[{"data":1,"prerenderedAt":706},["ShallowReactive",2],{"/en-us/blog/path-to-decomposing-gitlab-database-part3/":3,"navigation-en-us":35,"banner-en-us":452,"footer-en-us":467,"Dylan Griffith":678,"next-steps-en-us":691},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"content":16,"config":25,"_id":28,"_type":29,"title":30,"_source":31,"_file":32,"_stem":33,"_extension":34},"/en-us/blog/path-to-decomposing-gitlab-database-part3","blog",false,"",{"title":9,"description":10,"ogTitle":9,"ogDescription":10,"noIndex":6,"ogImage":11,"ogUrl":12,"ogSiteName":13,"ogType":14,"canonicalUrls":12,"schema":15},"Decomposing the GitLab backend database, Part 3: Challenges and surprises","This is the final installment in our three-part series about our yearlong project to decompose GitLab's Postgres database.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663397/Blog/Hero%20Images/logoforblogpost.jpg","https://about.gitlab.com/blog/path-to-decomposing-gitlab-database-part3","https://about.gitlab.com","article","\n                        {\n        \"@context\": \"https://schema.org\",\n        \"@type\": \"Article\",\n        \"headline\": \"Decomposing the GitLab backend database, Part 3: Challenges and surprises\",\n        \"author\": [{\"@type\":\"Person\",\"name\":\"Dylan Griffith\"}],\n        \"datePublished\": \"2022-08-04\",\n      }",{"title":9,"description":10,"authors":17,"heroImage":11,"date":19,"body":20,"category":21,"tags":22},[18],"Dylan Griffith","2022-08-04","\n_This blog post is part 3 in a three-part series. It focuses on some interesting\nlow-level challenges we faced along the way, as well as some surprises we found during\nthe migration._\n\n- To read about the design and planning phase, check out [part 1](/blog/path-to-decomposing-gitlab-database-part1/).\n- To read about how we executed the actual migration and our results, check out [part 2](/blog/path-to-decomposing-gitlab-database-part2/).\n\n## The challenge with taking GitLab.com offline\n\nOne key part of our migration process was to take all systems offline that\ncould potentially talk to the database. This may seem as simple as \"shutting\ndown the servers\" but given the scale and complexity of GitLab.com's\ninfrastructure this proved to be really quite complex. Here is just a subset of\nthe different things we had to shut down:\n\n1. Kubernetes pods corresponding to web, API, and Sidekiq services\n2. Cron jobs across various VMs\n\n## Surprises along the way\n\nEven though we had rehearsed the migration many times in staging, there were\nstill some things that caught us off-guard in production. Luckily, we had\nallocated sufficient buffer time during the migration to resolve all of these\nduring the call:\n\n1. Autovacuum on our largest CI tables take a long time and can run at any\n   time. This delayed our migration as we needed to gain table locks for our\n   [write block\n   triggers](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/83211).\n   Adding these triggers requires a `ShareRowExclusiveLock` which cannot be\n   acquired while the autovacuum is running for that table. We disabled\n   some manual vacuum processes we were aware of ahead of the call but\n   autovacuum can happen at any time and our `ci_builds` table just happen to\n   have autovacuum at the time we were trying to block writes to this table. To\n   work around this we needed to temporarily disable autovacuum for the\n   relevant tables and then find the `pid` for the autovacuum process and\n   terminate this which allowed our triggers to be successfully added.\n2. Sometimes a long-running SSH session by an SRE or developer can leave open a\n   surprising database connection that needs to be tracked down and closed.\n3. Cron jobs can be run on various hosts that start rails processes or database\n   connections at any time. We had many examples that were created with\n   different purposes for database maintenance over the years, and we missed at\n   least one in our practice runs. They weren't as easy to detect on staging as\n   they may not all be configured on staging, or they run a lot faster on\n   staging. Also, our staging runs all happened on week days, but our\n   production migration happened on a weekend where it seemed we were\n   deliberately running some database maintenance workloads during low\n   utilization hours.\n4. Our Sentry client-side error tracking caused us to overload our Sentry\n   server due to many of users leaving open GitLab browser tabs. As\n   the browser tabs periodically make asynchronous requests to GitLab and get\n   errors (since GitLab.com was down), they then send all these errors to Sentry\n   and this overloaded our Sentry error server to the point we couldn't load it\n   to check for errors. This was quickly diagnosed based on the URL all the\n   requests were sent to, but it did delay our migration as checking for new\n   errors was key to determining success or failure of the migration.\n\n## Cascading replication doubles latency (triples in our case)\n\nA key initial step in our phased rollout was to move all read-only CI traffic\nto dedicated CI replicas. These were cascading replicas from the main Patroni\ncluster. Furthermore, we made the decision to create the standby cluster leader\nas a replica of another replica in the Main Patroni cluster. Ultimately this\nmeant the replication process for our CI replicas was\n`Main Primary -> Main Replica -> CI Standby Leader -> CI Replica`.\n\nThis change meant that our CI replicas had roughly three times as much latency\ncompared with our Main replicas, which previously served CI read-only traffic.\nSince our read-only load balancing logic is based on users sticking to the primary\nuntil a replica catches up with the last write that they performed, users\nmight end up sticking to the primary longer than they previously would have.\nThis may have served to increase our load on the primary database after rolling\nout Phase 3.\n\nWe never measured this impact, but in hindsight it is something we\nshould have factored in and benchmarked with our gradual rollout of Phase 3.\nAdditionally, we should have considered mitigating this issue by having the `CI\nStandby Leader` replicating straight from the `Main Primary` or adding the `CI\nStandby Leader` to the pool of replicas that we could service CI read-only\ntraffic.\n\n## Re-balancing PGBouncer connections incrementally without saturating anything\n\n[Phase 4 of our rollout][phase4_change_request] turned out to be one of the\ntrickiest parts of the migration. Since we wanted all phases (where possible)\nto be rolled out incrementally we needed some way to [solve for\nincrementally re-balancing connection pool limits][phase4_gradual_rollout_issue]\nfrom `GitLab -> PGBouncer -> Postgres` without exceeding the total connection\nlimit of Postgres or opening too many connections to Postgres that might\nsaturate CPU. This was difficult because all the connection limits were very\nwell tuned, and we were close to saturation across all these limits.\n\nThe gradual rollout of traffic for Phase 4 looked like:\n\n```mermaid\ngraph LR;\n    PostgresMain[(PostgresMain - Limit K max_connections)]\n    GitLabRails-->|100-X % of CI queries|PGBouncerMain\n    GitLabRails-->|X% of CI queries|PGBouncerCi\n    PGBouncerMain-->|Limit N pool_size|PostgresMain\n    PGBouncerCi-->|Limit M pool_size|PostgresMain\n```\n\nWe wanted to gradually increase X from 0-100. But this presented a problem, because\nthe number of connections to the `PostgresMain` DB will change\nwith this number.\n\nWe assume it has some initial limit `K` connections, and we\nassume this limit is deliberately just high enough to handle the current\nconnections from `PGBouncerMain` and not overload the CPU. We need to carefully\ntune `N` and `M` `pool_size` values across the separate PGBouncer processes to\navoid overloading the limit K, and we also need to avoid saturating the\nPostgres server CPU with too much traffic. At the same time, we need to ensure\nthere are enough connections to handle the traffic to both PGBouncer pools.\n\nWe addressed this issue by taking very small steps during low\nutilization hours (where CPU and connection pools weren't near saturation) and\ndoing very detailed analysis after each step. We would wait a day or so to figure out how\nmany connections to move over with the following steps, based on the number of\nconnections that were used by the smaller step. We also used what data we had\nearly on from table-based metrics to get an insight into how many connections\nwe thought we'd need to move to the CI PGBouncer pool.\n\nIn the end, we did need to make small adjustments to our estimates along the way\nas we saw saturation occur, but there was never any major user-facing saturation\nincidents, as the steps were small enough.\n\n## Final thoughts\n\nWe're very happy with the results of this project overall.\n\nA key objective of this project, which was hard to predict, was how the complexity of\nan additional database might impact developer productivity. They can't do\ncertain types of joins and there is more information to be aware of.\nHowever, many months have now passed, and it seems clear now that the complexity is mostly abstracted by Rails models. With continued large number of developers contributing, we have seen\nlittle-to-no impact on productivity.\n\nCombining this success with the huge scalability headroom we've gained, we believe this was a great decision for GitLab.\n\n## More reading\n\nThis blog series contains many links to see our early designing, planning, and\nimplementation of various parts of this project. GitLab's\n[transparency value](https://handbook.gitlab.com/handbook/values/#transparency)\nmeans you can read all the details and get a sense of what it's like to work on\nprojects like this at GitLab. 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