From bcca1855a0122ecc01c09d700a1ddcda91e81650 Mon Sep 17 00:00:00 2001 From: ChrisChinchilla Date: Thu, 17 Sep 2020 12:50:37 +0200 Subject: [PATCH] Squashed commit of the following: MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit commit 0ee043f5d7b4c65df87e01caa013c30414a7ef1a Author: ChrisChinchilla Date: Wed Sep 16 18:11:50 2020 +0200 Add Netlify TOML file Signed-off-by: ChrisChinchilla commit a91c5eca33596dd94fe2c775eb8a5a22322d55a4 Author: ChrisChinchilla Date: Wed Sep 16 18:00:16 2020 +0200 Final Signed-off-by: ChrisChinchilla commit 356abb89bcbe816beaa4147606d550ab15c8fb8d Merge: febe502cf 47bd03b30 Author: ChrisChinchilla Date: Wed Sep 16 17:55:50 2020 +0200 Merge branch 'master' into braskin/hugo_site Signed-off-by: ChrisChinchilla # Conflicts: # docs/content/coordinator/_index.md # docs/content/coordinator/api/remote.md # docs/content/how_to/query.md # docs/content/m3query/api/query.md # docs/content/m3query/architecture/_index.md # docs/content/m3query/architecture/blocks.md # docs/content/m3query/architecture/fanout.md # docs/content/m3query/config/annotated_config.md # docs/content/m3query/config/annotated_config.yaml # docs/content/m3query/roadmap.md # docs/content/operational_guide/bootstrapping_crash_recovery.md # docs/content/operational_guide/monitoring.md # docs/content/operational_guide/repairs.md # docs/content/operational_guide/upgrading_m3.md # docs/content/overview/components.md # docs/coordinator/api/remote.md # docs/includes/headers_optional_read_all.md # docs/includes/headers_optional_read_limits.md # docs/includes/headers_optional_write_all.md # docs/index.md # docs/m3coordinator/api/remote.md # docs/m3db/architecture/storage.md # docs/m3query/api/index.md # docs/m3query/architecture/blocks.md # docs/m3query/architecture/fanout.md # docs/m3query/architecture/functions.md # docs/m3query/config/annotated_config.md # docs/m3query/config/annotated_config.yaml # docs/m3query/roadmap.md # docs/operational_guide/index.md # docs/operational_guide/mapping_rollup.md # docs/overview/media.md # docs/query_engine/api/index.md # docs/query_engine/architecture/blocks.md # docs/query_engine/architecture/fanout.md # docs/query_engine/config/annotated_config.md # docs/query_engine/config/annotated_config.yaml # docs/query_engine/roadmap.md # docs/static/aggregator.md commit febe502cf45a97de5d44babbff7a46e03dce765e Author: ChrisChinchilla Date: Wed Sep 16 15:34:17 2020 +0200 Fix paths Signed-off-by: ChrisChinchilla commit 8c300d5b1e53ca8326d07e7e514794c75bd33f6d Author: ChrisChinchilla Date: Wed Sep 16 14:55:57 2020 +0200 Link fixes Signed-off-by: ChrisChinchilla commit a4ca3db960bd51c7b593db954cfdfbb2b7b58c5a Merge: 3325ed863 45ad2f6d6 Author: ChrisChinchilla Date: Wed Sep 16 14:17:18 2020 +0200 Merge commit '45ad2f6d6f1c23e387b8232b678e8c0d1093a5ca' into braskin/hugo_site commit 45ad2f6d6f1c23e387b8232b678e8c0d1093a5ca Author: ChrisChinchilla Date: Wed Sep 16 14:17:18 2020 +0200 Squashed 'docs/themes/docs-theme/' changes from cc1e7c4b7..5103e6cc3 5103e6cc3 Remove centering git-subtree-dir: docs/themes/docs-theme git-subtree-split: 5103e6cc3427e97c469c2914246d4b46f7e27bd9 commit 3325ed863fb04e702a261e95293cec2ecca830e3 Author: ChrisChinchilla Date: Wed Sep 16 14:17:06 2020 +0200 Fix links Signed-off-by: ChrisChinchilla commit 47bd03b30de311113e6f70991f3e8aff4b57721d Author: Matt Schallert Date: Tue Sep 15 18:47:31 2020 -0700 [docs] Add recent talks (#2639) commit 705f4f0d0fb9289787337a328e622091601b3f9c Author: Ryan Allen Date: Tue Sep 15 20:54:45 2020 -0400 [dbnode] query limits - missed commit with feedback (#2640) commit b71785ea6dc91519be10834dc98dbf1e5b33a9f0 Author: Ryan Allen Date: Tue Sep 15 15:19:25 2020 -0400 [dbnode] additional query limit on bytes read (#2627) commit 3aa2f51f7db827e35c7a0f35eab9e739a6aed8e5 Author: Linas Medžiūnas Date: Tue Sep 15 21:13:41 2020 +0300 [large-tiles] Cross block reader changes plus Cross block iterator (#2612) * [dbnode] Add OrderedByIndex option for DataFileSetReader.Open (#2465) * [dbnode] Cross-block series reader (#2481) * [dbnode] AggregateTiles RPC - minimal E2E flow (#2466) * [large-tiles] Cross block reader changes plus Cross block iterator * Fix TestReadAggregateWrite * Fix TestShardAggregateTiles * Dummy * Fix TestReadAggregateWrite * typo * Convert spaces to tabs in rpc.thrift * Change capitalization of source/target namespace * Fix copyright year in large_tiles_test.go * Remove a noop * PR feedback * PR 2617 feedback * PR feedback * More PR feedback * Fix unit test * Reuse read objects during aggregation * Renamed StreamingMode to StreamingEnabled * PR feedback * Indentation * Address PR feedback * Skip flaky TestReadAggregateWrite Co-authored-by: arnikola commit 8bcf5ac7cfba809fe71c501c85da1360888a877d Author: Rob Skillington Date: Tue Sep 15 12:35:09 2020 -0400 [changelog] Update changelog for 0.15.14 release (#2638) commit 4b0fcb301a028aa8cecd46c100d8cde119737ccc Author: ChrisChinchilla Date: Tue Sep 15 16:40:53 2020 +0200 Squashed 'docs/themes/docs-theme/' content from commit cc1e7c4b7 git-subtree-dir: docs/themes/docs-theme git-subtree-split: cc1e7c4b7be99f7dd8ceaacf11e6094f297c5989 commit 0d8b1fa8028e7bc3735c3542a489780dfc3b5396 Merge: 99b541e8d 4b0fcb301 Author: ChrisChinchilla Date: Tue Sep 15 16:40:53 2020 +0200 Merge commit '4b0fcb301a028aa8cecd46c100d8cde119737ccc' as 'docs/themes/docs-theme' commit 99b541e8d4b931a7220419ff6d084ce8c8b41abc Author: ChrisChinchilla Date: Tue Sep 15 16:40:42 2020 +0200 Finalise glossary section Signed-off-by: ChrisChinchilla commit 90e38bcae988b686710ef4c97527d4cfa71e1810 Merge: 6d887232a 2b5490b21 Author: ChrisChinchilla Date: Tue Sep 15 13:44:15 2020 +0200 Merge commit '2b5490b211202f7959328b4f7e9b387ef5e4b6ab' into braskin/hugo_site commit 2b5490b211202f7959328b4f7e9b387ef5e4b6ab Author: ChrisChinchilla Date: Tue Sep 15 13:44:15 2020 +0200 Squashed 'docs/themes/docs-theme/' changes from 06ca4188b..dcbf4649b dcbf4649b Change glossary path REVERT: 06ca4188b Change glossary path git-subtree-dir: docs/themes/docs-theme git-subtree-split: dcbf4649bbaa43fc21c12cbdb936999d4b6719d8 commit 6d887232aaa0d87d6393d34a3df801b78fa7b0ff Merge: 957fb39ee ce857e005 Author: ChrisChinchilla Date: Tue Sep 15 12:28:23 2020 +0200 Merge commit 'ce857e005246025a90ec4ddd933cee7cfab4488a' into braskin/hugo_site commit ce857e005246025a90ec4ddd933cee7cfab4488a Author: ChrisChinchilla Date: Tue Sep 15 12:28:23 2020 +0200 Squashed 'docs/themes/docs-theme/' changes from 7ac79e398..06ca4188b 06ca4188b Change glossary path REVERT: 7ac79e398 Change glossary path git-subtree-dir: docs/themes/docs-theme git-subtree-split: 06ca4188b1b040ce0f6334c50aeaecf8352c789f commit 957fb39ee8ca82897468a8e8c28ebe19f7e537f0 Merge: 571aed1f5 0ee3f033d Author: ChrisChinchilla Date: Tue Sep 15 12:23:18 2020 +0200 Merge commit '0ee3f033d96a0af227c42edb71274a5e081a6e04' into braskin/hugo_site commit 0ee3f033d96a0af227c42edb71274a5e081a6e04 Author: ChrisChinchilla Date: Tue Sep 15 12:23:18 2020 +0200 Squashed 'docs/themes/docs-theme/' changes from d84a9b2e3..7ac79e398 7ac79e398 Change glossary path REVERT: d84a9b2e3 Change glossary path git-subtree-dir: docs/themes/docs-theme git-subtree-split: 7ac79e398305a2dbdbd88b3cd36e3b4c31e7483a commit 571aed1f54d3864ac58a95c02ed150696e119181 Merge: 8ed003229 fa9fe8b2f Author: ChrisChinchilla Date: Tue Sep 15 12:13:12 2020 +0200 Merge commit 'fa9fe8b2f4055b89cd23bd9f0a8b50fb70918fe3' into braskin/hugo_site commit fa9fe8b2f4055b89cd23bd9f0a8b50fb70918fe3 Author: ChrisChinchilla Date: Tue Sep 15 12:13:12 2020 +0200 Squashed 'docs/themes/docs-theme/' changes from 4dba9dd2e..d84a9b2e3 d84a9b2e3 Change glossary path git-subtree-dir: docs/themes/docs-theme git-subtree-split: d84a9b2e3d94bc46382e79bb7b14775f5f9d29ff commit 8ed0032291651f7943f1991a13a887b5409c48a2 Author: ChrisChinchilla Date: Tue Sep 15 12:13:03 2020 +0200 Remove defunct files Signed-off-by: ChrisChinchilla commit 2ae97921754de9a06f5d55652d8f372a732d9dbe Author: Rob Skillington Date: Tue Sep 15 00:39:21 2020 -0400 [dbnode] Add ability to configure writes to initializing shards (#2615) commit f83f5768f86647fca2c94e079334bd06035b57b1 Author: Rob Skillington Date: Mon Sep 14 22:19:15 2020 -0400 [changelog] Update out of date changelog for 0.15.13 (#2636) commit 876cee834d65e80dcbbf05dbb63edd40602ee190 Author: Linas Medžiūnas Date: Mon Sep 14 22:11:29 2020 +0300 [large-tiles] Large tiles aggregation flow (#2617) commit d38ff54aa5708474de0aec7a93f25a5f21542112 Author: Matt Schallert Date: Mon Sep 14 07:22:09 2020 -0700 [m3admin] Allow /set for new placements (#2625) Previously `/set` required that a placement already exists in order to set it. This hinders operations such as preemptively creating a placement or replacing one that had been deleted via safe APIs. This PR allows using `/set` even if a placement didn't previously exist at that key. commit d5fff76fac215da9f96e751e916bb82cca6dc785 Author: ChrisChinchilla Date: Mon Sep 14 15:33:05 2020 +0200 Squashed 'docs/themes/docs-theme/' content from commit 4dba9dd2e git-subtree-dir: docs/themes/docs-theme git-subtree-split: 4dba9dd2e0320cf0b9df05be01808a567781d5e3 commit 74ae18a546b202596f20a07f2b08eb4f1185b60f Merge: d707793aa d5fff76fa Author: ChrisChinchilla Date: Mon Sep 14 15:33:05 2020 +0200 Merge commit 'd5fff76fac215da9f96e751e916bb82cca6dc785' as 'docs/themes/docs-theme' commit d707793aab7574c4d570d8718534e86a5cd24fb8 Author: ChrisChinchilla Date: Mon Sep 14 15:32:55 2020 +0200 Move Signed-off-by: ChrisChinchilla commit 93f3cd1b2d690edb4f88b30ef88e5347e66b9871 Merge: 42dbf1931 8b276ed17 Author: ChrisChinchilla Date: Mon Sep 14 12:57:19 2020 +0200 Merge commit '8b276ed170a886058c276535a127567a38210426' as 'docs/themes/docs-theme' commit 8b276ed170a886058c276535a127567a38210426 Author: ChrisChinchilla Date: Mon Sep 14 12:57:19 2020 +0200 Squashed 'docs/themes/docs-theme/' content from commit 4bb3340df git-subtree-dir: docs/themes/docs-theme git-subtree-split: 4bb3340df8857eb8cd07b70515b546287617ca60 commit 42dbf1931f80cc3edc0961eb3dfb6e5afdbfe441 Author: ChrisChinchilla Date: Mon Sep 14 12:57:15 2020 +0200 Continue commit 07c034dfd6014ef9339d07403bc006963ecb9ed0 Author: teddywahle <69990143+teddywahle@users.noreply.github.com> Date: Mon Sep 14 01:03:57 2020 -0700 [query] Implemented movingSum, movingMax, movingMin (graphite functions) (#2570) commit e66e016d0249910df14f543164495e15a4cab0b5 Author: teddywahle <69990143+teddywahle@users.noreply.github.com> Date: Mon Sep 14 00:48:27 2020 -0700 [query] Add Graphite aliasByTags function (#2626) commit d3cd56679aa925dd7f3358abec2d46cae3215db8 Author: teddywahle <69990143+teddywahle@users.noreply.github.com> Date: Mon Sep 14 00:46:44 2020 -0700 [query] Implemented the Graphite `exponentialMovingAverage` function (#2622) commit 47eaa98b4e22ecc978ffc8210af2e1c1c1f3c26a Author: arnikola Date: Thu Sep 10 18:18:01 2020 -0400 [query] Expose Graphite Compile method. (#2624) commit 82236667b206c156794fb4a71ec5905079b20236 Author: nate Date: Thu Sep 10 11:00:49 2020 -0400 [dbnode] Fail if FetchTagged partially retrieves results due to error (#2610) commit d103bae30c1c9ea772608118f86e3a1c08f6f12d Author: ChrisChinchilla Date: Wed Sep 9 12:36:58 2020 +0200 Remove netlify build from branch commit 7759733416d90026cd9db0a3c45b6e4b43cea3c7 Author: ChrisChinchilla Date: Wed Sep 9 12:35:32 2020 +0200 Attempt to fix Netlify build commit 7440118380d30537c29909a5a50e66f8e4391c2a Author: ChrisChinchilla Date: Tue Sep 8 17:05:41 2020 +0200 More commit 76372dea6d83f865eb52c212caf3d3e95ceda999 Author: ChrisChinchilla Date: Fri Sep 4 16:43:26 2020 +0200 Struct commit e74c381eb1eaff1c27717080fbf0e84d643bb3fc Author: ChrisChinchilla Date: Fri Aug 28 12:42:48 2020 +0200 Further fixing commit 3e357ace4f5f924e4f6155de81a11c46796f8c89 Author: ChrisChinchilla Date: Fri Aug 28 12:10:14 2020 +0200 Continue commit 6b50f87f46de0e6e5e4c29fcf622217cbbf210b8 Author: ChrisChinchilla Date: Fri Aug 28 11:44:52 2020 +0200 Continue commit efddf37ab765cbf7ef4dea795b7b7d8818649678 Author: ChrisChinchilla Date: Fri Aug 28 11:37:08 2020 +0200 Start migration to Hugo commit 1bbca4b438f4ef4574018eb1cca9c04c343c3f14 Author: ChrisChinchilla Date: Mon Aug 17 16:29:09 2020 +0200 Start quickstart rewrite commit 97279a4e91cd111d22576ac2be5e47779622b670 Author: ChrisChinchilla Date: Thu Aug 13 16:33:02 2020 +0200 Add hugo publish dir to gitignore commit fdd275d9f972c9308489dcc023972366290ce2c1 Author: ChrisChinchilla Date: Thu Aug 13 16:25:58 2020 +0200 Update publish path commit e942b4cf5f750d1bad722b59fc711ba3e32bc4ba Author: ChrisChinchilla Date: Thu Aug 13 16:22:20 2020 +0200 Miss-formed YAML broke build commit 62b2faccd7ade413b6b437e92e1e7428a049b7c9 Author: ChrisChinchilla Date: Thu Aug 13 16:20:25 2020 +0200 Add netlify config for this preview branch commit 3232961afa7d04f43b0c15353854895cf8d3a110 Author: ChrisChinchilla Date: Thu Aug 13 16:06:43 2020 +0200 Remove rogue submodule commit d8842698d42211b9117ce98959c33d16c126cedd Author: ChrisChinchilla Date: Thu Aug 13 15:56:55 2020 +0200 Change submodule path commit 3e42ed8f216de26deebc008fc1c3207bd7058c18 Author: ChrisChinchilla Date: Thu Aug 13 15:53:59 2020 +0200 Add theme as submodule commit ce92f326785750df40021715c852de55878f7572 Author: ChrisChinchilla Date: Thu Aug 13 14:37:49 2020 +0200 Testing content commit 40d6f9f43c569af2052ba37bdf9e956c583af7da Author: ChrisChinchilla Date: Thu Aug 13 14:37:33 2020 +0200 Update config commit accedc19d77a9f6b42b03965c8e87c7d466987da Author: ChrisChinchilla Date: Thu Aug 13 14:37:25 2020 +0200 remove folder commit 6ed118bc91a1d94175716810754549c3ade61109 Author: Gibbs Cullen Date: Fri May 8 12:56:50 2020 -0400 edits to operator section commit 016785738d461150371bb0c03f9e0b104359c5f0 Author: Gibbs Cullen Date: Fri May 8 11:01:58 2020 -0400 initial formatting done commit 21dc3af202924d1f396c43435702f8e9dade09bf Author: Gibbs Cullen Date: Mon Apr 27 12:55:20 2020 -0400 content trasferred to hugo site commit ff7d60c21dd11392aee679f6a8d17662739d9777 Author: Gibbs Cullen Date: Tue Apr 21 21:07:00 2020 -0400 updated outline commit 642464cb26d244df19676b78d3c0ae0a86c84f73 Author: Gibbs Cullen Date: Thu Apr 2 12:50:30 2020 -0400 updated docs format in hugo commit c431580efde9569be74d0b091c189f18b2155336 Author: Gibbs Cullen Date: Mon Mar 30 18:51:17 2020 -0400 hugo updates commit 0249640bd4e34421d4e43bfb7d05dd2c949ccff8 Author: Gibbs Cullen Date: Fri Mar 27 19:28:34 2020 -0400 new format commit bb393154f0f0cbdc24d5d37feb33af8359229fbf Author: Benjamin Raskin Date: Mon Mar 2 15:22:36 2020 -0500 [WIP][docs] Add hugo theme Signed-off-by: ChrisChinchilla --- CHANGELOG.md | 25 + docs/config.toml | 118 ++ docs/content/_index.md | 43 + docs/{ => content}/case_studies/index.md | 0 docs/{ => content}/community/index.md | 0 docs/content/coordinator/_index.md | 14 + docs/{ => content}/ecosystem/index.md | 0 .../{faqs/index.md => 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src/dbnode/persist/fs/cross_block_reader.go create mode 100644 src/dbnode/persist/fs/cross_block_reader_test.go create mode 100644 src/dbnode/storage/limits/noop_query_limits.go create mode 100644 src/dbnode/storage/limits/query_limits.go create mode 100644 src/dbnode/storage/limits/query_limits_test.go create mode 100644 src/dbnode/storage/limits/types.go delete mode 100644 src/dbnode/storage/stats/query_stats.go delete mode 100644 src/dbnode/storage/stats/query_stats_default_tracker.go delete mode 100644 src/dbnode/storage/stats/query_stats_default_tracker_test.go delete mode 100644 src/dbnode/storage/stats/query_stats_test.go diff --git a/CHANGELOG.md b/CHANGELOG.md index 525dac6dd1..b60e5f39f8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,30 @@ # Changelog +# 0.15.14 + +## Features + +- **M3Query**: Add movingSum, movingMax, movingMin Graphite functions ([#2570](https://github.com/m3db/m3/pull/2570)) +- **M3Query**: Add aliasByTags Graphite function ([#2626](https://github.com/m3db/m3/pull/2626)) +- **M3Query**: Add exponentialMovingAverage Graphite function ([#2622](https://github.com/m3db/m3/pull/2622)) +- **M3Query**: Add timeSlice Graphite function ([#2581](https://github.com/m3db/m3/pull/2581)) +- **M3Query**: Add delay Graphite function ([#2567](https://github.com/m3db/m3/pull/2567)) +- **M3Query**: Add aggregateWithWildcards Graphite function ([#2582](https://github.com/m3db/m3/pull/2582)) +- **M3Query**: Add groupByNodes Graphite function ([#2579](https://github.com/m3db/m3/pull/2579)) +- **M3Coordinator**: Allow using the placement set API for creating new placements as well as upserting existing placements ([#2625](https://github.com/m3db/m3/pull/2625)) +- **M3DB**: Add bootstrap time migration config which supports seamless backwards and forwards compatible fileset upgrades ([#2521](https://github.com/m3db/m3/pull/2521)) + +# 0.15.13 + +## Bug Fixes + +- **M3DB**: Fix case insensitive regexp modifiers (?i) implementation for metrics selector ([#2564](https://github.com/m3db/m3/pull/2564)) + +## Misc + +- **M3DB**: Expose stream batch client options to config ([#2576](https://github.com/m3db/m3/pull/2576)) +- **M3Query**: Metrics visibility and ability to limit number of encoders per block ([#2516](https://github.com/m3db/m3/pull/2516)) + # 0.15.12 ## Bug Fixes diff --git a/docs/config.toml b/docs/config.toml new file mode 100644 index 0000000000..33ccbfbeff --- /dev/null +++ b/docs/config.toml @@ -0,0 +1,118 @@ +# Change the default theme to be use when building the site with Hugo +theme = "docs-theme" + +baseURL = "/" +languageCode = "en-US" +defaultContentLanguage = "en" +staticDir = ["static"] + +title = "M3DB Documentation" +metaDataFormat = "yaml" +defaultContentLanguageInSubdir= true + +# Highlighting config. +pygmentsCodeFences = true +pygmentsUseClasses = false +# Use the new Chroma Go highlighter in Hugo. +pygmentsUseClassic = false +#pygmentsOptions = "linenos=table" +# See https://help.farbox.com/pygments.html +# pygmentsStyle = "vs" + +enableGitInfo = true + +[frontmatter] +# date = ["date", ":filename", "publishDate", "lastmod"] + +# Image processing configuration. +[imaging] +resampleFilter = "CatmullRom" +quality = 75 +anchor = "smart" + +[services] +[services.googleAnalytics] +# Comment out the next line to disable GA tracking. Also disables the feature described in [params.ui.feedback]. +# id = "UA-00000000-0" + +#fullversion = "v1.18.0" +#version = "v1.18" +githubbranch = "master" +docsbranch = "master" +deprecated = false +#currentUrl = "https://kubernetes.io/docs/home/" +#nextUrl = "https://kubernetes-io-vnext-staging.netlify.com/" +githubWebsiteRepo = "https://github.com/m3db/m3" +githubWebsiteRaw = "raw.githubusercontent.com/m3db/m3" + +# Enable Algolia DocSearch +# algolia_docsearch = false + +# Enable Lunr.js offline search +offlineSearch = false + +[params] + # Prefix URL to edit current page. Will display an "Edit this page" button on top right hand corner of every page. + # Useful to give opportunity to people to create merge request for your doc. + # See the config.toml file from this documentation site to have an example. + editURL = "https://github.com/m3db/m3" + # Author of the site, will be used in meta information + author = "m3" + # Description of the site, will be used in meta information + description = "m3documentation" + # Shows a checkmark for visited pages on the menu + showVisitedLinks = false + # Disable search function. It will hide search bar + disableSearch = false + # Javascript and CSS cache are automatically busted when new version of site is generated. + # Set this to true to disable this behavior (some proxies don't handle well this optimization) + disableAssetsBusting = false + # Set this to true to disable copy-to-clipboard button for inline code. + disableInlineCopyToClipBoard = true + # A title for shortcuts in menu is set by default. Set this to true to disable it. + disableShortcutsTitle = false + # When using mulitlingual website, disable the switch language button. + disableLanguageSwitchingButton = false + # Hide breadcrumbs in the header and only show the current page title + disableBreadcrumb = true + # Hide Next and Previous page buttons normally displayed full height beside content + disableNextPrev = true + # Order sections in menu by "weight" or "title". Default to "weight" + ordersectionsby = "weight" + # Change default color scheme with a variant one. Can be "red", "blue", "green". + themeVariant = "blue" + twitter = "m3db_io" + +# TODO: Do not like doing this really +[markup] + [markup.goldmark] + [markup.goldmark.renderer] + unsafe = true + [markup.goldmark.parser] + attribute = true + autoHeadingID = true + autoHeadingIDType = "github" + [markup.tableOfContents] + endLevel = 3 + ordered = false + startLevel = 2 + [markup.goldmark.extensions] + definitionList = true + footnote = true + linkify = true + strikethrough = true + table = true + taskList = true + typographer = true + +[[Languages.en.menu.shortcuts]] +name = " GitHub" +identifier = "ds" +url = "https://github.com/m3db/m3" +weight = 10 + +[outputs] +home = [ "HTML", "RSS", "JSON"] +page = [ "HTML"] +section = [ "HTML"] +chapter = [ "HTML"] diff --git a/docs/content/_index.md b/docs/content/_index.md new file mode 100644 index 0000000000..33d877a32b --- /dev/null +++ b/docs/content/_index.md @@ -0,0 +1,43 @@ +--- +title: M3 Introduction +weight: 1 +--- + + +## About + +After using open-source metrics solutions and finding issues with them at scale – such as reliability, cost, and +operational complexity – [M3](https://github.com/m3db/m3) was created from the ground up to provide Uber with a +native, distributed time series database, a highly-dynamic and performant aggregation service, a query engine, and +other supporting infrastructure. + +## Key Features + +M3 has several features, provided as discrete components, which make it an ideal platform for time series data at scale: + +- A distributed time series database, [M3DB](m3db/), that provides scalable storage for time series data and a reverse index. +- A sidecar process, [M3Coordinator](integrations/prometheus), that allows M3DB to act as the long-term storage for Prometheus. +- A distributed query engine, [M3Query](query_engine), with native support for PromQL and Graphite (M3QL coming soon). + +- An aggregation tier, M3Aggregator, that runs as a dedicated metrics aggregator/downsampler allowing metrics to be stored at various retentions at different resolutions. + +## Getting Started + +**Note:** Make sure to read our [Operational Guides](operational_guide) before running in production! + +Getting started with M3 is as easy as following one of the How-To guides. + +- [Single M3DB node deployment](how_to/single_node) +- [Clustered M3DB deployment](how_to/cluster_hard_way) +- [M3DB on Kubernetes](how_to/kubernetes) +- [Isolated M3Query on deployment](how_to/query) + +## Support + +For support with any issues, questions about M3 or its operation, or to leave any comments, the team can be +reached in a variety of ways: + +- [Slack (main chat channel)](http://bit.ly/m3slack) +- [Gitter (old chat channel)](https://gitter.im/m3db/Lobby) +- [Email](https://groups.google.com/forum/#!forum/m3db) +- [Github issues](https://github.com/m3db/m3/issues) diff --git a/docs/case_studies/index.md b/docs/content/case_studies/index.md similarity index 100% rename from docs/case_studies/index.md rename to docs/content/case_studies/index.md diff --git a/docs/community/index.md b/docs/content/community/index.md similarity index 100% rename from docs/community/index.md rename to docs/content/community/index.md diff --git a/docs/content/coordinator/_index.md b/docs/content/coordinator/_index.md new file mode 100644 index 0000000000..e66a19c734 --- /dev/null +++ b/docs/content/coordinator/_index.md @@ -0,0 +1,14 @@ +--- +title: "M3 Coordinator, API for reading/writing metrics and management" +menuTitle: "M3 Coordinator" +weight: 4 +chapter: true +--- + +M3 Coordinator is a service that coordinates reads and writes between upstream systems, such as Prometheus, and downstream systems, such as M3DB. + +It also provides management APIs to setup and configure different parts of M3. + +The coordinator is generally a bridge for read and writing different types of metrics formats and a management layer for M3. + +**Note**: M3DB by default includes the M3 Coordinator accessible on port 7201. For production deployments it is recommended to deploy it as a dedicated service to ensure you can scale the write coordination role separately and independently to database nodes as an isolated application separate from the M3DB database role. diff --git a/docs/ecosystem/index.md b/docs/content/ecosystem/index.md similarity index 100% rename from docs/ecosystem/index.md rename to docs/content/ecosystem/index.md diff --git a/docs/faqs/index.md b/docs/content/faqs/_index.md similarity index 93% rename from docs/faqs/index.md rename to docs/content/faqs/_index.md index a28f9a4e10..1b16b6c5f6 100644 --- a/docs/faqs/index.md +++ b/docs/content/faqs/_index.md @@ -1,4 +1,8 @@ -# FAQs +--- +title: "FAQs" +weight: 9 +chapter: true +--- - **Is there a way to disable M3DB embedded `etcd` and just use an external `etcd` cluster?** Yes, you can definitely do that. It's all just about setting the etcd endpoints in config as etcd hosts instead of M3DB hosts. See [these docs](../operational_guide/etcd.md#external-etcd) for more information on configuring an external `etcd` cluster. @@ -7,7 +11,7 @@ Yes, you can definitely do that. It's all just about setting the etcd endpoints Yes, you can use the [Prometheus remote write client](https://github.com/m3db/prometheus_remote_client_golang/). - **Why does my dbnode keep OOM’ing?** -Refer to the [troubleshooting guide](../troubleshooting/index.md). +Refer to the [troubleshooting guide](/troubleshooting). - **Do you support PromQL?** Yes, M3Query and M3Coordinator both support PromQL. @@ -29,7 +33,7 @@ If you’re adding namespaces, the m3dbnode process will pickup the new namespac If you’re removing or modifying an existing namespace, you’ll need to restart the m3dbnode process in order to complete the namespace deletion/modification process. It is recommended to restart one node at a time and wait for a node to be completely bootstrapped before restarting another node. - **How do I set up aggregation in the coordinator?** -Refer to the [Aggregation section](../how_to/query.md) of the M3Query how-to guide. +Refer to the [Aggregation section](/how_to/query) of the M3Query how-to guide. - **How do I set up aggregation using a separate aggregation tier?** See this [WIP documentation](https://github.com/m3db/m3/pull/1741/files#diff-0a1009f86783ca8fd4499418e556c6f5). @@ -61,7 +65,7 @@ etcdClusters: ``` - **How can I get a heap dump, cpu profile, etc.** -See our docs on the [/debug/dump api](../troubleshooting/index.md) +See our docs on the [/debug/dump api](/troubleshooting) - **How much memory utilization should I run M3DB at?** We recommend not going above 50%. @@ -70,7 +74,7 @@ We recommend not going above 50%. TBA - **What is the recommended way to create a new namespace?** -Refer to the [Namespace configuration guide](../operational_guide/namespace_configuration.md). +Refer to the [Namespace configuration guide](/operational_guide/namespace_configuration). - **How can I see the cardinality of my metrics?** Currently, the best way is to go to the [M3DB Node Details Dashboard](https://grafana.com/grafana/dashboards/8126) and look at the `Ticking` panel. However, this is not entirely accurate because of the way data is stored in M3DB -- time series are stored inside time-based blocks that you configure. In actuality, the `Ticking` graph shows you how many unique series there are for the most recent block that has persisted. In the future, we plan to introduce easier ways to determine the number of unique time series. diff --git a/docs/content/glossary/bootstrapping.md b/docs/content/glossary/bootstrapping.md new file mode 100644 index 0000000000..ff28f8fb3c --- /dev/null +++ b/docs/content/glossary/bootstrapping.md @@ -0,0 +1,15 @@ +--- +title: Bootstrapping +id: bootstrapping +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +Process by which an M3DB node is brought up. Bootstrapping consists of determining the integrity of data that the node has, replay writes from the commit log, and/or stream missing data from its peers. \ No newline at end of file diff --git a/docs/content/glossary/cardinality.md b/docs/content/glossary/cardinality.md new file mode 100644 index 0000000000..b19d87d362 --- /dev/null +++ b/docs/content/glossary/cardinality.md @@ -0,0 +1,15 @@ +--- +title: Cardinality +id: cardinality +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +The number of unique metrics within the M3DB index. Cardinality increases with the number of unique tag/value combinations that are being emitted. \ No newline at end of file diff --git a/docs/content/glossary/datapoint.md b/docs/content/glossary/datapoint.md new file mode 100644 index 0000000000..bd6db25015 --- /dev/null +++ b/docs/content/glossary/datapoint.md @@ -0,0 +1,15 @@ +--- +title: Datapoint +id: datapoint +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +A single timestamp/value. Timeseries are composed of multiple datapoints and a series of tag/value pairs \ No newline at end of file diff --git a/docs/content/glossary/index.md b/docs/content/glossary/index.md new file mode 100644 index 0000000000..225afb5e2d --- /dev/null +++ b/docs/content/glossary/index.md @@ -0,0 +1,12 @@ +--- +title: Standardized Glossary +layout: glossary +noedit: true +default_active_tag: test +weight: 5 +card: + name: reference + weight: 10 + title: Glossary +--- + diff --git a/docs/content/glossary/labels.md b/docs/content/glossary/labels.md new file mode 100644 index 0000000000..3ce06367d8 --- /dev/null +++ b/docs/content/glossary/labels.md @@ -0,0 +1,16 @@ +--- +title: Labels +id: labels +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +Pairs of descriptive words that give meaning to a metric. Tags and Labels are interchangeable terms. + diff --git a/docs/content/glossary/m3.md b/docs/content/glossary/m3.md new file mode 100644 index 0000000000..56a8077411 --- /dev/null +++ b/docs/content/glossary/m3.md @@ -0,0 +1,16 @@ +--- +title: M3 +id: m3 +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +Highly scalable, distributed metrics platform that is comprised of a native, distributed time series database, a highly-dynamic and performant aggregation service, a query engine, and other supporting infrastructure. + diff --git a/docs/content/glossary/m3coordinator.md b/docs/content/glossary/m3coordinator.md new file mode 100644 index 0000000000..b6f0e21394 --- /dev/null +++ b/docs/content/glossary/m3coordinator.md @@ -0,0 +1,15 @@ +--- +title: M3Coordinator +id: m3coordinator +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +A service within M3 that coordinates reads and writes between upstream systems, such as Prometheus, and downstream systems, such as M3DB. \ No newline at end of file diff --git a/docs/content/glossary/m3db.md b/docs/content/glossary/m3db.md new file mode 100644 index 0000000000..6b7d90df94 --- /dev/null +++ b/docs/content/glossary/m3db.md @@ -0,0 +1,16 @@ +--- +title: M3DB +id: m3db +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +Distributed time series database influenced by Gorilla and Cassandra released as open source by Uber Technologies. + diff --git a/docs/content/glossary/m3query.md b/docs/content/glossary/m3query.md new file mode 100644 index 0000000000..094fcdf1fb --- /dev/null +++ b/docs/content/glossary/m3query.md @@ -0,0 +1,15 @@ +--- +title: M3Query +id: m3query +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +A distributed query engine for M3DB. Unlike M3Coordinator, M3Query only provides supports for reads. \ No newline at end of file diff --git a/docs/content/glossary/metric.md b/docs/content/glossary/metric.md new file mode 100644 index 0000000000..da6eb1f0f9 --- /dev/null +++ b/docs/content/glossary/metric.md @@ -0,0 +1,15 @@ +--- +title: Metric +id: metric +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +A collection of uniquely identifiable tags. \ No newline at end of file diff --git a/docs/content/glossary/namespace.md b/docs/content/glossary/namespace.md new file mode 100644 index 0000000000..c1448775d1 --- /dev/null +++ b/docs/content/glossary/namespace.md @@ -0,0 +1,16 @@ +--- +title: Namespace +id: namespace +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +Similar to a table in other types of databases, namespaces in M3DB have a unique name and a set of configuration options, such as data retention and block size. + diff --git a/docs/content/glossary/placement.md b/docs/content/glossary/placement.md new file mode 100644 index 0000000000..4e58a21929 --- /dev/null +++ b/docs/content/glossary/placement.md @@ -0,0 +1,17 @@ +--- +title: Placement +id: placement +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +Map of the M3DB cluster's shard replicas to nodes. Each M3DB cluster has only one placement. Placement and Topology are interchangeable terms. + + diff --git a/docs/content/glossary/shard.md b/docs/content/glossary/shard.md new file mode 100644 index 0000000000..531cf09ff6 --- /dev/null +++ b/docs/content/glossary/shard.md @@ -0,0 +1,15 @@ +--- +title: Shard +id: shard +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +Effectively the same as a "virtual shard" in Cassandra in that it provides an arbitrary distribution of time series data via a simple hash of the series ID. \ No newline at end of file diff --git a/docs/content/glossary/tags.md b/docs/content/glossary/tags.md new file mode 100644 index 0000000000..7e69e8609a --- /dev/null +++ b/docs/content/glossary/tags.md @@ -0,0 +1,15 @@ +--- +title: Tags +id: tags +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +Pairs of descriptive words that give meaning to a metric. Tags and Labels are interchangeable terms. \ No newline at end of file diff --git a/docs/content/glossary/timeseries.md b/docs/content/glossary/timeseries.md new file mode 100644 index 0000000000..63a7b24a23 --- /dev/null +++ b/docs/content/glossary/timeseries.md @@ -0,0 +1,15 @@ +--- +title: Timeseries +id: timeseries +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +A series of data points tracking a particular metric over time. \ No newline at end of file diff --git a/docs/content/glossary/topology.md b/docs/content/glossary/topology.md new file mode 100644 index 0000000000..0f3770d2f8 --- /dev/null +++ b/docs/content/glossary/topology.md @@ -0,0 +1,16 @@ +--- +title: Topology +id: topology +date: 2019-06-15 +full_link: +short_description: > + I am a test term. + +aka: +tags: +- example +- illustration +--- + +Map of the M3DB cluster's shard replicas to nodes. Each M3DB cluster has only one placement. Placement and Topology are interchangeable terms. + diff --git a/docs/content/how_to/_index.md b/docs/content/how_to/_index.md new file mode 100644 index 0000000000..af7e951e12 --- /dev/null +++ b/docs/content/how_to/_index.md @@ -0,0 +1,6 @@ ++++ +title = "How To Guides" +date = 2020-04-01T19:26:56-04:00 +weight = 6 +chapter = true ++++ \ No newline at end of file diff --git a/docs/content/how_to/aggregator.md b/docs/content/how_to/aggregator.md new file mode 100644 index 0000000000..d1223049b6 --- /dev/null +++ b/docs/content/how_to/aggregator.md @@ -0,0 +1,414 @@ +--- +title: Setting up M3Aggregator +menuTitle: M3Aggregator +weight: 5 +--- + + + +## Introduction + +`m3aggregator` is used to cluster stateful downsampling and rollup of metrics before they are store in M3DB. The M3 Coordinator also performs this role but is not cluster aware. This means metrics will not get aggregated properly if you send metrics in round robin fashion to multiple M3 Coordinators for the same metrics ingestion source (e.g. Prometheus server). + +Similar to M3DB, `m3aggregator` supports clustering and replication by default. This means that metrics are correctly routed to the instance(s) responsible for aggregating each metric and multiple `m3aggregator` replicas can be configured such that there are no single points of failure for aggregation. + +## Configuration + +Before setting up m3aggregator, make sure that you have at least [one M3DB node running](single_node) and a dedicated m3coordinator setup. + +We highly recommend running with at least a replication factor 2 for a `m3aggregator` deployment. If you run with replication factor 1 then when you restart an aggregator it will temporarily interrupt good the stream of aggregated metrics and there will be some data loss. + +### Topology + +#### Initializing aggregator topology + +You can setup a m3aggregator topology by issuing a request to your coordinator (be sure to use your own hostnames, number of shards and replication factor): +```bash +curl -vvvsSf -H "Cluster-Environment-Name: namespace/m3db-cluster-name" -X POST http://m3dbnode-with-embedded-coordinator:7201/api/v1/services/m3aggregator/placement/init -d '{ + "num_shards": 64, + "replication_factor": 2, + "instances": [ + { + "id": "m3aggregator01:6000", + "isolation_group": "availability-zone-a", + "zone": "embedded", + "weight": 100, + "endpoint": "m3aggregator01:6000", + "hostname": "m3aggregator01", + "port": 6000 + }, + { + "id": "m3aggregator02:6000", + "isolation_group": "availability-zone-b", + "zone": "embedded", + "weight": 100, + "endpoint": "m3aggregator02:6000", + "hostname": "m3aggregator02", + "port": 6000 + } + ] +}' +``` + +#### Initializing m3msg topic for m3aggregator to receive from m3coordinators to aggregate metrics + +Now we must setup a topic for the `m3aggregator` to receive unaggregated metrics from `m3coordinator` instances: + +```bash +curl -vvvsSf -H "Cluster-Environment-Name: namespace/m3db-cluster-name" -H "Topic-Name: aggregator_ingest" -X POST http://m3dbnode-with-embedded-coordinator:7201/api/v1/topic/init -d '{ + "numberOfShards": 64 +}' +``` + +#### Add m3aggregagtor consumer group to ingest topic + +Add the `m3aggregator` placement to receive traffic from the topic (make sure to set message TTL to match your desired maximum in memory retry message buffer): +```bash +curl -vvvsSf -H "Cluster-Environment-Name: namespace/m3db-cluster-name" -H "Topic-Name: aggregator_ingest" -X POST http://m3dbnode-with-embedded-coordinator:7201/api/v1/topic -d '{ + "consumerService": { + "serviceId": { + "name": "m3aggregator", + "environment": "namespace/m3db-cluster-name", + "zone": "embedded" + }, + "consumptionType": "REPLICATED", + "messageTtlNanos": "300000000000" + } +}' +``` + +**Note:** 300000000000 nanoseconds is a TTL of 5 minutes for messages to rebuffer for retry. + +#### Initializing m3msg topic for m3coordinator to receive from m3aggregator to write to M3DB + +Now we must setup a topic for the `m3coordinator` to receive aggregated metrics from `m3aggregator` instances to write to M3DB: +```bash +curl -vvvsSf -H "Cluster-Environment-Name: namespace/m3db-cluster-name" -H "Topic-Name: aggregated_metrics" -X POST http://m3dbnode-with-embedded-coordinator:7201/api/v1/topic/init -d '{ + "numberOfShards": 64 +}' +``` + +#### Initializing m3coordinator topology + +Then `m3coordinator` instances need to be configured to receive traffic for this topic (note ingest at port 7507 must match the configured port for your `m3coordinator` ingest server, see config at bottom of this guide): +```bash +curl -vvvsSf -H "Cluster-Environment-Name: namespace/m3db-cluster-name" -X POST http://m3dbnode-with-embedded-coordinator:7201/api/v1/services/m3coordinator/placement/init -d '{ + "instances": [ + { + "id": "m3coordinator01", + "zone": "embedded", + "endpoint": "m3coordinator01:7507", + "hostname": "m3coordinator01", + "port": 7507 + } + ] +}' +``` + +**Note:** When you add or remove `m3coordinator` instances they must be added to this placement. + +#### Add m3coordinator consumer group to outbound topic + +Add the `m3coordinator` placement to receive traffic from the topic (make sure to set message TTL to match your desired maximum in memory retry message buffer): +```bash +curl -vvvsSf -H "Cluster-Environment-Name: namespace/m3db-cluster-name" -H "Topic-Name: aggregated_metrics" -X POST http://m3dbnode-with-embedded-coordinator:7201/api/v1/topic -d '{ + "consumerService": { + "serviceId": { + "name": "m3coordinator", + "environment": "namespace/m3db-cluster-name", + "zone": "embedded" + }, + "consumptionType": "SHARED", + "messageTtlNanos": "300000000000" + } +}' +``` + +**Note:** 300000000000 nanoseconds is a TTL of 5 minutes for messages to rebuffer for retry. + +### Running + +#### Dedicated Coordinator + +Metrics will still arrive at the `m3coordinator`, they simply need to be forwarded to an `m3aggregator`. The `m3coordinator` then also needs to receive metrics that have been aggregated from the `m3aggregator` and store them in M3DB, so running an ingestion server should be configured. + +Here is the config you should add to your `m3coordinator`: +```yaml +# This is for sending metrics to the remote m3aggregators +downsample: + remoteAggregator: + client: + type: m3msg + m3msg: + producer: + writer: + topicName: aggregator_ingest + topicServiceOverride: + zone: embedded + environment: namespace/m3db-cluster-name + placement: + isStaged: true + placementServiceOverride: + namespaces: + placement: /placement + connection: + numConnections: 4 + messagePool: + size: 16384 + watermark: + low: 0.2 + high: 0.5 + +# This is for configuring the ingestion server that will receive metrics from the m3aggregators on port 7507 +ingest: + ingester: + workerPoolSize: 10000 + opPool: + size: 10000 + retry: + maxRetries: 3 + jitter: true + logSampleRate: 0.01 + m3msg: + server: + listenAddress: "0.0.0.0:7507" + retry: + maxBackoff: 10s + jitter: true +``` + +#### M3 Aggregator + +You can run `m3aggregator` by either building and running the binary yourself: + +```bash +make m3aggregator +./bin/m3aggregator -f ./src/aggregator/config/m3aggregator.yml +``` + +Or you can run it with Docker using the Docker file located at `docker/m3aggregator/Dockerfile` or the publicly provided image `quay.io/m3db/m3aggregator:latest`. + +You can use a config like so, making note of the topics used such as `aggregator_ingest` and `aggregated_metrics` and the corresponding environment `namespace/m3db-cluster-name`: + +```yaml +logging: + level: info + +metrics: + scope: + prefix: m3aggregator + prometheus: + onError: none + handlerPath: /metrics + listenAddress: 0.0.0.0:6002 + timerType: histogram + sanitization: prometheus + samplingRate: 1.0 + extended: none + +m3msg: + server: + listenAddress: 0.0.0.0:6000 + retry: + maxBackoff: 10s + jitter: true + consumer: + messagePool: + size: 16384 + watermark: + low: 0.2 + high: 0.5 + +http: + listenAddress: 0.0.0.0:6001 + readTimeout: 60s + writeTimeout: 60s + +kvClient: + etcd: + env: namespace/m3db-cluster-name + zone: embedded + service: m3aggregator + cacheDir: /var/lib/m3kv + etcdClusters: + - zone: embedded + endpoints: + - dbnode01:2379 + +runtimeOptions: + kvConfig: + environment: namespace/m3db-cluster-name + zone: embedded + writeValuesPerMetricLimitPerSecondKey: write-values-per-metric-limit-per-second + writeValuesPerMetricLimitPerSecond: 0 + writeNewMetricLimitClusterPerSecondKey: write-new-metric-limit-cluster-per-second + writeNewMetricLimitClusterPerSecond: 0 + writeNewMetricNoLimitWarmupDuration: 0 + +aggregator: + hostID: + resolver: environment + envVarName: M3AGGREGATOR_HOST_ID + instanceID: + type: host_id + verboseErrors: true + metricPrefix: "" + counterPrefix: "" + timerPrefix: "" + gaugePrefix: "" + aggregationTypes: + counterTransformFnType: empty + timerTransformFnType: suffix + gaugeTransformFnType: empty + aggregationTypesPool: + size: 1024 + quantilesPool: + buckets: + - count: 256 + capacity: 4 + - count: 128 + capacity: 8 + stream: + eps: 0.001 + capacity: 32 + streamPool: + size: 4096 + samplePool: + size: 4096 + floatsPool: + buckets: + - count: 4096 + capacity: 16 + - count: 2048 + capacity: 32 + - count: 1024 + capacity: 64 + client: + type: m3msg + m3msg: + producer: + writer: + topicName: aggregator_ingest + topicServiceOverride: + zone: embedded + environment: namespace/m3db-cluster-name + placement: + isStaged: true + placementServiceOverride: + namespaces: + placement: /placement + messagePool: + size: 16384 + watermark: + low: 0.2 + high: 0.5 + placementManager: + kvConfig: + namespace: /placement + environment: namespace/m3db-cluster-name + zone: embedded + placementWatcher: + key: m3aggregator + initWatchTimeout: 10s + hashType: murmur32 + bufferDurationBeforeShardCutover: 10m + bufferDurationAfterShardCutoff: 10m + bufferDurationForFutureTimedMetric: 10m # Allow test to write into future. + resignTimeout: 1m + flushTimesManager: + kvConfig: + environment: namespace/m3db-cluster-name + zone: embedded + flushTimesKeyFmt: shardset/%d/flush + flushTimesPersistRetrier: + initialBackoff: 100ms + backoffFactor: 2.0 + maxBackoff: 2s + maxRetries: 3 + electionManager: + election: + leaderTimeout: 10s + resignTimeout: 10s + ttlSeconds: 10 + serviceID: + name: m3aggregator + environment: namespace/m3db-cluster-name + zone: embedded + electionKeyFmt: shardset/%d/lock + campaignRetrier: + initialBackoff: 100ms + backoffFactor: 2.0 + maxBackoff: 2s + forever: true + jitter: true + changeRetrier: + initialBackoff: 100ms + backoffFactor: 2.0 + maxBackoff: 5s + forever: true + jitter: true + resignRetrier: + initialBackoff: 100ms + backoffFactor: 2.0 + maxBackoff: 5s + forever: true + jitter: true + campaignStateCheckInterval: 1s + shardCutoffCheckOffset: 30s + flushManager: + checkEvery: 1s + jitterEnabled: true + maxJitters: + - flushInterval: 5s + maxJitterPercent: 1.0 + - flushInterval: 10s + maxJitterPercent: 0.5 + - flushInterval: 1m + maxJitterPercent: 0.5 + - flushInterval: 10m + maxJitterPercent: 0.5 + - flushInterval: 1h + maxJitterPercent: 0.25 + numWorkersPerCPU: 0.5 + flushTimesPersistEvery: 10s + maxBufferSize: 5m + forcedFlushWindowSize: 10s + flush: + handlers: + - dynamicBackend: + name: m3msg + hashType: murmur32 + producer: + writer: + topicName: aggregated_metrics + topicServiceOverride: + zone: embedded + environment: namespace/m3db-cluster-name + messagePool: + size: 16384 + watermark: + low: 0.2 + high: 0.5 + passthrough: + enabled: true + forwarding: + maxConstDelay: 5m # Need to add some buffer window, since timed metrics by default are delayed by 1min. + entryTTL: 1h + entryCheckInterval: 10m + maxTimerBatchSizePerWrite: 140 + defaultStoragePolicies: [] + maxNumCachedSourceSets: 2 + discardNaNAggregatedValues: true + entryPool: + size: 4096 + counterElemPool: + size: 4096 + timerElemPool: + size: 4096 + gaugeElemPool: + size: 4096 +``` + +## Usage + +Send metrics as usual to your `m3coordinator` instances in round robin fashion (or any other load balancing strategy), the metrics will be forwarded to the `m3aggregator` instances, then once aggregated they will be returned to the `m3coordinator` instances to write to M3DB. diff --git a/docs/how_to/cluster_hard_way.md b/docs/content/how_to/cluster_hard_way.md similarity index 76% rename from docs/how_to/cluster_hard_way.md rename to docs/content/how_to/cluster_hard_way.md index b2c89fea73..10367b583f 100644 --- a/docs/how_to/cluster_hard_way.md +++ b/docs/content/how_to/cluster_hard_way.md @@ -1,13 +1,16 @@ -# M3DB Cluster Deployment, Manually (The Hard Way) - -## Introduction +--- +title: M3DB Cluster Deployment, Manually (The Hard Way) +menuTitle: Manual Cluster Deployment +weight: 2 +--- This document lists the manual steps involved in deploying a M3DB cluster. In practice, you'd be automating this using Terraform or using Kubernetes rather than doing this by hand; guides for doing so are available under the How-To section. ## Primer Architecture + A quick primer on M3DB architecture. Here’s what a typical deployment looks like: -![Typical Deployment](cluster_architecture.png) +![Typical Deployment](/cluster_architecture.png) A few different things to highlight about the diagram: @@ -15,18 +18,18 @@ A few different things to highlight about the diagram: There are three ‘role types’ for a m3db deployment - -- Coordinator: `m3coordinator` serves to coordinate reads and writes across all hosts in the cluster. It’s a lightweight process, and does not store any data. This role would typically be run alongside a Prometheus instance, or be baked into a collector agent. - -- Storage Node: `m3dbnode` processes running on these hosts are the workhorses of the database, they store data; and serve reads and writes. +- Coordinator: `m3coordinator` serves to coordinate reads and writes across all hosts in the cluster. It’s a lightweight process, and does not store any data. This role would typically be run alongside a Prometheus instance, or be baked into a collector agent. +- Storage Node: `m3dbnode` processes running on these hosts are the workhorses of the database, they store data; and serve reads and writes. +- Seed Node: First and foremost, these hosts are storage nodes themselves. In addition to that responsibility, they run an embedded ETCD server. This is to allow the various M3DB processes running across the cluster to reason about the topology/configuration of the cluster in a consistent manner. -- Seed Node: First and foremost, these hosts are storage nodes themselves. In addition to that responsibility, they run an embedded ETCD server. This is to allow the various M3DB processes running across the cluster to reason about the topology/configuration of the cluster in a consistent manner. - -Note: In very large deployments, you’d use a dedicated ETCD cluster, and only use M3DB Storage and Coordinator Nodes +**Note**: In very large deployments, you’d use a dedicated ETCD cluster, and only use M3DB Storage and Coordinator Nodes ## Provisioning + Enough background, lets get you going with a real cluster! Provision your host (be it VMs from AWS/GCP/etc) or bare-metal servers in your DC with the latest and greatest flavour of Linux you favor. M3DB works on all popular distributions - Ubuntu/RHEL/CentOS, let us know if you run into issues on another platform and we’ll be happy to assist. ### Network + If you’re using AWS or GCP it is highly advised to use static IPs so that if you need to replace a host, you don’t have to update your configuration files on all the hosts, you simply decomission the old seed node and provision a new seed node with the same host ID and static IP that the old seed node had. For AWS you can use a [Elastic Network Interface](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-eni.html) on a VPC and for GCP you can simply use an [internal static IP address](https://cloud.google.com/compute/docs/ip-addresses/reserve-static-internal-ip-address). In this example you will be creating three static IP addresses for the three seed nodes. @@ -38,32 +41,36 @@ In GCP the name of your instance when you create it will automatically be it's h In AWS it might be simpler to just use whatever the hostname you get for the provisioned VM as your host ID when specifying M3DB placement. Either that or use the `environment` host ID resolver and pass your host ID when launching the database process with an environment variable. You can set to the host ID and specify the environment variable name in config as `envVarName: M3DB_HOST_ID` if you are using an environment variable named `M3DB_HOST_ID`. Relevant config snippet: -``` + +```yaml hostID: resolver: environment envVarName: M3DB_HOST_ID ``` Then start your process with: -``` + +```shell M3DB_HOST_ID=m3db001 m3dbnode -f config.yml ``` ### Kernel -Ensure you review our [recommended kernel configuration](../operational_guide/kernel_configuration.md) before running M3DB in production as M3DB may exceed the default limits for some default kernel values. + +Ensure you review our [recommended kernel configuration](/operational_guide/kernel_configuration) before running M3DB in production as M3DB may exceed the default limits for some default kernel values. ## Config files + We wouldn’t feel right to call this guide, “The Hard Way” and not require you to change some configs by hand. -Note: the steps that follow assume you have the following 3 seed nodes - make necessary adjustment if you have more or are using a dedicated ETCD cluster. Example seed nodes: +**Note**: the steps that follow assume you have the following 3 seed nodes - make necessary adjustment if you have more or are using a dedicated ETCD cluster. Example seed nodes: -- m3db001 (Region=us-east1, Zone=us-east1-a, Static IP=10.142.0.1) -- m3db002 (Region=us-east1, Zone=us-east1-b, Static IP=10.142.0.2) -- m3db003 (Region=us-east1, Zone=us-east1-c, Static IP=10.142.0.3) +- m3db001 (Region=us-east1, Zone=us-east1-a, Static IP=10.142.0.1) +- m3db002 (Region=us-east1, Zone=us-east1-b, Static IP=10.142.0.2) +- m3db003 (Region=us-east1, Zone=us-east1-c, Static IP=10.142.0.3) We’re going to start with the M3DB config template and modify it to work for your cluster. Start by downloading the [config](https://github.com/m3db/m3/blob/master/src/dbnode/config/m3dbnode-cluster-template.yml). Update the config ‘service’ and 'seedNodes' sections to read as follows: -``` +```yaml config: service: env: default_env @@ -86,24 +93,24 @@ config: endpoint: http://10.142.0.3:2380 ``` - ## Start the seed nodes + Transfer the config you just crafted to each host in the cluster. And then starting with the seed nodes, start up the m3dbnode process: -``` +```shell m3dbnode -f ``` -Note, remember to daemon-ize this using your favourite utility: systemd/init.d/supervisor/etc +**Note**, remember to daemon-ize this using your favourite utility: systemd/init.d/supervisor/etc ## Create Namespace and Initialize Topology The recommended way to create a namespace and initialize a topology is to use the `/api/v1/database/create` api. Below is an example. -**Note:** In order to create a more custom setup, please refer to the [namespace configuration](../operational_guide/namespace_configuration.md) and -[placement configuration](../operational_guide/placement_configuration.md) guides, though this is discouraged. +**Note:** In order to create a more custom setup, please refer to the [namespace configuration](/operational_guide/namespace_configuration) and +[placement configuration](/operational_guide/placement_configuration) guides, though this is discouraged. -```json +```shell curl -X POST http://localhost:7201/api/v1/database/create -d '{ "type": "cluster", "namespaceName": "1week_namespace", @@ -143,7 +150,7 @@ curl -X POST http://localhost:7201/api/v1/database/create -d '{ Shortly after, you should see your node complete bootstrapping: -``` +```shell 20:10:12.911218[I] updating database namespaces [{adds [default]} {updates []} {removals []}] 20:10:13.462798[I] node tchannelthrift: listening on 0.0.0.0:9000 20:10:13.463107[I] cluster tchannelthrift: listening on 0.0.0.0:9001 @@ -160,17 +167,17 @@ If you need to setup multiple namespaces, you can run the above `/api/v1/databas ### Replication factor (RF) -Recommended is RF3, where each replica is spread across failure domains such as a rack, data center or availability zone. See [Replication Factor Recommendations](../operational_guide/replication_and_deployment_in_zones.md) for more specifics. +Recommended is RF3, where each replica is spread across failure domains such as a rack, data center or availability zone. See [Replication Factor Recommendations](/operational_guide/replication_and_deployment_in_zones) for more specifics. ### Shards -See [placement configuration](../operational_guide/placement_configuration.md) to determine the appropriate number of shards to specify. +See [placement configuration](/operational_guide/placement_configuration) to determine the appropriate number of shards to specify. ## Test it out Now you can experiment with writing tagged metrics: -```json +```shell curl -sS -X POST localhost:9003/writetagged -d '{ "namespace": "metrics", "id": "foo", @@ -193,7 +200,7 @@ curl -sS -X POST localhost:9003/writetagged -d '{ And reading the metrics you've written: -```json +```shell curl -sS -X POST http://localhost:9003/query -d '{ "namespace": "metrics", "query": { @@ -209,4 +216,4 @@ curl -sS -X POST http://localhost:9003/query -d '{ ## Integrations -[Prometheus as a long term storage remote read/write endpoint](../integrations/prometheus.md). +[Prometheus as a long term storage remote read/write endpoint](/integrations/prometheus). diff --git a/docs/content/how_to/grafana.md b/docs/content/how_to/grafana.md new file mode 100644 index 0000000000..2a9a205f94 --- /dev/null +++ b/docs/content/how_to/grafana.md @@ -0,0 +1,35 @@ +--- +title: "Integrating with Grafana" +date: 2020-04-21T20:53:35-04:00 +draft: true +--- + +### Grafana +You can also set up m3query as a datasource in Grafana. To do this, add a new datasource with a type of Prometheus. The URL should point to the host/port running m3query. By default, m3query runs on port 7201. + +#### Querying With Grafana +When using the Prometheus integration with Grafana, there are two different ways you can query for your metrics. The first option is to configure Grafana to query Prometheus directly by following these instructions. +Alternatively, you can configure Grafana to read metrics directly from M3Coordinator in which case you will bypass Prometheus entirely and use M3's PromQL engine instead. To set this up, follow the same instructions from the previous step, but set the url to: http://:7201. + +### Querying +M3 supports the the majority of graphite query functions and can be used to query metrics that were ingested via the ingestion pathway described above. + +### Grafana +M3Coordinator implements the Graphite source interface, so you can add it as a graphite source in Grafana by following these instructions. +Note that you'll need to set the URL to: http://:7201/api/v1/graphite +Direct +You can query for metrics directly by issuing HTTP GET requests directly against the M3Coordinator /api/v1/graphite/render endpoint which runs on port 7201 by default. For example: +(export now=$(date +%s) && curl "localhost:7201/api/v1/graphite/render?target=transformNull(foo.*.baz)&from=$(($now-300))" | jq .) + +will query for all metrics matching the foo.*.baz pattern, applying the transformNull function, and returning all datapoints for the last 5 minutes. + +### Grafana +M3 supports a variety of Grafana integrations. + +#### Prometheus / Graphite Sources +M3Coordinator can function as a datasource for Prometheus as well as Graphite. See the Prometheus integration and Graphite integration documents respectively for more information. +#### Pre-configured Prometheus Dashboards +All M3 applications expose Prometheus metrics on port 7203 by default as described in the Prometheus integration guide, so if you're already monitoring your M3 stack with Prometheus and Grafana you can use our pre-configured dashboards. +M3DB Prometheus / Grafana dashboard +M3Coordinator Prometheus / Grafana dashboard: TODO +Alternatively, you can obtain the JSON for our most up-to-date dashboards from our git repo directly. diff --git a/docs/content/how_to/graphite.md b/docs/content/how_to/graphite.md new file mode 100644 index 0000000000..a5a5b77217 --- /dev/null +++ b/docs/content/how_to/graphite.md @@ -0,0 +1,120 @@ +--- +title: "Integrating with Graphite" +date: 2020-04-21T20:52:20-04:00 +draft: true +--- + +### Graphite +This document is a getting started guide to integrating the M3 stack with Graphite. + +### Overview +M3 supports ingesting Graphite metrics using the Carbon plaintext protocol. We also support a variety of aggregation and storage policies for the ingestion pathway (similar to storage-schemas.conf when using Graphite Carbon) that are documented below. Finally, on the query side, we support the majority of graphite query functions. + +#### Ingestion +Setting up the M3 stack to ingest carbon metrics is straightforward. First, make sure you've followed our other documentation to get m3coordinator and M3DB setup. Also, familiarize yourself with how M3 handles aggregation. + +Once you have both of those services running properly, modify your m3coordinator configuration to add the following lines and restart it: +carbon: + ingester: + listenAddress: "0.0.0.0:7204" + +This will enable a line-based TCP carbon ingestion server on the specified port. By default, the server will write all carbon metrics to every aggregated namespace specified in the m3coordinator configuration file and aggregate them using a default strategy of mean (equivalent to Graphite's Average). +This default setup makes sense if your carbon metrics are unaggregated, however, if you've already aggregated your data using something like statsite then you may want to disable M3 aggregation. In that case, you can do something like the following: +carbon: + ingester: + listenAddress: "0.0.0.0:7204" + rules: + - pattern: .* + aggregation: + enabled: false + policies: + - resolution: 1m + retention: 48h + +This replaces M3's default behavior with a single rule which states that all metrics (since .* will match any string) should be written to whichever aggregated M3DB namespace has been configured with a resolution of 1 minute and a retention of 48 hours, bypassing aggregation / downsampling altogether. Note that there must be a configured M3DB namespace with the specified resolution/retention or the coordinator will fail to start. +In the situation that you choose to use M3's aggregation functionality, there are a variety of aggregation types you can choose from. For example: +carbon: + ingester: + listenAddress: "0.0.0.0:7204" + rules: + - pattern: .* + aggregation: + type: last + policies: + - resolution: 1m + retention: 48h + +The config above will aggregate ingested carbon metrics into 1 minute tiles, but instead of taking the mean of every datapoint, it will emit the last datapoint that was received within a given tile's window. +Similar to Graphite's storage-schemas.conf, M3 carbon ingestion rules are applied in order and only the first pattern that matches is applied. In addition, the rules can be as simple or as complex as you like. For example: +carbon: + ingester: + listenAddress: "0.0.0.0:7204" + rules: + - pattern: stats.internal.financial-service.* + aggregation: + type: max + policies: + - resolution: 1m + retention: 4320h + - resolution: 10s + retention: 24h + - pattern: stats.internal.rest-proxy.* + aggregation: + type: mean + policies: + - resolution: 10s + retention: 2h + - pattern: stats.cloud.* + aggregation: + enabled: false + policies: + - resolution: 1m + retention: 2h + - pattern: .* + aggregation: + type: mean + policies: + - resolution: 1m + retention: 48h + +Lets break that down. +The first rule states that any metric matching the pattern stats.internal.financial-service.* should be aggregated using the max function (meaning the datapoint with the highest value that is received in a given window will be retained) to generate two different tiles, one with 1 minute resolution and another with 10 second resolution which will be written out to M3DB namespaces with 4320 hour and 24 hour retentions respectively. + +The second rule will aggregate all the metrics coming from our rest-proxy service using a mean type aggregation (all datapoints within a given window will be averaged) to generate 10 second tiles and write them out to an M3DB namespace that stores data for two hours. + +The third will match any metrics coming from our cloud environment. In this hypoethical example, our cloud metrics are already aggregated using an application like statsite, so instead of aggregating them again, we just write them directly to an M3DB namespace that retains data for two hours. Note that while we're not aggregating the data in M3 here, we still need to provide a resolution so that the ingester can match the storage policy to a known M3DB namespace, as well as so that when we fan out queries to multiple namespaces we know the resolution of the data contained in each namespace. + +Finally, our last rule uses a "catch-all" pattern to capture any metrics that don't match any of our other rules and aggregate them using the mean function into 1 minute tiles which we store for 48 hours. + +#### Debug mode +If at any time you're not sure which metrics are being matched by which patterns, or want more visibility into how the carbon ingestion rule are being evaluated, modify the config to enable debug mode: +carbon: + ingester: + debug: true + listenAddress: "0.0.0.0:7204" + +This will make the carbon ingestion emit logs for every step that is taking. Note: If your coordinator is ingesting a lot of data, enabling this mode could bring the proccess to a halt due to the I/O overhead, so use this feature cautiously in production environments. + +Supported Aggregation Functions +last +min +max +mean +median +count +sum +sumsq +stdev +p10 +p20 +p30 +p40 +p50 +p60 +p70 +p80 +p90 +p95 +p99 +p999 +p9999 diff --git a/docs/how_to/kubernetes.md b/docs/content/how_to/kubernetes.md similarity index 87% rename from docs/how_to/kubernetes.md rename to docs/content/how_to/kubernetes.md index afc459c2ed..f68e29a034 100644 --- a/docs/how_to/kubernetes.md +++ b/docs/content/how_to/kubernetes.md @@ -1,13 +1,17 @@ -# M3DB on Kubernetes +--- +title: M3DB on Kubernetes +weight: 3 +--- + **Please note:** If possible _[PLEASE USE THE OPERATOR](https://operator.m3db.io/)_ to deploy to Kubernetes if you -can. It is a considerly more streamlined setup. +can. It is a considerably more streamlined setup. The [operator](https://operator.m3db.io/) leverages [custom resource definitions](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) (CRDs) to automatically handle operations such as managing cluster topology. The guide below provides static manifests to bootstrap a cluster on Kubernetes and should be considered -as a guide to running M3 on Kubernetes, if and only if you have significant custom requirements not satisified by +as a guide to running M3 on Kubernetes, if and only if you have significant custom requirements not satisfied by the operator. ## Prerequisites @@ -21,7 +25,8 @@ Because of this, the included manifests reference a provided to provide such a StorageClass on AWS / Azure / GCP using the respective cloud provider's premium disk class. If you do not already have a StorageClass named `fast`, create one using one of the provided manifests: -``` + +```shell # AWS EBS (class io1) kubectl apply -f https://raw.githubusercontent.com/m3db/m3/master/kube/storage-fast-aws.yaml @@ -49,23 +54,25 @@ similar enforcement mechanism that prevents adding capabilities to containers. ## Deploying Apply the following manifest to create your cluster: -``` + +```shell kubectl apply -f https://raw.githubusercontent.com/m3db/m3/master/kube/bundle.yaml ``` Applying this bundle will create the following resources: -1. An `m3db` [Namespace](https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/) for - all M3DB-related resources. -2. A 3-node etcd cluster in the form of a - [StatefulSet](https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/) backed by persistent - remote SSDs. This cluster stores the DB topology and other runtime configuration data. -3. A 3-node M3DB cluster in the form of a StatefulSet. -4. [Headless services](https://kubernetes.io/docs/concepts/services-networking/dns-pod-service/#services) for - the etcd and m3db StatefulSets to provide stable DNS hostnames per-pod. +1. An `m3db` [Namespace](https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/) for + all M3DB-related resources. +2. A 3-node etcd cluster in the form of a + [StatefulSet](https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/) backed by persistent + remote SSDs. This cluster stores the DB topology and other runtime configuration data. +3. A 3-node M3DB cluster in the form of a StatefulSet. +4. [Headless services](https://kubernetes.io/docs/concepts/services-networking/dns-pod-service/#services) for + the etcd and m3db StatefulSets to provide stable DNS hostnames per-pod. Wait until all created pods are listed as ready: -``` + +```shell $ kubectl -n m3db get po NAME READY STATUS RESTARTS AGE etcd-0 1/1 Running 0 22m @@ -78,14 +85,15 @@ m3dbnode-2 1/1 Running 0 22m You can now proceed to initialize a namespace and placement for the cluster the same as you would for our other how-to guides: -``` + +```shell # Open a local connection to the coordinator service: $ kubectl -n m3db port-forward svc/m3coordinator 7201 Forwarding from 127.0.0.1:7201 -> 7201 Forwarding from [::1]:7201 -> 7201 ``` -```json +```shell # Create an initial cluster topology curl -sSf -X POST localhost:7201/api/v1/placement/init -d '{ "num_shards": 1024, @@ -122,7 +130,7 @@ curl -sSf -X POST localhost:7201/api/v1/placement/init -d '{ }' ``` -```json +```shell # Create a namespace to hold your metrics curl -X POST localhost:7201/api/v1/namespace -d '{ "name": "default", @@ -150,7 +158,8 @@ curl -X POST localhost:7201/api/v1/namespace -d '{ ``` Shortly after you should see your nodes finish bootstrapping: -``` + +```shell $ kubectl -n m3db logs -f m3dbnode-0 21:36:54.831698[I] cluster database initializing topology 21:36:54.831732[I] cluster database resolving topology @@ -169,13 +178,14 @@ $ kubectl -n m3db logs -f m3dbnode-0 ``` You can now write and read metrics using the API on the DB nodes: -``` + +```shell $ kubectl -n m3db port-forward svc/m3dbnode 9003 Forwarding from 127.0.0.1:9003 -> 9003 Forwarding from [::1]:9003 -> 9003 ``` -```json +```shell curl -sSf -X POST localhost:9003/writetagged -d '{ "namespace": "default", "id": "foo", @@ -196,7 +206,7 @@ curl -sSf -X POST localhost:9003/writetagged -d '{ }' ``` -```json +```shell $ curl -sSf -X POST http://localhost:9003/query -d '{ "namespace": "default", "query": { @@ -238,18 +248,20 @@ $ curl -sSf -X POST http://localhost:9003/query -d '{ ### Adding nodes You can easily scale your M3DB cluster by scaling the StatefulSet and informing the cluster topology of the change: -``` + +```shell kubectl -n m3db scale --replicas=4 statefulset/m3dbnode ``` Once the pod is ready you can modify the cluster topology: -``` + +```shell kubectl -n m3db port-forward svc/m3coordinator 7201 Forwarding from 127.0.0.1:7201 -> 7201 Forwarding from [::1]:7201 -> 7201 ``` -```json +```shell curl -sSf -X POST localhost:7201/api/v1/placement -d '{ "instances": [ { @@ -269,12 +281,12 @@ curl -sSf -X POST localhost:7201/api/v1/placement -d '{ ### Prometheus -As mentioned in our integrations [guide](../integrations/prometheus.md), M3DB can be used as a [remote read/write +As mentioned in our integrations [guide](/integrations/prometheus), M3DB can be used as a [remote read/write endpoint](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#%3Cremote_write%3E) for Prometheus. If you run Prometheus on your Kubernetes cluster you can easily point it at M3DB in your Prometheus server config: -``` +```YAML remote_read: - url: "http://m3coordinator.m3db.svc.cluster.local:7201/api/v1/prom/remote/read" # To test reading even when local Prometheus has the data @@ -297,8 +309,8 @@ affinities](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/) [tolerations](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/) to prefer to run on certain nodes. Specifically: -1. The pods tolerate the taint `"dedicated-m3db"` to run on nodes that are specifically dedicated to m3db if you so - choose. -2. Via `nodeAffinity` the pods prefer to run on nodes with the label `m3db.io/dedicated-m3db="true"`. +1. The pods tolerate the taint `"dedicated-m3db"` to run on nodes that are specifically dedicated to m3db if you so + choose. +2. Via `nodeAffinity` the pods prefer to run on nodes with the label `m3db.io/dedicated-m3db="true"`. -[kernel]: ../operational_guide/kernel_configuration.md +[kernel]: /operational_guide/kernel_configuration diff --git a/docs/content/how_to/monitoring_m3/_index.md b/docs/content/how_to/monitoring_m3/_index.md new file mode 100644 index 0000000000..15fdd1f3cf --- /dev/null +++ b/docs/content/how_to/monitoring_m3/_index.md @@ -0,0 +1,6 @@ +--- +title: "Monitoring M3" +date: 2020-04-21T20:56:58-04:00 +draft: true +--- + diff --git a/docs/content/how_to/monitoring_m3/alerts.md b/docs/content/how_to/monitoring_m3/alerts.md new file mode 100644 index 0000000000..f621820083 --- /dev/null +++ b/docs/content/how_to/monitoring_m3/alerts.md @@ -0,0 +1,6 @@ +--- +title: "Alerts" +date: 2020-04-21T20:56:27-04:00 +draft: true +--- + diff --git a/docs/content/how_to/monitoring_m3/metrics.md b/docs/content/how_to/monitoring_m3/metrics.md new file mode 100644 index 0000000000..9ac42b158e --- /dev/null +++ b/docs/content/how_to/monitoring_m3/metrics.md @@ -0,0 +1,6 @@ +--- +title: "Metrics" +date: 2020-04-21T20:56:34-04:00 +draft: true +--- + diff --git a/docs/content/how_to/monitoring_m3/tracing.md b/docs/content/how_to/monitoring_m3/tracing.md new file mode 100644 index 0000000000..65303127ff --- /dev/null +++ b/docs/content/how_to/monitoring_m3/tracing.md @@ -0,0 +1,55 @@ +--- +title: "Tracing" +date: 2020-04-21T20:56:40-04:00 +draft: true +--- + +### Tracing +M3DB is integrated with opentracing to provide insight into query performance and errors. + +#### Jaeger +To enable Jaeger as the tracing backend, set tracing.backend to "jaeger" (see also our sample local config: +tracing: + backend: jaeger # enables jaeger with default configs + jaeger: + # optional configuration for jaeger -- see + # https://github.com/jaegertracing/jaeger-client-go/blob/master/config/config.go#L37 + # for options + ... + +Jaeger can be run locally with docker as described in https://www.jaegertracing.io/docs/1.9/getting-started/. + +The default configuration will report traces via udp to localhost:6831; using the all-in-one jaeger container, they will be accessible at +http://localhost:16686 + +N.B.: for production workloads, you will almost certainly want to use sampler.type=remote with adaptive sampling for Jaeger, as write volumes are likely orders of magnitude higher than read volumes in most timeseries systems. + +#### LightStep +To use LightStep as the tracing backend, set tracing.backend to "lightstep" and configure necessary information for your client under lightstep. Any options exposed in lightstep-tracer-go can be set in config. Any environment variables may be interpolated. For example: +tracing: + serviceName: m3coordinator + backend: lightstep + lightstep: + access_token: ${LIGHTSTEP_ACCESS_TOKEN:""} + collector: + scheme: https + host: my-satellite-address.domain + port: 8181 + +#### Alternative backends +If you'd like additional backends, we'd love to support them! +File an issue against M3 and we can work with you on how best to add the backend. The first time's going to be a little rough--opentracing unfortunately doesn't support Go plugins (yet--see https://github.com/opentracing/opentracing-go/issues/133), and glide's update model means that adding dependencies directly will update everything, which isn't ideal for an isolated dependency change. These problems are all solvable though, and we'll work with you to make it happen! + +#### Use cases +Note: all URLs assume a local jaeger setup as described in Jaeger's docs. + +**Finding slow queries** +To find prom queries longer than , filter for minDuration >= on operation="GET /api/v1/query_range". +Sample query: http://localhost:16686/search?end=1548876672544000&limit=20&lookback=1h&maxDuration&minDuration=1ms&operation=GET%20%2Fapi%2Fv1%2Fquery_range&service=m3query&start=1548873072544000 + +**Finding queries with errors** +Search for error=true on operation="GET /api/v1/query_range" http://localhost:16686/search?operation=GET%20%2Fapi%2Fv1%2Fquery_range&service=m3query&tags=%7B%22error%22%3A%22true%22%7D + +**Finding 500 (Internal Server Error) responses** +Search for http.status_code=500. +http://localhost:16686/search?limit=20&lookback=24h&maxDuration&minDuration&operation=GET%20%2Fapi%2Fv1%2Fquery_range&service=m3query&start=1548802430108000&tags=%7B"http.status_code"%3A"500"% diff --git a/docs/content/how_to/other/_index.md b/docs/content/how_to/other/_index.md new file mode 100644 index 0000000000..b1d874b66c --- /dev/null +++ b/docs/content/how_to/other/_index.md @@ -0,0 +1,6 @@ +--- +title: "Other" +date: 2020-04-21T20:53:05-04:00 +draft: true +--- + diff --git a/docs/content/how_to/other/tsdb.md b/docs/content/how_to/other/tsdb.md new file mode 100644 index 0000000000..0ca766b2e1 --- /dev/null +++ b/docs/content/how_to/other/tsdb.md @@ -0,0 +1,136 @@ +--- +title: "Integrating with general TSDB" +date: 2020-04-21T20:50:45-04:00 +draft: true +--- + +### Using M3DB as a general purpose time series database + +#### Overview +M3 has native integrations that make it particularly easy to use it as a metrics storage for Prometheus and Graphite. M3DB can also be used as a general purpose distributed time series database by itself. +Data Model + +#### IDs and Tags +M3DB's data model allows multiple namespaces, each of which can be configured and tuned independently. +Each namespace can also be configured with its own schema (see "Schema Modeling" section below). +Within a namespace, each time series is uniquely identified by an ID which can be any valid string / byte array. In addition, tags can be attached to any series which makes the series queryable using the inverted index. + +M3DB's inverted index supports term (exact match) and regular expression queries over all tag values, and individual tag queries can be arbitrarily combined using AND, OR, and NOT operators. +For example, imagine an application that tracks a fleet of vehicles. One potential structure for the time series could be as follows: + + +Timeseries 1 +Timeseries 2 +Timeseries 3 +Timeseries 4 +Timeseries ID +vehicle_id_1 +vehicle_id_2 +vehicle_id_3 +vehicle_id_4 +"type" tag value +sedan +bike +scooter +scooter +"city" tag value +san_francisco +san_francisco +new_york +chicago +"version" tag value +0_1_0 +0_1_0 +0_1_1 +0_1_2 +This would allow users to issue queries that answer questions like: +"What time series IDs exist for any vehicle type operating in San Francisco?" +"What time series IDs exist for scooters that are NOT operating in Chicago?" +"What time series IDs exist where the "version" tag matches the regular expression: 0_1_[12]" +TODO(rartoul): Discuss the ability to perform limited amounts of aggregation queries here as well. +TODO(rartoul): Discuss ID / tags mutability. + +#### Data Points +Each time series in M3DB stores data as a stream of data points in the form of tuples. Timestamp resolution can be as granular as individual nanoseconds. +The value portion of the tuple is a Protobuf message that matches the configured namespace schema, which requires that all values in the current time series must also match this schema. This limitation may be lifted in the future. + +#### Schema Modeling +Every M3DB namespace can be configured with a Protobuf-defined schema that every value in the time series must conform to +For example, continuing with the vehicle fleet tracking example introduced earlier, a schema might look as follows: +syntax = "proto3"; + +message VehicleLocation { + double latitude = 1; + double longitude = 2; + double fuel_percent = 3; + string status = 4; +} + +While M3DB strives to support the entire proto3 language spec, only the following features are currently supported: +Scalar values +Nested messages +Repeated fields +Map fields +Reserved fields +The following features are currently not supported: +Any fields +Oneof fields +Options of any type +Custom field types +Compression + +While M3DB supports schemas that contain nested messages, repeated fields, and map fields, currently it can only effectively compress top level scalar fields. For example, M3DB can compress every field in the following schema: +syntax = "proto3"; + +message VehicleLocation { + double latitude = 1; + double longitude = 2; + double fuel_percent = 3; + string status = 4; +} + +however, it will not apply any form of compression to the attributes field in this schema: +syntax = "proto3"; + +message VehicleLocation { + double latitude = 1; + double longitude = 2; + double fuel_percent = 3; + string status = 4; + map attributes = 5; +} + +While the latter schema is valid, the attributes field will not be compressed; users should weigh the tradeoffs between more expressive schema and better compression for each use case. +For more details on the compression scheme and its limitations, review the documentation for M3DB's compressed Protobuf encoding. + +### Getting Started + +#### M3DB setup +For more advanced setups, it's best to follow the guides on how to configure an M3DB cluster manually or using Kubernetes. However, this tutorial will walk you through configuring a single node setup locally for development. + +First, run the following command to pull the latest M3DB image: +docker pull quay.io/m3db/m3dbnode:latest + +Next, run the following command to start the M3DB container: +docker run -p 7201:7201 -p 7203:7203 -p 9000:9000 -p 9001:9001 -p 9002:9002 -p 9003:9003 -p 9004:9004 -p 2379:2379 --name m3db -v $(pwd)/m3db_data:/var/lib/m3db -v $(pwd)/src/dbnode/config/m3dbnode-local-etcd-proto.yml:/etc/m3dbnode/m3dbnode.yml -v :/etc/m3dbnode/default_schema.proto quay.io/m3db/m3dbnode:latest + +#### Breaking that down: +All the -p flags expose the necessary ports. +The -v $(pwd)/m3db_data:/var/lib/m3db section creates a bind mount that enables M3DB to persist data between container restarts. +The -v :/etc/m3dbnode/m3dbnode.yml section mounts the specified configuration file in the container which allows configuration changes by restarting the container (rather than rebuilding it). This example file can be used as a good starting point. It configures the database to have the Protobuf feature enabled and expects one namespace with the name default and a Protobuf message name of VehicleLocation for the schema. You'll need to update that portion of the config if you intend to use a different schema than the example one used throughout this document. Note that hard-coding paths to the schema should only be done for local development and testing. For production use-cases, M3DB supports storing the current schema in etcd so that it can be update dynamically. TODO(rartoul): Document how to do that as well as what kind of schema changes are safe / backwards compatible. +The -v :/etc/m3dbnode/default_schema.proto section mounts the Protobuf file containing the schema in the container, similar to the configuration file this allows the schema to be changed by restarting the container instead of rebuilding it. You can use this example schema as a starting point. Is is also the same example schema that is used by the sample Go program discussed below in the "Clients" section. Also see the bullet point above about not hard coding schema files in production. +Once the M3DB container has started, issue the following CURL statement to create the default namespace: +curl -X POST http://localhost:7201/api/v1/database/create -d '{ + "type": "local", + "namespaceName": "default", + "retentionTime": "4h" +}' + +Note that the retentionTime is set artificially low to conserve resources. +After a few moments, the M3DB container should finish bootstrapping. At this point it should be ready to serve write and read queries. + +#### Clients +Note: M3DB only has a Go client; this is unlikely to change in the future due to the fact that the client is "fat" and contains a substantial amount of logic that would be difficult to port to other languages. +Users interested in interacting with M3DB directly from Go applications can reference this runnable example to get an understanding of how to interact with M3DB in Go. Note that the example above uses the same default namespace and VehicleLocation schema used throughout this document so it can be run directly against an M3DB docker container setup using the "M3DB setup" instructions above. +M3DB will eventually support other languages by exposing an M3Coordinator endpoint which will allow users to write/read from M3DB directly using GRPC/JSON. + diff --git a/docs/content/how_to/other/upgrading.md b/docs/content/how_to/other/upgrading.md new file mode 100644 index 0000000000..714916c75d --- /dev/null +++ b/docs/content/how_to/other/upgrading.md @@ -0,0 +1,71 @@ +--- +title: "Upgrading M3" +date: 2020-04-21T20:50:39-04:00 +draft: true +--- + +### Overview +This guide explains how to upgrade M3 from one version to another (e.g. from 0.14.0 to 0.15.0). This includes upgrading: +m3dbnode +m3coordinator +m3query +m3aggregator +m3dbnode + +#### Graphs to monitor +While upgrading M3DB nodes, it's important to monitor the status of bootstrapping the individual nodes. This can be monitored using the M3DB Node Details graph. Typically, the Bootstrapped graph under Background Tasks and the graphs within the CPU and Memory Utilization give a good understanding of how well bootstrapping is going. + +#### Non-Kubernetes +It is very important that for each replica set, only one node gets upgraded at a time. However, multiple nodes can be upgraded across replica sets. + +1) Download new binary (linux example below). +wget "https://github.com/m3db/m3/releases/download/v$VERSION/m3_$VERSION_linux_amd64.tar.gz" && tar xvzf m3_$VERSION_linux_amd64.tar.gz && rm m3_$VERSION_linux_amd64.tar.gz + +2) Stop and upgrade one M3DB node at a time per replica set using the systemd unit. +# stop m3dbnode +sudo systemctl stop m3dbnode + +# start m3dbnode with the new binary (which should be placed in the path specified in the systemd unit) +sudo systemctl start m3dbnode + +Note: If unable to stop m3dbnode using systemctl, use pkill instead. +# stop m3dbnode +pkill m3dbnode + +# start m3dbnode with new binary +./m3_$VERSION_linux_amd64/m3dbnode -f + +3) Confirm m3dbnode has finished bootstrapping. +20:10:12.911218[I] updating database namespaces [{adds [default]} {updates []} {removals []}] +20:10:13.462798[I] node tchannelthrift: listening on 0.0.0.0:9000 +20:10:13.463107[I] cluster tchannelthrift: listening on 0.0.0.0:9001 +20:10:13.747173[I] node httpjson: listening on 0.0.0.0:9002 +20:10:13.747506[I] cluster httpjson: listening on 0.0.0.0:9003 +20:10:13.747763[I] bootstrapping shards for range starting ... +... +20:10:13.757834[I] bootstrap finished [{namespace metrics} {duration 10.1261ms}] +20:10:13.758001[I] bootstrapped +20:10:14.764771[I] successfully updated topology to 3 hosts + +4) Repeat steps 2 and 3 until all nodes have been upgraded. +Kubernetes +If running M3DB on Kubernetes, upgrade by completing the following steps. +Identify the version of m3dbnode to upgrade to on Quay. +Replace the Docker image in the StatefulSet manifest (or m3db-operator manifest) to be the new version of m3dbnode. +spec: + image: quay.io/m3db/m3dbnode:$VERSION + +Once updated, apply the updated manifest and a rolling restart will be performed. +kubectl apply -f + +### Downgrading +The upgrading steps above can also be used to downgrade M3DB. However, it is important to refer to the release notes to make sure that versions are backwards compatible. +m3coordinator +m3coordinator can be upgraded using similar steps as m3dbnode, however, the images can be found here instead. +m3query +m3query can be upgraded using similar steps as m3dbnode, however, the images can be found here instead. +m3aggregator +m3aggregator can be upgraded using similar steps as m3dbnode, however, the images can be found here instead. + + + diff --git a/docs/content/how_to/prometheus.md b/docs/content/how_to/prometheus.md new file mode 100644 index 0000000000..6e4853f96c --- /dev/null +++ b/docs/content/how_to/prometheus.md @@ -0,0 +1,117 @@ +--- +title: "Integrating with Prometheus" +date: 2020-04-21T20:48:58-04:00 +draft: true +--- + +### Prometheus +As mentioned in our integrations guide, M3DB can be used as a remote read/write endpoint for Prometheus. +If you run Prometheus on your Kubernetes cluster you can easily point it at M3DB in your Prometheus server config: + +remote_read: + - url: "http://m3coordinator.m3db.svc.cluster.local:7201/api/v1/prom/remote/read" + # To test reading even when local Prometheus has the data + read_recent: true + +remote_write: + - url: "http://m3coordinator.m3db.svc.cluster.local:7201/api/v1/prom/remote/write" + # To differentiate between local and remote storage we will add a storage label + write_relabel_configs: + - target_label: metrics_storage + replacement: m3db_remote + + +### M3 Coordinator configuration +To write to a remote M3DB cluster the simplest configuration is to run m3coordinator as a sidecar alongside Prometheus. + +Start by downloading the config template. Update the namespaces and the client section for a new cluster to match your cluster's configuration. + +You'll need to specify the static IPs or hostnames of your M3DB seed nodes, and the name and retention values of the namespace you set up. You can leave the namespace storage metrics type as unaggregated since it's required by default to have a cluster that receives all Prometheus metrics unaggregated. In the future you might also want to aggregate and downsample metrics for longer retention, and you can come back and update the config once you've setup those clusters. You can read more about our aggregation functionality here. + +It should look something like: +listenAddress: + type: "config" + value: "0.0.0.0:7201" + +logging: + level: info + +metrics: + scope: + prefix: "coordinator" + prometheus: + handlerPath: /metrics + listenAddress: 0.0.0.0:7203 # until https://github.com/m3db/m3/issues/682 is resolved + sanitization: prometheus + samplingRate: 1.0 + extended: none + +tagOptions: + idScheme: quoted + +clusters: + - namespaces: +# We created a namespace called "default" and had set it to retention "48h". + - namespace: default + retention: 48h + type: unaggregated + client: + config: + service: + env: default_env + zone: embedded + service: m3db + cacheDir: /var/lib/m3kv + etcdClusters: + - zone: embedded + endpoints: +# We have five M3DB nodes but only three are seed nodes, they are listed here. + - M3DB_NODE_01_STATIC_IP_ADDRESS:2379 + - M3DB_NODE_02_STATIC_IP_ADDRESS:2379 + - M3DB_NODE_03_STATIC_IP_ADDRESS:2379 + writeConsistencyLevel: majority + readConsistencyLevel: unstrict_majority + writeTimeout: 10s + fetchTimeout: 15s + connectTimeout: 20s + writeRetry: + initialBackoff: 500ms + backoffFactor: 3 + maxRetries: 2 + jitter: true + fetchRetry: + initialBackoff: 500ms + backoffFactor: 2 + maxRetries: 3 + jitter: true + backgroundHealthCheckFailLimit: 4 + backgroundHealthCheckFailThrottleFactor: 0.5 + +Now start the process up: +m3coordinator -f + +Or, use the docker container: +docker pull quay.io/m3db/m3coordinator:latest +docker run -p 7201:7201 --name m3coordinator -v :/etc/m3coordinator/m3coordinator.yml quay.io/m3db/m3coordinator:latest + +### Prometheus configuration +Add to your Prometheus configuration the m3coordinator sidecar remote read/write endpoints, something like: +remote_read: + - url: "http://localhost:7201/api/v1/prom/remote/read" + # To test reading even when local Prometheus has the data + read_recent: true +remote_write: + - url: "http://localhost:7201/api/v1/prom/remote/write" + +Also, we recommend adding M3DB and M3Coordinator/M3Query to your list of jobs under scrape_configs so that you can monitor them using Prometheus. With this scraping setup, you can also use our pre-configured M3DB Grafana dashboard. +- job_name: 'm3db' + static_configs: + - targets: [':7203', ':7203', ':7203'] +- job_name: 'm3coordinator' + static_configs: + - targets: [':7203'] + +NOTE: If you are running M3DB with embedded M3Coordinator, you should only have one job. We recommend just calling this job m3. For example: +- job_name: 'm3' + static_configs: + - targets: [':7203'] diff --git a/docs/how_to/query.md b/docs/content/how_to/query.md similarity index 83% rename from docs/how_to/query.md rename to docs/content/how_to/query.md index 0c442aed61..1bc279732e 100644 --- a/docs/how_to/query.md +++ b/docs/content/how_to/query.md @@ -1,18 +1,21 @@ -# Setting up m3query +--- +title: Setting up m3query +menuTitle: M3Query +weight: 4 +--- -## Introduction -m3query is used to query data that is stored in M3DB. For instance, if you are using the Prometheus remote write endpoint with [m3coordinator](../integrations/prometheus.md), you can use m3query instead of the Prometheus remote read endpoint. By doing so, you get all of the benefits of m3query's engine such as [block processing](../m3query/architecture/blocks.md). Furthermore, since m3query provides a Prometheus compatible API, you can use 3rd party graphing and alerting solutions like Grafana. +m3query is used to query data that is stored in M3DB. For instance, if you are using the Prometheus remote write endpoint with [m3coordinator](/integrations/prometheus), you can use m3query instead of the Prometheus remote read endpoint. By doing so, you get all of the benefits of m3query's engine such as [block processing](/m3query/architecture/blocks/). Furthermore, since m3query provides a Prometheus compatible API, you can use 3rd party graphing and alerting solutions like Grafana. ## Configuration -Before setting up m3query, make sure that you have at least [one M3DB node running](single_node.md). In order to start m3query, you need to configure a `yaml` file, that will be used to connect to M3DB. Here is a link to a [sample config](https://github.com/m3db/m3/blob/master/src/query/config/m3query-local-etcd.yml) file that is used for an embedded etcd cluster within M3DB. +Before setting up m3query, make sure that you have at least [one M3DB node running](/how_to/single_node). In order to start m3query, you need to configure a `yaml` file, that will be used to connect to M3DB. Here is a link to a [sample config](https://github.com/m3db/m3/blob/master/src/query/config/m3query-local-etcd.yml) file that is used for an embedded etcd cluster within M3DB. ### Running You can run m3query by either building and running the binary yourself: -```bash +```shell make m3query ./bin/m3query -f ./src/query/config/m3query-local-etcd.yml ``` @@ -21,11 +24,11 @@ Or you can run it with Docker using the Docker file located at `$GOPATH/src/gith ### Namespaces -All namespaces that you wish to query from must be configured when [setting up M3DB](single_node.md). If you wish to add or change an existing namespace, please follow the namespace operational guide [here](../operational_guide/namespace_configuration.md). +All namespaces that you wish to query from must be configured when [setting up M3DB](/how_to/single_node). If you wish to add or change an existing namespace, please follow the namespace operational guide [here](/operational_guide/namespace_configuration). ### etcd -The configuration file linked above uses an embedded etcd cluster, which is fine for development purposes. However, if you wish to use this in production, you will want an [external etcd](../operational_guide/etcd.md) cluster. +The configuration file linked above uses an embedded etcd cluster, which is fine for development purposes. However, if you wish to use this in production, you will want an [external etcd](/operational_guide/etcd) cluster. @@ -65,7 +68,7 @@ tagOptions: As an example of how these schemes generate IDs, consider a series with the following 4 tags, `[{"t1":v1}, {t2:"v2"}, {t3:v3}, {t4:v4}]`. The following is an example of how different schemes will generate IDs. -``` +```shell legacy: "t1"=v1,t2="v2",t3=v3,t4=v4, prepend_meta: 4,2,2,4,2,2,2,2!"t1"v1t2"v2"t3v3t4v4 quoted: {\"t1\"="v1",t2="\"v2\"",t3="v3",t4="v4"} diff --git a/docs/how_to/single_node.md b/docs/content/how_to/single_node.md similarity index 70% rename from docs/how_to/single_node.md rename to docs/content/how_to/single_node.md index b0e505b799..9dada5fe52 100644 --- a/docs/how_to/single_node.md +++ b/docs/content/how_to/single_node.md @@ -1,26 +1,31 @@ -# M3DB Single Node Deployment +--- +weight: 1 +title: M3DB Single Node Deployment +--- Deploying a single-node cluster is a great way to experiment with M3DB and get a feel for what it has to offer. Our Docker image by default configures a single M3DB instance as one binary containing: -- An M3DB storage instance (`m3dbnode`) for timeseries storage. This includes an embedded tag-based - metrics index, as well as as an embedded etcd server for storing the above mentioned cluster - topology and runtime configuration. -- A "coordinator" instance (`m3coordinator`) for writing and querying tagged metrics, as well as - managing cluster topology and runtime configuration. +- An M3DB storage instance (`m3dbnode`) for timeseries storage. This includes an embedded tag-based + metrics index, as well as as an embedded etcd server for storing the above mentioned cluster + topology and runtime configuration. +- A "coordinator" instance (`m3coordinator`) for writing and querying tagged metrics, as well as + managing cluster topology and runtime configuration. To begin, first start up a Docker container with port `7201` (used to manage the cluster topology), port `7203` which is where Prometheus scrapes metrics produced by `M3DB` and `M3Coordinator`, and port `9003` (used to read and write metrics) exposed. We recommend you create a persistent data directory on your host for durability: -``` +```shell docker pull quay.io/m3db/m3dbnode:latest docker run -p 7201:7201 -p 7203:7203 -p 9003:9003 --name m3db -v $(pwd)/m3db_data:/var/lib/m3db quay.io/m3db/m3dbnode:latest ``` -**Note:** For the single node case, we use this [sample config file](https://github.com/m3db/m3/blob/master/src/dbnode/config/m3dbnode-local-etcd.yml). If you inspect the file, you'll see that all the configuration is namespaced by `coordinator` or `db`. That's because this setup runs `M3DB` and `M3Coordinator` as one application. While this is convenient for testing and development, you'll want to run clustered `M3DB` with a separate `M3Coordinator` in production. You can read more about that [here.](cluster_hard_way.md). +**Note:** For the single node case, we use this [sample config file](https://github.com/m3db/m3/blob/master/src/dbnode/config/m3dbnode-local-etcd.yml). If you inspect the file, you'll see that all the configuration is grouped by `coordinator` or `db`. That's because this setup runs `M3DB` and `M3Coordinator` as one application. While this is convenient for testing and development, you'll want to run clustered `M3DB` with a separate `M3Coordinator` in production. You can read more about that [here.](/how_to/cluster_hard_way). + +Next, create an initial namespace for your metrics in the database using the cURL below. Keep in mind that the provided `namespaceName` must match the namespace in the `local` section of the `M3Coordinator` YAML configuration, and if you choose to [add any additional namespaces](/operational_guide/namespace_configuration) you'll need to add them to the `local` section of `M3Coordinator`'s YAML config as well. -Next, create an initial namespace for your metrics in the database using the cURL below. Keep in mind that the provided `namespaceName` must match the namespace in the `local` section of the `M3Coordinator` YAML configuration, and if you choose to [add any additional namespaces](../operational_guide/namespace_configuration.md) you'll need to add them to the `local` section of `M3Coordinator`'s YAML config as well. + ```json curl -X POST http://localhost:7201/api/v1/database/create -d '{ @@ -30,11 +35,11 @@ curl -X POST http://localhost:7201/api/v1/database/create -d '{ }' ``` -**Note**: The `api/v1/database/create` endpoint is abstraction over two concepts in M3DB called [placements](../operational_guide/placement.md) and [namespaces](../operational_guide/namespace_configuration.md). If a placement doesn't exist, it will create one based on the `type` argument, otherwise if the placement already exists, it just creates the specified namespace. For now it's enough to just understand that it creates M3DB namespaces (tables), but if you're going to run a clustered M3 setup in production, make sure you familiarize yourself with the links above. +**Note**: The `api/v1/database/create` endpoint is abstraction over two concepts in M3DB called [placements](/operational_guide/placement) and [namespaces](/operational_guide/namespace_configuration). If a placement doesn't exist, it will create one based on the `type` argument, otherwise if the placement already exists, it just creates the specified namespace. For now it's enough to just understand that it creates M3DB namespaces (tables), but if you're going to run a clustered M3 setup in production, make sure you familiarize yourself with the links above. Placement initialization may take a minute or two and you can check on the status of this by running the following: -``` +```shell curl http://localhost:7201/api/v1/placement | jq . ``` @@ -42,7 +47,7 @@ Once all of the shards become `AVAILABLE`, you should see your node complete boo /var/lib/m3kv/m3db_embedded.json`. Those are expected for a local instance and in general any warn-level errors (prefixed with `[W]`) should not block bootstrapping. -``` +```shell 02:28:30.008072[I] updating database namespaces [{adds [default]} {updates []} {removals []}] 02:28:30.270681[I] node tchannelthrift: listening on 0.0.0.0:9000 02:28:30.271909[I] cluster tchannelthrift: listening on 0.0.0.0:9001 @@ -55,9 +60,10 @@ warn-level errors (prefixed with `[W]`) should not block bootstrapping. The node also self-hosts its OpenAPI docs, outlining available endpoints. You can access this by going to `localhost:7201/api/v1/openapi` in your browser. -![OpenAPI Doc](redoc_screenshot.png) +![OpenAPI Doc](/redoc_screenshot.png) Now you can experiment with writing tagged metrics: + ```json curl -sS -X POST http://localhost:9003/writetagged -d '{ "namespace": "default", @@ -86,14 +92,15 @@ curl -sS -X POST http://localhost:9003/writetagged -d '{ **Note:** In the above example we include the tag `__name__`. This is because `__name__` is a reserved tag in Prometheus and will make querying the metric much easier. For example, if you have -[M3Query](query.md) setup as a Prometheus datasource in Grafana, you can then query for the metric +[M3Query](/how_to/query) setup as a Prometheus datasource in Grafana, you can then query for the metric using the following PromQL query: -``` +```shell user_login{city="new_york",endpoint="/request"} ``` And reading the metrics you've written using the M3DB `/query` endpoint: + ```json curl -sS -X POST http://localhost:9003/query -d '{ "namespace": "default", @@ -137,4 +144,4 @@ curl -sS -X POST http://localhost:9003/query -d '{ } ``` -Now that you've got the M3 stack up and running, take a look at the rest of our documentation to see how you can integrate with [Prometheus](../integrations/prometheus.md) and [Graphite](../integrations/graphite.md) +Now that you've got the M3 stack up and running, take a look at the rest of our documentation to see how you can integrate with [Prometheus](/integrations/prometheus) and [Graphite](/integrations/graphite) diff --git a/docs/how_to/use_as_tsdb.md b/docs/content/how_to/use_as_tsdb.md similarity index 57% rename from docs/how_to/use_as_tsdb.md rename to docs/content/how_to/use_as_tsdb.md index 7ba22021e2..90c2caba8e 100644 --- a/docs/how_to/use_as_tsdb.md +++ b/docs/content/how_to/use_as_tsdb.md @@ -1,14 +1,18 @@ -# Using M3DB as a general purpose time series database +--- +weight: 6 +title: Using M3DB as a general purpose time series database +--- + ## Overview -M3 has native integrations that make it particularly easy to use it as a metrics storage for [Prometheus](../integrations/prometheus.md) and [Graphite](../integrations/graphite.md). M3DB can also be used as a general purpose distributed time series database by itself. +M3 has native integrations that make it particularly easy to use it as a metrics storage for [Prometheus](/integrations/prometheus) and [Graphite](/integrations/graphite). M3DB can also be used as a general purpose distributed time series database by itself. ## Data Model ### IDs and Tags -M3DB's data model allows multiple namespaces, each of which can be [configured and tuned independently](../operational_guide/namespace_configuration.md). +M3DB's data model allows multiple namespaces, each of which can be [configured and tuned independently](/operational_guide/namespace_configuration). Each namespace can also be configured with its own schema (see "Schema Modeling" section below). @@ -18,18 +22,18 @@ M3DB's inverted index supports term (exact match) and regular expression queries For example, imagine an application that tracks a fleet of vehicles. One potential structure for the time series could be as follows: -| | Timeseries 1 | Timeseries 2 | Timeseries 3 | Timeseries 4 | -|---------------------|---------------|---------------|------------------|------------------| -| Timeseries ID | vehicle_id_1 | vehicle_id_2 | vehicle_id_3 | vehicle_id_4 | -| "type" tag value | sedan | bike | scooter | scooter | -| "city" tag value | san_francisco | san_francisco | new_york | chicago | -| "version" tag value | 0_1_0 | 0_1_0 | 0_1_1 | 0_1_2 | +| | Timeseries 1 | Timeseries 2 | Timeseries 3 | Timeseries 4 | +| ------------------- | ------------- | ------------- | ------------ | ------------ | +| Timeseries ID | vehicle_id_1 | vehicle_id_2 | vehicle_id_3 | vehicle_id_4 | +| "type" tag value | sedan | bike | scooter | scooter | +| "city" tag value | san_francisco | san_francisco | new_york | chicago | +| "version" tag value | 0_1_0 | 0_1_0 | 0_1_1 | 0_1_2 | This would allow users to issue queries that answer questions like: -1. "What time series IDs exist for any vehicle type operating in San Francisco?" -2. "What time series IDs exist for scooters that are NOT operating in Chicago?" -3. "What time series IDs exist where the "version" tag matches the regular expression: `0_1_[12]`" +1. "What time series IDs exist for any vehicle type operating in San Francisco?" +2. "What time series IDs exist for scooters that are NOT operating in Chicago?" +3. "What time series IDs exist where the "version" tag matches the regular expression: `0_1_[12]`" TODO(rartoul): Discuss the ability to perform limited amounts of aggregation queries here as well. @@ -47,7 +51,7 @@ Every M3DB namespace can be configured with a Protobuf-defined schema that every For example, continuing with the vehicle fleet tracking example introduced earlier, a schema might look as follows: -```Protobuf +```protobuf syntax = "proto3"; message VehicleLocation { @@ -58,20 +62,20 @@ message VehicleLocation { } ``` -While M3DB strives to support the entire [proto3 language spec](https://developers.google.com/protocol-buffers/docs/proto3), only [the following features are currently supported](https://github.com/m3db/m3/blob/master/src/dbnode/encoding/proto/docs/encoding.md): +While M3DB strives to support the entire [proto3 language spec](https://developers.google.com/protocol-buffers/docs/proto3), only [the following features are currently supported](https://github.com/m3db/m3/blob/master/src/dbnode/encoding/proto/docs/encoding): -1. [Scalar values](https://developers.google.com/protocol-buffers/docs/proto3#scalar) -2. Nested messages -3. Repeated fields -4. Map fields -5. Reserved fields +1. [Scalar values](https://developers.google.com/protocol-buffers/docs/proto3#scalar) +2. Nested messages +3. Repeated fields +4. Map fields +5. Reserved fields The following features are currently not supported: -1. `Any` fields -2. [`Oneof` fields](https://developers.google.com/protocol-buffers/docs/proto#oneof) -3. Options of any type -4. Custom field types +1. `Any` fields +2. [`Oneof` fields](https://developers.google.com/protocol-buffers/docs/proto#oneof) +3. Options of any type +4. Custom field types #### Compression @@ -104,37 +108,32 @@ message VehicleLocation { While the latter schema is valid, the attributes field will not be compressed; users should weigh the tradeoffs between more expressive schema and better compression for each use case. -For more details on the compression scheme and its limitations, review [the documentation for M3DB's compressed Protobuf encoding](https://github.com/m3db/m3/blob/master/src/dbnode/encoding/proto/docs/encoding.md). - +For more details on the compression scheme and its limitations, review [the documentation for M3DB's compressed Protobuf encoding](https://github.com/m3db/m3/blob/master/src/dbnode/encoding/proto/docs/encoding). ### Getting Started #### M3DB setup -For more advanced setups, it's best to follow the guides on how to configure an M3DB cluster [manually](./cluster_hard_way.md) or [using Kubernetes](./kubernetes.md). However, this tutorial will walk you through configuring a single node setup locally for development. +For more advanced setups, it's best to follow the guides on how to configure an M3DB cluster [manually](./cluster_hard_way) or [using Kubernetes](./kubernetes). However, this tutorial will walk you through configuring a single node setup locally for development. First, run the following command to pull the latest M3DB image: -``` -docker pull quay.io/m3db/m3dbnode:latest -``` + docker pull quay.io/m3db/m3dbnode:latest Next, run the following command to start the M3DB container: -``` -docker run -p 7201:7201 -p 7203:7203 -p 9000:9000 -p 9001:9001 -p 9002:9002 -p 9003:9003 -p 9004:9004 -p 2379:2379 --name m3db -v $(pwd)/m3db_data:/var/lib/m3db -v $(pwd)/src/dbnode/config/m3dbnode-local-etcd-proto.yml:/etc/m3dbnode/m3dbnode.yml -v :/etc/m3dbnode/default_schema.proto quay.io/m3db/m3dbnode:latest -``` + docker run -p 7201:7201 -p 7203:7203 -p 9000:9000 -p 9001:9001 -p 9002:9002 -p 9003:9003 -p 9004:9004 -p 2379:2379 --name m3db -v $(pwd)/m3db_data:/var/lib/m3db -v $(pwd)/src/dbnode/config/m3dbnode-local-etcd-proto.yml:/etc/m3dbnode/m3dbnode.yml -v :/etc/m3dbnode/default_schema.proto quay.io/m3db/m3dbnode:latest Breaking that down: -- All the `-p` flags expose the necessary ports. -- The `-v $(pwd)/m3db_data:/var/lib/m3db` section creates a bind mount that enables M3DB to persist data between container restarts. -- The `-v :/etc/m3dbnode/m3dbnode.yml` section mounts the specified configuration file in the container which allows configuration changes by restarting the container (rather than rebuilding it). [This example file](https://github.com/m3db/m3/blob/master/src/dbnode/config/m3dbnode-local-etcd-proto.yml) can be used as a good starting point. It configures the database to have the Protobuf feature enabled and expects one namespace with the name `default` and a Protobuf message name of `VehicleLocation` for the schema. You'll need to update that portion of the config if you intend to use a different schema than the example one used throughout this document. Note that hard-coding paths to the schema should only be done for local development and testing. For production use-cases, M3DB supports storing the current schema in `etcd` so that it can be update dynamically. TODO(rartoul): Document how to do that as well as what kind of schema changes are safe / backwards compatible. -- The `-v :/etc/m3dbnode/default_schema.proto` section mounts the Protobuf file containing the schema in the container, similar to the configuration file this allows the schema to be changed by restarting the container instead of rebuilding it. You can use [this example schema](https://github.com/m3db/m3/tree/master/examples/dbnode/proto_client/schema.proto) as a starting point. Is is also the same example schema that is used by the sample Go program discussed below in the "Clients" section. Also see the bullet point above about not hard coding schema files in production. +- All the `-p` flags expose the necessary ports. +- The `-v $(pwd)/m3db_data:/var/lib/m3db` section creates a bind mount that enables M3DB to persist data between container restarts. +- The `-v :/etc/m3dbnode/m3dbnode.yml` section mounts the specified configuration file in the container which allows configuration changes by restarting the container (rather than rebuilding it). [This example file](https://github.com/m3db/m3/blob/master/src/dbnode/config/m3dbnode-local-etcd-proto.yml) can be used as a good starting point. It configures the database to have the Protobuf feature enabled and expects one namespace with the name `default` and a Protobuf message name of `VehicleLocation` for the schema. You'll need to update that portion of the config if you intend to use a different schema than the example one used throughout this document. Note that hard-coding paths to the schema should only be done for local development and testing. For production use-cases, M3DB supports storing the current schema in `etcd` so that it can be update dynamically. TODO(rartoul): Document how to do that as well as what kind of schema changes are safe / backwards compatible. +- The `-v :/etc/m3dbnode/default_schema.proto` section mounts the Protobuf file containing the schema in the container, similar to the configuration file this allows the schema to be changed by restarting the container instead of rebuilding it. You can use [this example schema](https://github.com/m3db/m3/tree/master/examples/dbnode/proto_client/schema.proto) as a starting point. Is is also the same example schema that is used by the sample Go program discussed below in the "Clients" section. Also see the bullet point above about not hard coding schema files in production. Once the M3DB container has started, issue the following CURL statement to create the `default` namespace: -``` +```shell curl -X POST http://localhost:7201/api/v1/database/create -d '{ "type": "local", "namespaceName": "default", @@ -152,4 +151,4 @@ Note: M3DB only has a Go client; this is unlikely to change in the future due to Users interested in interacting with M3DB directly from Go applications can reference [this runnable example](https://github.com/m3db/m3/tree/master/examples/dbnode/proto_client) to get an understanding of how to interact with M3DB in Go. Note that the example above uses the same `default` namespace and `VehicleLocation` schema used throughout this document so it can be run directly against an M3DB docker container setup using the "M3DB setup" instructions above. -M3DB will eventually support other languages by exposing an `M3Coordinator` endpoint which will allow users to write/read from M3DB directly using GRPC/JSON. \ No newline at end of file +M3DB will eventually support other languages by exposing an `M3Coordinator` endpoint which will allow users to write/read from M3DB directly using GRPC/JSON. diff --git a/docs/content/integrations/_index.md b/docs/content/integrations/_index.md new file mode 100644 index 0000000000..f54fbcb5a1 --- /dev/null +++ b/docs/content/integrations/_index.md @@ -0,0 +1,5 @@ ++++ +title = "Integrations" +weight = 8 +chapter = true ++++ \ No newline at end of file diff --git a/docs/integrations/grafana.md b/docs/content/integrations/grafana.md similarity index 66% rename from docs/integrations/grafana.md rename to docs/content/integrations/grafana.md index 30f32cddd0..c5617cee46 100644 --- a/docs/integrations/grafana.md +++ b/docs/content/integrations/grafana.md @@ -1,14 +1,18 @@ -# Grafana +--- +title: "Grafana" +weight: 3 +--- + M3 supports a variety of Grafana integrations. ## Prometheus / Graphite Sources -M3Coordinator can function as a datasource for Prometheus as well as Graphite. See the [Prometheus integration](./prometheus.md) and [Graphite integration](./graphite.md) documents respectively for more information. +M3Coordinator can function as a datasource for Prometheus as well as Graphite. See the [Prometheus integration](./prometheus) and [Graphite integration](./graphite) documents respectively for more information. ## Pre-configured Prometheus Dashboards -All M3 applications expose Prometheus metrics on port `7203` by default as described in the [Prometheus integration guide](./prometheus.md), so if you're already monitoring your M3 stack with Prometheus and Grafana you can use our pre-configured dashboards. +All M3 applications expose Prometheus metrics on port `7203` by default as described in the [Prometheus integration guide](./prometheus), so if you're already monitoring your M3 stack with Prometheus and Grafana you can use our pre-configured dashboards. [M3DB Prometheus / Grafana dashboard](https://grafana.com/dashboards/8126) diff --git a/docs/integrations/graphite.md b/docs/content/integrations/graphite.md similarity index 95% rename from docs/integrations/graphite.md rename to docs/content/integrations/graphite.md index 465d4dfa8f..db96f9ae13 100644 --- a/docs/integrations/graphite.md +++ b/docs/content/integrations/graphite.md @@ -1,4 +1,7 @@ -# Graphite +--- +title: "Graphite" +weight: 2 +--- This document is a getting started guide to integrating the M3 stack with Graphite. @@ -8,7 +11,7 @@ M3 supports ingesting Graphite metrics using the [Carbon plaintext protocol](htt ## Ingestion -Setting up the M3 stack to ingest carbon metrics is straightforward. First, make sure you've followed our [other documentation](../how_to/single_node.md) to get m3coordinator and M3DB setup. Also, familiarize yourself with how M3 [handles aggregation](../how_to/query.md). +Setting up the M3 stack to ingest carbon metrics is straightforward. First, make sure you've followed our [other documentation](/how_to/single_node) to get m3coordinator and M3DB setup. Also, familiarize yourself with how M3 [handles aggregation](/how_to/query). Once you have both of those services running properly, modify your m3coordinator configuration to add the following lines and restart it: @@ -18,7 +21,7 @@ carbon: listenAddress: "0.0.0.0:7204" ``` -This will enable a line-based TCP carbon ingestion server on the specified port. By default, the server will write all carbon metrics to every aggregated namespace specified in the m3coordinator [configuration file](../how_to/query.md) and aggregate them using a default strategy of `mean` (equivalent to Graphite's `Average`). +This will enable a line-based TCP carbon ingestion server on the specified port. By default, the server will write all carbon metrics to every aggregated namespace specified in the m3coordinator [configuration file](/how_to/query) and aggregate them using a default strategy of `mean` (equivalent to Graphite's `Average`). This default setup makes sense if your carbon metrics are unaggregated, however, if you've already aggregated your data using something like [statsite](https://github.com/statsite/statsite) then you may want to disable M3 aggregation. In that case, you can do something like the following: diff --git a/docs/integrations/influxdb.md b/docs/content/integrations/influx.md similarity index 97% rename from docs/integrations/influxdb.md rename to docs/content/integrations/influx.md index 3333516eeb..66bf2d9f49 100644 --- a/docs/integrations/influxdb.md +++ b/docs/content/integrations/influx.md @@ -1,4 +1,8 @@ -# InfluxDB +--- +title: "InfluxDB" +weight: 4 +--- + This document is a getting started guide to integrating InfluxDB data pipelines with M3. diff --git a/docs/integrations/prometheus.md b/docs/content/integrations/prometheus.md similarity index 98% rename from docs/integrations/prometheus.md rename to docs/content/integrations/prometheus.md index 6d533b4870..ca2c0e580f 100644 --- a/docs/integrations/prometheus.md +++ b/docs/content/integrations/prometheus.md @@ -1,4 +1,7 @@ -# Prometheus +--- +title: "Prometheus" +weight: 1 +--- This document is a getting started guide to integrating M3DB with Prometheus. @@ -8,7 +11,7 @@ To write to a remote M3DB cluster the simplest configuration is to run `m3coordi Start by downloading the [config template](https://github.com/m3db/m3/blob/master/src/query/config/m3coordinator-cluster-template.yml). Update the `namespaces` and the `client` section for a new cluster to match your cluster's configuration. -You'll need to specify the static IPs or hostnames of your M3DB seed nodes, and the name and retention values of the namespace you set up. You can leave the namespace storage metrics type as `unaggregated` since it's required by default to have a cluster that receives all Prometheus metrics unaggregated. In the future you might also want to aggregate and downsample metrics for longer retention, and you can come back and update the config once you've setup those clusters. You can read more about our aggregation functionality [here](../how_to/query.md). +You'll need to specify the static IPs or hostnames of your M3DB seed nodes, and the name and retention values of the namespace you set up. You can leave the namespace storage metrics type as `unaggregated` since it's required by default to have a cluster that receives all Prometheus metrics unaggregated. In the future you might also want to aggregate and downsample metrics for longer retention, and you can come back and update the config once you've setup those clusters. You can read more about our aggregation functionality [here](/how_to/query). It should look something like: diff --git a/docs/m3coordinator/api/remote.md b/docs/content/m3coordinator/api/remote.md similarity index 89% rename from docs/m3coordinator/api/remote.md rename to docs/content/m3coordinator/api/remote.md index 8553fc6115..0bdb42a456 100644 --- a/docs/m3coordinator/api/remote.md +++ b/docs/content/m3coordinator/api/remote.md @@ -1,4 +1,7 @@ -# API +--- +title: "API" +weight: 1 +--- The M3 Coordinator implements the Prometheus Remote Read and Write HTTP endpoints, they also can be used however as general purpose metrics write and read APIs. Any metrics that are written to the remote write API can be queried using PromQL through the query APIs as well as being able to be read back by the Prometheus Remote Read endpoint. @@ -22,18 +25,15 @@ None. #### Optional ---8<-- -docs/common/headers_optional_read_write_all.md ---8<-- ---8<-- -docs/common/headers_optional_write_all.md ---8<-- +{{% fileinclude file="/includes/headers_optional_read_write_all.md" %}} + +{{% fileinclude file="/includes/headers_optional_write_all.md" %}} ### Data Params Binary [snappy compressed](http://google.github.io/snappy/) Prometheus [WriteRequest protobuf message](https://github.com/prometheus/prometheus/blob/10444e8b1dc69ffcddab93f09ba8dfa6a4a2fddb/prompb/remote.proto#L22-L24). -### Available Tuning Params +### Available Tuning Params Refer [here](https://prometheus.io/docs/practices/remote_write/) for an up to date list of remote tuning parameters. @@ -55,6 +55,7 @@ Here is an example of writing the datapoint at the current unix timestamp with v Note: keep this example similar to the one found in query API documentation for consistency/ease of readers. --> + ```bash docker run -it --rm \ quay.io/m3db/prometheus_remote_client_golang:latest \ @@ -74,7 +75,7 @@ promremotecli_log 2019/06/25 04:13:56 write success # quay.io/m3db/prometheus_remote_client_golang@sha256:fc56df819bff9a5a087484804acf3a584dd4a78c68900c31a28896ed66ca7e7b ``` -For more details on querying data in PromQL that was written using this endpoint, see the [query API documentation](../../m3query/api/). +For more details on querying data in PromQL that was written using this endpoint, see the [query API documentation](/m3query/api/). ## Remote Read @@ -96,12 +97,9 @@ None. #### Optional ---8<-- -docs/common/headers_optional_read_write_all.md ---8<-- ---8<-- -docs/common/headers_optional_read_all.md ---8<-- +{{% fileinclude file="/includes/headers_optional_read_write_all.md" %}} + +{{% fileinclude file="/includes/headers_optional_read_all.md" %}} ### Data Params diff --git a/docs/m3coordinator/index.md b/docs/content/m3coordinator/index.md similarity index 100% rename from docs/m3coordinator/index.md rename to docs/content/m3coordinator/index.md diff --git a/docs/m3db/index.md b/docs/content/m3db/_index.md similarity index 64% rename from docs/m3db/index.md rename to docs/content/m3db/_index.md index 00077a5438..df5693f0cb 100644 --- a/docs/m3db/index.md +++ b/docs/content/m3db/_index.md @@ -1,4 +1,9 @@ -# M3DB, a distributed time series database +--- +title: "M3DB, a distributed time series database" +menuTitle: "M3DB" +weight: 3 +chapter: true +--- ## About @@ -6,12 +11,12 @@ M3DB, inspired by [Gorilla][gorilla] and [Cassandra][cassandra], is a distribute Here are some attributes of the project: -* Distributed time series storage, single nodes use a WAL commit log and persists time windows per shard independently -* Cluster management built on top of [etcd][etcd] -* Built-in synchronous replication with configurable durability and read consistency (one, majority, all, etc) -* M3TSZ float64 compression inspired by Gorilla TSZ compression, configurable as lossless or lossy -* Arbitrary time precision configurable from seconds to nanoseconds precision, able to switch precision with any write -* Configurable out of order writes, currently limited to the size of the configured time window's block size +- Distributed time series storage, single nodes use a WAL commit log and persists time windows per shard independently +- Cluster management built on top of [etcd][etcd] +- Built-in synchronous replication with configurable durability and read consistency (one, majority, all, etc) +- M3TSZ float64 compression inspired by Gorilla TSZ compression, configurable as lossless or lossy +- Arbitrary time precision configurable from seconds to nanoseconds precision, able to switch precision with any write +- Configurable out of order writes, currently limited to the size of the configured time window's block size ## Current Limitations @@ -22,6 +27,9 @@ The project has aimed to avoid compactions when at all possible, currently the o The project has also optimized the storage and retrieval of float64 values, as such there is no way to use it as a general time series database of arbitrary data structures just yet. [gorilla]: http://www.vldb.org/pvldb/vol8/p1816-teller.pdf + [cassandra]: http://cassandra.apache.org/ + [etcd]: https://github.com/etcd-io/etcd + [ubeross]: http://uber.github.io diff --git a/docs/content/m3db/architecture/_index.md b/docs/content/m3db/architecture/_index.md new file mode 100644 index 0000000000..06c8ea1023 --- /dev/null +++ b/docs/content/m3db/architecture/_index.md @@ -0,0 +1,17 @@ +--- +title: "Architecture" +weight: 2 +chapter: true +--- + +## Overview + +M3DB is written entirely in Go and does not have any required dependencies. For larger deployments, one may use an etcd cluster to manage M3DB cluster membership and topology definition. + +## High Level Goals + +Some of the high level goals for the project are defined as: + +- **Monitoring support:** M3DB was primarily developed for collecting a high volume of monitoring time series data, distributing the storage in a horizontally scalable manner and most efficiently leveraging the hardware. As such time series that are not read frequently are not kept in memory. +- **Highly configurable:** Provide a high level of configuration to support a wide set of use cases and runtime environments. +- **Variable durability:** Providing variable durability guarantees for the write and read side of storing time series data enables a wider variety of applications to use M3DB. This is why replication is primarily synchronous and is provided with configurable consistency levels, to enable consistent writes and reads. It must be possible to use M3DB with strong guarantees that data was replicated to a quorum of nodes and that the data was durable if desired. diff --git a/docs/m3db/architecture/caching.md b/docs/content/m3db/architecture/caching.md similarity index 98% rename from docs/m3db/architecture/caching.md rename to docs/content/m3db/architecture/caching.md index 258deabead..a2642a3ccb 100644 --- a/docs/m3db/architecture/caching.md +++ b/docs/content/m3db/architecture/caching.md @@ -1,4 +1,7 @@ -# Caching policies +--- +title: "Caching" +weight: 7 +--- ## Overview diff --git a/docs/m3db/architecture/commitlogs.md b/docs/content/m3db/architecture/commitlogs.md similarity index 72% rename from docs/m3db/architecture/commitlogs.md rename to docs/content/m3db/architecture/commitlogs.md index 92a68497ce..ff77632c08 100644 --- a/docs/m3db/architecture/commitlogs.md +++ b/docs/content/m3db/architecture/commitlogs.md @@ -1,4 +1,8 @@ -# Commit Logs And Snapshot Files +--- +title: "Commit Logs And Snapshot Files" +menuTitle: "Commit Logs" +weight: 5 +--- ## Overview @@ -8,8 +12,8 @@ M3DB has a commit log that is equivalent to the commit log or write-ahead-log in There are two integrity levels available for commit logs: -- **Synchronous:** write operations must wait until it has finished writing an entry in the commit log to complete. -- **Behind:** write operations must finish enqueueing an entry to the commit log write queue to complete. +- **Synchronous:** write operations must wait until it has finished writing an entry in the commit log to complete. +- **Behind:** write operations must finish enqueueing an entry to the commit log write queue to complete. Depending on the data loss requirements users can choose either integrity level. @@ -23,7 +27,7 @@ Commit logs for a given time window are kept in a single file. An info structure The structures can be conceptually described as: -``` +```golang CommitLogInfo { start int64 duration int64 @@ -49,10 +53,10 @@ CommitLogMetadata { ### Compaction / Snapshotting -Commit log files are compacted via the snapshotting proccess which (if enabled at the namespace level) will snapshot all data in memory into compressed files which have the same structure as the [fileset files](storage.md) but are stored in a different location. Once these snapshot files are created, then all the commit log files whose data are captured by the snapshot files can be deleted. This can result in significant disk savings for M3DB nodes running with large block sizes and high write volume where the size of the (uncompressed) commit logs can quickly get out of hand. +Commit log files are compacted via the snapshotting proccess which (if enabled at the namespace level) will snapshot all data in memory into compressed files which have the same structure as the [fileset files](storage) but are stored in a different location. Once these snapshot files are created, then all the commit log files whose data are captured by the snapshot files can be deleted. This can result in significant disk savings for M3DB nodes running with large block sizes and high write volume where the size of the (uncompressed) commit logs can quickly get out of hand. In addition, since the snapshot files are already compressed, bootstrapping from them is much faster than bootstrapping from raw commit log files because the individual data points don't need to be decoded and then M3TSZ encoded. The M3DB node just needs to read the raw bytes off disk and load them into memory. ### Cleanup -Commit log files are automatically deleted once all the data they contain has been flushed to disk as immutable compressed filesets *or* all the data they contain has been captured by a compressed snapshot file. Similarly, snapshot files are deleted once all the data they contain has been flushed to disk as filesets. \ No newline at end of file +Commit log files are automatically deleted once all the data they contain has been flushed to disk as immutable compressed filesets _or_ all the data they contain has been captured by a compressed snapshot file. Similarly, snapshot files are deleted once all the data they contain has been flushed to disk as filesets. diff --git a/docs/content/m3db/architecture/consistencylevels.md b/docs/content/m3db/architecture/consistencylevels.md new file mode 100644 index 0000000000..024759af33 --- /dev/null +++ b/docs/content/m3db/architecture/consistencylevels.md @@ -0,0 +1,29 @@ +--- +title: "Consistency Levels" +weight: 3 +--- + +M3DB provides variable consistency levels for read and write operations, as well as cluster connection operations. These consistency levels are handled at the client level. + +## Write consistency levels + +- **One:** Corresponds to a single node succeeding for an operation to succeed. +- **Majority:** Corresponds to the majority of nodes succeeding for an operation to succeed. +- **All:** Corresponds to all nodes succeeding for an operation to succeed. + +## Read consistency levels + +- **One**: Corresponds to reading from a single node to designate success. +- **UnstrictMajority**: Corresponds to reading from the majority of nodes but relaxing the constraint when it cannot be met, falling back to returning success when reading from at least a single node after attempting reading from the majority of nodes. +- **Majority**: Corresponds to reading from the majority of nodes to designate success. +- **All:** Corresponds to reading from all of the nodes to designate success. + +## Connect consistency levels + +Connect consistency levels are used to determine when a client session is deemed as connected before operations can be attempted. + +- **Any:** Corresponds to connecting to any number of nodes for all shards, this strategy will attempt to connect to all, then the majority, then one and then fallback to none and as such will always succeed. +- **None:** Corresponds to connecting to no nodes for all shards and as such will always succeed. +- **One:** Corresponds to connecting to a single node for all shards. +- **Majority:** Corresponds to connecting to the majority of nodes for all shards. +- **All:** Corresponds to connecting to all of the nodes for all shards. diff --git a/docs/content/m3db/architecture/engine.md b/docs/content/m3db/architecture/engine.md new file mode 100644 index 0000000000..692197b49d --- /dev/null +++ b/docs/content/m3db/architecture/engine.md @@ -0,0 +1,248 @@ +--- +title: "Storage Engine" +weight: 1 +--- + +M3DB is a time series database that was primarily designed to be horizontally scalable and able to handle high data throughput. + +## Time Series Compression + +One of M3DB's biggest strengths as a time series database (as opposed to using a more general-purpose horizontally scalable, distributed database like Cassandra) is its ability to compress time series data resulting in huge memory and disk savings. There are two compression algorithms used in M3DB: M3TSZ and protobuf encoding. + +### M3TSZ + +M3TSZ is used when values are floats. A variant of the streaming time series compression algorithm described in [Facebook's Gorilla paper](http://www.vldb.org/pvldb/vol8/p1816-teller.pdf), it achieves a high compression ratio. The compression ratio will vary depending on the workload and configuration, but we found that we were able to achieve a compression ratio of 1.45 bytes/datapoint with Uber's production workloads. This was a 40% improvement over standard TSZ, which only gave us a compression ratio of 2.42 bytes/datapoint under the same conditions. + +### Protobuf Encoding + +For more complex value types, M3DB also supports generic Protobuf messages with [a few exceptions](https://github.com/m3db/m3/blob/master/src/dbnode/encoding/proto/docs/encoding.md#supported-syntax). The algorithm takes on a hybrid approach and uses different compression schemes depending on the field types within the Protobuf message. + +Details on the encoding, marshaling and unmarshaling methods can be read [here](https://github.com/m3db/m3/tree/master/src/dbnode/encoding/proto). + +## Architecture + +M3DB is a persistent database with durable storage, but it is best understood via the boundary between its in-memory object layout and on-disk representations. + +### In-Memory Object Layout + + ┌────────────────────────────────────────────────────────────┐ + │ Database │ + ├────────────────────────────────────────────────────────────┤ + │ │ + │ ┌────────────────────────────────────────────────────┐ │ + │ │ Namespaces │ │ + │ ├────────────────────────────────────────────────────┤ │ + │ │ │ │ + │ │ ┌────────────────────────────────────────────┐ │ │ + │ │ │ Shards │ │ │ + │ │ ├────────────────────────────────────────────┤ │ │ + │ │ │ │ │ │ + │ │ │ ┌────────────────────────────────────┐ │ │ │ + │ │ │ │ Series │ │ │ │ + │ │ │ ├────────────────────────────────────┤ │ │ │ + │ │ │ │ │ │ │ │ + │ │ │ │ ┌────────────────────────────┐ │ │ │ │ + │ │ │ │ │ Buffer │ │ │ │ │ + │ │ │ │ └────────────────────────────┘ │ │ │ │ + │ │ │ │ │ │ │ │ + │ │ │ │ │ │ │ │ + │ │ │ │ ┌────────────────────────────┐ │ │ │ │ + │ │ │ │ │ Cached blocks │ │ │ │ │ + │ │ │ │ └────────────────────────────┘ │ │ │ │ + │ │ │ │ ... │ │ │ │ + │ │ │ │ │ │ │ │ + │ │ │ └────────────────────────────────────┘ │ │ │ + │ │ │ ... │ │ │ + │ │ │ │ │ │ + │ │ └────────────────────────────────────────────┘ │ │ + │ │ ... │ │ + │ │ │ │ + │ └────────────────────────────────────────────────────┘ │ + │ ... │ + │ │ + └────────────────────────────────────────────────────────────┘ + +The in-memory portion of M3DB is implemented via a hierarchy of objects: + +1. A `database` of which there is only one per M3DB process. The `database` owns multiple `namespace`s. +2. A `namespace` is similar to a table in other databases. Each `namespace` has a unique name and a set of configuration options, such as data retention and block size (which we will discuss in more detail later). A namespace owns multiple `shard`s. +3. A [`shard`](sharding) is effectively the same as a "virtual shard" in Cassandra in that it provides an arbitrary distribution of time series data via a simple hash of the series ID. A shard owns multiple `series`. +4. A `series` represents a sequence of time series datapoints. For example, the CPU utilization for a host could be represented as a series with the ID "host1.system.cpu.utilization" and a vector of (TIMESTAMP, CPU_LEVEL) tuples. Visualizing this example in a graph, there would a single line with time on the x-axis and CPU utilization on the y-axis. A `series` owns a `buffer` and any cached `block`s. +5. The `buffer` is where all data that has yet to be written to disk gets stored in memory. This includes both new writes to M3DB and data obtained through bootstrapping. More details on the [buffer](engine.md#buffer) is explained below. Upon [flushing](engine.md#flushing), the buffer creates a `block` of its data to be persisted to disk. +6. A `block` represents a stream of compressed time series data for a pre-configured block size, for example, a block could hold data for 6-8PM (block size of two hours). A `block` can arrive directly into the series only as a result of getting cached after a read request. Since blocks are in a compressed format, individual datapoints cannot be read from it. In other words, in order to read a single datapoint, the entire block up to that datapoint needs to be decompressed beforehand. + +### Persistent storage + +While in-memory databases can be useful (and M3DB supports operating in a memory-only mode), some form of persistence is required for durability. In other words, without a persistence strategy, it would be impossible for M3DB to restart (or recover from a crash) without losing all of its data. + +In addition, with large volumes of data, it becomes prohibitively expensive to keep all of the data in memory. This is especially true for monitoring workloads which often follow a "write-once, read-never" pattern where less than a few percent of all the data that's stored is ever read. With that type of workload, it's wasteful to keep all of that data in memory when it could be persisted on disk and retrieved when required. + +M3DB takes a two-pronged approach to persistant storage that involves combining a [commit log](commitlogs) for disaster recovery with periodic flushing (writing [fileset files](storage) to disk) for efficient retrieval: + +1. All writes are persisted to a commit log (the commit log can be configured to fsync every write, or optionally batch writes together which is much faster but leaves open the possibility of small amounts of data loss in the case of a catastrophic failure). The commit log is completely uncompressed and exists only to recover unflushed data in the case of a database shutdown (intentional or not) and is never used to satisfy a read request. +2. Periodically (based on the configured block size), all data in the buffer is flushed to disk as immutable [fileset files](storage). These files are highly compressed and can be indexed into via their complementary index files. Check out the [flushing section](engine.md#flushing) to learn more about the background flushing process. + +The block size parameter is the most important variable that needs to be tuned for a particular workload. A small block size will mean more frequent flushing and a smaller memory footprint for the data that is being actively compressed, but it will also reduce the compression ratio and data will take up more space on disk. + +If the database is stopped for any reason in between flushes, then when the node is started back up those writes will be recovered by reading the commit log or streaming in the data from a peer responsible for the same shard (if the replication factor is larger than one). + +While the fileset files are designed to support efficient data retrieval via the series ID, there is still a heavy cost associated with any query that has to retrieve data from disk because going to disk is always much slower than accessing main memory. To compensate for that, M3DB supports various [caching policies](caching) which can significantly improve the performance of reads by caching data in memory. + +## Write Path + +We now have enough context of M3DB's architecture to discuss the lifecycle of a write. A write begins when an M3DB client calls the [`writeBatchRaw`](https://github.com/m3db/m3/blob/06d3ecc94d13cff67b82a791271816caa338dcab/src/dbnode/generated/thrift/rpc.thrift#L59) endpoint on M3DB's embedded thrift server. The write itself will contain the following information: + +1. The namespace +2. The series ID (byte blob) +3. The timestamp +4. The value itself + +M3DB will consult the database object to check if the namespace exists, and if it does, then it will hash the series ID to determine which shard it belongs to. If the node receiving the write owns that shard, then it will lookup the series in the shard object. If the series exists, then an encoder in the buffer will encode the datapoint into the compressed stream. If the encoder doesn't exist (no writes for this series have occurred yet as part of this block) then a new encoder will be allocated and it will begin a compressed M3TSZ stream with that datapoint. There is also some additional logic for handling multiple encoders and filesets which is discussed in the [buffer](engine.md#buffer) section. + +At the same time, the write will be appended to the commit log, which is periodically compacted via a snapshot process. Details of this is outlined in the [commit log](commitlogs) page. + +**Note:** Regardless of the success or failure of the write in a single node, the client will return a success or failure to the caller for the write based on the configured [consistency level](consistencylevels). + +## Read Path + +A read begins when an M3DB client calls the [`FetchBatchResult`](https://github.com/m3db/m3/blob/master/src/dbnode/generated/thrift/rpc.thrift) or [`FetchBlocksRawResult`](https://github.com/m3db/m3/blob/master/src/dbnode/generated/thrift/rpc.thrift) endpoint on M3DB's embedded thrift server. The read request will contain the following information: + +1. The namespace +2. The series ID (byte blob) +3. The period of time being requested (start and end) + +M3DB will consult the database object to check if the namespace exists, and if it does, it will hash the series ID to determine which shard it belongs to. If the node receiving the read owns that shard, then M3DB needs to determine two things: + +1. Whether the series exists and if it does, +2. Whether the data exists in the buffer, cached in-memory, on disk, or some combination of all three. + +Determining whether the series exists is simple. M3DB looks up the series in the shard object. If it exists, then the series exists. If it doesn't, then M3DB consults in-memory bloom filters(s) for all shard/block start combinations(s) that overlap the query range to determine if the series exists on disk. + +If the series exists, then for every block that the request spans, M3DB needs to consolidate data from the buffer, in-memory cache, and fileset files (disk). + +Let's imagine a read for a given series that requests the last 6 hours worth of data, and an M3DB namespace that is configured with a block size of 2 hours, i.e. we need to find 3 different blocks. + +If the current time is 8PM, then the location of the requested blocks might be as follows: + + [2PM - 4PM (fileset file)] - Flushed block that isn't cached + [4PM - 6PM (in-memory cache)] - Flushed block that is cached + [4PM - 6PM (cold write in active buffer)] - Cold write that hasn't been flushed yet + [6PM - 8PM (active buffer)] - Hasn't been flushed yet + +Then M3DB will need to consolidate: + +1. The not-yet-sealed block from the buffer (located inside an internal lookup in the Series object) **[6PM - 8PM]** +2. The in-memory cached block (also located inside an internal lookup in the Series object). Since there are also cold writes in this block, the cold writes will be consolidated in memory with data found in the cached block before returning. **[4PM - 6PM]** +3. The block from disk (the block will be retrieved from disk and will then be cached according to the current [caching policy](caching)) **[2PM - 4PM]** + +Retrieving blocks from the buffer and in-memory cache is simple, the data is already present in memory and easily accessible via hashmaps keyed by series ID. Retrieving a block from disk is more complicated. The flow for retrieving a block from disk is as follows: + +1. Consult the in-memory bloom filter to determine if it's possible the series exists on disk. +2. If the bloom filter returns negative, we are sure that the series isn't there, so return that result. If the bloom filter returns positive, then binary search the in-memory index summaries to find the nearest index entry that is _before_ the series ID that we're searching for. Review the `index_lookup.go` file for implementation details. +3. Jump to the offset in the index file that we obtained from the binary search in the previous step, and begin scanning forward until we identify the index entry for the series ID we're looking for _or_ we get far enough in the index file that it becomes clear that the ID we're looking for doesn't exist (this is possible because the index file is sorted by ID) +4. Jump to the offset in the data file that we obtained from scanning the index file in the previous step, and begin streaming data. + +Once M3DB has retrieved the three blocks from their respective locations in memory / on-disk, it will transmit all of the data back to the client. Whether or not the client returns a success to the caller for the read is dependent on the configured [consistency level](consistencylevels). + +**Note:** Since M3DB nodes return compressed blocks (the M3DB client decompresses them), it's not possible to return "partial results" for a given block. If any portion of a read request spans a given block, then that block in its entirety must be transmitted back to the client. In practice, this ends up being not much of an issue because of the high compression ratio that M3DB is able to achieve. + +## Buffer + +Each series object contains a buffer, which is in charge of handling all data that has yet to be flushed - new writes and bootstrapped data. To accomplish this, it keeps mutable "buckets" of encoders (for new writes) and immutable blocks (for bootstrapped data). M3TSZ, the database's encoding scheme, is designed for compressing time series data in which each datapoint has a timestamp that is larger than the last encoded datapoint. For metrics workloads this works very well because every subsequent datapoint is almost always after the previous one. However, out of order writes will occasionally be received, for example due to clock skew. When this happens, M3DB will allocate a new encoder for the out of order datapoints. These encoders are contained in a bucket along with any blocks that got bootstrapped. + +Upon a flush (discussed further below), all data within a bucket gets merged and its version gets incremented - the specific version it gets set to depends on the number of times this block has previously been flushed. This bucket versioning allows the buffer to know which data has been flushed so that subsequent flushes will not try to flush it again. It also indicates to the clean up process (also discussed below) that that data can be evicted. + +Given this complex, concurrent logic, this has been [modeled in TLA](https://github.com/m3db/m3/blob/master/specs/dbnode/flush/FlushVersion.tla). + + ┌─────────────────────────┐ + │ Buffer │ + ├─────────────────────────┤ + │ │ + │ ┌─────────────────┐ │ + │ │ 2-4PM buckets │ │ + │ └─────────────────┘ │ + │ │ + │ ┌─────────────────┐ │ + ┌────│───│ 4-6PM buckets │ | + │ │ └─────────────────┘ │ + │ │ │ + │ │ ... │ + │ └─────────────────────────┘ + │ + │ + v After flush: + ┌─────────────────────┐ ┌─────────────────────┐ + │ 4-6PM buckets │ │ 4-6PM buckets │ + ├─────────────────────┤ ├─────────────────────┤ + │ │ │ │ + │ ┌─────────────┐ │ │ ┌─────────────┐ │ + │ │ Bucket v0 │<--│--writes │ │ Bucket v3 │ │ + │ └─────────────┘ │ │ └─────────────┘ │ + │ │ │ │ + │ │ │ │ + │ │ │ │ + │ │ │ │ + │ │ │ │ + └─────────────────────┘ └─────────────────────┘ + + + More writes after flush: After clean up: + ┌─────────────────────┐ ┌─────────────────────┐ + │ 4-6PM buckets │ │ 4-6PM buckets │ + ├─────────────────────┤ ├─────────────────────┤ + │ │ │ │ + │ ┌─────────────┐ │ │ ┌─────────────┐ │ + │ │ Bucket v3 │ │ │ │ Bucket v0 │<--│--writes + │ └─────────────┘ │ │ └─────────────┘ │ + │ │ │ │ + │ ┌─────────────┐ │ │ │ + │ │ Bucket v0 │<--│--writes │ │ + │ └─────────────┘ │ │ │ + │ │ │ │ + └─────────────────────┘ └─────────────────────┘ + +## Background processes + +M3DB has a variety of processes that run in the background during normal operation. + +### Flushing + +As discussed in the [architecture](engine.md#architecture) section, writes are actively buffered / compressed in memory and the commit log is continuously being written to, but eventually data needs to be flushed to disk in the form of [fileset files](storage) to facilitate efficient storage and retrieval. + +This is where the configurable "block size" comes into play. The block size is simply a duration of time that dictates how long new writes will be compressed (in a streaming manner) in memory before being flushed to disk. Let's use a block size of two hours as an example. + +If the block size is set to two hours, then all writes for all series for a given shard will be buffered in memory for two hours at a time. At the end of the two hour period all of the fileset files will be generated, written to disk, and then the in-memory objects can be released and replaced with new ones for the new block. The old objects will be removed from memory in the subsequent tick. + +If a flush happens for a namespace/shard/series/block for which there is already a fileset, in-memory data will get merged with data on disk from the fileset. The resultant merged data will then be flushed as a separate fileset. + +### Ticking + +The ticking process runs continously in the background and is responsible for a variety of tasks: + +1. Merging all encoders for a given series / block start combination +2. Removing expired / flushed series and blocks from memory +3. Clean up of expired data (fileset/commit log) from the filesystem + +#### Merging all encoders + +If there are multiple encoders for a block, they need to be merged before flushing the data to disk. To prevent huge memory spikes during the flushing process we continuously merge out of order encoders in the background. + +#### Removing expired / flushed series and blocks from memory + +Depending on the configured [caching policy](caching), the [in-memory object layout](engine.md#in-memory-object-layout) can end up with references to series or data blocks that are expired (have fallen out of the retention period) or no longer needed to be in memory (due to the data being flushed to disk or no longer needing to be cached). The background tick will identify these structures and release them from memory. + +#### Clean up of expired data + +Fileset files can become no longer necessary for two reasons: + +1. The fileset files for a block that has fallen out of retention +2. A flush occurred for a block that already has a fileset file. The new fileset will be a superset of the existing fileset with any new data that for that block, hence, the existing fileset is no longer required + +During the clean up process, these fileset files will get deleted. + +## Caveats / Limitations + +1. Currently M3DB does not support deletes. +2. M3DB does not support storing data with an indefinite retention period, every namespace in M3DB is required to have a retention policy which specifies how long data in that namespace will be retained for. While there is no upper bound on that value, it's still required and generally speaking M3DB is optimized for workloads with a well-defined [TTL](https://en.wikipedia.org/wiki/Time_to_live). +3. M3DB does not support either background data repair or Cassandra-style [read repairs](https://docs.datastax.com/en/cassandra/2.1/cassandra/operations/opsRepairNodesReadRepair.html). Future versions of M3DB will support automatic repairs of data as an ongoing background process. +4. M3DB does not support writing far into the future. Support for this will be added in future. diff --git a/docs/content/m3db/architecture/peer_streaming.md b/docs/content/m3db/architecture/peer_streaming.md new file mode 100644 index 0000000000..f7b4e55107 --- /dev/null +++ b/docs/content/m3db/architecture/peer_streaming.md @@ -0,0 +1,108 @@ +--- +title: "Peer Streaming" +weight: 6 +--- + +## Client + +Peer streaming is managed by the M3DB client. It fetches all blocks from peers for a specified time range for bootstrapping purposes. It performs the following steps: + +1. Fetch all metadata for blocks from all peers who own the specified shard +2. Compares metadata from different peers and determines the best peer(s) from which to stream the actual data +3. Streams the block data from peers + +Steps 1, 2 and 3 all happen concurrently. As metadata streams in, we begin determining which peer is the best source to stream a given block's data for a given series from, and then we begin streaming data from that peer while we continue to receive metadata. If the checksum for a given series block matches all three replicas then the least loaded (in terms of outstanding requests) and recently attempted will be selected to stream from. If the checksum differs for the series block across any of the peers then a fanout fetch of the series block is performed. + +In terms of error handling, the client will respect the consistency level specified for bootstrap. This means that when fetching metadata, indefinite retry is performed until the consistency level is achieved, for instance for quorum a majority of peers must successfully return metadata. For fetching the block data, if checksum matches from all peers then one successful fetch must occur, unless bootstrap consistency level "none" is specified, and if checksum mismatches then the specified consistency level must be achieved when the series block fetch is fanned out to peers. Fetching block data as well will indefinitely retry until the consistency level is achieved. + +The client supports dynamically changing the bootstrap consistency level, which is helpful in disaster scenarios where the consistency level cannot be achieved. To break the indefinite streaming attempt an operator can change the consistency level to "none" and a purely best-effort will be made to fetch the metadata and correspondingly to fetch the block data. + +The diagram below depicts the control flow and concurrency (goroutines and channels) in detail: + + ┌───────────────────────────────────────────────┐ + │ │ + │ FetchBootstrapBlocksFromPeers │ + │ │ + └───────────────────────────────────────────────┘ + │ + │ + ┌────────────────────┘ + │ + ▼ + ┌───────────────────────────────┐ + │ Main routine │ + │ │ + │ 1) Create metadataCh │────────────────┐ + │ 2) Spin up background routine │ │ + └───────────────────────────────┘ Create with metadataCh + │ │ + │ ▼ + │ ┌───────────────────────────────┐ + │ │ │ + │ │ Background routine │ + │ │ │ + │ └───────────────────────────────┘ + │ │ + │ For each peer + │ │ + │ ┌────────────────┼─────────────────┐ + │ │ │ │ + │ │ │ │ + │ ▼ ▼ ▼ + │ ┌───────────────────────────────────────────┐ + │ │ StreamBlocksMetadataFromPeer │ + │ │ │ + │ │ Stream paginated blocks metadata from a │ + │ │ peer while pageToken != nil │ + │ │ │ + │ │ For each blocks' metadata --> put │ + │ │ metadata into metadataCh │ + │ └───────────────────────────────────────────┘ + ▼ + ┌───────────────────────────────────────────┐ + │ StreamBlocksFromPeers │ + │ │ + │ 1) Create a background goroutine (details │ + │ to the right) │ + │ │ + │ 2) Create a queue per-peer which each have│ + │ their own internal goroutine and will │ + │ stream blocks back per-series from a │──────────┐ + │ specific peer │ │ + │ │ │ + │ 3) Loop through the enqueCh and pick an │ Creates with metadataCh + │appropriate peer(s) for each series (based │ and enqueueCh + │on whether all the peers have the same data│ │ + │ or not) and then put that into the queue │ │ + │for that peer so the data will be streamed │ │ + └───────────────────────────────────────────┘ │ + │ ▼ + │ ┌──────────────────────────────────────────────────────────┐ + │ │ streamAndGroupCollectedBlocksMetadata (injected via │ + │ │ streamMetadataFn variable) │ + │ │ │ + │ │ Loop through the metadataCh aggregating blocks metadata │ + │ │per series/block combination from different peers until we│ + │ │ have them from all peers for a series/block metadata │ + │ │ combination and then "submit" them to the enqueueCh │ + │ │ │ + │ │At the end, flush any remaining series/block combinations │ + │ │(that we received from less than N peers) into the enqueCh│ + │ │ as well. │ + │ └──────────────────────────────────────────────────────────┘ + │ + For each peer + │ + ┌────────────┼─────────────┐ + │ │ │ + │ │ │ + ▼ ▼ ▼ + ┌─────────────────────────────────────────────────────────────┐ + │ newPeerBlocksQueue (processFn = streamBlocksBatchFromPeer) │ + │ │ + │For each peer we're creating a new peerBlocksQueue which will│ + │ stream data blocks from a specific peer (using the │ + │ streamBlocksBatchFromPeer function) and add them to the │ + │ blocksResult │ + │ │ + └─────────────────────────────────────────────────────────────┘ diff --git a/docs/m3db/architecture/sharding.md b/docs/content/m3db/architecture/sharding.md similarity index 70% rename from docs/m3db/architecture/sharding.md rename to docs/content/m3db/architecture/sharding.md index 386a2e9e12..b7e2e39ee5 100644 --- a/docs/m3db/architecture/sharding.md +++ b/docs/content/m3db/architecture/sharding.md @@ -1,4 +1,7 @@ -# Sharding +--- +title: "Sharding" +weight: 2 +--- Timeseries keys are hashed to a fixed set of virtual shards. Virtual shards are then assigned to physical nodes. M3DB can be configured to use any hashing function and a configured number of shards. By default [murmur3](https://en.wikipedia.org/wiki/MurmurHash) is used as the hashing function and 4096 virtual shards are configured. @@ -6,9 +9,9 @@ Timeseries keys are hashed to a fixed set of virtual shards. Virtual shards are Shards provide a variety of benefits throughout the M3DB stack: -1. They make horizontal scaling easier and adding / removing nodes without downtime trivial at the cluster level. -2. They provide more fine grained lock granularity at the memory level. -3. They inform the filesystem organization in that data belonging to the same shard will be used / dropped together and can be kept in the same file. +1. They make horizontal scaling easier and adding / removing nodes without downtime trivial at the cluster level. +2. They provide more fine grained lock granularity at the memory level. +3. They inform the filesystem organization in that data belonging to the same shard will be used / dropped together and can be kept in the same file. ## Replication @@ -22,7 +25,7 @@ Each replica has its own assignment of a single logical shard per virtual shard. Conceptually it can be defined as: -``` +```golang Replica { id uint32 shards []Shard @@ -33,7 +36,7 @@ Replica { Each shard can be conceptually defined as: -``` +```golang Shard { id uint32 assignments []ShardAssignment @@ -55,17 +58,17 @@ enum ShardState { The assignment of shards is stored in etcd. When adding, removing or replacing a node shard goal states are assigned for each shard assigned. -For a write to appear as successful for a given replica it must succeed against all assigned hosts for that shard. That means if there is a given shard with a host assigned as *LEAVING* and another host assigned as *INITIALIZING* for a given replica writes to both these hosts must appear as successful to return success for a write to that given replica. Currently however only *AVAILABLE* shards count towards consistency, the work to group the *LEAVING* and *INITIALIZING* shards together when calculating a write success/error is not complete, see [issue 417](https://github.com/m3db/m3/issues/417). +For a write to appear as successful for a given replica it must succeed against all assigned hosts for that shard. That means if there is a given shard with a host assigned as _LEAVING_ and another host assigned as _INITIALIZING_ for a given replica writes to both these hosts must appear as successful to return success for a write to that given replica. Currently however only _AVAILABLE_ shards count towards consistency, the work to group the _LEAVING_ and _INITIALIZING_ shards together when calculating a write success/error is not complete, see [issue 417](https://github.com/m3db/m3/issues/417). -It is up to the nodes themselves to bootstrap shards when the assignment of new shards to it are discovered in the *INITIALIZING* state and to transition the state to *AVAILABLE* once bootstrapped by calling the cluster management APIs when done. Using a compare and set this atomically removes the *LEAVING* shard still assigned to the node that previously owned it and transitions the shard state on the new node from *INITIALIZING* state to *AVAILABLE*. +It is up to the nodes themselves to bootstrap shards when the assignment of new shards to it are discovered in the _INITIALIZING_ state and to transition the state to _AVAILABLE_ once bootstrapped by calling the cluster management APIs when done. Using a compare and set this atomically removes the _LEAVING_ shard still assigned to the node that previously owned it and transitions the shard state on the new node from _INITIALIZING_ state to _AVAILABLE_. -Nodes will not start serving reads for the new shard until it is *AVAILABLE*, meaning not until they have bootstrapped data for those shards. +Nodes will not start serving reads for the new shard until it is _AVAILABLE_, meaning not until they have bootstrapped data for those shards. ## Cluster operations ### Node add -When a node is added to the cluster it is assigned shards that relieves load fairly from the existing nodes. The shards assigned to the new node will become *INITIALIZING*, the nodes then discover they need to be bootstrapped and will begin bootstrapping the data using all replicas available. The shards that will be removed from the existing nodes are marked as *LEAVING*. +When a node is added to the cluster it is assigned shards that relieves load fairly from the existing nodes. The shards assigned to the new node will become _INITIALIZING_, the nodes then discover they need to be bootstrapped and will begin bootstrapping the data using all replicas available. The shards that will be removed from the existing nodes are marked as _LEAVING_. ### Node down @@ -73,4 +76,4 @@ A node needs to be explicitly taken out of the cluster. If a node goes down and ### Node remove -When a node is removed the shards it owns are assigned to existing nodes in the cluster. Remaining servers discover they are now in possession of shards that are *INITIALIZING* and need to be bootstrapped and will begin bootstrapping the data using all replicas available. +When a node is removed the shards it owns are assigned to existing nodes in the cluster. Remaining servers discover they are now in possession of shards that are _INITIALIZING_ and need to be bootstrapped and will begin bootstrapping the data using all replicas available. diff --git a/docs/m3db/architecture/storage.md b/docs/content/m3db/architecture/storage.md similarity index 99% rename from docs/m3db/architecture/storage.md rename to docs/content/m3db/architecture/storage.md index 22e18dd042..541a71982b 100644 --- a/docs/m3db/architecture/storage.md +++ b/docs/content/m3db/architecture/storage.md @@ -1,4 +1,7 @@ -# Storage +--- +title: "Storage" +weight: 4 +--- ## Overview diff --git a/docs/m3db/monodraw/m3db-file-layout.monopic b/docs/content/m3db/monodraw/m3db-file-layout.monopic similarity index 100% rename from docs/m3db/monodraw/m3db-file-layout.monopic rename to docs/content/m3db/monodraw/m3db-file-layout.monopic diff --git a/docs/m3db/monodraw/m3db_structs.monopic b/docs/content/m3db/monodraw/m3db_structs.monopic similarity index 100% rename from docs/m3db/monodraw/m3db_structs.monopic rename to docs/content/m3db/monodraw/m3db_structs.monopic diff --git a/docs/m3db/monodraw/peer_bootstrap.monopic b/docs/content/m3db/monodraw/peer_bootstrap.monopic similarity index 100% rename from docs/m3db/monodraw/peer_bootstrap.monopic rename to docs/content/m3db/monodraw/peer_bootstrap.monopic diff --git a/docs/m3db/monodraw/placement_monodraw_template.monopic b/docs/content/m3db/monodraw/placement_monodraw_template.monopic similarity index 100% rename from docs/m3db/monodraw/placement_monodraw_template.monopic rename to docs/content/m3db/monodraw/placement_monodraw_template.monopic diff --git a/docs/m3db/monodraw/placement_state_machine.monopic b/docs/content/m3db/monodraw/placement_state_machine.monopic similarity index 100% rename from docs/m3db/monodraw/placement_state_machine.monopic rename to docs/content/m3db/monodraw/placement_state_machine.monopic diff --git a/docs/m3query/index.md b/docs/content/m3query/_index.md similarity index 85% rename from docs/m3query/index.md rename to docs/content/m3query/_index.md index 2cac842cae..a64700fbf2 100644 --- a/docs/m3query/index.md +++ b/docs/content/m3query/_index.md @@ -1,4 +1,9 @@ -# M3 Query, a statelees query server for M3DB and Prometheus +--- +title: "M3 Query, a statelees query server for M3DB and Prometheus" +menuTitle: "M3 Query" +weight: 5 +chapter: true +--- M3 Query is a service that exposes all metrics query endpoints along with metrics time series metadata APIs that return dimensions and labels of metrics diff --git a/docs/m3query/api/index.md b/docs/content/m3query/api/query.md similarity index 90% rename from docs/m3query/api/index.md rename to docs/content/m3query/api/query.md index 3dc23a17dc..2ab12b6860 100644 --- a/docs/m3query/api/index.md +++ b/docs/content/m3query/api/query.md @@ -1,4 +1,9 @@ -# API +--- +title: "API" +menuTitle: "Query" +weight: 1 +--- + **Please note:** This documentation is a work in progress and more detail is required. @@ -32,12 +37,9 @@ Query using PromQL and returns JSON datapoints compatible with the Prometheus Gr #### Optional ---8<-- -docs/common/headers_optional_read_write_all.md ---8<-- ---8<-- -docs/common/headers_optional_read_all.md ---8<-- +{{% fileinclude file="/includes/headers_optional_read_write_all.md" %}} + +{{% fileinclude file="/includes/headers_optional_read_all.md" %}} ### Data Params @@ -45,8 +47,8 @@ None. ### Sample Call - ```bash diff --git a/docs/content/m3query/architecture/_index.md b/docs/content/m3query/architecture/_index.md new file mode 100644 index 0000000000..b9747a458e --- /dev/null +++ b/docs/content/m3query/architecture/_index.md @@ -0,0 +1,12 @@ +--- +title: "Architecture" +weight: 1 +chapter: true +--- + + +**Please note:** This documentation is a work in progress and more detail is required. + +## Overview + +M3 Query and M3 Coordinator are written entirely in Go, M3 Query is as a query engine for [M3DB](https://m3db.github.io/m3/) and M3 Coordinator is a remote read/write endpoint for Prometheus and M3DB. To learn more about Prometheus's remote endpoints and storage, [see here](https://prometheus.io/docs/operating/integrations/#remote-endpoints-and-storage). diff --git a/docs/m3query/architecture/blocks.md b/docs/content/m3query/architecture/blocks.md similarity index 99% rename from docs/m3query/architecture/blocks.md rename to docs/content/m3query/architecture/blocks.md index 80e76b2d35..474afb51e9 100644 --- a/docs/m3query/architecture/blocks.md +++ b/docs/content/m3query/architecture/blocks.md @@ -1,4 +1,7 @@ -# Blocks +--- +title: "Blocks" +weight: 1 +--- **Please note:** This documentation is a work in progress and more detail is required. diff --git a/docs/m3query/architecture/fanout.md b/docs/content/m3query/architecture/fanout.md similarity index 78% rename from docs/m3query/architecture/fanout.md rename to docs/content/m3query/architecture/fanout.md index cf098e591a..bf08feeea9 100644 --- a/docs/m3query/architecture/fanout.md +++ b/docs/content/m3query/architecture/fanout.md @@ -1,4 +1,8 @@ -# Fetching and querying +--- +title: "Fetching and querying" +menuTitle: "Query Fanout" +weight: 2 +--- ## Fetch fanout @@ -6,4 +10,4 @@ Since m3query does not currently have a view into the M3DB index, fanout to mult The general approach is therefore to attempt to fanout to any namespace which has a complete view of all metrics, for example, `Unaggregated`, and take that if it fulfills the query range; if not, m3query will attempt to stitch together namespaces with longer retentions to try and build the most complete possible view of stored metrics. -For further details, please ask questions on [Slack](http://bit.ly/m3slack), and we'll be happy to help! +For further details, please ask questions on [our gitter](https://gitter.im/m3db/Lobby), and we'll be happy to help! diff --git a/docs/content/m3query/architecture/functions.md b/docs/content/m3query/architecture/functions.md new file mode 100644 index 0000000000..44a41fbbe7 --- /dev/null +++ b/docs/content/m3query/architecture/functions.md @@ -0,0 +1,74 @@ +--- +title: "Function Processing" +weight: 3 +--- + +## Supported Functions + +| M3QL | Prometheus | Graphite | +| ------------------------------------------------------ | -------------------------- | -------------------------------------------------------------------- | +| abs/absolute | abs() | absolute(seriesList) | +| alias [alias] | | alias(seriesList, newName) | +| aliasByTags [tag] | | aliasByTags(seriesList, \*tags) | +| aliasByBucket/aliasByHistogramBucket [tag] | | | +| anomalies [flags] | | | +| asPercent | / | asPercent(seriesList, total=None, \*nodes) | +| avg/averageSeries [tag] | avg() | averageSeries(\*seriesLists) | +| changed | | changed(seriesList) | +| constantLine [value] | | constantLine(value) | +| count | count() | countSeries(\*seriesLists) | +| derivative | | derivative(seriesList) | +| diff | - | diffSeries(\*seriesLists) | +| divideSeries | / | divideSeries(dividendSeriesList, divisorSeries) | +| eq/== [value] | == | removeBelowValue(seriesList, n)/removeAboveValue(seriesList, n) | +| ne/!= [value] | != | removeBelowValue(seriesList, n)/removeAboveValue(seriesList, n) | +| excludeByTag [tag, pattern] | | exclude(seriesList, pattern) | +| execute/exec [fetch] | | | +| fallbackSeries [replacement] | | fallbackSeries(seriesList, fallback) | +| fetch | | | +| ge/=> [value] | >= | removeBelowValue(seriesList, n) | +| gt/> [value] | > | removeBelowValue(seriesList, n) | +| head [limit] | topk() | highest(seriesList, n=1, func='average') | +| histogramCDF [idTag, rangeTag, value] | | | +| histogramPercentile [idTag, rangeTag, percentileValue] | | | +| identity [name] | | identity(name) | +| integral | | integral(seriesList) | +| intersect [tags] | and/or | | +| isNonNull | | isNonNull(seriesList) | +| jainCP | | | +| keepLastValue | | keepLastValue(seriesList, limit=inf) | +| le/<= [value] | <= | removeAboveValue(seriesList, n) | +| logarithm | ln() | logarithm(seriesList, base=10) | +| lt/< [value] | < | removeAboveValue(seriesList, n) | +| max/maxSeries [tag] | max() | maxSeries(\*seriesLists) | +| min/minSeries [tag] | min() | minSeries(\*seriesLists) | +| moving [interval, func] | \_over_time() | movingMax, movingMin, movingMedian, movingAverage, etc. | +| multiply/multiplySeries [tag] | \* | multiplySeries(\*seriesLists) | +| nonNegativeDerivative [maxValue] | | nonNegativeDerivative(seriesList, maxValue=None) | +| nPercentile [percentile] | | nPercentile(seriesList, n) | +| offset [amount] | | offset(seriesList, factor) | +| percentileOfSeries [n, true/false, tag] | | percentileOfSeries(seriesList, n, interpolate=False) | +| perSecond | rate() | perSecond(seriesList, maxValue=None) | +| promHistogramPercentile [percentileValue] | | | +| range [tag] | | rangeOfSeries(\*seriesLists) | +| removeAbovePercentile [percentile] | | removeAbovePercentile(seriesList, n) | +| removeBelowPercentile [percentile] | | removeBelowPercentile(seriesList, n) | +| removeAboveValue [value] | | removeAboveValue(seriesList, n) | +| removeBelowValue [value] | | removeBelowValue(seriesList, n) | +| removeEmpty | | removeEmptySeries(seriesList, xFilesFactor=None) | +| scale [factor] | | scale(seriesList, factor) | +| scaleToSeconds [seconds] | | scaleToSeconds(seriesList, seconds) | +| setDiff [tags] | | | +| showAnomalyThresholds [level, model] | | | +| showTags [true/false, tagName(s)] | | | +| sort/sortSeries [avg, current, max, stddev, sum] | sort() | sortBy(seriesList, func='average', reverse=False) | +| stdev [points, windowTolerance] | stddev() | stdev(seriesList, points, windowTolerance=0.1) | +| sqrt/squareRoot | sqrt() | squareRoot(seriesList) | +| summarize [interval, func, alignToFrom] | | summarize(seriesList, intervalString, func='sum', alignToFrom=False) | +| sum/sumSeries [tag] | sum() | sumSeries(\*seriesLists) | +| sustain [duration] | | | +| sustainedAbove & sustainedBelow | | | +| tail [limit] | bottomk() | lowest(seriesList, n=1, func='average') | +| timeshift [duration] | | timeShift(seriesList, timeShift, resetEnd=True, alignDST=False) | +| timestamp | timestamp() | | +| transformNull [value] | | transformNull(seriesList, default=0, referenceSeries=None) | diff --git a/docs/m3query/config/index.md b/docs/content/m3query/config/_index.md similarity index 93% rename from docs/m3query/config/index.md rename to docs/content/m3query/config/_index.md index 319d30c011..ff8b14909d 100644 --- a/docs/m3query/config/index.md +++ b/docs/content/m3query/config/_index.md @@ -1,4 +1,9 @@ -# Configuration +--- +title: "Configuration" +weight: 1 +chapter: true +--- + ## Default query engine @@ -14,7 +19,7 @@ http://localhost:7201/api/v1/query?query=count(http_requests)&time=1590147165 But you can switch between the two in the following ways: -- Changing default query engine in config file (see `defaultEngine` parameter in [Configuration](annotated_config.md)) +- Changing default query engine in config file (see `defaultEngine` parameter in [Configuration](annotated_config)) - Passing HTTP header `M3-Engine`: ```curl -H "M3-Engine: m3query" "http://localhost:7201/api/v1/query?query=count(http_requests)&time=1590147165"``` diff --git a/docs/m3query/config/annotated_config.md b/docs/content/m3query/config/annotated_config.md similarity index 57% rename from docs/m3query/config/annotated_config.md rename to docs/content/m3query/config/annotated_config.md index 26ffea59b3..b6180b03a3 100644 --- a/docs/m3query/config/annotated_config.md +++ b/docs/content/m3query/config/annotated_config.md @@ -1,3 +1,7 @@ -# Annotated Config +--- +title: "Annotated Config" +weight: 2 +chapter: true +--- Please [see here](./annotated_config.yaml) for a link to the annotated config. diff --git a/docs/m3query/config/annotated_config.yaml b/docs/content/m3query/config/annotated_config.yaml similarity index 89% rename from docs/m3query/config/annotated_config.yaml rename to docs/content/m3query/config/annotated_config.yaml index c262f804a2..4c7080847e 100644 --- a/docs/m3query/config/annotated_config.yaml +++ b/docs/content/m3query/config/annotated_config.yaml @@ -65,7 +65,7 @@ writeWorkerPoolPolicy: size: tagOptions: - # See here for more information: https://docs.m3db.io/how_to/query/#id-generation + # See here for more information: http://m3db.github.io/m3/how_to/query/#id-generation idScheme: # lookbackDuration defines, at each step, how long we lookback until we see a non-NaN value. @@ -82,9 +82,3 @@ resultOptions: # for quick local setup (which this config will send data to). tracing: backend: jaeger - -# Query configuration. -query: - timeout: - # The default query engine is 'prometheus' but it could be switched to 'm3query' - defaultEngine: \ No newline at end of file diff --git a/docs/content/operational_guide/_index.md b/docs/content/operational_guide/_index.md new file mode 100644 index 0000000000..37f9161455 --- /dev/null +++ b/docs/content/operational_guide/_index.md @@ -0,0 +1,7 @@ ++++ +title = "Operational Guides" +weight = 7 +chapter = true ++++ + +This list of operational guides provide documentation for operating M3. \ No newline at end of file diff --git a/docs/operational_guide/availability_consistency_durability.md b/docs/content/operational_guide/availability_consistency_durability.md similarity index 94% rename from docs/operational_guide/availability_consistency_durability.md rename to docs/content/operational_guide/availability_consistency_durability.md index c2e4cd6437..5fd7b07165 100644 --- a/docs/operational_guide/availability_consistency_durability.md +++ b/docs/content/operational_guide/availability_consistency_durability.md @@ -1,6 +1,7 @@ -# Tuning Availability, Consistency, and Durability - -## Overview +--- +title: "Tuning Availability, Consistency, and Durability" +weight: 5 +--- M3DB is designed as a High Availability [HA](https://en.wikipedia.org/wiki/High_availability) system because it doesn't use a consensus protocol like Raft or Paxos to enforce strong consensus and consistency guarantees. However, even within the category of HA systems, there is a broad spectrum of consistency and durability guarantees that a database can provide. @@ -19,7 +20,7 @@ While reading it, we recommend referring to [the default configuration file](htt We recommend running the client with `writeConsistencyLevel` set to `majority` and `readConsistencyLevel` set to `unstrict_majority`. This means that all write must be acknowledged by a quorums of nodes in order to be considered succesful, and that reads will attempt to achieve quorum, but will return the data from a single node if they are unable to achieve quorum. This ensures that reads will normally ensure consistency, but degraded conditions will cause reads to fail outright as long as at least a single node can satisfy the request. -You can read about the consistency levels in more detail in [the Consistency Levels section](../m3db/architecture/consistencylevels.md) +You can read about the consistency levels in more detail in [the Consistency Levels section](/m3db/architecture/consistencylevels) ### Commitlog Configuration @@ -28,7 +29,7 @@ This means that writes will be reported as successful by the client, though the For example, consider the default configuration: -``` +```yaml commitlog: flushMaxBytes: 524288 flushEvery: 1s @@ -39,8 +40,8 @@ commitlog: This configuration states that the commitlog should be flushed whenever either of the following is true: -1. 524288 or more bytes have been written since the last time M3DB flushed the commitlog. -2. One or more seconds has elapsed since the last time M3DB flushed the commitlog. +1. 524288 or more bytes have been written since the last time M3DB flushed the commitlog. +2. One or more seconds has elapsed since the last time M3DB flushed the commitlog. In addition, the configuration also states that M3DB should allow up to `2097152` writes to be buffered in the commitlog queue before the database node will begin rejecting incoming writes so it can attempt to drain the queue and catch up. Increasing the size of this queue can often increase the write throughput of an M3DB node at the cost of potentially losing more data if the node experiences a sudden failure like a hard crash or power loss. @@ -48,7 +49,7 @@ In addition, the configuration also states that M3DB should allow up to `2097152 The default M3DB YAML configuration will contain the following as a top-level key under the `db` section: -``` +```yaml writeNewSeriesAsync: true ``` @@ -58,7 +59,7 @@ However, since new time series are created asynchronously, it's possible that th M3DB also allows operators to rate limit the number of new series that can be created per second via the following configuration: -``` +```yaml writeNewSeriesLimitPerSecond: 1048576 ``` @@ -71,7 +72,7 @@ However, in situations where the process crashed/exited unexpectedly or the node In such situations, M3DB will read as much of the commitlog as possible in an attempt to recover the maximum amount of data. However, it then needs to make a decision: it can either **(a)** come up successfully and tolerate an ostensibly minor amount of data or loss, or **(b)** attempt to stream the missing data from its peers. This behavior is controlled by the following default configuration: -``` +```yaml bootstrap: commitlog: returnUnfulfilledForCorruptCommitLogFiles: false @@ -86,20 +87,19 @@ This issue requires an operator with significant M3DB operational experience to ### Client Write and Read consistency The most important thing to understand is that **if you want to guarantee that you will be able to read the result of every successful write, then both writes and reads must be done with `majority` consistency.** -This means that both writes *and* reads will fail if a quorum of nodes are unavailable for a given shard. -You can read about the consistency levels in more detail in [the Consistency Levels section](../m3db/architecture/consistencylevels.md) +This means that both writes _and_ reads will fail if a quorum of nodes are unavailable for a given shard. +You can read about the consistency levels in more detail in [the Consistency Levels section](/m3db/architecture/consistencylevels) ### Commitlog Configuration M3DB supports running the commitlog synchronously such that every write is flushed to disk and fsync'd before the client receives a successful acknowledgement, but this is not currently exposed to users in the YAML configuration and generally leads to a massive performance degradation. -We only recommend operating M3DB this way for workloads where data consistency and durability is strictly required, and even then there may be better alternatives such as running M3DB with the bootstrapping configuration: `filesystem,peers,uninitialized_topology` as described in our [bootstrapping operational guide](./bootstrapping_crash_recovery.md). - +We only recommend operating M3DB this way for workloads where data consistency and durability is strictly required, and even then there may be better alternatives such as running M3DB with the bootstrapping configuration: `filesystem,peers,uninitialized_topology` as described in our [bootstrapping operational guide](./bootstrapping_crash_recovery). ### Writing New Series Asynchronously If you want to guarantee that M3DB will immediately allow you to read data for writes that have been acknowledged by the client, including the situation where the previous write was for a brand new timeseries, then you will need to change the default M3DB configuration to set `writeNewSeriesAsync: false` as a top-level key under the `db` section: -``` +```yaml writeNewSeriesAsync: false ``` @@ -107,7 +107,7 @@ This instructs M3DB to handle writes for new timeseries (for a given time block) Since this operation is so expensive, M3DB allows operator to rate limit the number of new series that can be created per second via the following configuration (also a top-level key under the `db` section): -``` +```yaml writeNewSeriesLimitPerSecond: 1048576 ``` @@ -115,7 +115,7 @@ writeNewSeriesLimitPerSecond: 1048576 As described in the "Tuning for Performance and Availability" section, we recommend configuring M3DB to ignore corrupt commitlog files on bootstrap. However, if you want to avoid any amount of inconsistency or data loss, no matter how minor, then you should configure M3DB to return unfulfilled when the commitlog bootstrapper encounters corrupt commitlog files. You can do so by modifying your configuration to look like this: -``` +```yaml bootstrap: commitlog: returnUnfulfilledForCorruptCommitLogFiles: true diff --git a/docs/operational_guide/bootstrapping_crash_recovery.md b/docs/content/operational_guide/bootstrapping_crash_recovery.md similarity index 96% rename from docs/operational_guide/bootstrapping_crash_recovery.md rename to docs/content/operational_guide/bootstrapping_crash_recovery.md index 1d05c20b79..c0c4435470 100644 --- a/docs/operational_guide/bootstrapping_crash_recovery.md +++ b/docs/content/operational_guide/bootstrapping_crash_recovery.md @@ -1,8 +1,10 @@ -# Bootstrapping & Crash Recovery - +--- +title: "Bootstrapping & Crash Recovery" +weight: 9 +--- ## Introduction -We recommend reading the [placement operational guide](placement.md) before reading the rest of this document. +We recommend reading the [placement operational guide](/operational_guide/placement.md) before reading the rest of this document. When an M3DB node is turned on (goes through a placement change) it needs to go through a bootstrapping process to determine the integrity of data that it has, replay writes from the commit log, and/or stream missing data from its peers. In most cases, as long as you're running with the default and recommended bootstrapper configuration of: `filesystem,commitlog,peers,uninitialized_topology` then you should not need to worry about the bootstrapping process at all and M3DB will take care of doing the right thing such that you don't lose data and consistency guarantees are met. Note that the order of the configured bootstrappers *does* matter. @@ -27,7 +29,7 @@ For example, imagine a M3DB node that is responsible for shards 1, 5, 13, and 25 ### Filesystem Bootstrapper -The `filesystem` bootstrapper's responsibility is to determine which immutable [Fileset files](../m3db/architecture/storage.md) exist on disk, and if so, mark them as fulfilled. The `filesystem` bootstrapper achieves this by scanning M3DB's directory structure and determining which Fileset files exist on disk. Unlike the other bootstrappers, the `filesystem` bootstrapper does not need to load any data into memory, it simply verifies the checksums of the data on disk and other components of the M3DB node will handle reading (and caching) the data dynamically once it begins to serve reads. +The `filesystem` bootstrapper's responsibility is to determine which immutable [Fileset files](/m3db/architecture/storage.md) exist on disk, and if so, mark them as fulfilled. The `filesystem` bootstrapper achieves this by scanning M3DB's directory structure and determining which Fileset files exist on disk. Unlike the other bootstrappers, the `filesystem` bootstrapper does not need to load any data into memory, it simply verifies the checksums of the data on disk and other components of the M3DB node will handle reading (and caching) the data dynamically once it begins to serve reads. ### Commitlog Bootstrapper diff --git a/docs/operational_guide/etcd.md b/docs/content/operational_guide/etcd.md similarity index 88% rename from docs/operational_guide/etcd.md rename to docs/content/operational_guide/etcd.md index 0d6ee98c70..f6580801a8 100644 --- a/docs/operational_guide/etcd.md +++ b/docs/content/operational_guide/etcd.md @@ -1,10 +1,13 @@ -# etcd +--- +title: "etcd" +weight: 12 +--- The M3 stack leverages `etcd` as a distributed key-value storage to: -1. Update cluster configuration in realtime -2. Manage placements for our distributed / sharded tiers like M3DB and M3Aggregator -3. Perform leader-election in M3Aggregator +1. Update cluster configuration in realtime +2. Manage placements for our distributed / sharded tiers like M3DB and M3Aggregator +3. Perform leader-election in M3Aggregator and much more! @@ -66,7 +69,7 @@ config: - http://external_etcd3:2379 ``` -**Note**: `M3DB` placements and namespaces are stored in `etcd` so if you want to switch to an external `etcd` cluster you'll need to recreate all your placements and namespaces. You can do this manually or use `etcdctl`'s [Mirror Maker](https://github.com/etcd-io/etcd/blob/v3.3.11/etcdctl/doc/mirror_maker.md) functionality. +**Note**: `M3DB` placements and namespaces are stored in `etcd` so if you want to switch to an external `etcd` cluster you'll need to recreate all your placements and namespaces. You can do this manually or use `etcdctl`'s [Mirror Maker](https://github.com/etcd-io/etcd/blob/v3.3.11/etcdctl/doc/mirror_maker) functionality. ### M3Coordinator @@ -76,8 +79,8 @@ config: ### Embedded etcd -If you're running `M3DB seed nodes` with embedded `etcd` (which we do not recommend for production workloads) and need to perform a node add/replace/remove then follow our [placement configuration guide](./placement_configuration.md) and pay special attention to follow the special instructions for `seed nodes`. +If you're running `M3DB seed nodes` with embedded `etcd` (which we do not recommend for production workloads) and need to perform a node add/replace/remove then follow our [placement configuration guide](/operational_guide/placement_configuration) and pay special attention to follow the special instructions for `seed nodes`. ### External etcd -Just follow the instructions in the [etcd docs.](https://github.com/etcd-io/etcd/tree/master/Documentation) \ No newline at end of file +Just follow the instructions in the [etcd docs.](https://github.com/etcd-io/etcd/tree/master/Documentation) diff --git a/docs/operational_guide/kernel_configuration.md b/docs/content/operational_guide/kernel_configuration.md similarity index 96% rename from docs/operational_guide/kernel_configuration.md rename to docs/content/operational_guide/kernel_configuration.md index 4c9ec161ca..e7e897adde 100644 --- a/docs/operational_guide/kernel_configuration.md +++ b/docs/content/operational_guide/kernel_configuration.md @@ -1,5 +1,7 @@ -Docker & Kernel Configuration -==================== +--- +title: "Docker & Kernel Configuration" +weight: 10 +--- This document lists the Kernel tweaks M3DB needs to run well. If you are running on Kubernetes, you may use our `sysctl-setter` [DaemonSet](https://github.com/m3db/m3/blob/master/kube/sysctl-daemonset.yaml) that will set these @@ -10,63 +12,68 @@ values for you. Please read the comment in that manifest to understand the impli When running M3DB inside Docker, it is recommended to add the `SYS_RESOURCE` capability to the container (using the `--cap-add` argument to `docker run`) so that it can raise its file limits: -``` +```shell docker run --cap-add SYS_RESOURCE quay.io/m3/m3dbnode:latest ``` If M3DB is being run as a non-root user, M3's `setcap` images are required: -``` + +```shell docker run --cap-add SYS_RESOURCE -u 1000:1000 quay.io/m3/m3dbnode:latest-setcap ``` More information on Docker's capability settings can be found [here][docker-caps]. ## vm.max_map_count + M3DB uses a lot of mmap-ed files for performance, as a result, you might need to bump `vm.max_map_count`. We suggest setting this value to `3000000`, so you don’t have to come back and debug issues later. On Linux, you can increase the limits by running the following command as root: -``` + +```shell sysctl -w vm.max_map_count=3000000 ``` To set this value permanently, update the `vm.max_map_count` setting in `/etc/sysctl.conf`. ## vm.swappiness + `vm.swappiness` controls how much the virtual memory subsystem will try to swap to disk. By default, the kernel configures this value to `60`, and will try to swap out items in memory even when there is plenty of RAM available to the system. We recommend sizing clusters such that M3DB is running on a substrate (hosts/containers) such that no-swapping is necessary, i.e. the process is only using 30-50% of the maximum available memory. And therefore recommend setting the value of `vm.swappiness` to `1`. This tells the kernel to swap as little as possible, without altogether disabling swapping. On Linux, you can configure this by running the following as root: -``` + +```shell sysctl -w vm.swappiness=1 ``` To set this value permanently, update the `vm.swappiness` setting in `/etc/sysctl.conf`. - ## rlimits + M3DB also can use a high number of files and we suggest setting a high max open number of files due to per partition fileset volumes. You may need to override the system and process-level limits set by the kernel with the following commands. To check the existing values run: -``` +```shell sysctl -n fs.file-max ``` and -``` +```shell sysctl -n fs.nr_open ``` to see the kernel and process limits respectively. If either of the values are less than three million (our minimum recommended value), then you can update them with the following commands: -``` +```shell sysctl -w fs.file-max=3000000 ``` -``` +```shell sysctl -w fs.nr_open=3000000 ``` diff --git a/docs/operational_guide/mapping_rollup.md b/docs/content/operational_guide/mapping_rollup.md similarity index 99% rename from docs/operational_guide/mapping_rollup.md rename to docs/content/operational_guide/mapping_rollup.md index d6cfbf0a82..9be76d85e2 100644 --- a/docs/operational_guide/mapping_rollup.md +++ b/docs/content/operational_guide/mapping_rollup.md @@ -1,3 +1,8 @@ +--- +title: "Mapping Rules" +weight: 13 +--- + ## Mapping Rules Mapping rules are used to configure the storage policy for metrics. The storage policy diff --git a/docs/operational_guide/monitoring.md b/docs/content/operational_guide/monitoring.md similarity index 99% rename from docs/operational_guide/monitoring.md rename to docs/content/operational_guide/monitoring.md index 5ac49f8021..2960f44696 100644 --- a/docs/operational_guide/monitoring.md +++ b/docs/content/operational_guide/monitoring.md @@ -1,4 +1,7 @@ -## Metrics +--- +title: "Metrics" +weight: 2 +--- It is best to use Prometheus to monitor M3DB, M3 Coordinator and M3 Query using the [Grafana dashboards](https://github.com/m3db/m3/blob/master/integrations/grafana/). diff --git a/docs/operational_guide/multiple_m3db_clusters.md b/docs/content/operational_guide/multiple_m3db_clusters.md similarity index 96% rename from docs/operational_guide/multiple_m3db_clusters.md rename to docs/content/operational_guide/multiple_m3db_clusters.md index 1d9d1e0eca..567bc624ac 100644 --- a/docs/operational_guide/multiple_m3db_clusters.md +++ b/docs/content/operational_guide/multiple_m3db_clusters.md @@ -1,6 +1,8 @@ -## Write to multiple M3DB clusters via m3coordinator - -### Overview: +--- +title: "Write to multiple M3DB clusters via m3coordinator" +weight: 2 +draft: true +--- Default M3 architecture has the m3coordinator writing to and aggregating metrics from a single M3DB cluster. To map a single coordinator to more than one M3DB cluster, follow the below instructions. diff --git a/docs/operational_guide/namespace_configuration.md b/docs/content/operational_guide/namespace_configuration.md similarity index 86% rename from docs/operational_guide/namespace_configuration.md rename to docs/content/operational_guide/namespace_configuration.md index 69ffc87e16..c9a33df06d 100644 --- a/docs/operational_guide/namespace_configuration.md +++ b/docs/content/operational_guide/namespace_configuration.md @@ -1,8 +1,9 @@ -# Namespace Configuration +--- +title: "Namespace Configuration" +weight: 8 +--- -## Introduction - -Namespaces in M3DB are analogous to tables in other databases. Each namespace has a unique name as well as distinct configuration with regards to data retention and blocksize. For more information about namespaces and the technical details of their implementation, read our [storage engine documentation](../m3db/architecture/engine.md). +Namespaces in M3DB are analogous to tables in other databases. Each namespace has a unique name as well as distinct configuration with regards to data retention and blocksize. For more information about namespaces and the technical details of their implementation, read our [storage engine documentation](/m3db/architecture/engine). ## Namespace Operations @@ -12,9 +13,7 @@ The operations below include sample cURLs, but you can always review the API doc Additionally, the following headers can be used in the namespace operations: ---8<-- -docs/common/headers_placement_namespace.md ---8<-- +{{% fileinclude file="/includes/headers_placement_namespace.md" %}} ### Adding a Namespace @@ -42,7 +41,7 @@ If you feel the need to configure the namespace options yourself (for performanc The "advanced" API allows you to configure every aspect of the namespace that you're adding which can sometimes be helpful for development, debugging, and tuning clusters for maximum performance. Adding a namespace is a simple as using the `POST` `api/v1/namespace` API on an M3Coordinator instance. -``` +```shell curl -X POST :/api/v1/namespace -d '{ "name": "default_unaggregated", "options": { @@ -94,7 +93,7 @@ in duration format as opposed to nanoseconds (default). ### bootstrapEnabled -This controls whether M3DB will attempt to [bootstrap](bootstrapping_crash_recovery.md) the namespace on startup. This value should always be set to `true` unless you have a very good reason to change it as setting it to `false` can cause data loss when restarting nodes. +This controls whether M3DB will attempt to [bootstrap](/operational_guide/bootstrapping_crash_recovery) the namespace on startup. This value should always be set to `true` unless you have a very good reason to change it as setting it to `false` can cause data loss when restarting nodes. Can be modified without creating a new namespace: `yes` @@ -112,7 +111,7 @@ Can be modified without creating a new namespace: `yes` ### snapshotEnabled -This controls whether M3DB will periodically write out [snapshot files](../m3db/architecture/commitlogs.md) for this namespace which act as compacted commitlog files. This value should always be set to `true` unless you have a very good reason to change it as setting it to `false` will increasing bootstrapping times (reading commitlog files is slower than reading snapshot files) and increase disk utilization (snapshot files are compressed but commitlog files are uncompressed). +This controls whether M3DB will periodically write out [snapshot files](/m3db/architecture/commitlogs) for this namespace which act as compacted commitlog files. This value should always be set to `true` unless you have a very good reason to change it as setting it to `false` will increasing bootstrapping times (reading commitlog files is slower than reading snapshot files) and increase disk utilization (snapshot files are compressed but commitlog files are uncompressed). Can be modified without creating a new namespace: `yes` @@ -130,14 +129,14 @@ Can be modified without creating a new namespace: `yes` #### blockSize -This is the most important value to consider when tuning the performance of an M3DB namespace. Read the [storage engine documentation](../m3db/architecture/storage.md) for more details, but the basic idea is that larger blockSizes will use more memory, but achieve higher compression. Similarly, smaller blockSizes will use less memory, but have worse compression. In testing, good compression occurs with blocksizes containing around 720 samples per timeseries. +This is the most important value to consider when tuning the performance of an M3DB namespace. Read the [storage engine documentation](/m3db/architecture/storage) for more details, but the basic idea is that larger blockSizes will use more memory, but achieve higher compression. Similarly, smaller blockSizes will use less memory, but have worse compression. In testing, good compression occurs with blocksizes containing around 720 samples per timeseries. Can be modified without creating a new namespace: `no` Below are recommendations for block size based on resolution: | Resolution | Block Size | -|------------|------------| +| ---------- | ---------- | | 5s | 60m | | 15s | 3h | | 30s | 6h | @@ -148,7 +147,7 @@ Below are recommendations for block size based on resolution: These values control how far into the future and the past (compared to the system time on an M3DB node) writes for the namespace will be accepted. For example, consider the following configuration: -``` +```text bufferPast: 10m bufferFuture: 20m currentSystemTime: 2:35:00PM @@ -156,7 +155,7 @@ currentSystemTime: 2:35:00PM Now consider the following writes (all of which arrive at 2:35:00PM system time, but include datapoints with the specified timestamps): -``` +```text 2:25:00PM - Accepted, within the 10m bufferPast 2:24:59PM - Rejected, outside the 10m bufferPast diff --git a/docs/content/operational_guide/namespace_mgmt.md b/docs/content/operational_guide/namespace_mgmt.md new file mode 100644 index 0000000000..2e8b86fe59 --- /dev/null +++ b/docs/content/operational_guide/namespace_mgmt.md @@ -0,0 +1,53 @@ +--- +title: "Namespace Management" +weight: 8 +draft: true +--- + +M3DB uses the concept of namespaces to determine how metrics are stored and retained. The M3DB operator allows a user to define their own namespaces, or to use a set of presets we consider to be suitable for production use cases. + +Namespaces are configured as part of an m3dbcluster spec. + +## Presets +10s:2d +This preset will store metrics at 10 second resolution for 2 days. For example, in your cluster spec: + +spec: +... + namespaces: + - name: metrics-short-term + preset: 10s:2d +1m:40d +This preset will store metrics at 1 minute resolution for 40 days. + +spec: +... + namespaces: + - name: metrics-long-term + preset: 1m:40d + +#### Custom Namespaces +You can also define your own custom namespaces by setting the NamespaceOptions within a cluster spec. The API lists all available fields. As an example, a namespace to store 7 days of data may look like: + +... +spec: +... + namespaces: + - name: custom-7d + options: + bootstrapEnabled: true + flushEnabled: true + writesToCommitLog: true + cleanupEnabled: true + snapshotEnabled: true + repairEnabled: false + retentionOptions: + retentionPeriod: 168h + blockSize: 12h + bufferFuture: 20m + bufferPast: 20m + blockDataExpiry: true + blockDataExpiryAfterNotAccessPeriod: 5m + indexOptions: + enabled: true + blockSize: 12h \ No newline at end of file diff --git a/docs/operational_guide/placement.md b/docs/content/operational_guide/placement.md similarity index 99% rename from docs/operational_guide/placement.md rename to docs/content/operational_guide/placement.md index ff36241ffe..02de573fe6 100644 --- a/docs/operational_guide/placement.md +++ b/docs/content/operational_guide/placement.md @@ -1,16 +1,17 @@ -# Placement +--- +title: "Placement" +weight: 6 +--- -## Overview - -**Note**: The words *placement* and *topology* are used interchangeably throughout the M3DB documentation and codebase. +**Note**: The words _placement_ and _topology_ are used interchangeably throughout the M3DB documentation and codebase. A M3DB cluster has exactly one Placement. That placement maps the cluster's shard replicas to nodes. A cluster also has 0 or more namespaces (analogous to tables in other databases), and each node serves every namespace for the shards it owns. In other words, if the cluster topology states that node A owns shards 1, 2, and 3 then node A will own shards 1, 2, 3 for all configured namespaces in the cluster. M3DB stores its placement (mapping of which NODES are responsible for which shards) in [etcd](https://etcd.io/). There are three possible states that each node/shard pair can be in: -1. `Initializing` -2. `Available` -3. `Leaving` +1. `Initializing` +2. `Available` +3. `Leaving` Note that these states are not a reflection of the current status of an M3DB node, but an indication of whether a given node has ever successfully bootstrapped and taken ownership of a given shard (achieved goal state). For example, in a new cluster all the nodes will begin with all of their shards in the `Initializing` state. Once all the nodes finish bootstrapping, they will mark all of their shards as `Available`. If all the M3DB nodes are stopped at the same time, the cluster placement will still show all of the shards for all of the nodes as `Available`. @@ -32,7 +33,7 @@ The `Leaving` state indicates that a node has been marked for removal from the c Node adds are performed by adding the new node to the placement. Some portion of the existing shards will be assigned to the new node based on its weight, and they will begin in the `Initializing` state. Similarly, the shards will be marked as `Leaving` on the node that are destined to lose ownership of them. Once the new node finishes bootstrapping the shards, it will update the placement to indicate that the shards it owns are `Available` and that the `Leaving` node should no longer own that shard in the placement. -``` +```yaml Replication factor: 3 ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ @@ -78,7 +79,7 @@ Replication factor: 3 Node removes are performed by updating the placement such that all the shards on the node that will be removed from the cluster are marked as `Leaving` and those shards are distributed to the remaining nodes (based on their weight) and assigned a state of `Initializing`. Once the existing nodes that are taking ownership of the leaving nodes shards finish bootstrapping, they will update the placement to indicate that the shards that they just acquired are `Available` and that the leaving node should no longer own those shards in the placement. -``` +```yaml Replication factor: 3 ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ @@ -124,7 +125,7 @@ Replication factor: 3 Node replaces are performed by updating the placement such that all the shards on the node that will be removed from the cluster are marked as `Leaving` and those shards are all added to the node that is being added and assigned a state of `Initializing`. Once the replacement node finishes bootstrapping, it will update the placement to indicate that the shards that it acquired are `Available` and that the leaving node should no longer own those shards in the placement. -``` +```yaml Replication factor: 3 ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ @@ -170,7 +171,7 @@ Replication factor: 3 The diagram below depicts the sequence of events that happen during a node replace and illustrates which entity is performing the placement update (in etcd) at each step. -``` +```yaml ┌────────────────────────────────┐ │ Node A │ │ │ diff --git a/docs/operational_guide/placement_configuration.md b/docs/content/operational_guide/placement_configuration.md similarity index 88% rename from docs/operational_guide/placement_configuration.md rename to docs/content/operational_guide/placement_configuration.md index 389c46dda7..7b75643f33 100644 --- a/docs/operational_guide/placement_configuration.md +++ b/docs/content/operational_guide/placement_configuration.md @@ -1,10 +1,11 @@ -# Placement Configuration - -## Overview +--- +title: "Placement Configuration" +weight: 7 +--- M3DB was designed from the ground up to be a distributed (clustered) database that is availability zone or rack aware (by using isolation groups). Clusters will seamlessly scale with your data, and you can start with a small number of nodes and grow it to a size of several hundred nodes with no downtime or expensive migrations. -Before reading the rest of this document, we recommend familiarizing yourself with the [M3DB placement documentation](placement.md) +Before reading the rest of this document, we recommend familiarizing yourself with the [M3DB placement documentation](/operational_guide/placement) **Note**: The primary limiting factor for the maximum size of an M3DB cluster is the number of shards. Picking an appropriate number of shards is more of an art than a science, but our recommendation is as follows: @@ -16,7 +17,7 @@ some guidelines depending on how many nodes you will have in your cluster eventu cannot change this once the cluster is created. | Number of Nodes | Number of Shards | -|-----------------|------------------| +| --------------- | ---------------- | | 3 | 64 | | 6 | 128 | | 12 | 256 | @@ -75,13 +76,11 @@ The instructions below all contain sample curl commands, but you can always revi `http://:/api/v1/openapi` or our [online API documentation](https://m3db.io/openapi/). -**Note**: The [peers bootstrapper](bootstrapping_crash_recovery.md) must be configured on all nodes in the M3DB cluster for placement changes to work. The `peers` bootstrapper is enabled by default, so you only need to worry about this if you modified the default bootstrapping configuration +**Note**: The [peers bootstrapper](bootstrapping_crash_recovery) must be configured on all nodes in the M3DB cluster for placement changes to work. The `peers` bootstrapper is enabled by default, so you only need to worry about this if you modified the default bootstrapping configuration Additionally, the following headers can be used in the placement operations: ---8<-- -docs/common/headers_placement_namespace.md ---8<-- +{{% fileinclude file="/includes/headers_placement_namespace.md" %}} #### Placement Initialization @@ -157,13 +156,13 @@ After sending the delete command you will need to wait for the M3DB cluster to r #### Adding / Removing Seed Nodes -If you find yourself adding or removing etcd seed nodes then we highly recommend setting up an [external etcd](etcd.md) cluster, as +If you find yourself adding or removing etcd seed nodes then we highly recommend setting up an [external etcd](etcd) cluster, as the overhead of operating two stateful systems at once is non-trivial. As this is not a recommended production setup, this section is intentionally brief. To add or remove nodes to the etcd cluster, use `etcdctl member add` and `etcdctl member remove` as found in `Replacing a Seed Node` below. A general rule to keep in mind is that any time the M3DB process starts on a seed node, the list of -cluster members in `etcdctl member list` must match *exactly* the list in config. +cluster members in `etcdctl member list` must match _exactly_ the list in config. #### Replacing a Node @@ -192,17 +191,17 @@ curl -X POST :/api/ If you are using the embedded etcd mode (which is only recommended for test purposes) and replacing a seed node then there are a few more steps to be done, as you are essentially doing two replace operations at once (replacing an etcd -node *and* and M3DB node). To perform these steps you will need the `etcdctl` binary (version 3.2 or later), which can +node _and_ and M3DB node). To perform these steps you will need the `etcdctl` binary (version 3.2 or later), which can be downloaded from the [etcd releases](https://github.com/etcd-io/etcd/releases) page. Many of the instructions here are mentioned in the [etcd operational -guide](https://github.com/etcd-io/etcd/blob/v3.3.11/Documentation/op-guide/runtime-configuration.md), which we recommend +guide](https://github.com/etcd-io/etcd/blob/v3.3.11/Documentation/op-guide/runtime-configuration), which we recommend reading for more context. To provide some context for the commands below, let's assume your cluster was created with the below configuration, and that you'd like to replace `host3` with a new host `host4`, which has IP address `1.1.1.4`: -``` +```yaml initialCluster: - hostID: host1 endpoint: http://1.1.1.1:2380 @@ -212,10 +211,10 @@ initialCluster: endpoint: http://1.1.1.3:2380 ``` -1. On an existing node in the cluster that is **not** the one you're removing, use `etcdctl` to remove `host3` from the - cluster: +1. On an existing node in the cluster that is **not** the one you're removing, use `etcdctl` to remove `host3` from the + cluster: -``` +```shell $ ETCDCTL_API=3 etcdctl member list 9d29673cf1328d1, started, host1, http://1.1.1.1:2380, http://1.1.1.1:2379 f14613b6c8a336b, started, host2, http://1.1.1.2:2380, http://1.1.1.2:2379 @@ -225,17 +224,17 @@ $ ETCDCTL_API=3 etcdctl member remove 2fd477713daf243 Removed member 2fd477713daf243 from cluster ``` -2. From the same host, use `etcdctl` to add `host4` to the cluster: +2. From the same host, use `etcdctl` to add `host4` to the cluster: -``` +```shell $ ETCDCTL_API=3 etcdctl member add host4 --peer-urls http://1.1.1.4:2380 ``` -3. **Before** starting M3DB on `host4`, modify the initial cluster list to indicate `host4` has a cluster state of - `existing`. Note: if you had previously started M3DB on this host, you'll have to remove the `member` subdirectory in - `$M3DB_DATA_DIR/etcd/`. +3. **Before** starting M3DB on `host4`, modify the initial cluster list to indicate `host4` has a cluster state of + `existing`. Note: if you had previously started M3DB on this host, you'll have to remove the `member` subdirectory in + `$M3DB_DATA_DIR/etcd/`. -``` +```yaml initialCluster: - hostID: host1 endpoint: http://1.1.1.1:2380 @@ -246,21 +245,21 @@ initialCluster: endpoint: http://1.1.1.4:2380 ``` -4. Start M3DB on `host4`. +4. Start M3DB on `host4`. -5. On all other seed nodes, update their `initialCluster` list to be exactly equal to the list on `host4` from step 3. - Rolling restart the hosts one at a time, waiting until they indicate they are bootstrapped (indicated in the - `/health`) endpoint before continuing to the next. +5. On all other seed nodes, update their `initialCluster` list to be exactly equal to the list on `host4` from step 3. + Rolling restart the hosts one at a time, waiting until they indicate they are bootstrapped (indicated in the + `/health`) endpoint before continuing to the next. -6. Follow the steps from `Replacing a Seed Node` to replace `host3` with `host4` in the M3DB placement. +6. Follow the steps from `Replacing a Seed Node` to replace `host3` with `host4` in the M3DB placement. #### Setting a new placement (Not Recommended) This endpoint is unsafe since it creates a brand new placement and therefore should be used with extreme caution. Some use cases for using this endpoint include: -- Changing IP addresses of nodes -- Rebalancing shards +- Changing IP addresses of nodes +- Rebalancing shards If the placement for `M3DB` needs to be recreated, the `/api/v1/services/m3db/placement/set` can be used to do so. Please note, a placement already needs to exist to use this endpoint. If no placement exists, use the `Placement Initialization` @@ -310,8 +309,8 @@ curl -X POST localhost:7201/api/v1/services/m3db/placement/set -d '{ ``` **Note:** The `set` endpoint can also be used to set the placements in `M3Aggregator` and `M3Coordinator` using the following endpoints, respectively: + ```bash /api/v1/m3aggregator/set /api/v1/m3coordinator/set ``` - diff --git a/docs/operational_guide/repairs.md b/docs/content/operational_guide/repairs.md similarity index 71% rename from docs/operational_guide/repairs.md rename to docs/content/operational_guide/repairs.md index a191bd1b08..703ee039a7 100644 --- a/docs/operational_guide/repairs.md +++ b/docs/content/operational_guide/repairs.md @@ -1,4 +1,7 @@ -# Background Repairs (beta) +--- +title: "Background Repairs" +weight: 14 +--- **Note:** This feature is in beta and only available for use with M3DB when run with the inverted index off. It can be run with the inverted index on however metrics will not be re-indexed if they are repaired so will be invisible to that node for queries. @@ -44,5 +47,5 @@ The `throttle` field controls how long the M3DB node will pause between repairin ## Caveats and Limitations -1. Background repairs do not currently support M3DB's inverted index; as a result, it can only be used for clusters / namespaces where the indexing feature is disabled. -2. Background repairs will wait until (`block start` + `block size` + `buffer past`) has elapsed before attempting to repair a block. For example, if M3DB is configured with a 2 hour block size and a 20 minute buffer past that M3DB will not attempt to repair the `12PM->2PM` block until at least `2:20PM`. This limitation is in place primarily to reduce "churn" caused by repairing mutable data that is actively being modified. **Note**: This limitation has no impact or negative interaction with M3DB's cold writes feature. In other words, even though it may take some time before a block becomes available for repairs, M3DB will repair the same block repeatedly until it falls out of retention so mismatches between nodes that were caused by "cold" writes will still eventually be repaired. +1. Background repairs do not currently support M3DB's inverted index; as a result, it can only be used for clusters / namespaces where the indexing feature is disabled. +2. Background repairs will wait until (`block start` + `block size` + `buffer past`) has elapsed before attempting to repair a block. For example, if M3DB is configured with a 2 hour block size and a 20 minute buffer past that M3DB will not attempt to repair the `12PM->2PM` block until at least `2:20PM`. This limitation is in place primarily to reduce "churn" caused by repairing mutable data that is actively being modified. **Note**: This limitation has no impact or negative interaction with M3DB's cold writes feature. In other words, even though it may take some time before a block becomes available for repairs, M3DB will repair the same block repeatedly until it falls out of retention so mismatches between nodes that were caused by "cold" writes will still eventually be repaired. diff --git a/docs/operational_guide/replication_and_deployment_in_zones.md b/docs/content/operational_guide/replication_and_deployment_in_zones.md similarity index 87% rename from docs/operational_guide/replication_and_deployment_in_zones.md rename to docs/content/operational_guide/replication_and_deployment_in_zones.md index 35972aa2cc..242f8e1272 100644 --- a/docs/operational_guide/replication_and_deployment_in_zones.md +++ b/docs/content/operational_guide/replication_and_deployment_in_zones.md @@ -1,19 +1,19 @@ -# Replication and Deployment in Zones - -## Overview +--- +title: "Replication and Deployment in Zones" +weight: 1 +--- M3DB supports both deploying across multiple zones in a region or deploying to a single zone with rack-level isolation. It can also be deployed across multiple regions for a global view of data, though both latency and bandwidth costs may increase as a result. -In addition, M3DB has support for automatically replicating data between isolated M3DB clusters (potentially running in different zones / regions). More details can be found in the [Replication between clusters](./replication_between_clusters.md) operational guide. +In addition, M3DB has support for automatically replicating data between isolated M3DB clusters (potentially running in different zones / regions). More details can be found in the [Replication between clusters](/operational_guide/replication_between_clusters) operational guide. -### Replication +## Replication -A replication factor of at least 3 is highly recommended for any M3DB deployment, due to the consistency levels (for both reads and writes) that require quorum in order to complete an operation. For more information on consistency levels, see the documentation concerning [tuning availability, consistency and durability](availability_consistency_durability.md). +A replication factor of at least 3 is highly recommended for any M3DB deployment, due to the consistency levels (for both reads and writes) that require quorum in order to complete an operation. For more information on consistency levels, see the documentation concerning [tuning availability, consistency and durability](/operational_guide/availability_consistency_durability). M3DB will do its best to distribute shards evenly among the availability zones while still taking each individual node's weight into account, but if some of the availability zones have less available hosts than others then each host in that zone will be responsible for more shards than hosts in the other zones and will thus be subjected to heavier load. - -### Replication Factor Recommendations +## Replication Factor Recommendations Running with `RF=1` or `RF=2` is not recommended for any multi-node use cases (testing or production). In [the future][1107] such topologies may be rejected by M3DB entirely. It is also recommended to only run with an odd number of @@ -36,16 +36,16 @@ following table demonstrates the quorum size and failure tolerance of various RF tolerance][failure-tolerance] documentation. | Replica Factor | Quorum Size | Failure Tolerance | -|:-:|:-:|:-:| -| 1 | 1 | 0 | -| 2 | 2 | 0 | -| 3 | 2 | 1 | -| 4 | 3 | 1 | -| 5 | 3 | 2 | -| 6 | 4 | 2 | -| 7 | 4 | 3 | +| :------------: | :---------: | :---------------: | +| 1 | 1 | 0 | +| 2 | 2 | 0 | +| 3 | 2 | 1 | +| 4 | 3 | 1 | +| 5 | 3 | 2 | +| 6 | 4 | 2 | +| 7 | 4 | 3 | -### Upgrading hosts in a deployment +## Upgrading hosts in a deployment When an M3DB node is restarted it has to perform a bootstrap process before it can serve reads. During this time the node will continue to accept writes, but will not be available for reads. @@ -90,5 +90,7 @@ For example, in a multi-region deployment with four shards spread over five regi Typically, deployments have many more than four shards - this is a simple example that illustrates how M3DB maintains availability while losing up to two regions, as three of five replicas are still intact. [1107]: https://github.com/m3db/m3/issues/1107 + [failure-tolerance]: https://github.com/etcd-io/etcd/blob/cca0d5c1bed134ac30e1354241f7655d2a118db4/Documentation/faq.md#what-is-failure-tolerance -[isolation-group]: ./placement_configuration.md#isolation-group + +[isolation-group]: /operational_guide/placement_configuration.md#isolation-group diff --git a/docs/operational_guide/replication_between_clusters.md b/docs/content/operational_guide/replication_between_clusters.md similarity index 65% rename from docs/operational_guide/replication_between_clusters.md rename to docs/content/operational_guide/replication_between_clusters.md index cc731de9cb..dfbe9f59fc 100644 --- a/docs/operational_guide/replication_between_clusters.md +++ b/docs/content/operational_guide/replication_between_clusters.md @@ -1,25 +1,26 @@ -# Replication between clusters (beta) - -## Overview +--- +title: "Replication between clusters" +weight: 15 +--- M3DB clusters can be configured to passively replicate data from other clusters. This feature is most commonly used when operators wish to run two (or more) regional clusters that function independently while passively replicating data from the other cluster in an eventually consistent manner. -The cross-cluster replication feature is built on-top of the [background repairs](repairs.md) feature. As a result, it has all the same caveats and limitations. Specifically, it does not currently work with clusters that use M3DB's indexing feature and the replication delay between two clusters will be at least (`block size` + `bufferPast`) for data written at the beginning of a block for a given namespace. For use-cases where a large replication delay is unacceptable, the current recommendation is to dual-write to both clusters in parallel and then rely upon the cross-cluster replication feature to repair any discrepancies between the clusters caused by failed dual-writes. This recommendation is likely to change in the future once support for low-latency replication is added to M3DB in the form of commitlog tailing. +The cross-cluster replication feature is built on-top of the [background repairs](/operational_guide/repairs) feature. As a result, it has all the same caveats and limitations. Specifically, it does not currently work with clusters that use M3DB's indexing feature and the replication delay between two clusters will be at least (`block size` + `bufferPast`) for data written at the beginning of a block for a given namespace. For use-cases where a large replication delay is unacceptable, the current recommendation is to dual-write to both clusters in parallel and then rely upon the cross-cluster replication feature to repair any discrepancies between the clusters caused by failed dual-writes. This recommendation is likely to change in the future once support for low-latency replication is added to M3DB in the form of commitlog tailing. While cross-cluster replication is built on top of the background repairs feature, background repairs do not need to be enabled for cross-cluster replication to be enabled. In other words, clusters can be configured such that: -1. Background repairs (within a cluster) are disabled and replication is also disabled. -2. Background repairs (within a cluster) are enabled, but replication is disabled. -3. Background repairs (within a cluster) are disabled, but replication is enabled. -4. Background repairs (within a cluster) are enabled and replication is also enabled. +1. Background repairs (within a cluster) are disabled and replication is also disabled. +2. Background repairs (within a cluster) are enabled, but replication is disabled. +3. Background repairs (within a cluster) are disabled, but replication is enabled. +4. Background repairs (within a cluster) are enabled and replication is also enabled. ## Configuration **Important**: All M3DB clusters involved in the cross-cluster replication process must be configured such that they have the exact same: -1. Number of shards -2. Replication factor -3. Namespace configuration +1. Number of shards +2. Replication factor +3. Namespace configuration The replication feature can be enabled by adding the following configuration to `m3dbnode.yml` under the `db` section: diff --git a/docs/operational_guide/replication_global.png b/docs/content/operational_guide/replication_global.png similarity index 100% rename from docs/operational_guide/replication_global.png rename to docs/content/operational_guide/replication_global.png diff --git a/docs/operational_guide/replication_region.png b/docs/content/operational_guide/replication_region.png similarity index 100% rename from docs/operational_guide/replication_region.png rename to docs/content/operational_guide/replication_region.png diff --git a/docs/operational_guide/replication_single_zone.png b/docs/content/operational_guide/replication_single_zone.png similarity index 100% rename from docs/operational_guide/replication_single_zone.png rename to docs/content/operational_guide/replication_single_zone.png diff --git a/docs/operational_guide/resource_limits.md b/docs/content/operational_guide/resource_limits.md similarity index 97% rename from docs/operational_guide/resource_limits.md rename to docs/content/operational_guide/resource_limits.md index 15924ee0bf..b2fc5cef5a 100644 --- a/docs/operational_guide/resource_limits.md +++ b/docs/content/operational_guide/resource_limits.md @@ -1,4 +1,7 @@ -# Resource Limits and Preventing Abusive Reads/Writes +--- +title: "Resource Limits and Preventing Abusive Reads/Writes" +weight: 4 +--- This operational guide provides an overview of how to set resource limits on M3 components to prevent abusive reads/writes impacting availability or @@ -30,7 +33,7 @@ a 5s lookback. ### Annotated configuration -``` +```yaml limits: # If set, will enforce a maximum cap on time series blocks matched for # queries searching time series by dimensions. @@ -90,7 +93,7 @@ and a warning included in the response body. ### Annotated configuration -``` +```yaml limits: # If set will override default limits set per query. perQuery: @@ -128,6 +131,4 @@ limits: The following headers can also be used to override configured limits on a per request basis (to allow for different limits dependent on caller): ---8<-- -docs/common/headers_optional_read_limits.md ---8<-- +{{% fileinclude file="/includes/headers_optional_read_limits.md" %}} \ No newline at end of file diff --git a/docs/operational_guide/upgrading_m3.md b/docs/content/operational_guide/upgrading_m3.md similarity index 98% rename from docs/operational_guide/upgrading_m3.md rename to docs/content/operational_guide/upgrading_m3.md index 323670bc6f..85ca56f5d2 100644 --- a/docs/operational_guide/upgrading_m3.md +++ b/docs/content/operational_guide/upgrading_m3.md @@ -1,4 +1,7 @@ -# Upgrading M3 +--- +title: "Upgrading M3" +weight: 3 +--- ## Overview diff --git a/docs/content/overview/_index.md b/docs/content/overview/_index.md new file mode 100644 index 0000000000..a5ba6b3c67 --- /dev/null +++ b/docs/content/overview/_index.md @@ -0,0 +1,5 @@ +--- +title: "Overview" +weight: 2 +chapter: true +--- \ No newline at end of file diff --git a/docs/overview/components.md b/docs/content/overview/components.md similarity index 96% rename from docs/overview/components.md rename to docs/content/overview/components.md index fe952f1586..7233ac2358 100644 --- a/docs/overview/components.md +++ b/docs/content/overview/components.md @@ -1,4 +1,7 @@ -# Components +--- +title: "Components" +weight: 1 +--- ## M3 Coordinator @@ -6,11 +9,11 @@ M3 Coordinator is a service that coordinates reads and writes between upstream s ## M3DB -M3DB is a distributed time series database that provides scalable storage and a reverse index of time series. It is optimized as a cost effective and reliable realtime and long term retention metrics store and index. For more details, see the [M3DB documentation](../m3db/). +M3DB is a distributed time series database that provides scalable storage and a reverse index of time series. It is optimized as a cost effective and reliable realtime and long term retention metrics store and index. For more details, see the [M3DB documentation](/m3db/). ## M3 Query -M3 Query is a service that houses a distributed query engine for querying both realtime and historical metrics, supporting several different query languages. It is designed to support both low latency realtime queries and queries that can take longer to execute, aggregating over much larger datasets, for analytical use cases. For more details, see the [query engine documentation](../m3query/). +M3 Query is a service that houses a distributed query engine for querying both realtime and historical metrics, supporting several different query languages. It is designed to support both low latency realtime queries and queries that can take longer to execute, aggregating over much larger datasets, for analytical use cases. For more details, see the [query engine documentation](/m3query/). ## M3 Aggregator diff --git a/docs/overview/media.md b/docs/content/overview/media.md similarity index 95% rename from docs/overview/media.md rename to docs/content/overview/media.md index b7d474b6b5..627df206c9 100644 --- a/docs/overview/media.md +++ b/docs/content/overview/media.md @@ -1,4 +1,8 @@ -# Media +--- +title: "Media" +weight: 3 +--- + ## Blogs @@ -18,6 +22,8 @@ Recordings of all past meetups can be found on a [Vimeo M3 Community Meetings fo ## Recorded Talks +- [OpenObservability Talks: Long-Term Metrics with M3 and Prometheus](https://youtu.be/XQ_yCLyAMLU) By Matt Schallert. Aug 27, 2020. + - [CNCF Webinar: Maximizing M3 – Pushing performance boundaries in a distributed metrics engine](https://www.cncf.io/webinars/maximizing-m3-pushing-performance-boundaries-in-a-distributed-metrics-engine-at-global-scale/) By Ryan Allen - Aug 6, 2020. - [OSCON 2019: Large-Scale Automated Storage on Kubernetes](https://youtu.be/N9A7xSE9n-c) By Matt Schallert - Jul 18, 2019. [Slides](https://schallert.io/OSCON%20Large-Scale%20Automated%20Storage%20on%20Kubernetes.pdf) @@ -43,4 +49,3 @@ Recordings of all past meetups can be found on a [Vimeo M3 Community Meetings fo - [PromCon 2018 Panel Discussion: Prometheus Long-Term Storage Approaches](https://youtube.com/watch?v=3pTG_N8yGSU) including highlights of the M3 stack by Nikunj Aggarwal - Aug 9, 2018. - [Putting billions of time series to work at Uber with autonomous monitoring](https://vimeo.com/274821002) By Prateek Rungta - Jun 6, 2018. [Slides](http://bit.ly/m3db-monitorama2018) - diff --git a/docs/overview/motivation.md b/docs/content/overview/motivation.md similarity index 89% rename from docs/overview/motivation.md rename to docs/content/overview/motivation.md index 14e175d81e..4cf88f837d 100644 --- a/docs/overview/motivation.md +++ b/docs/content/overview/motivation.md @@ -1,3 +1,7 @@ -# Motivation +--- +title: "Motivation" +weight: 2 +chapter: true +--- We decided to open source the M3 platform as a scalable remote storage backend for Prometheus and Graphite so that others may attempt to reuse our work and avoid building yet another scalable metrics platform. As documentation for Prometheus states, it is limited by single nodes in its scalability and durability. The M3 platform aims to provide a turnkey, scalable, and configurable multi-tenant store for Prometheus, Graphite and other standard metrics schemas. diff --git a/docs/overview/roadmap.md b/docs/content/overview/roadmap.md similarity index 91% rename from docs/overview/roadmap.md rename to docs/content/overview/roadmap.md index c38e3e8d4e..629b300581 100644 --- a/docs/overview/roadmap.md +++ b/docs/content/overview/roadmap.md @@ -1,4 +1,7 @@ -# Roadmap +--- +title: "Roadmap" +weight: 4 +--- This roadmap is open for suggestions and currently just a small snapshot of what is coming up. diff --git a/docs/content/podtemplate.json b/docs/content/podtemplate.json new file mode 100644 index 0000000000..4906077adc --- /dev/null +++ b/docs/content/podtemplate.json @@ -0,0 +1,22 @@ +{ + "apiVersion": "v1", + "kind": "PodTemplate", + "metadata": { + "name": "nginx" + }, + "template": { + "metadata": { + "labels": { + "name": "nginx" + }, + "generateName": "nginx-" + }, + "spec": { + "containers": [{ + "name": "nginx", + "image": "dockerfile/nginx", + "ports": [{"containerPort": 80}] + }] + } + } + } \ No newline at end of file diff --git a/docs/content/style-guide.md b/docs/content/style-guide.md new file mode 100644 index 0000000000..d3df83fcfe --- /dev/null +++ b/docs/content/style-guide.md @@ -0,0 +1,42 @@ ++++ +title = "Home" +date = 2020-04-01T19:26:56-04:00 +weight = 4 +chapter = false ++++ + +{{< tabs name="tab_with_md" >}} +{{% tab name="Markdown" %}} +This is **some markdown.** +```bash +code +``` +{{% /tab %}} +{{< tab name="HTML" >}} + +HTML + +{{< /tab >}} +{{< tab name="JSON" >}} + +Include code from elsewhere. + +{{% codeinclude file="/static/podtemplate.json" language="json" %}} + +{{< /tab >}} +{{< /tabs >}} + +{{< glossary_tooltip text="m3" term_id="m3" >}} + +```go {hl_lines=[2]} +func GetTitleFunc(style string) func(s string) string { + switch strings.ToLower(style) { + case "go": + return strings.Title + case "chicago": + return transform.NewTitleConverter(transform.ChicagoStyle) + default: + return transform.NewTitleConverter(transform.APStyle) + } + +``` \ No newline at end of file diff --git a/docs/content/troubleshooting/_index.md b/docs/content/troubleshooting/_index.md new file mode 100644 index 0000000000..9981a2ac8e --- /dev/null +++ b/docs/content/troubleshooting/_index.md @@ -0,0 +1,76 @@ +--- +title: "Troubleshooting" +weight: 8 +chapter: true +--- + +Some common problems and resolutions + +## Ports 9001-9004 aren't open after starting m3db. + +These ports will not open until a namespace and placement have been created and the nodes have bootstrapped. + +## Bootstrapping is slow + +Double check your configuration against the [bootstrapping guide](/operational_guide/bootstrapping_crash_recovery). The nodes will log what bootstrapper they are using and what time range they are using it for. + +If you're using the commitlog bootstrapper, and it seems to be slow, ensure that snapshotting is enabled for your namespace. Enabling snapshotting will require a node restart to take effect. + +If an m3db node hasn't been able to snapshot for awhile, or is stuck in the commitlog bootstrapping phase for a long time due to accumulating a large number of commitlogs, consider using the peers bootstrapper. In situations where a large number of commitlogs need to be read, the peers bootstrapper will outperform the commitlog bootstrapper (faster and less memory usage) due to the fact that it will receive already-compressed data from its peers. Keep in mind that this will only work with a replication factor of 3 or larger and if the nodes peers are healthy and bootstrapped. Review the [bootstrapping guide](/operational_guide/bootstrapping_crash_recovery) for more information. + +## Nodes a crashing with memory allocation errors, but there's plenty of available memory + +Ensure you've set `vm.max_map_count` to something like 262,144 using sysctl. Find out more in the [Clustering the Hard Way](/how_to/cluster_hard_way.md#kernel) document. + +## What to do if my M3DB node is OOM’ing? + +1. Ensure that you are not co-locating coordinator, etcd or query nodes with your M3DB nodes. Colocation or embedded mode is fine for a development environment, but highly discouraged in production. +2. Check to make sure you are running adequate block sizes based on the retention of your namespace. See [namespace configuration](/operational_guide/namespace_configuration) for more information. +3. Ensure that you use at most 50-60% memory utilization in the normal running state. You want to ensure enough overhead to handle bursts of metrics, especially ones with new IDs as those will take more memory initially. +4. High cardinality metrics can also lead to OOMs especially if you are not adequately provisioned. If you have many unique timeseries such as ones containing UUIDs or timestamps as tag values, you should consider mitigating their cardinality. + +## Using the /debug/dump API + +The `/debug/dump` API returns a number of helpful debugging outputs. Currently, we support the following: + +- CPU profile: determines where a program spends its time while actively consuming CPU cycles (as opposed to while sleeping or waiting for I/O). Currently set to take a 5 second profile. +- Heap profile: reports memory allocation samples; used to monitor current and historical memory usage, and to check for memory leaks. +- Goroutines profile: reports the stack traces of all current goroutines. +- Host profile: returns data about the underlying host such as PID, working directory, etc. +- Namespace: returns information about the namespaces setup in M3DB +- Placement: returns information about the placement setup in M3DB + +This endpoint can be used on both the db nodes as well as the coordinator/query nodes. However, namespace and placement info are only available on the coordinator debug endpoint. + +To use this, simply run the following on either the M3DB debug listen port or the regular port on M3Coordinator. + + curl :/debug/dump > + + # unzip the file + unzip + +Now, you will have the following files, which you can use for troubleshooting using the below commands: + +**cpuSource** + + go tool pprof -http=:16000 cpuSource + +**heapSource** + + go tool pprof -http=:16000 heapSource + +**goroutineProfile** + + less goroutineProfile + +**hostSource** + + less hostSource | jq . + +**namespaceSource** + + less namespaceSource | jq . + +**placementSource** + + less placementSource | jq . \ No newline at end of file diff --git a/docs/glossary/index.md b/docs/glossary/index.md deleted file mode 100644 index b015e5192a..0000000000 --- a/docs/glossary/index.md +++ /dev/null @@ -1,43 +0,0 @@ -# Glossary - -- **Bootstrapping**: Process by which an M3DB node is brought up. Bootstrapping consists of determining -the integrity of data that the node has, replay writes from the commit log, and/or stream missing data -from its peers. - -- **Cardinality**: The number of unique metrics within the M3DB index. Cardinality increases with the -number of unique tag/value combinations that are being emitted. - -- **Datapoint**: A single timestamp/value. Timeseries are composed of multiple datapoints and a series -of tag/value pairs. - -- **Labels**: Pairs of descriptive words that give meaning to a metric. `Tags` and `Labels` are interchangeable terms. - -- **Metric**: A collection of uniquely identifiable tags. - -- **M3**: Highly scalable, distributed metrics platform that is comprised of a native, distributed time -series database, a highly-dynamic and performant aggregation service, a query engine, and other -supporting infrastructure. - -- **M3Coordinator**: A service within M3 that coordinates reads and writes between upstream systems, -such as Prometheus, and downstream systems, such as M3DB. - -- **M3DB**: Distributed time series database influenced by [Gorilla](http://www.vldb.org/pvldb/vol8/p1816-teller.pdf) -and [Cassandra](http://cassandra.apache.org/) released as open source by Uber Technologies. - -- **M3Query**: A distributed query engine for M3DB. Unlike M3Coordinator, M3Query only provides supports for reads. - -- **Namespace**: Similar to a table in other types of databases, namespaces in M3DB have a unique name -and a set of configuration options, such as data retention and block size. - -- **Placement**: Map of the M3DB cluster's shard replicas to nodes. Each M3DB cluster has only one placement. -`Placement` and `Topology` are interchangeable terms. - -- **Shard**: Effectively the same as a "virtual shard" in Cassandra in that it provides an arbitrary -distribution of time series data via a simple hash of the series ID. - -- **Tags**: Pairs of descriptive words that give meaning to a metric. `Tags` and `Labels` are interchangeable terms. - -- **Timeseries**: A series of data points tracking a particular metric over time. - -- **Topology**: Map of the M3DB cluster's shard replicas to nodes. Each M3DB cluster has only one placement. -`Placement` and `Topology` are interchangeable terms. diff --git a/docs/common/headers_optional_read_all.md b/docs/includes/headers_optional_read_all.md similarity index 89% rename from docs/common/headers_optional_read_all.md rename to docs/includes/headers_optional_read_all.md index a21d0fbca6..214da63df9 100644 --- a/docs/common/headers_optional_read_all.md +++ b/docs/includes/headers_optional_read_all.md @@ -1,6 +1,5 @@ ---8<-- -docs/common/headers_optional_read_limits.md ---8<-- +{{% fileinclude file="/includes/headers_optional_read_limits.md" %}} + - `M3-Restrict-By-Tags-JSON`: If this header is set it can ensure specific label matching is performed as part of every query including series metadata endpoints. As an example, the following diff --git a/docs/common/headers_optional_read_limits.md b/docs/includes/headers_optional_read_limits.md similarity index 100% rename from docs/common/headers_optional_read_limits.md rename to docs/includes/headers_optional_read_limits.md diff --git a/docs/includes/headers_optional_read_write.md b/docs/includes/headers_optional_read_write.md new file mode 100644 index 0000000000..7712c113fa --- /dev/null +++ b/docs/includes/headers_optional_read_write.md @@ -0,0 +1,11 @@ +- `M3-Metrics-Type`: + If this header is set, it determines what type of metric to store this metric value as. Otherwise by default, metrics will be stored in all namespaces that are configured. You can also disable this default behavior by setting `downsample` options to `all: false` for a namespace in the coordinator config, for more see [disabling automatic aggregation](/how_to/query.md#disabling-automatic-aggregation). + + Must be one of: + `unaggregated`: Write metrics directly to configured unaggregated namespace. + `aggregated`: Write metrics directly to a configured aggregated namespace (bypassing any aggregation), this requires the `M3-Storage-Policy` header to be set to resolve which namespace to write metrics to. + +- `M3-Storage-Policy`: + If this header is set, it determines which aggregated namespace to read/write metrics directly to/from (bypassing any aggregation). + The value of the header must be in the format of `resolution:retention` in duration shorthand. e.g. `1m:48h` specifices 1 minute resolution and 48 hour retention. Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h".

+ Here is [an example](https://github.com/m3db/m3/blob/master/scripts/docker-integration-tests/prometheus/test.sh#L126-L146) of querying metrics from a specific namespace. diff --git a/docs/common/headers_optional_read_write_all.md b/docs/includes/headers_optional_read_write_all.md similarity index 100% rename from docs/common/headers_optional_read_write_all.md rename to docs/includes/headers_optional_read_write_all.md diff --git a/docs/common/headers_optional_write_all.md b/docs/includes/headers_optional_write_all.md similarity index 100% rename from docs/common/headers_optional_write_all.md rename to docs/includes/headers_optional_write_all.md diff --git a/docs/common/headers_placement_namespace.md b/docs/includes/headers_placement_namespace.md similarity index 100% rename from docs/common/headers_placement_namespace.md rename to docs/includes/headers_placement_namespace.md diff --git a/docs/index.md b/docs/index.md index 9283ac6f0a..95c1613148 100644 --- a/docs/index.md +++ b/docs/index.md @@ -11,22 +11,22 @@ other supporting infrastructure. M3 has several features, provided as discrete components, which make it an ideal platform for time series data at scale: -* A distributed time series database, [M3DB](m3db/index.md), that provides scalable storage for time series data and a reverse index. -* A sidecar process, [M3Coordinator](integrations/prometheus.md), that allows M3DB to act as the long-term storage for Prometheus. -* A distributed query engine, [M3Query](m3query/index.md), with native support for PromQL and Graphite (M3QL coming soon). +* A distributed time series database, [M3DB](/m3db/index.md), that provides scalable storage for time series data and a reverse index. +* A sidecar process, [M3Coordinator](/integrations/prometheus.md), that allows M3DB to act as the long-term storage for Prometheus. +* A distributed query engine, [M3Query](/m3query/index.md), with native support for PromQL and Graphite (M3QL coming soon). * An aggregation tier, M3Aggregator, that runs as a dedicated metrics aggregator/downsampler allowing metrics to be stored at various retentions at different resolutions. ## Getting Started -**Note:** Make sure to read our [Operational Guides](operational_guide/index.md) before running in production! +**Note:** Make sure to read our [Operational Guides](/operational_guide/index.md) before running in production! Getting started with M3 is as easy as following one of the How-To guides. -* [Single M3DB node deployment](how_to/single_node.md) -* [Clustered M3DB deployment](how_to/cluster_hard_way.md) -* [M3DB on Kubernetes](how_to/kubernetes.md) -* [Isolated M3Query on deployment](how_to/query.md) +* [Single M3DB node deployment](/how_to/single_node.md) +* [Clustered M3DB deployment](/how_to/cluster_hard_way.md) +* [M3DB on Kubernetes](/how_to/kubernetes.md) +* [Isolated M3Query on deployment](/how_to/query.md) ## Support diff --git a/docs/m3db/architecture/consistencylevels.md b/docs/m3db/architecture/consistencylevels.md deleted file mode 100644 index d66e2099c0..0000000000 --- a/docs/m3db/architecture/consistencylevels.md +++ /dev/null @@ -1,38 +0,0 @@ -# Consistency Levels - -M3DB provides variable consistency levels for read and write operations, as well as cluster connection operations. These consistency levels are handled at the client level. - -## Write consistency levels - -* **One:** Corresponds to a single node succeeding for an operation to succeed. - -* **Majority:** Corresponds to the majority of nodes succeeding for an operation to succeed. - -* **All:** Corresponds to all nodes succeeding for an operation to succeed. - -## Read consistency levels - -* **One**: Corresponds to reading from a single node to designate success. - -* **UnstrictMajority**: Corresponds to reading from the majority of nodes but relaxing the constraint when it cannot be met, falling back to returning success when reading from at least a single node after attempting reading from the majority of nodes. - -* **Majority**: Corresponds to reading from the majority of nodes to designate success. - -* **All:** Corresponds to reading from all of the nodes to designate success. - -## Connect consistency levels - -Connect consistency levels are used to determine when a client session is deemed as connected before operations can be attempted. - -* **Any:** Corresponds to connecting to any number of nodes for all shards, this strategy will attempt to connect to all, then the majority, then one and then fallback to none and as such will always succeed. - -* **None:** Corresponds to connecting to no nodes for all shards and as such will always succeed. - -* **One:** Corresponds to connecting to a single node for all shards. - - -* **Majority:** Corresponds to connecting to the majority of nodes for all shards. - - -* **All:** Corresponds to connecting to all of the nodes for all shards. - diff --git a/docs/m3db/architecture/engine.md b/docs/m3db/architecture/engine.md deleted file mode 100644 index d4727b32ad..0000000000 --- a/docs/m3db/architecture/engine.md +++ /dev/null @@ -1,256 +0,0 @@ -# Storage Engine Overview - -M3DB is a time series database that was primarily designed to be horizontally scalable and able to handle high data throughput. - -## Time Series Compression - -One of M3DB's biggest strengths as a time series database (as opposed to using a more general-purpose horizontally scalable, distributed database like Cassandra) is its ability to compress time series data resulting in huge memory and disk savings. There are two compression algorithms used in M3DB: M3TSZ and protobuf encoding. - -### M3TSZ - -M3TSZ is used when values are floats. A variant of the streaming time series compression algorithm described in [Facebook's Gorilla paper](http://www.vldb.org/pvldb/vol8/p1816-teller.pdf), it achieves a high compression ratio. The compression ratio will vary depending on the workload and configuration, but we found that we were able to achieve a compression ratio of 1.45 bytes/datapoint with Uber's production workloads. This was a 40% improvement over standard TSZ, which only gave us a compression ratio of 2.42 bytes/datapoint under the same conditions. - -### Protobuf Encoding - -For more complex value types, M3DB also supports generic Protobuf messages with [a few exceptions](https://github.com/m3db/m3/blob/master/src/dbnode/encoding/proto/docs/encoding.md#supported-syntax). The algorithm takes on a hybrid approach and uses different compression schemes depending on the field types within the Protobuf message. - -Details on the encoding, marshaling and unmarshaling methods can be read [here](https://github.com/m3db/m3/tree/master/src/dbnode/encoding/proto). - -## Architecture - -M3DB is a persistent database with durable storage, but it is best understood via the boundary between its in-memory object layout and on-disk representations. - -### In-Memory Object Layout - -``` - ┌────────────────────────────────────────────────────────────┐ - │ Database │ - ├────────────────────────────────────────────────────────────┤ - │ │ - │ ┌────────────────────────────────────────────────────┐ │ - │ │ Namespaces │ │ - │ ├────────────────────────────────────────────────────┤ │ - │ │ │ │ - │ │ ┌────────────────────────────────────────────┐ │ │ - │ │ │ Shards │ │ │ - │ │ ├────────────────────────────────────────────┤ │ │ - │ │ │ │ │ │ - │ │ │ ┌────────────────────────────────────┐ │ │ │ - │ │ │ │ Series │ │ │ │ - │ │ │ ├────────────────────────────────────┤ │ │ │ - │ │ │ │ │ │ │ │ - │ │ │ │ ┌────────────────────────────┐ │ │ │ │ - │ │ │ │ │ Buffer │ │ │ │ │ - │ │ │ │ └────────────────────────────┘ │ │ │ │ - │ │ │ │ │ │ │ │ - │ │ │ │ │ │ │ │ - │ │ │ │ ┌────────────────────────────┐ │ │ │ │ - │ │ │ │ │ Cached blocks │ │ │ │ │ - │ │ │ │ └────────────────────────────┘ │ │ │ │ - │ │ │ │ ... │ │ │ │ - │ │ │ │ │ │ │ │ - │ │ │ └────────────────────────────────────┘ │ │ │ - │ │ │ ... │ │ │ - │ │ │ │ │ │ - │ │ └────────────────────────────────────────────┘ │ │ - │ │ ... │ │ - │ │ │ │ - │ └────────────────────────────────────────────────────┘ │ - │ ... │ - │ │ - └────────────────────────────────────────────────────────────┘ -``` - -The in-memory portion of M3DB is implemented via a hierarchy of objects: - -1. A `database` of which there is only one per M3DB process. The `database` owns multiple `namespace`s. - -2. A `namespace` is similar to a table in other databases. Each `namespace` has a unique name and a set of configuration options, such as data retention and block size (which we will discuss in more detail later). A namespace owns multiple `shard`s. - -3. A [`shard`](sharding.md) is effectively the same as a "virtual shard" in Cassandra in that it provides an arbitrary distribution of time series data via a simple hash of the series ID. A shard owns multiple `series`. - -4. A `series` represents a sequence of time series datapoints. For example, the CPU utilization for a host could be represented as a series with the ID "host1.system.cpu.utilization" and a vector of (TIMESTAMP, CPU_LEVEL) tuples. Visualizing this example in a graph, there would a single line with time on the x-axis and CPU utilization on the y-axis. A `series` owns a `buffer` and any cached `block`s. - -5. The `buffer` is where all data that has yet to be written to disk gets stored in memory. This includes both new writes to M3DB and data obtained through bootstrapping. More details on the [buffer](engine.md#buffer) is explained below. Upon [flushing](engine.md#flushing), the buffer creates a `block` of its data to be persisted to disk. - -6. A `block` represents a stream of compressed time series data for a pre-configured block size, for example, a block could hold data for 6-8PM (block size of two hours). A `block` can arrive directly into the series only as a result of getting cached after a read request. Since blocks are in a compressed format, individual datapoints cannot be read from it. In other words, in order to read a single datapoint, the entire block up to that datapoint needs to be decompressed beforehand. - -### Persistent storage - -While in-memory databases can be useful (and M3DB supports operating in a memory-only mode), some form of persistence is required for durability. In other words, without a persistence strategy, it would be impossible for M3DB to restart (or recover from a crash) without losing all of its data. - -In addition, with large volumes of data, it becomes prohibitively expensive to keep all of the data in memory. This is especially true for monitoring workloads which often follow a "write-once, read-never" pattern where less than a few percent of all the data that's stored is ever read. With that type of workload, it's wasteful to keep all of that data in memory when it could be persisted on disk and retrieved when required. - -M3DB takes a two-pronged approach to persistant storage that involves combining a [commit log](commitlogs.md) for disaster recovery with periodic flushing (writing [fileset files](storage.md) to disk) for efficient retrieval: - -1. All writes are persisted to a commit log (the commit log can be configured to fsync every write, or optionally batch writes together which is much faster but leaves open the possibility of small amounts of data loss in the case of a catastrophic failure). The commit log is completely uncompressed and exists only to recover unflushed data in the case of a database shutdown (intentional or not) and is never used to satisfy a read request. -2. Periodically (based on the configured block size), all data in the buffer is flushed to disk as immutable [fileset files](storage.md). These files are highly compressed and can be indexed into via their complementary index files. Check out the [flushing section](engine.md#flushing) to learn more about the background flushing process. - -The block size parameter is the most important variable that needs to be tuned for a particular workload. A small block size will mean more frequent flushing and a smaller memory footprint for the data that is being actively compressed, but it will also reduce the compression ratio and data will take up more space on disk. - -If the database is stopped for any reason in between flushes, then when the node is started back up those writes will be recovered by reading the commit log or streaming in the data from a peer responsible for the same shard (if the replication factor is larger than one). - -While the fileset files are designed to support efficient data retrieval via the series ID, there is still a heavy cost associated with any query that has to retrieve data from disk because going to disk is always much slower than accessing main memory. To compensate for that, M3DB supports various [caching policies](caching.md) which can significantly improve the performance of reads by caching data in memory. - -## Write Path - -We now have enough context of M3DB's architecture to discuss the lifecycle of a write. A write begins when an M3DB client calls the [`writeBatchRaw`](https://github.com/m3db/m3/blob/06d3ecc94d13cff67b82a791271816caa338dcab/src/dbnode/generated/thrift/rpc.thrift#L59) endpoint on M3DB's embedded thrift server. The write itself will contain the following information: - -1. The namespace -2. The series ID (byte blob) -3. The timestamp -4. The value itself - -M3DB will consult the database object to check if the namespace exists, and if it does, then it will hash the series ID to determine which shard it belongs to. If the node receiving the write owns that shard, then it will lookup the series in the shard object. If the series exists, then an encoder in the buffer will encode the datapoint into the compressed stream. If the encoder doesn't exist (no writes for this series have occurred yet as part of this block) then a new encoder will be allocated and it will begin a compressed M3TSZ stream with that datapoint. There is also some additional logic for handling multiple encoders and filesets which is discussed in the [buffer](engine.md#buffer) section. - -At the same time, the write will be appended to the commit log, which is periodically compacted via a snapshot process. Details of this is outlined in the [commit log](commitlogs.md) page. - -**Note:** Regardless of the success or failure of the write in a single node, the client will return a success or failure to the caller for the write based on the configured [consistency level](consistencylevels.md). - -## Read Path - -A read begins when an M3DB client calls the [`FetchBatchResult`](https://github.com/m3db/m3/blob/master/src/dbnode/generated/thrift/rpc.thrift) or [`FetchBlocksRawResult`](https://github.com/m3db/m3/blob/master/src/dbnode/generated/thrift/rpc.thrift) endpoint on M3DB's embedded thrift server. The read request will contain the following information: - -1. The namespace -2. The series ID (byte blob) -3. The period of time being requested (start and end) - -M3DB will consult the database object to check if the namespace exists, and if it does, it will hash the series ID to determine which shard it belongs to. If the node receiving the read owns that shard, then M3DB needs to determine two things: - -1. Whether the series exists and if it does, -2. Whether the data exists in the buffer, cached in-memory, on disk, or some combination of all three. - -Determining whether the series exists is simple. M3DB looks up the series in the shard object. If it exists, then the series exists. If it doesn't, then M3DB consults in-memory bloom filters(s) for all shard/block start combinations(s) that overlap the query range to determine if the series exists on disk. - -If the series exists, then for every block that the request spans, M3DB needs to consolidate data from the buffer, in-memory cache, and fileset files (disk). - -Let's imagine a read for a given series that requests the last 6 hours worth of data, and an M3DB namespace that is configured with a block size of 2 hours, i.e. we need to find 3 different blocks. - -If the current time is 8PM, then the location of the requested blocks might be as follows: - -``` -[2PM - 4PM (fileset file)] - Flushed block that isn't cached -[4PM - 6PM (in-memory cache)] - Flushed block that is cached -[4PM - 6PM (cold write in active buffer)] - Cold write that hasn't been flushed yet -[6PM - 8PM (active buffer)] - Hasn't been flushed yet -``` - -Then M3DB will need to consolidate: - -1. The not-yet-sealed block from the buffer (located inside an internal lookup in the Series object) **[6PM - 8PM]** -2. The in-memory cached block (also located inside an internal lookup in the Series object). Since there are also cold writes in this block, the cold writes will be consolidated in memory with data found in the cached block before returning. **[4PM - 6PM]** -3. The block from disk (the block will be retrieved from disk and will then be cached according to the current [caching policy](caching.md)) **[2PM - 4PM]** - -Retrieving blocks from the buffer and in-memory cache is simple, the data is already present in memory and easily accessible via hashmaps keyed by series ID. Retrieving a block from disk is more complicated. The flow for retrieving a block from disk is as follows: - -1. Consult the in-memory bloom filter to determine if it's possible the series exists on disk. -2. If the bloom filter returns negative, we are sure that the series isn't there, so return that result. If the bloom filter returns positive, then binary search the in-memory index summaries to find the nearest index entry that is *before* the series ID that we're searching for. Review the `index_lookup.go` file for implementation details. -3. Jump to the offset in the index file that we obtained from the binary search in the previous step, and begin scanning forward until we identify the index entry for the series ID we're looking for *or* we get far enough in the index file that it becomes clear that the ID we're looking for doesn't exist (this is possible because the index file is sorted by ID) -4. Jump to the offset in the data file that we obtained from scanning the index file in the previous step, and begin streaming data. - -Once M3DB has retrieved the three blocks from their respective locations in memory / on-disk, it will transmit all of the data back to the client. Whether or not the client returns a success to the caller for the read is dependent on the configured [consistency level](consistencylevels.md). - -**Note:** Since M3DB nodes return compressed blocks (the M3DB client decompresses them), it's not possible to return "partial results" for a given block. If any portion of a read request spans a given block, then that block in its entirety must be transmitted back to the client. In practice, this ends up being not much of an issue because of the high compression ratio that M3DB is able to achieve. - -## Buffer - -Each series object contains a buffer, which is in charge of handling all data that has yet to be flushed - new writes and bootstrapped data. To accomplish this, it keeps mutable "buckets" of encoders (for new writes) and immutable blocks (for bootstrapped data). M3TSZ, the database's encoding scheme, is designed for compressing time series data in which each datapoint has a timestamp that is larger than the last encoded datapoint. For metrics workloads this works very well because every subsequent datapoint is almost always after the previous one. However, out of order writes will occasionally be received, for example due to clock skew. When this happens, M3DB will allocate a new encoder for the out of order datapoints. These encoders are contained in a bucket along with any blocks that got bootstrapped. - -Upon a flush (discussed further below), all data within a bucket gets merged and its version gets incremented - the specific version it gets set to depends on the number of times this block has previously been flushed. This bucket versioning allows the buffer to know which data has been flushed so that subsequent flushes will not try to flush it again. It also indicates to the clean up process (also discussed below) that that data can be evicted. - -Given this complex, concurrent logic, this has been [modeled in TLA](https://github.com/m3db/m3/blob/master/specs/dbnode/flush/FlushVersion.tla). - -``` - ┌─────────────────────────┐ - │ Buffer │ - ├─────────────────────────┤ - │ │ - │ ┌─────────────────┐ │ - │ │ 2-4PM buckets │ │ - │ └─────────────────┘ │ - │ │ - │ ┌─────────────────┐ │ - ┌────│───│ 4-6PM buckets │ | - │ │ └─────────────────┘ │ - │ │ │ - │ │ ... │ - │ └─────────────────────────┘ - │ - │ - v After flush: - ┌─────────────────────┐ ┌─────────────────────┐ - │ 4-6PM buckets │ │ 4-6PM buckets │ - ├─────────────────────┤ ├─────────────────────┤ - │ │ │ │ - │ ┌─────────────┐ │ │ ┌─────────────┐ │ - │ │ Bucket v0 │<--│--writes │ │ Bucket v3 │ │ - │ └─────────────┘ │ │ └─────────────┘ │ - │ │ │ │ - │ │ │ │ - │ │ │ │ - │ │ │ │ - │ │ │ │ - └─────────────────────┘ └─────────────────────┘ - - - More writes after flush: After clean up: - ┌─────────────────────┐ ┌─────────────────────┐ - │ 4-6PM buckets │ │ 4-6PM buckets │ - ├─────────────────────┤ ├─────────────────────┤ - │ │ │ │ - │ ┌─────────────┐ │ │ ┌─────────────┐ │ - │ │ Bucket v3 │ │ │ │ Bucket v0 │<--│--writes - │ └─────────────┘ │ │ └─────────────┘ │ - │ │ │ │ - │ ┌─────────────┐ │ │ │ - │ │ Bucket v0 │<--│--writes │ │ - │ └─────────────┘ │ │ │ - │ │ │ │ - └─────────────────────┘ └─────────────────────┘ -``` - -## Background processes - -M3DB has a variety of processes that run in the background during normal operation. - -### Flushing - -As discussed in the [architecture](engine.md#architecture) section, writes are actively buffered / compressed in memory and the commit log is continuously being written to, but eventually data needs to be flushed to disk in the form of [fileset files](storage.md) to facilitate efficient storage and retrieval. - -This is where the configurable "block size" comes into play. The block size is simply a duration of time that dictates how long new writes will be compressed (in a streaming manner) in memory before being flushed to disk. Let's use a block size of two hours as an example. - -If the block size is set to two hours, then all writes for all series for a given shard will be buffered in memory for two hours at a time. At the end of the two hour period all of the fileset files will be generated, written to disk, and then the in-memory objects can be released and replaced with new ones for the new block. The old objects will be removed from memory in the subsequent tick. - -If a flush happens for a namespace/shard/series/block for which there is already a fileset, in-memory data will get merged with data on disk from the fileset. The resultant merged data will then be flushed as a separate fileset. - -### Ticking - -The ticking process runs continously in the background and is responsible for a variety of tasks: - -1. Merging all encoders for a given series / block start combination -2. Removing expired / flushed series and blocks from memory -3. Clean up of expired data (fileset/commit log) from the filesystem - -#### Merging all encoders - -If there are multiple encoders for a block, they need to be merged before flushing the data to disk. To prevent huge memory spikes during the flushing process we continuously merge out of order encoders in the background. - -#### Removing expired / flushed series and blocks from memory - -Depending on the configured [caching policy](caching.md), the [in-memory object layout](engine.md#in-memory-object-layout) can end up with references to series or data blocks that are expired (have fallen out of the retention period) or no longer needed to be in memory (due to the data being flushed to disk or no longer needing to be cached). The background tick will identify these structures and release them from memory. - -#### Clean up of expired data - -Fileset files can become no longer necessary for two reasons: - -1. The fileset files for a block that has fallen out of retention -2. A flush occurred for a block that already has a fileset file. The new fileset will be a superset of the existing fileset with any new data that for that block, hence, the existing fileset is no longer required - -During the clean up process, these fileset files will get deleted. - -## Caveats / Limitations - -1. Currently M3DB does not support deletes. -2. M3DB does not support storing data with an indefinite retention period, every namespace in M3DB is required to have a retention policy which specifies how long data in that namespace will be retained for. While there is no upper bound on that value, it's still required and generally speaking M3DB is optimized for workloads with a well-defined [TTL](https://en.wikipedia.org/wiki/Time_to_live). -3. M3DB does not support either background data repair or Cassandra-style [read repairs](https://docs.datastax.com/en/cassandra/2.1/cassandra/operations/opsRepairNodesReadRepair.html). Future versions of M3DB will support automatic repairs of data as an ongoing background process. -4. M3DB does not support writing far into the future. Support for this will be added in future. diff --git a/docs/m3db/architecture/index.md b/docs/m3db/architecture/index.md deleted file mode 100644 index 6767ce46e4..0000000000 --- a/docs/m3db/architecture/index.md +++ /dev/null @@ -1,15 +0,0 @@ -# Architecture - -## Overview - -M3DB is written entirely in Go and does not have any required dependencies. For larger deployments, one may use an etcd cluster to manage M3DB cluster membership and topology definition. - -## High Level Goals - -Some of the high level goals for the project are defined as: - -* **Monitoring support:** M3DB was primarily developed for collecting a high volume of monitoring time series data, distributing the storage in a horizontally scalable manner and most efficiently leveraging the hardware. As such time series that are not read frequently are not kept in memory. - -* **Highly configurable:** Provide a high level of configuration to support a wide set of use cases and runtime environments. - -* **Variable durability:** Providing variable durability guarantees for the write and read side of storing time series data enables a wider variety of applications to use M3DB. This is why replication is primarily synchronous and is provided with configurable consistency levels, to enable consistent writes and reads. It must be possible to use M3DB with strong guarantees that data was replicated to a quorum of nodes and that the data was durable if desired. diff --git a/docs/m3db/architecture/peer_streaming.md b/docs/m3db/architecture/peer_streaming.md deleted file mode 100644 index 532f6c6aac..0000000000 --- a/docs/m3db/architecture/peer_streaming.md +++ /dev/null @@ -1,107 +0,0 @@ -# Peer Streaming - -## Client - -Peer streaming is managed by the M3DB client. It fetches all blocks from peers for a specified time range for bootstrapping purposes. It performs the following steps: - -1. Fetch all metadata for blocks from all peers who own the specified shard -2. Compares metadata from different peers and determines the best peer(s) from which to stream the actual data -3. Streams the block data from peers - -Steps 1, 2 and 3 all happen concurrently. As metadata streams in, we begin determining which peer is the best source to stream a given block's data for a given series from, and then we begin streaming data from that peer while we continue to receive metadata. If the checksum for a given series block matches all three replicas then the least loaded (in terms of outstanding requests) and recently attempted will be selected to stream from. If the checksum differs for the series block across any of the peers then a fanout fetch of the series block is performed. - -In terms of error handling, the client will respect the consistency level specified for bootstrap. This means that when fetching metadata, indefinite retry is performed until the consistency level is achieved, for instance for quorum a majority of peers must successfully return metadata. For fetching the block data, if checksum matches from all peers then one successful fetch must occur, unless bootstrap consistency level "none" is specified, and if checksum mismatches then the specified consistency level must be achieved when the series block fetch is fanned out to peers. Fetching block data as well will indefinitely retry until the consistency level is achieved. - -The client supports dynamically changing the bootstrap consistency level, which is helpful in disaster scenarios where the consistency level cannot be achieved. To break the indefinite streaming attempt an operator can change the consistency level to "none" and a purely best-effort will be made to fetch the metadata and correspondingly to fetch the block data. - -The diagram below depicts the control flow and concurrency (goroutines and channels) in detail: - -``` - ┌───────────────────────────────────────────────┐ - │ │ - │ FetchBootstrapBlocksFromPeers │ - │ │ - └───────────────────────────────────────────────┘ - │ - │ - ┌────────────────────┘ - │ - ▼ -┌───────────────────────────────┐ -│ Main routine │ -│ │ -│ 1) Create metadataCh │────────────────┐ -│ 2) Spin up background routine │ │ -└───────────────────────────────┘ Create with metadataCh - │ │ - │ ▼ - │ ┌───────────────────────────────┐ - │ │ │ - │ │ Background routine │ - │ │ │ - │ └───────────────────────────────┘ - │ │ - │ For each peer - │ │ - │ ┌────────────────┼─────────────────┐ - │ │ │ │ - │ │ │ │ - │ ▼ ▼ ▼ - │ ┌───────────────────────────────────────────┐ - │ │ StreamBlocksMetadataFromPeer │ - │ │ │ - │ │ Stream paginated blocks metadata from a │ - │ │ peer while pageToken != nil │ - │ │ │ - │ │ For each blocks' metadata --> put │ - │ │ metadata into metadataCh │ - │ └───────────────────────────────────────────┘ - ▼ -┌───────────────────────────────────────────┐ -│ StreamBlocksFromPeers │ -│ │ -│ 1) Create a background goroutine (details │ -│ to the right) │ -│ │ -│ 2) Create a queue per-peer which each have│ -│ their own internal goroutine and will │ -│ stream blocks back per-series from a │──────────┐ -│ specific peer │ │ -│ │ │ -│ 3) Loop through the enqueCh and pick an │ Creates with metadataCh -│appropriate peer(s) for each series (based │ and enqueueCh -│on whether all the peers have the same data│ │ -│ or not) and then put that into the queue │ │ -│for that peer so the data will be streamed │ │ -└───────────────────────────────────────────┘ │ - │ ▼ - │ ┌──────────────────────────────────────────────────────────┐ - │ │ streamAndGroupCollectedBlocksMetadata (injected via │ - │ │ streamMetadataFn variable) │ - │ │ │ - │ │ Loop through the metadataCh aggregating blocks metadata │ - │ │per series/block combination from different peers until we│ - │ │ have them from all peers for a series/block metadata │ - │ │ combination and then "submit" them to the enqueueCh │ - │ │ │ - │ │At the end, flush any remaining series/block combinations │ - │ │(that we received from less than N peers) into the enqueCh│ - │ │ as well. │ - │ └──────────────────────────────────────────────────────────┘ - │ - For each peer - │ - ┌────────────┼─────────────┐ - │ │ │ - │ │ │ - ▼ ▼ ▼ -┌─────────────────────────────────────────────────────────────┐ -│ newPeerBlocksQueue (processFn = streamBlocksBatchFromPeer) │ -│ │ -│For each peer we're creating a new peerBlocksQueue which will│ -│ stream data blocks from a specific peer (using the │ -│ streamBlocksBatchFromPeer function) and add them to the │ -│ blocksResult │ -│ │ -└─────────────────────────────────────────────────────────────┘ -``` diff --git a/docs/m3query/architecture/functions.md b/docs/m3query/architecture/functions.md deleted file mode 100644 index 578de59c9e..0000000000 --- a/docs/m3query/architecture/functions.md +++ /dev/null @@ -1,71 +0,0 @@ -# Function Processing - -## Supported Functions - -| M3QL | Prometheus | Graphite | -| ------ | ------ | ------ | -| abs/absolute | abs() | absolute(seriesList) | -| alias [alias] | | alias(seriesList, newName) | -| aliasByTags [tag] | | aliasByTags(seriesList, *tags) | -| aliasByBucket/aliasByHistogramBucket [tag] | | | -| anomalies [flags] | | | -| asPercent | / | asPercent(seriesList, total=None, *nodes) | -| avg/averageSeries [tag] | avg() | averageSeries(*seriesLists) | -| changed | | changed(seriesList) | -| constantLine [value] | | constantLine(value) | -| count | count() | countSeries(*seriesLists) | -| derivative | | derivative(seriesList) | -| diff | - | diffSeries(*seriesLists) | -| divideSeries | / | divideSeries(dividendSeriesList, divisorSeries) | -| eq/== [value] | == | removeBelowValue(seriesList, n)/removeAboveValue(seriesList, n) | -| ne/!= [value] | != | removeBelowValue(seriesList, n)/removeAboveValue(seriesList, n) | -| excludeByTag [tag, pattern] | | exclude(seriesList, pattern) | -| execute/exec [fetch] | | | -| fallbackSeries [replacement] | | fallbackSeries(seriesList, fallback) | -| fetch | | | -| ge/=> [value] | >= | removeBelowValue(seriesList, n) | -| gt/> [value] | > | removeBelowValue(seriesList, n) | -| head [limit] | topk() | highest(seriesList, n=1, func='average') | -| histogramCDF [idTag, rangeTag, value] | | | -| histogramPercentile [idTag, rangeTag, percentileValue] | | | -| identity [name] | | identity(name) | -| integral | | integral(seriesList) | -| intersect [tags] | and/or | | -| isNonNull | | isNonNull(seriesList) | -| jainCP | | | -| keepLastValue | | keepLastValue(seriesList, limit=inf) | -| le/<= [value] | <= | removeAboveValue(seriesList, n) | -| logarithm | ln() | logarithm(seriesList, base=10) | -| lt/< [value] | < | removeAboveValue(seriesList, n) | -| max/maxSeries [tag] | max() | maxSeries(*seriesLists) | -| min/minSeries [tag] | min() | minSeries(*seriesLists) | -| moving [interval, func] | _over_time() | movingMax, movingMin, movingMedian, movingAverage, etc. | -| multiply/multiplySeries [tag] | * | multiplySeries(*seriesLists) | -| nonNegativeDerivative [maxValue] | | nonNegativeDerivative(seriesList, maxValue=None) | -| nPercentile [percentile] | | nPercentile(seriesList, n) | -| offset [amount] | | offset(seriesList, factor) | -| percentileOfSeries [n, true/false, tag] | | percentileOfSeries(seriesList, n, interpolate=False) | -| perSecond | rate() | perSecond(seriesList, maxValue=None) | -| promHistogramPercentile [percentileValue] | | | -| range [tag] | | rangeOfSeries(*seriesLists) | -| removeAbovePercentile [percentile] | | removeAbovePercentile(seriesList, n) | -| removeBelowPercentile [percentile] | | removeBelowPercentile(seriesList, n) | -| removeAboveValue [value] | | removeAboveValue(seriesList, n) | -| removeBelowValue [value] | | removeBelowValue(seriesList, n) | -| removeEmpty | | removeEmptySeries(seriesList, xFilesFactor=None) | -| scale [factor] | | scale(seriesList, factor) | -| scaleToSeconds [seconds] | | scaleToSeconds(seriesList, seconds) | -| setDiff [tags] | | | -| showAnomalyThresholds [level, model] | | | -| showTags [true/false, tagName(s)] | | | -| sort/sortSeries [avg, current, max, stddev, sum] | sort() | sortBy(seriesList, func='average', reverse=False) | -| stdev [points, windowTolerance] | stddev() | stdev(seriesList, points, windowTolerance=0.1) | -| sqrt/squareRoot | sqrt() | squareRoot(seriesList) | -| summarize [interval, func, alignToFrom] | | summarize(seriesList, intervalString, func='sum', alignToFrom=False) | -| sum/sumSeries [tag] | sum() | sumSeries(*seriesLists) | -| sustain [duration] | | | -| sustainedAbove & sustainedBelow | | | -| tail [limit] | bottomk() | lowest(seriesList, n=1, func='average') | -| timeshift [duration] | | timeShift(seriesList, timeShift, resetEnd=True, alignDST=False) | -| timestamp | timestamp() | | -| transformNull [value] | | transformNull(seriesList, default=0, referenceSeries=None) | diff --git a/docs/m3query/architecture/index.md b/docs/m3query/architecture/index.md deleted file mode 100644 index 6fcc7bcf84..0000000000 --- a/docs/m3query/architecture/index.md +++ /dev/null @@ -1,7 +0,0 @@ -# Architecture - -**Please note:** This documentation is a work in progress and more detail is required. - -## Overview - -M3 Query and M3 Coordinator are written entirely in Go, M3 Query is as a query engine for [M3DB](https://docs.m3db.io/) and M3 Coordinator is a remote read/write endpoint for Prometheus and M3DB. To learn more about Prometheus's remote endpoints and storage, [see here](https://prometheus.io/docs/operating/integrations/#remote-endpoints-and-storage). diff --git a/docs/m3query/roadmap.md b/docs/m3query/roadmap.md deleted file mode 100644 index dd4f622fa0..0000000000 --- a/docs/m3query/roadmap.md +++ /dev/null @@ -1,44 +0,0 @@ -# Roadmap - -## Q3-Q4 2019: M3QL support - -- Complete support for the [M3 Query Language (M3QL)](architecture/functions.md) - -## Q1-Q2 2019: Graphite and Prometheus feature parity - -- Ingestion and querying support for Graphite (Added in v0.5.0: [#1308](https://github.com/m3db/m3/pull/1308)) -- Full support for Prometheus functions (Added in v0.6.0: [#1367](https://github.com/m3db/m3/pull/1367) and [#1373](https://github.com/m3db/m3/pull/1373)) -- Tracing support using [Jaeger](https://www.jaegertracing.io/) (Added in v0.7.0: [#1321](https://github.com/m3db/m3/pull/1321)) -- Basic cost accounting: (Currently in master: [#1207](https://github.com/m3db/m3/pull/1207)) -- Performance improvements: (Ongoing) - -## Q3-Q4 2018: Dashboards can directly interact with M3 Query or M3 Coordinator to get data from M3DB (Q3-Q4 2018) - -- Write a PromQL parser. -- Write the current M3QL interfaces to conform to the common DAG structure. -- Suggest auto aggregation rules. -- Provide advanced query tracking to figure out bottlenecks. - -## Q2 2018: M3 Query and optimizations for M3 Coordinator (Q2 2018) - -- Dedicated query engine service, M3 Query. -- Support authentication and rate limiting. -- Cost accounting per query and memory management to prevent M3 Query and M3 Coordinator from going OOM. -- Push computation to storage nodes whenever possible. -- Execution state manager to keep track of running queries. -- Port the distributed computation to M3 Query service. - -## Q1 2018: Launch M3 Coordinator as a bridge for the read/write path of M3DB into open source - -### Late Q1 2018: V2 - -- Support cross datacenter calls with remote aggregations. -- Benchmark the performance of the coordinator and M3DB using popular datasets. -- Support for multiple M3DB clusters. - -### Early Q1 2018: V1 - -- Create a gRPC/Protobuf service. -- Handlers for Prometheus remote read/write endpoints. -- Perform fanout to M3DB nodes. -- Tooling to set up M3 Coordinator alongside Prometheus. diff --git a/docs/misc/writing_docs.md b/docs/misc/writing_docs.md deleted file mode 100644 index 4ac258fe14..0000000000 --- a/docs/misc/writing_docs.md +++ /dev/null @@ -1,335 +0,0 @@ -# Tips For Writing Documentation - -Writing is easy. Writing _well_ is hard. Writing documentation is even harder and, as one might -expect, writing _good documentation_ can be very hard. The challenge ultimately stems from the fact -that good documentation requires attention to several things: consistency, clarity, detail, -information density, unambiguity, and grammar (of course). - -This page describes tips for writing documentation (for M3 or otherwise). There are many different, -equally-valid writing styles; this document isn't meant as a prescription for how to write, but -instead as optional (but encouraged) guidelines geared toward writing consistent, clear, -easy-to-read documentation. - -## Tip #1: Avoid first- and second-person pronouns. - -Documentation should always be written from a [third-person point of view][wp3p] (specifically, -with a [third-person objective narrative voice][wp3ponv]); avoid using [first-][wp1p] and -[second-person][wp2p]. The point of documentation is not to be a message from the author or to -convey their point of view, but rather to be objective, factual, and canonical. - -| Examples | | -| :-: | --- | -| BAD | ~~You will need to set Value X to "foo", as our testing indicated that "bar" can cause issues when solar rays are present.~~ | -| BAD | ~~We suggest setting Value X to "foo", as testing indicated that "bar" can cause issues when solar rays are present.~~ | -| GOOD | **Value X must not be set to "bar", as testing indicated that it may cause issues when solar rays are present.** | -| GOOD | **Value X must be set to "foo"; a value of "bar" may cause issues when solar rays are present.** | - -## Tip #2: Avoid subjective language. - -Subjective language – language that is open to interpretation, particularly based on perspective – -should be avoided wherever possible. Documentation should state objective or broadly empirical -truths (and should be supported by facts). Introducing perspective inherently creates ambiguity and -vagueness in the document, as its meaning becomes reader-dependent. Importantly, cases of subjective -language are distinct from cases of conditional behavior. - -| Examples | | -| :-: | --- | -| BAD | ~~Setting PowerLevel to 9000 is required, because efficiency is better than reliability in high-rate situations.~~ | -| GOOD | **In high-rate situations (≥1k req/s), PowerLevel should be set to 9000 in order to maximize efficiency; otherwise, the system defaults to ensuring reliability.** | - -## Tip #3: Focus on concrete ideas. - -It can be easy to get sidetracked within documentation, or to bury critical points amidst tangent, -ancillary, or otherwise extraneous information. When writing documentation, always ask, "What am I -trying to say?". [Using terse bullets][skeleton] can help structure information, and using a -[self-defined guideline for sentence (or paragraph) length][concise-sentences] can help to optimize -the message such that the information density is high (i.e., no fluff or filler). - -## Tip #4: Use concise sentences (or fragments). - -Sentences and sentence fragments should be simple and to the point (but not stuttering). Each -sentence should ideally convey a single idea, but clearly-structured compound sentences are -acceptable as well. - -| Examples | | -| :-: | --- | -| BAD | ~~In order to time travel the MaxSpeed setting must be set to 88MPH because lower speeds will not achieve the desired result, but users should be careful around turns as high speeds are dangerous.~~ | -| BAD | ~~Time travel requires MaxSpeed to be set. The ideal value is 88MPH. Lower speeds will not achieve the desired result. Users should be careful around turns as high speeds are dangerous.~~ | -| GOOD | **Setting MaxSpeed to 88MPH is required to enable time travel – lower speeds will not achieve the desired result. However, users should take care around turns, as high speeds are dangerous.** | - -## Tip #5: Write down questions that the documentation should answer. - -The point of writing documentation is to document the subject and educate the reader, but every -piece of documentation should be written with an understanding of which questions it is answering. -Questions can be as specific as "How do I deploy service X?", or as vague as "How does the system -work?", but for vaguer questions, there are ostensibly sub-questions (or -[skeleton bullets][skeleton]) that help to flesh them out. - -These questions can be included as a preface to the documentation if desired, which can serve to set -the reader's expectations and frame of reference (both of which can help to improve comprehension -and retention). - -A contrived example: - -``` -After reading this document, users should be able to answer: - - - What does the system's topology look like? - - What are components X, Y, and Z, and how are they related? - - When should one use Modes 1-3 of component X? - - How can component Y be deployed in a HA capacity? - - How can components X and Y be tuned to mitigate load on component Z? - - How can all of the components be configured for correctness versus availability? -``` - -## Tip #6: Write skeleton documentation first. - -Starting with a skeleton shows the full breadth and depth of a document (or set of documents) before -anything is written, enabling a more holistic view of the content and helping to ensure cohesion and -flow. Conversely, by fleshing out each section as it's added, it can be easy to lose track of the -scope of the section relative to the document, or the scope of the document itself. This approach -also serves as a TODO list, helping to ensure that sections aren't forgotten about and enabling the -documentation to be divided and conquered. - -A contrived example: - -``` -# The Document Title - -## Overview - - - One-sentence or elevator pitch summary - - Introduce components X, Y, and Z - -## Component X - -### Overview - - Responsible for all client ingress - - Sole entrypoint into the system - - Can be deployed to support multiple client encodings - - Adapter layer for translating all ingress from clients into - - Traffic goes: Client -> Component X -> Component Y -> Component Z - -### Deployment modes - - HA - - Lossless - - High-throughput - -## Component Y - -### Overview - - Dynamic, stateless, full-mesh cluster topology - - Deduplicates all ingress from Component X - - Partitions data among Component Z backends using perfect hashing - -### Deployment modes - - Dynamic cluster - - Static cluster - - Full mesh - - Ring network - - Isolated - -## Component Z - -### Overview - - Persists normalized client data - - Maintains a sharded, in-memory cache - -### Deployment modes - - HA - - Prioritized throughput - - Writethrough cache - - Readthrough cache - - Ring buffer - - LRU -``` - -## Tip #7: When in doubt, less is more. - -While it may seem counterintuitive to start by writing less in a given document, terseness – -specifically, fewer sentences or words to explain an idea – is easier to fix than verboseness. -It is far easier to find gaps in a reader's comprehension that are caused by missing documentation -than it is to try and find the minimal subset of documentation necessary to convey an idea. In other -words: start short, and build out. - -| Examples | | -| :-: | --- | -| BAD | ~~Configuring the cache to be a LRU readthrough cache ensures that the minimal amount of memory is used at the expense of guaranteed initial cache misses. Using minimal memory is important, as servers have finite memory that does not scale with the size of persisted data, and thus the bulk of the data will incur cache misses after a certain point (though compression may be used to mitigate this in the future).~~ | -| GOOD | **Configuring the cache as a LRU readthrough cache results in a minimal memory footprint (due to the combined behavior of both readthrough and LRU caching) at the expense of initial cache misses. Cache efficiency may be improved in the future.** | - - -## Tip #8: Write for an uninitiated target audience (and call out prerequisites). - -Every piece of technical documentation has at least one baseline prerequisite that the documentation -builds upon; otherwise, every document would need to explain -[how computers work][how-computers-work]. However, in the case of M3 (as an example), the reader -might be someone writing code that emits metrics, an infrastructure engineer that wants to run a -metrics stack, someone with experience running other metrics stacks that is looking for nuanced -tradeoffs between this and other platforms, or a distributed systems enthusiast (or a thousand other -folks). - -It's relatively straightforward to tailor a document to a given audience: an end-user looking to -emit metrics is a _much_ different demographic than someone wanting to run M3 in their -infrastructure (etc), and as such, there is often no conflation. However, the skill level or -technical depth of a given audience is often varied: a person who only vaguely understands what a -metric really is will require a much broader set of information than a person who has experience -using metrics in complex and interesting ways. - -Thus, it's important to cater to a baseline: don't expect that every piece of technical jargon will -be understood. Similarly, it's not a document's responsibility to satisfy its own prerequisites - -it's okay to inform the reader that they should be familiar with X, Y, and Z, else they won't get -much out of the document. If there are prerequisites that normalize the baseline of most or all -readers, call that out at the beginning (e.g., "Users should read and understand Document X before -reading this document"). - -## Tip #9: Make sure that documentation is reviewed thoroughly. - -Erroneous documentation can lead to confusion, misunderstanding, incorrect assumptions, or – in the -worst case – a user unknowingly implementing bad or faulty behavior. Documentation should ideally be -reviewed like code: thoroughly and pedantically. Depending on the complexity, it may be worth -[asking reviewers to answer basic questions][write-down-questions] and update the documentation -based on their responses – for example, if the question "When should `ConfigValueX` be set, and -what are its side effects?" can't be answered, it might merit adding more documentation around the -semantics of `ConfigValueX`, as well as its direct and side effects. - -## Tip #10: Have _at least_ one less-familiar person review documentation. - -It's _extremely_ easy to let assumptions, implicit points, jargon, etc sneak in. Folks who are -familiar with a subject are more likely to subconsciously or contextually fill in the gaps, but they -are not the documentation's intended audience. Instead, try to find one or more folks who are closer -to the target audience (relatively speaking) review documentation, and evaluate their comprehension -(e.g., ask basic questions that the documentation should provide answers for). - -Reviewers should sanity check all assumptions, and ask any questions freely – it's important to not -assume that they just missed the boat on some critical information that is obvious to everyone else -(most documentation should be explanatory and not expect readers to continually read between the -lines). - -## Tip #11: Document both reality _and_ intent. - -While documentation should reflect the current state of the world, in many cases, it's also -important to document the context or rationale around that state (the "what" and the "why"). -Documenting only the "what" leaves readers to draw their own inferences and conclusions, while the -"why" answers many of those questions outright (or, at the least, imparts a line of thinking that -will more accurately inform those inferences). - -## Tip #12: Focus on readability. - -The goal of documentation is to provide information to the reader. This means that, aside from -correctness, the most important metrics of documentation are its efficiency (words per idea) and -efficacy (readability of words and retention of ideas). - -Reading a sentence in isolation helps to analyze the readability of an idea in a narrow context, -but reading multiple sentences (or a paragraph) at a time can be a good signal of how well -information flows. - -## Tip #13: Be pedantic about grammar. - -English grammar can often be flexible, subjectively readable, or confusing (there is more than one -way to skin a cat). Being clear and using [simpler, well-structured sentences][concise-sentences] -can [help with readability][readability] by presenting digestible, unambiguous ideas. -[Verb tense][verb-tense] and [pronouns][pronouns] can play an important role as well. Ultimately, -the best litmus test may be the simplest: whether a reader needs to read a sentence twice to -understand it (technical terms notwithstanding). - -Realistically, it is impractical to strive for or expect perfect grammar. The goal isn't to write -flawless English; the goal is to write _clear, concise, unambiguous, and readable_ documentation, -for which grammar is an important (albeit not the only) tool. - -## Tip #14: Use verb tenses consistently. - -Generally speaking, the bulk of documentation only makes sense when written in the present tense -(its "common tense", so to speak), as objects are typically referred to in the abstract (e.g. -"Component X" is abstract, "The running instance of Component X" proverbially concrete) and do not -usually involve time (e.g. "Component X is an upstream of Component Y" refers to nominal abstract -state, "Component X will be an upstream of Component Y" is prospective state). Of course, this is -not always true: documentation can tell a story, and tenses should be used as needed to express ideas -relative to time. - -However, cases of general fact, such as describing cause and effect: - - Value X -> Behavior Y - -should be described in the present tense: - - Setting Value X causes Behavior Y. - -versus the future tense: - - Setting Value X will cause Behavior Y. - -in order to be consistent with the documentation's common tense, to maintain a time-less (and thus -ongoing) objective truth, and to contrast actual time-relative statements (e.g. "Setting Value X -causes Behavior Y, but will be updated to cause Behavior Z in the future"). - -## Tip #15: Make consistent references. - -When communicating verbally, there is no need to distinguish between represented forms for a given -artifact, value, method, etc - one can simply say "foo bar" to reference "the member function or -property `Bar` on type `Foo`", or "foo dot bar" to reference "the YAML key `bar` nested under -the key `foo`" (as simple examples). - -In written documentation, however, these references can either maintain continuity throughout a -document (when used consistently), or they can cause confusion (when used inconsistently). A simple -framework for references is, "if it's code (or code-like), it should look like code; otherwise, -treat it like a proper noun". Thankfully, most references fall into two such categories: - -- **Code (and code-like) references**, such as `MyClass` or `myVar` or `some.config.property`. In - these cases, `PreformattedText` should be used, and should reference the code verbatim. All - instances of such a reference should be consistent, and should not be abbreviated. -- **Proper (or effectively proper) names**, such as "My Component" or "My System Name". In these - cases, "Title Case" should be used, with the following exceptions: - - Established projects, such as "etcd", "PostgreSQL", and "GitHub", should use the project's - official name and format, versus arbitrary ones (e.g. "Etcd", "Postgres", "Github", etc). - - First-party components, such as "the M3 Aggregator", can use shorthand when doing so does not - contextually result in ambiguity (e.g. "the Aggregator", when already discussing the Aggregator), - but should still be proper nouns. - -For other or nuanced cases, it's more important to be consistent than it is to fit a reference into -one of these simple categories. - -| Examples | | -| :-: | --- | -| BAD | ~~The Flux Capactitor is a Y-shaped component that enables time travel. Importantly, the enclosing vehicle must be capable of providing 1.21 gigawatts of power, as the flux capacitor requires this energy to function.~~ | -| BAD | ~~FluxCapacitor's power level property should be set to 1.21.~~ | -| GOOD | **The Flux Capacitor is a Y-shaped component that enables time travel. Importantly, the enclosing vehicle must be capable of providing 1.21 gigawatts of power, as the Flux Capacitor requires this energy to function.** | -| GOOD | **`FluxCapacitor`'s `powerLevel` property should be set to `1.21`.** | - -## Tip #16: Use `TODO`/`TODOC` placeholders liberally. - -Often, it might not yet be the right time to write a part of the documentation – either because a -feature isn't ready yet, it's not the most practical time investment at present, or other reasons. -In these cases, consider adding a `TODO` (in exactly the same way that code `TODO`s are used). If -it's necessary/helpful to disambiguate code `TODO`s from documentation `TODO`s, `TODOC` can be used -instead: it's similar enough to match a naive `TODO` search, but both `TODO` and `TODOC` can be -reasoned about independently as desired. For example: - -```go -// FluxCapacitor TODOC(anyone) -type FluxCapacitor struct { - // TODO(anyone): use a discrete GigaWatts type instead of a float - RequiredPower float64 -} -``` - -```shell -$ grep -rnH 'TODOC(' . -./time/timetravel/flux_capacitor.go:123: // FluxCapacitor TODOC(anyone) -``` - -```shell -$ grep -rnH 'TODO(' . -./time/timetravel/flux_capacitor.go:125: // TODO(anyone): use a discrete GigaWatts type instead of a float -``` - -[concise-sentences]: #tip-4-use-concise-sentences-or-fragments -[how-computers-work]: https://softwareengineering.stackexchange.com/a/81715/4860 -[pronouns]: #tip--1-avoid-first-andsecond-person-pronouns -[readability]: #tip-12-focus-on-readability -[skeleton]: #tip-6-write-skeleton-documentation-first -[verb-tense]: #tip-14-use-verb-tenses-consistently -[wp1p]: https://en.wikipedia.org/wiki/Narration#First-person -[wp2p]: https://en.wikipedia.org/wiki/Narration#Second-person -[wp3p]: https://en.wikipedia.org/wiki/Narration#Third-person -[wp3ponv]: https://en.wikipedia.org/wiki/Narration#Third-person,_objective -[write-down-questions]: #tip-5-write-down-questions-that-the-documentation-should-answer diff --git a/docs/operational_guide/index.md b/docs/operational_guide/index.md deleted file mode 100644 index 1f1db09877..0000000000 --- a/docs/operational_guide/index.md +++ /dev/null @@ -1,3 +0,0 @@ -# Operational Guides - -This list of operational guides provide documentation for operating M3. diff --git a/docs/how_to/aggregator.md b/docs/static/aggregator.md similarity index 100% rename from docs/how_to/aggregator.md rename to docs/static/aggregator.md diff --git a/docs/how_to/cluster_architecture.png b/docs/static/cluster_architecture.png similarity index 100% rename from docs/how_to/cluster_architecture.png rename to docs/static/cluster_architecture.png diff --git a/docs/static/docker-install.gif b/docs/static/docker-install.gif new file mode 100644 index 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