diff --git a/docs/_print/index.html b/docs/_print/index.html
index dc3b2cab7..cbc422049 100644
--- a/docs/_print/index.html
+++ b/docs/_print/index.html
@@ -5,7 +5,7 @@
implemented the Apache TinkerPop3 framework and is fully compatible with the Gremlin query language,
With complete toolchain components, it helps users to easily build applications and products based on graph databases. HugeGraph supports fast import of more than 10 billion vertices and edges, and provides millisecond-level relational query capability (OLTP).
It supports large-scale distributed graph computing (OLAP).
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.
Features
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:
Ease of use: HugeGraph supports Gremlin graph query language and RESTful API, provides common interfaces for graph retrieval, and has peripheral tools with complete functions to easily implement various graph-based query and analysis operations.
Efficiency: HugeGraph has been deeply optimized in graph storage and graph computing, and provides a variety of batch import tools, which can easily complete the rapid import of tens of billions of data, and achieve millisecond-level response for graph retrieval through optimized queries. Supports simultaneous online real-time operations of thousands of users.
Universal: HugeGraph supports the Apache Gremlin standard graph query language and the Property Graph standard graph modeling method, and supports graph-based OLTP and OLAP schemes. Integrate Apache Hadoop and Apache Spark big data platform.
Scalable: supports distributed storage, multiple copies of data and horizontal expansion, built-in multiple back-end storage engines, and can easily expand the back-end storage engine through plug-ins.
Open: HugeGraph code is open source (Apache 2 License), customers can modify and customize independently, and selectively give back to the open source community.
The functions of this system include but are not limited to:
Supports batch import of data from multiple data sources (including local files, HDFS files, MySQL databases and other data sources), and supports import of multiple file formats (including TXT, CSV, JSON and other formats)
With a visual operation interface, it can be used for operation, analysis and display diagrams, reducing the threshold for users to use
Optimized graph interface: shortest path (Shortest Path), K-step connected subgraph (K-neighbor), K-step to reach the adjacent point (K-out), personalized recommendation algorithm PersonalRank, etc.
Implemented based on Apache TinkerPop3 framework, supports Gremlin graph query language
Support attribute graph, attributes can be added to vertices and edges, and support rich attribute types
Has independent schema metadata information, has powerful graph modeling capabilities, and facilitates third-party system integration
Support multi-vertex ID strategy: support primary key ID, support automatic ID generation, support user-defined string ID, support user-defined digital ID
The attributes of edges and vertices can be indexed to support precise query, range query, and full-text search
The storage system adopts plug-in mode, supporting RocksDB, Cassandra, ScyllaDB, HBase, MySQL, PostgreSQL, Palo, and InMemory, etc.
Integrate with big data systems such as Hadoop and Spark GraphX, and support Bulk Load operations
Support high availability HA, multiple copies of data, backup recovery, monitoring, etc.
Modules
HugeGraph-Server: HugeGraph-Server is the core part of the HugeGraph project, including submodules such as Core, Backend, and API;
Core: Graph engine implementation, connecting the Backend module downward and supporting the API module upward;
Backend: Realize the storage of graph data to the backend. The supported backends include: Memory, Cassandra, ScyllaDB, RocksDB, HBase, MySQL and PostgreSQL. Users can choose one according to the actual situation;
API: Built-in REST Server, provides RESTful API to users, and is fully compatible with Gremlin query.
HugeGraph-Client: HugeGraph-Client provides a RESTful API client for connecting to HugeGraph-Server. Currently, only Java version is implemented. Users of other languages can implement it by themselves;
HugeGraph-Loader: HugeGraph-Loader is a data import tool based on HugeGraph-Client, which converts ordinary text data into graph vertices and edges and inserts them into graph database;
HugeGraph-Computer: HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of Pregel. It runs on Kubernetes framework;
HugeGraph-Hubble: HugeGraph-Hubble is HugeGraph’s web visualization management platform, a one-stop visual analysis platform. The platform covers the whole process from data modeling, to rapid data import, to online and offline analysis of data, and unified management of graphs;
HugeGraph-Tools: HugeGraph-Tools is HugeGraph’s deployment and management tools, including functions such as managing graphs, backup/restore, Gremlin execution, etc.
Contact Us
GitHub Issues: Feedback on usage issues and functional requirements (priority)
Note: The latest graph analysis and display platform is Hubble, which supports server v0.10 +.
3 - Quick Start
3.1 - HugeGraph-Server Quick Start
1 HugeGraph-Server Overview
HugeGraph-Server is the core part of the HugeGraph Project, contains submodules such as Core、Backend、API.
The Core Module is an implementation of the Tinkerpop interface; The Backend module is used to save the graph data to the data store, currently supported backends include:Memory、Cassandra、ScyllaDB、RocksDB; The API Module provides HTTP Server, which converts Client’s HTTP request into a call to Core Module.
There will be two spellings HugeGraph-Server and HugeGraphServer in the document, and other modules are similar. There is no big difference in the meaning of these two ways of writing, which can be distinguished as follows: HugeGraph-Server represents the code of server-related components, HugeGraphServer represents the service process.
2 Dependency
2.1 Install Java11 (JDK 11)
Consider use Java 11 to run HugeGraph-Server(also compatible with Java 8), and configure by yourself.
Be sure to execute the java -version command to check the jdk version before reading
java -version
+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:
Ease of use: HugeGraph supports Gremlin graph query language and RESTful API, provides common interfaces for graph retrieval, and has peripheral tools with complete functions to easily implement various graph-based query and analysis operations.
Efficiency: HugeGraph has been deeply optimized in graph storage and graph computing, and provides a variety of batch import tools, which can easily complete the rapid import of tens of billions of data, and achieve millisecond-level response for graph retrieval through optimized queries. Supports simultaneous online real-time operations of thousands of users.
Universal: HugeGraph supports the Apache Gremlin standard graph query language and the Property Graph standard graph modeling method, and supports graph-based OLTP and OLAP schemes. Integrate Apache Hadoop and Apache Spark big data platform.
Scalable: supports distributed storage, multiple copies of data and horizontal expansion, built-in multiple back-end storage engines, and can easily expand the back-end storage engine through plug-ins.
Open: HugeGraph code is open source (Apache 2 License), customers can modify and customize independently, and selectively give back to the open source community.
The functions of this system include but are not limited to:
Supports batch import of data from multiple data sources (including local files, HDFS files, MySQL databases and other data sources), and supports import of multiple file formats (including TXT, CSV, JSON and other formats)
With a visual operation interface, it can be used for operation, analysis and display diagrams, reducing the threshold for users to use
Optimized graph interface: shortest path (Shortest Path), K-step connected subgraph (K-neighbor), K-step to reach the adjacent point (K-out), personalized recommendation algorithm PersonalRank, etc.
Implemented based on Apache TinkerPop3 framework, supports Gremlin graph query language
Support attribute graph, attributes can be added to vertices and edges, and support rich attribute types
Has independent schema metadata information, has powerful graph modeling capabilities, and facilitates third-party system integration
Support multi-vertex ID strategy: support primary key ID, support automatic ID generation, support user-defined string ID, support user-defined digital ID
The attributes of edges and vertices can be indexed to support precise query, range query, and full-text search
The storage system adopts plug-in mode, supporting RocksDB, Cassandra, ScyllaDB, HBase, MySQL, PostgreSQL, Palo, and InMemory, etc.
Integrate with big data systems such as Hadoop and Spark GraphX, and support Bulk Load operations
Support high availability HA, multiple copies of data, backup recovery, monitoring, etc.
Modules
HugeGraph-Server: HugeGraph-Server is the core part of the HugeGraph project, including submodules such as Core, Backend, and API;
Core: Graph engine implementation, connecting the Backend module downward and supporting the API module upward;
Backend: Realize the storage of graph data to the backend. The supported backends include: Memory, Cassandra, ScyllaDB, RocksDB, HBase, MySQL and PostgreSQL. Users can choose one according to the actual situation;
API: Built-in REST Server, provides RESTful API to users, and is fully compatible with Gremlin query.
HugeGraph-Client: HugeGraph-Client provides a RESTful API client for connecting to HugeGraph-Server. Currently, only Java version is implemented. Users of other languages can implement it by themselves;
HugeGraph-Loader: HugeGraph-Loader is a data import tool based on HugeGraph-Client, which converts ordinary text data into graph vertices and edges and inserts them into graph database;
HugeGraph-Computer: HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of Pregel. It runs on Kubernetes framework;
HugeGraph-Hubble: HugeGraph-Hubble is HugeGraph’s web visualization management platform, a one-stop visual analysis platform. The platform covers the whole process from data modeling, to rapid data import, to online and offline analysis of data, and unified management of graphs;
HugeGraph-Tools: HugeGraph-Tools is HugeGraph’s deployment and management tools, including functions such as managing graphs, backup/restore, Gremlin execution, etc.
Contact Us
GitHub Issues: Feedback on usage issues and functional requirements (priority)
Note: The latest graph analysis and display platform is Hubble, which supports server v0.10 +.
3 - Quick Start
3.1 - HugeGraph-Server Quick Start
1 HugeGraph-Server Overview
HugeGraph-Server is the core part of the HugeGraph Project, contains submodules such as Core、Backend、API.
The Core Module is an implementation of the Tinkerpop interface; The Backend module is used to save the graph data to the data store, currently supported backends include:Memory、Cassandra、ScyllaDB、RocksDB; The API Module provides HTTP Server, which converts Client’s HTTP request into a call to Core Module.
There will be two spellings HugeGraph-Server and HugeGraphServer in the document, and other modules are similar. There is no big difference in the meaning of these two ways of writing, which can be distinguished as follows: HugeGraph-Server represents the code of server-related components, HugeGraphServer represents the service process.
2 Dependency
2.1 Install Java11 (JDK 11)
Consider use Java 11 to run HugeGraph-Server(also compatible with Java 8), and configure by yourself.
Be sure to execute the java -version command to check the jdk version before reading
java -version
2.2 Install GCC-4.3.0(GLIBCXX_3.4.10) or update version (optional)
If you are using the RocksDB backend, be sure to execute the gcc --version command to check the gcc version; if you are using other backends, this is not required.
gcc --version
3 Deploy
There are three ways to deploy HugeGraph-Server components:
Method 1: One-click deployment
Method 2: Download the tarball
Method 3: Source code compilation
3.1 One-click deployment
HugeGraph-Tools provides a command-line tool for one-click deployment, users can use this tool to quickly download、decompress、configure and start HugeGraphServer and HugeGraphStudio with one click.
of course, you still have to download the tarball of HugeGraph-Tools first.
wget https://github.com/hugegraph/hugegraph-tools/releases/download/v${version}/hugegraph-tools-${version}.tar.gz
diff --git a/docs/download/download/index.html b/docs/download/download/index.html
index 1edde1c5a..425a30955 100644
--- a/docs/download/download/index.html
+++ b/docs/download/download/index.html
@@ -9,17 +9,17 @@
components
description …">
+components description download HugeGraph-Server The main program of HugeGraph 1.0.0 HugeGraph-Toolchain A collection of tools for graph data import/export/backup, web visualization, etc. 1.0.0 Binary Versions mapping Version Release Date server toolchain computer Release Notes 1.0.0 2022-12-31 [Binary] [Sign] [SHA512] [Binary] [Sign] [SHA512] [Binary] [Sign] [SHA512] Release-Notes Source Versions mapping Version Release Date server toolchain computer common Release Notes 1.">
Download HugeGraph
TODO: update 1.0.0 download link with the CN doc
Latest version
The latest HugeGraph: 1.0.0, released on 2022-12-31.
diff --git a/docs/index.xml b/docs/index.xml
index d56eecfcf..c18ca8794 100644
--- a/docs/index.xml
+++ b/docs/index.xml
@@ -3523,22 +3523,12 @@ HugeGraph目前采用EdgeCut的分区方案。</p>
<tbody>
<tr>
<td>HugeGraph-Server</td>
-<td>HugeGraph 的主程序</td>
+<td>The main program of HugeGraph</td>
<td><a href="https://dist.apache.org/repos/dist/dev/incubator/hugegraph/1.0.0/apache-hugegraph-incubating-1.0.0.tar.gz">1.0.0</a></td>
</tr>
<tr>
-<td>HugeGraph-Hubble</td>
-<td>基于 Web 的可视化图形界面</td>
-<td>1.0.0</td>
-</tr>
-<tr>
-<td>HugeGraph-Loader</td>
-<td>数据导入工具</td>
-<td>1.0.0</td>
-</tr>
-<tr>
-<td>HugeGraph-Tools</td>
-<td>命令行工具集</td>
+<td>HugeGraph-Toolchain</td>
+<td>A collection of tools for graph data import/export/backup, web visualization, etc.</td>
<td><a href="https://dist.apache.org/repos/dist/dev/incubator/hugegraph/1.0.0/apache-hugegraph-toolchain-incubating-1.0.0.tar.gz">1.0.0</a></td>
</tr>
</tbody>
diff --git a/en/sitemap.xml b/en/sitemap.xml
index b98aa094f..9e2ea5306 100644
--- a/en/sitemap.xml
+++ b/en/sitemap.xml
@@ -1 +1 @@
-/docs/guides/architectural/2022-11-27T21:05:55+08:00/docs/config/config-guide/2022-04-17T11:36:55+08:00/docs/language/hugegraph-gremlin/2023-01-01T16:16:43+08:00/docs/contribution-guidelines/contribute/2022-09-15T12:59:59+08:00/docs/performance/hugegraph-benchmark-0.5.6/2022-09-15T12:59:59+08:00/docs/quickstart/hugegraph-server/2023-01-01T16:16:43+08:00/docs/introduction/readme/2022-11-27T21:44:37+08:00/docs/changelog/hugegraph-1.0.0-release-notes/2023-01-01T16:16:43+08:00/docs/clients/restful-api/2022-04-17T11:36:55+08:00/docs/clients/restful-api/schema/2022-04-17T11:36:55+08:00/docs/performance/api-preformance/hugegraph-api-0.5.6-rocksdb/2022-04-17T11:36:55+08:00/docs/config/config-option/2023-01-01T16:16:43+08:00/docs/guides/desgin-concept/2022-04-17T11:36:55+08:00/docs/download/download/2023-01-01T16:16:43+08:00/docs/language/hugegraph-example/2023-01-01T16:16:43+08:00/docs/clients/hugegraph-client/2023-01-01T16:16:43+08:00/docs/performance/api-preformance/2022-04-17T11:36:55+08:00/docs/quickstart/hugegraph-loader/2023-01-01T16:16:43+08:00/docs/clients/restful-api/propertykey/2022-05-12T21:24:05+08:00/docs/changelog/hugegraph-0.12.0-release-notes/2023-01-01T16:16:43+08:00/docs/contribution-guidelines/subscribe/2022-09-15T12:59:59+08:00/docs/performance/api-preformance/hugegraph-api-0.5.6-cassandra/2022-04-17T11:36:55+08:00/docs/config/config-authentication/2022-04-17T11:36:55+08:00/docs/clients/gremlin-console/2022-05-25T21:16:41+08:00/docs/guides/custom-plugin/2022-09-15T12:59:59+08:00/docs/performance/hugegraph-loader-performance/2022-04-17T11:36:55+08:00/docs/quickstart/hugegraph-tools/2022-09-15T12:59:59+08:00/docs/quickstart/2022-04-17T11:36:55+08:00/docs/performance/api-preformance/hugegraph-api-0.4.4/2022-04-17T11:36:55+08:00/docs/contribution-guidelines/validate-release/2023-01-01T16:16:43+08:00/docs/clients/restful-api/vertexlabel/2022-04-17T11:36:55+08:00/docs/guides/backup-restore/2022-04-17T11:36:55+08:00/docs/config/2022-04-17T11:36:55+08:00/docs/config/config-https/2022-04-17T11:36:55+08:00/docs/clients/restful-api/edgelabel/2022-04-17T11:36:55+08:00/docs/performance/api-preformance/hugegraph-api-0.2/2022-04-17T11:36:55+08:00/docs/quickstart/hugegraph-hubble/2022-09-15T12:59:59+08:00/docs/clients/2022-04-17T11:36:55+08:00/docs/config/config-computer/2023-01-01T16:16:43+08:00/docs/guides/faq/2023-01-01T16:16:43+08:00/docs/clients/restful-api/indexlabel/2022-04-17T11:36:55+08:00/docs/quickstart/hugegraph-client/2023-01-01T16:16:43+08:00/docs/guides/2022-04-17T11:36:55+08:00/docs/clients/restful-api/rebuild/2022-05-09T18:43:53+08:00/docs/quickstart/hugegraph-computer/2023-01-01T16:16:43+08:00/docs/language/2022-04-17T11:36:55+08:00/docs/clients/restful-api/vertex/2022-09-15T15:16:23+08:00/docs/clients/restful-api/edge/2022-09-15T15:16:23+08:00/docs/performance/2022-04-17T11:36:55+08:00/docs/contribution-guidelines/2022-12-30T19:36:31+08:00/docs/clients/restful-api/traverser/2022-04-17T11:36:55+08:00/docs/changelog/2022-04-28T21:26:41+08:00/docs/clients/restful-api/rank/2022-09-15T12:59:59+08:00/docs/clients/restful-api/variable/2022-04-17T11:36:55+08:00/docs/clients/restful-api/graphs/2022-05-27T09:27:37+08:00/docs/clients/restful-api/task/2022-09-15T12:59:59+08:00/docs/clients/restful-api/gremlin/2022-04-17T11:36:55+08:00/docs/clients/restful-api/auth/2022-04-17T11:36:55+08:00/docs/clients/restful-api/other/2022-04-17T11:36:55+08:00/docs/2022-12-30T19:57:48+08:00/blog/news/2022-03-21T18:55:33+08:00/blog/releases/2022-03-21T18:55:33+08:00/blog/2018/10/06/easy-documentation-with-docsy/2022-03-21T18:55:33+08:00/blog/2018/10/06/the-second-blog-post/2022-03-21T18:55:33+08:00/blog/2018/01/04/another-great-release/2022-03-21T18:55:33+08:00/docs/cla/2022-03-21T19:51:14+08:00/docs/performance/hugegraph-benchmark-0.4.4/2022-09-15T12:59:59+08:00/docs/summary/2022-11-27T21:05:55+08:00/blog/2022-03-21T18:55:33+08:00/categories//community/2022-03-21T18:55:33+08:00/2022-12-28T13:42:04+08:00/search/2022-03-21T18:55:33+08:00/tags/
\ No newline at end of file
+/docs/guides/architectural/2022-11-27T21:05:55+08:00/docs/config/config-guide/2022-04-17T11:36:55+08:00/docs/language/hugegraph-gremlin/2023-01-01T16:16:43+08:00/docs/contribution-guidelines/contribute/2022-09-15T12:59:59+08:00/docs/performance/hugegraph-benchmark-0.5.6/2022-09-15T12:59:59+08:00/docs/quickstart/hugegraph-server/2023-01-01T16:16:43+08:00/docs/introduction/readme/2022-11-27T21:44:37+08:00/docs/changelog/hugegraph-1.0.0-release-notes/2023-01-01T16:16:43+08:00/docs/clients/restful-api/2022-04-17T11:36:55+08:00/docs/clients/restful-api/schema/2022-04-17T11:36:55+08:00/docs/performance/api-preformance/hugegraph-api-0.5.6-rocksdb/2022-04-17T11:36:55+08:00/docs/config/config-option/2023-01-01T16:16:43+08:00/docs/guides/desgin-concept/2022-04-17T11:36:55+08:00/docs/download/download/2023-01-01T22:24:37+08:00/docs/language/hugegraph-example/2023-01-01T16:16:43+08:00/docs/clients/hugegraph-client/2023-01-01T16:16:43+08:00/docs/performance/api-preformance/2022-04-17T11:36:55+08:00/docs/quickstart/hugegraph-loader/2023-01-01T16:16:43+08:00/docs/clients/restful-api/propertykey/2022-05-12T21:24:05+08:00/docs/changelog/hugegraph-0.12.0-release-notes/2023-01-01T16:16:43+08:00/docs/contribution-guidelines/subscribe/2022-09-15T12:59:59+08:00/docs/performance/api-preformance/hugegraph-api-0.5.6-cassandra/2022-04-17T11:36:55+08:00/docs/config/config-authentication/2022-04-17T11:36:55+08:00/docs/clients/gremlin-console/2022-05-25T21:16:41+08:00/docs/guides/custom-plugin/2022-09-15T12:59:59+08:00/docs/performance/hugegraph-loader-performance/2022-04-17T11:36:55+08:00/docs/quickstart/hugegraph-tools/2022-09-15T12:59:59+08:00/docs/quickstart/2022-04-17T11:36:55+08:00/docs/performance/api-preformance/hugegraph-api-0.4.4/2022-04-17T11:36:55+08:00/docs/contribution-guidelines/validate-release/2023-01-01T16:16:43+08:00/docs/clients/restful-api/vertexlabel/2022-04-17T11:36:55+08:00/docs/guides/backup-restore/2022-04-17T11:36:55+08:00/docs/config/2022-04-17T11:36:55+08:00/docs/config/config-https/2022-04-17T11:36:55+08:00/docs/clients/restful-api/edgelabel/2022-04-17T11:36:55+08:00/docs/performance/api-preformance/hugegraph-api-0.2/2022-04-17T11:36:55+08:00/docs/quickstart/hugegraph-hubble/2022-09-15T12:59:59+08:00/docs/clients/2022-04-17T11:36:55+08:00/docs/config/config-computer/2023-01-01T16:16:43+08:00/docs/guides/faq/2023-01-01T16:16:43+08:00/docs/clients/restful-api/indexlabel/2022-04-17T11:36:55+08:00/docs/quickstart/hugegraph-client/2023-01-01T16:16:43+08:00/docs/guides/2022-04-17T11:36:55+08:00/docs/clients/restful-api/rebuild/2022-05-09T18:43:53+08:00/docs/quickstart/hugegraph-computer/2023-01-01T16:16:43+08:00/docs/language/2022-04-17T11:36:55+08:00/docs/clients/restful-api/vertex/2022-09-15T15:16:23+08:00/docs/clients/restful-api/edge/2022-09-15T15:16:23+08:00/docs/performance/2022-04-17T11:36:55+08:00/docs/contribution-guidelines/2022-12-30T19:36:31+08:00/docs/clients/restful-api/traverser/2022-04-17T11:36:55+08:00/docs/changelog/2022-04-28T21:26:41+08:00/docs/clients/restful-api/rank/2022-09-15T12:59:59+08:00/docs/clients/restful-api/variable/2022-04-17T11:36:55+08:00/docs/clients/restful-api/graphs/2022-05-27T09:27:37+08:00/docs/clients/restful-api/task/2022-09-15T12:59:59+08:00/docs/clients/restful-api/gremlin/2022-04-17T11:36:55+08:00/docs/clients/restful-api/auth/2022-04-17T11:36:55+08:00/docs/clients/restful-api/other/2022-04-17T11:36:55+08:00/docs/2022-12-30T19:57:48+08:00/blog/news/2022-03-21T18:55:33+08:00/blog/releases/2022-03-21T18:55:33+08:00/blog/2018/10/06/easy-documentation-with-docsy/2022-03-21T18:55:33+08:00/blog/2018/10/06/the-second-blog-post/2022-03-21T18:55:33+08:00/blog/2018/01/04/another-great-release/2022-03-21T18:55:33+08:00/docs/cla/2022-03-21T19:51:14+08:00/docs/performance/hugegraph-benchmark-0.4.4/2022-09-15T12:59:59+08:00/docs/summary/2022-11-27T21:05:55+08:00/blog/2022-03-21T18:55:33+08:00/categories//community/2022-03-21T18:55:33+08:00/2022-12-28T13:42:04+08:00/search/2022-03-21T18:55:33+08:00/tags/
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index d5c66d1d6..e958e3042 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -1 +1 @@
-/en/sitemap.xml2023-01-01T16:16:43+08:00/cn/sitemap.xml2023-01-01T16:16:43+08:00
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+/en/sitemap.xml2023-01-01T22:24:37+08:00/cn/sitemap.xml2023-01-01T16:16:43+08:00
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