | 9791.46 |
| Memory(core) | 41.077 | 1825.905 | * | * |
| Cassandra（core） | 39.783 | 862.744 | 2423.136 | 6564.191 |
| RocksDB（core） | 33.383 | 199.894 | 763.869 | 1677.813 |
说明
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
| com-lj.ungraph.txt | 3997961 | 34681189 | 479MB |
HugeGraph版本：0.5.6，RestServer和Gremlin Server和backends都在同一台服务器上
Titan版本：0.5.4, 使用thrift+Cassandra模式
Neo4j版本：2.0.1
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.629 | 5.711 | 5.243 | 67.033 |
| Titan | 10.15 | 108.569 | 150.266 | 1217.944 |
| Neo4j | 3.884 | 18.938 | 24.890 | 281.537 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) | com-lj.ungraph(400w) |
|---|---|---|---|---|
| HugeGraph | 4.072 | 45.118 | 66.006 | 609.083 |
| Titan | 8.084 | 92.507 | 184.543 | 1099.371 |
| Neo4j | 2.424 | 10.537 | 11.609 | 106.919 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 1.540 | 10.764 | 11.243 | 151.271 |
| Titan | 7.361 | 93.344 | 169.218 | 1085.235 |
| Neo4j | 1.673 | 4.775 | 4.284 | 40.507 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.494 | 0.103 | 3.364 | 8.155 |
| Titan | 11.818 | 0.239 | 377.709 | 575.678 |
| Neo4j | 1.719 | 1.800 | 1.956 | 8.530 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| HugeGraph(core) | 20.804 | 242.099 | 744.780 | 1700.547 |
| Titan | 45.790 | 820.633 | 2652.235 | 9568.623 |
| Neo4j | 5.913 | 50.267 | 142.354 | 460.880 |
说明
| CPU | Memory | 网卡 | 磁盘 |
|---|---|---|---|
| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB SSD |
测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
Clustering，基于Louvain Method的社区发现算法
测试使用人造数据和真实数据
MIW、SIW和QW使用SNAP数据集
CW使用LFR-Benchmark generator生成的人造数据
| 名称 | vertex数目 | edge数目 | 文件大小 |
|---|---|---|---|
| email-enron.txt | 36,691 | 367,661 | 4MB |
| com-youtube.ungraph.txt | 1,157,806 | 2,987,624 | 38.7MB |
| amazon0601.txt | 403,393 | 3,387,388 | 47.9MB |
| com-lj.ungraph.txt | 3997961 | 34681189 | 479MB |
HugeGraph版本：0.5.6，RestServer和Gremlin Server和backends都在同一台服务器上
Titan版本：0.5.4, 使用thrift+Cassandra模式
Neo4j版本：2.0.1
graphdb-benchmark适配的Titan版本为0.5.4
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.629 | 5.711 | 5.243 | 67.033 |
| Titan | 10.15 | 108.569 | 150.266 | 1217.944 |
| Neo4j | 3.884 | 18.938 | 24.890 | 281.537 |
说明
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w) | com-lj.ungraph(400w) |
|---|---|---|---|---|
| HugeGraph | 4.072 | 45.118 | 66.006 | 609.083 |
| Titan | 8.084 | 92.507 | 184.543 | 1099.371 |
| Neo4j | 2.424 | 10.537 | 11.609 | 106.919 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 1.540 | 10.764 | 11.243 | 151.271 |
| Titan | 7.361 | 93.344 | 169.218 | 1085.235 |
| Neo4j | 1.673 | 4.775 | 4.284 | 40.507 |
说明
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w) | com-lj.ungraph(3000w) |
|---|---|---|---|---|
| HugeGraph | 0.494 | 0.103 | 3.364 | 8.155 |
| Titan | 11.818 | 0.239 | 377.709 | 575.678 |
| Neo4j | 1.719 | 1.800 | 1.956 | 8.530 |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM |
| v111 | 时间 | 0.027s | 0.034s | 0.115 | 1.36s | OOM | – |
| v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM |
说明
| 顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度 |
|---|---|---|---|---|---|---|---|
| v1 | 时间 | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM |
| 度 | 10 | 133 | 2453 | 50,830 | 1,128,688 | ||
| v111 | 时间 | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM |
| 度 | 10 | 211 | 4944 | 113150 | 2,629,970 | ||
| v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM |
| 度 | 10 | 140 | 2555 | 50825 | 1,070,230 |
说明
| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
|---|---|---|---|---|
| HugeGraph(core) | 20.804 | 242.099 | 744.780 | 1700.547 |
| Titan | 45.790 | 820.633 | 2652.235 | 9568.623 |
| Neo4j | 5.913 | 50.267 | 142.354 | 460.880 |
说明
当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用HugeGraph-Loader工具持续、高速导入图数据
测试均采用网址数据的边数据
当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用HugeGraph-Loader工具持续、高速导入图数据
测试均采用网址数据的边数据
NOTE: it will only be saved for one hour
kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system
-If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.
More algorithms please see: Built-In algorithms
TODO
TODO
+If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.
More algorithms please see: Built-In algorithms
TODO
TODO
diff --git a/docs/quickstart/hugegraph-client/index.html b/docs/quickstart/hugegraph-client/index.html index cefbacfb2..b7b067110 100644 --- a/docs/quickstart/hugegraph-client/index.html +++ b/docs/quickstart/hugegraph-client/index.html @@ -297,7 +297,7 @@ hugeClient.close(); } } -Before running Example, you need to start the Server. For the startup process, seeHugeGraph-Server Quick Start.
SeeIntroduce basic API of HugeGraph-Client.
Before running Example, you need to start the Server. For the startup process, seeHugeGraph-Server Quick Start.
SeeIntroduce basic API of HugeGraph-Client.
NOTE: it will only be saved for one hour
kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system
-If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.
More algorithms please see: Built-In algorithms
TODO
TODO
If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.
More algorithms please see: Built-In algorithms
TODO
TODO
HugeGraph is an analysis-oriented graph database system that supports batch operations, which fully supports Apache TinkerPop3 framework and Gremlin graph query language. It provides a complete tool chain ecology such as export, backup, and recovery, and effectively solve the storage, query and correlation analysis needs of massive graph data. HugeGraph is widely used in the fields of risk control, insurance claims, recommendation search, public security crime crackdown, knowledge graph, network security, IT operation and maintenance of bank securities companies, and is committed to allowing more industries, organizations and users to enjoy a wider range of data comprehensive value.
HugeGraph-Hubble is HugeGraph’s one-stop visual analysis platform. The platform covers the whole process from data modeling, to efficient data import, to real-time and offline analysis of data, and unified management of graphs, realizing the whole process wizard of graph application. It is designed to improve the user’s use fluency, lower the user’s use threshold, and provide a more efficient and easy-to-use user experience.
The platform mainly includes the following modules:
The graph management module realizes the unified management of multiple graphs and graph access, editing, deletion, and query by creating graph and connecting the platform and graph data.
The metadata modeling module realizes the construction and management of graph models by creating attribute libraries, vertex types, edge types, and index types. The platform provides two modes, list mode and graph mode, which can display the metadata model in real time, which is more intuitive. At the same time, it also provides a metadata reuse function across graphs, which saves the tedious and repetitive creation process of the same metadata, greatly improves modeling efficiency and enhances ease of use.
Data import is to convert the user’s business data into the vertices and edges of the graph and insert it into the graph database. The platform provides a wizard-style visual import module. By creating import tasks, the management of import tasks and the parallel operation of multiple import tasks are realized. Improve import performance. After entering the import task, you only need to follow the platform step prompts, upload files as needed, and fill in the content to easily implement the import process of graph data. At the same time, it supports breakpoint resuming, error retry mechanism, etc., which reduces import costs and improves efficiency.
By inputting the graph traversal language Gremlin, high-performance general analysis of graph data can be realized, and functions such as customized multidimensional path query of vertices can be provided, and three kinds of graph result display methods are provided, including: graph form, table form, Json form, and multidimensional display. The data form meets the needs of various scenarios used by users. It provides functions such as running records and collection of common statements, realizing the traceability of graph operations, and the reuse and sharing of query input, which is fast and efficient. It supports the export of graph data, and the export format is Json format.
For Gremlin tasks that need to traverse the whole graph, index creation and reconstruction and other time-consuming asynchronous tasks, the platform provides corresponding task management functions to achieve unified management and result viewing of asynchronous tasks.
The module usage process of the platform is as follows:
Under the graph management module, click [Create graph], and realize the connection of multiple graphs by filling in the graph ID, graph name, host name, port number, username, and password information.
Create graph by filling in the content as follows:：
Realize the information access of the graph space. After entering, you can perform operations such as multidimensional query analysis, metadata management, data import, and algorithm analysis of the graph.
Left navigation:
List mode：
Graph mode：
Select reuse items:
Check reuse items:
List mode:
Graph mode:
Editing operations are available. The vertex style, association type, vertex display content, and attribute index can be edited, and the rest cannot be edited.
You can delete a single item or delete it in batches.
List mode:
Graph mode：
Displays vertex and edge indices for vertex types and edge types.
The usage process of data import is as follows:
Left navigation:
Set up data mapping for uploaded files, including file settings and type settings
File settings: Check or fill in whether to include the header, separator, encoding format and other settings of the file itself, all set the default values, no need to fill in manually
Type setting:
Vertex map and edge map:
【Vertex Type】: Select the vertex type, and upload the column data in the file for its ID mapping;
【Edge Type】: Select the edge type and map the column data of the uploaded file to the ID column of its start point type and end point type;
Mapping settings: upload the column data in the file for the attribute mapping of the selected vertex type. Here, if the attribute name is the same as the header name of the file, the mapping attribute can be automatically matched, and there is no need to manually fill in the selection.
After completing the setting, the setting list will be displayed before proceeding to the next step. It supports the operations of adding, editing and deleting mappings.
Fill in the settings map:
Mapping list：
Before importing, you need to fill in the import setting parameters. After filling in, you can start importing data into the gallery.
Left navigation:
By switching the entrance on the left, flexibly switch the operation space of multiple graphs
HugeGraph supports Gremlin, a graph traversal query language of Apache TinkerPop3. Gremlin is a general graph database query language. By entering Gremlin statements and clicking execute, you can perform query and analysis operations on graph data, and create and delete vertices/edges. , vertex/edge attribute modification, etc.
After Gremlin query, below is the graph result display area, which provides 3 kinds of graph result display modes: [Graph Mode], [Table Mode], [Json Mode].
Support zoom, center, full screen, export and other operations.
【Picture Mode】
【Table mode】
【Json mode】
Click the vertex/edge entity to view the data details of the vertex/edge, including: vertex/edge type, vertex ID, attribute and corresponding value, expand the information display dimension of the graph, and improve the usability.
In addition to the global query, in-depth customized query and hidden operations can be performed for the vertices in the query result to realize customized mining of graph results.
Right-click a vertex, and the menu entry of the vertex appears, which can be displayed, inquired, hidden, etc.
Double-clicking a vertex also displays the vertex associated with the selected point.
In the graph area, two entries can be used to dynamically add vertices, as follows:
Complete the addition of vertices by selecting or filling in the vertex type, ID value, and attribute information.
The entry is as follows:
Add the vertex content as follows:
Right-click a vertex in the graph result to add the outgoing or incoming edge of that point.
Left navigation:
Click to view the entry to jump to the task management list, as follows:
There is no visual OLAP algorithm execution on Hubble. You can call the RESTful API to perform OLAP algorithm tasks, find the corresponding tasks by ID in the task management, and view the progress and results.
HugeGraph is an analysis-oriented graph database system that supports batch operations, which fully supports Apache TinkerPop3 framework and Gremlin graph query language. It provides a complete tool chain ecology such as export, backup, and recovery, and effectively solve the storage, query and correlation analysis needs of massive graph data. HugeGraph is widely used in the fields of risk control, insurance claims, recommendation search, public security crime crackdown, knowledge graph, network security, IT operation and maintenance of bank securities companies, and is committed to allowing more industries, organizations and users to enjoy a wider range of data comprehensive value.
HugeGraph-Hubble is HugeGraph’s one-stop visual analysis platform. The platform covers the whole process from data modeling, to efficient data import, to real-time and offline analysis of data, and unified management of graphs, realizing the whole process wizard of graph application. It is designed to improve the user’s use fluency, lower the user’s use threshold, and provide a more efficient and easy-to-use user experience.
The platform mainly includes the following modules:
The graph management module realizes the unified management of multiple graphs and graph access, editing, deletion, and query by creating graph and connecting the platform and graph data.
The metadata modeling module realizes the construction and management of graph models by creating attribute libraries, vertex types, edge types, and index types. The platform provides two modes, list mode and graph mode, which can display the metadata model in real time, which is more intuitive. At the same time, it also provides a metadata reuse function across graphs, which saves the tedious and repetitive creation process of the same metadata, greatly improves modeling efficiency and enhances ease of use.
Data import is to convert the user’s business data into the vertices and edges of the graph and insert it into the graph database. The platform provides a wizard-style visual import module. By creating import tasks, the management of import tasks and the parallel operation of multiple import tasks are realized. Improve import performance. After entering the import task, you only need to follow the platform step prompts, upload files as needed, and fill in the content to easily implement the import process of graph data. At the same time, it supports breakpoint resuming, error retry mechanism, etc., which reduces import costs and improves efficiency.
By inputting the graph traversal language Gremlin, high-performance general analysis of graph data can be realized, and functions such as customized multidimensional path query of vertices can be provided, and three kinds of graph result display methods are provided, including: graph form, table form, Json form, and multidimensional display. The data form meets the needs of various scenarios used by users. It provides functions such as running records and collection of common statements, realizing the traceability of graph operations, and the reuse and sharing of query input, which is fast and efficient. It supports the export of graph data, and the export format is Json format.
For Gremlin tasks that need to traverse the whole graph, index creation and reconstruction and other time-consuming asynchronous tasks, the platform provides corresponding task management functions to achieve unified management and result viewing of asynchronous tasks.
The module usage process of the platform is as follows:
Under the graph management module, click [Create graph], and realize the connection of multiple graphs by filling in the graph ID, graph name, host name, port number, username, and password information.
Create graph by filling in the content as follows:：
Realize the information access of the graph space. After entering, you can perform operations such as multidimensional query analysis, metadata management, data import, and algorithm analysis of the graph.
Left navigation:
List mode：
Graph mode：
Select reuse items:
Check reuse items:
List mode:
Graph mode:
Editing operations are available. The vertex style, association type, vertex display content, and attribute index can be edited, and the rest cannot be edited.
You can delete a single item or delete it in batches.
List mode:
Graph mode：
Displays vertex and edge indices for vertex types and edge types.
The usage process of data import is as follows:
Left navigation:
Set up data mapping for uploaded files, including file settings and type settings
File settings: Check or fill in whether to include the header, separator, encoding format and other settings of the file itself, all set the default values, no need to fill in manually
Type setting:
Vertex map and edge map:
【Vertex Type】: Select the vertex type, and upload the column data in the file for its ID mapping;
【Edge Type】: Select the edge type and map the column data of the uploaded file to the ID column of its start point type and end point type;
Mapping settings: upload the column data in the file for the attribute mapping of the selected vertex type. Here, if the attribute name is the same as the header name of the file, the mapping attribute can be automatically matched, and there is no need to manually fill in the selection.
After completing the setting, the setting list will be displayed before proceeding to the next step. It supports the operations of adding, editing and deleting mappings.
Fill in the settings map:
Mapping list：
Before importing, you need to fill in the import setting parameters. After filling in, you can start importing data into the gallery.
Left navigation:
By switching the entrance on the left, flexibly switch the operation space of multiple graphs
HugeGraph supports Gremlin, a graph traversal query language of Apache TinkerPop3. Gremlin is a general graph database query language. By entering Gremlin statements and clicking execute, you can perform query and analysis operations on graph data, and create and delete vertices/edges. , vertex/edge attribute modification, etc.
After Gremlin query, below is the graph result display area, which provides 3 kinds of graph result display modes: [Graph Mode], [Table Mode], [Json Mode].
Support zoom, center, full screen, export and other operations.
【Picture Mode】
【Table mode】
【Json mode】
Click the vertex/edge entity to view the data details of the vertex/edge, including: vertex/edge type, vertex ID, attribute and corresponding value, expand the information display dimension of the graph, and improve the usability.
In addition to the global query, in-depth customized query and hidden operations can be performed for the vertices in the query result to realize customized mining of graph results.
Right-click a vertex, and the menu entry of the vertex appears, which can be displayed, inquired, hidden, etc.
Double-clicking a vertex also displays the vertex associated with the selected point.
In the graph area, two entries can be used to dynamically add vertices, as follows:
Complete the addition of vertices by selecting or filling in the vertex type, ID value, and attribute information.
The entry is as follows:
Add the vertex content as follows:
Right-click a vertex in the graph result to add the outgoing or incoming edge of that point.
Left navigation:
Click to view the entry to jump to the task management list, as follows:
There is no visual OLAP algorithm execution on Hubble. You can call the RESTful API to perform OLAP algorithm tasks, find the corresponding tasks by ID in the task management, and view the progress and results.
For detailed API, please refer toRESTful-API
$cd hugegraph-${version}
$bin/stop-hugegraph.sh
-./bin/hugegraph --url http://127.0.0.1:8080 --graph hugegraph migrate --target-url http://127.0.0.1:8090 --target-graph hugegraph
-HugeGraph supports fast import performance in the case of more than 10 billion Vertices and Edges
Graph,millisecond-level OLTP query capability, and large-scale distributed
graph processing (OLAP). The main scenarios of HugeGraph include
correlation search, fraud detection, and knowledge graph.
Not only supports Gremlin graph query language and RESTful API but also provides commonly used graph algorithm APIs. To help users easily implement various queries and analyses, HugeGraph has a full range of accessory tools, such as supporting distributed storage, data replication, scaling horizontally, and supports many built-in backends of storage engines.
Has been deeply optimized in graph storage and graph computation. It provides multiple batch import tools that can easily complete the fast-import of tens of billions of data, achieves millisecond-level response for graph retrieval through ameliorated queries, and supports concurrent online and real-time operations for thousands of users.
Adapts to the Apache Gremlin standard graph query language and the Property Graph standard modeling method, and both support graph-based OLTP and OLAP schemes. Furthermore, HugeGraph can be integrated with Hadoop and Spark’s big data platforms, and easily extend the back-end storage engine through plug-ins.
Follow the official account “HugeGraph” to get the latest news
PS: twitter account it’s on the way
HugeGraph is a convenient, efficient, and adaptable graph database
compatible with the Apache TinkerPop3 framework and the Gremlin query language.
HugeGraph supports fast import performance in the case of more than 10 billion Vertices and Edges
Graph,millisecond-level OLTP query capability, and large-scale distributed
graph processing (OLAP). The main scenarios of HugeGraph include
correlation search, fraud detection, and knowledge graph.
Not only supports Gremlin graph query language and RESTful API but also provides commonly used graph algorithm APIs. To help users easily implement various queries and analyses, HugeGraph has a full range of accessory tools, such as supporting distributed storage, data replication, scaling horizontally, and supports many built-in backends of storage engines.
Has been deeply optimized in graph storage and graph computation. It provides multiple batch import tools that can easily complete the fast-import of tens of billions of data, achieves millisecond-level response for graph retrieval through ameliorated queries, and supports concurrent online and real-time operations for thousands of users.
Adapts to the Apache Gremlin standard graph query language and the Property Graph standard modeling method, and both support graph-based OLTP and OLAP schemes. Furthermore, HugeGraph can be integrated with Hadoop and Spark’s big data platforms, and easily extend the back-end storage engine through plug-ins.
Follow the official account “HugeGraph” to get the latest news
PS: twitter account it’s on the way
Apache EventMesh is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.