221 lines
10 KiB
Markdown
221 lines
10 KiB
Markdown
---
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status: in progress
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creation-date: "2021-01-21"
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authors: [ "@grzesiek" ]
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coach: "@grzesiek"
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approvers: [ "@cheryl.li", "@jreporter" ]
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owning-stage: "~devops::verify"
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---
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# CI/CD Scaling
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## Summary
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GitLab CI/CD is one of the most data and compute intensive components of GitLab.
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Since its [initial release in November 2012](https://about.gitlab.com/blog/2012/11/13/continuous-integration-server-from-gitlab/),
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the CI/CD subsystem has evolved significantly. It was [integrated into GitLab in September 2015](https://about.gitlab.com/releases/2015/09/22/gitlab-8-0-released/)
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and has become [one of the most beloved CI/CD solutions](https://about.gitlab.com/blog/2017/09/27/gitlab-leader-continuous-integration-forrester-wave/).
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GitLab CI/CD has come a long way since the initial release, but the design of
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the data storage for pipeline builds remains almost the same since 2012. We
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store all the builds in PostgreSQL in `ci_builds` table, and because we are
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creating more than 5 million builds each day on GitLab.com we are reaching
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database limits that are slowing our development velocity down.
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On February 1st, 2021, GitLab.com surpassed 1 billion CI/CD builds created. In
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February 2022 we reached 2 billion of CI/CD build stored in the database. The
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number of builds continues to grow exponentially.
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The screenshot below shows our forecast created at the beginning of 2021, that
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turned out to be quite accurate.
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## Goals
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**Enable future growth by making processing 20M builds in a day possible.**
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## Challenges
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The current state of CI/CD product architecture needs to be updated if we want
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to sustain future growth.
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### We were running out of the capacity to store primary keys: DONE
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The primary key in `ci_builds` table is an integer value, generated in a sequence.
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Historically, Rails used to use [integer](https://www.postgresql.org/docs/14/datatype-numeric.html)
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type when creating primary keys for a table. We did use the default when we
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[created the `ci_builds` table in 2012](https://gitlab.com/gitlab-org/gitlab/-/blob/046b28312704f3131e72dcd2dbdacc5264d4aa62/db/ci/migrate/20121004165038_create_builds.rb).
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[The behavior of Rails has changed](https://github.com/rails/rails/pull/26266)
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since the release of Rails 5. The framework is now using `bigint` type that is 8
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bytes long, however we have not migrated primary keys for `ci_builds` table to
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`bigint` yet.
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In early 2021 we had estimated that would run out of the capacity of the integer
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type to store primary keys in `ci_builds` table before December 2021. If it had
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happened without a viable workaround or an emergency plan, GitLab.com would go
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down. `ci_builds` was just one of many tables that were running out of the
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primary keys available in Int4 sequence.
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Before October 2021, our Database team had managed to migrate all the risky
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tables' primary keys to big integers.
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See the [related Epic](https://gitlab.com/groups/gitlab-org/-/epics/5657) for more details.
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### Some CI/CD database tables are too large: IN PROGRESS
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There is more than two billion rows in `ci_builds` table. We store many
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terabytes of data in that table, and the total size of indexes is measured in
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terabytes as well.
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This amount of data contributes to a significant number of performance
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problems we experience on our CI PostgreSQL database.
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Most of the problems are related to how PostgreSQL database works internally,
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and how it is making use of resources on a node the database runs on. We are at
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the limits of vertical scaling of the CI primary database nodes and we
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frequently see a negative impact of the `ci_builds` table on the overall
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performance, stability, scalability and predictability of the CI database
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GitLab.com depends on.
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The size of the table also hinders development velocity because queries that
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seem fine in the development environment may not work on GitLab.com. The
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difference in the dataset size between the environments makes it difficult to
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predict the performance of even the most simple queries.
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Team members and the wider community members are struggling to contribute the
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Verify area, because we restricted the possibility of extending `ci_builds`
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even further. Our static analysis tools prevent adding more columns to this
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table. Adding new queries is unpredictable because of the size of the dataset
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and the amount of queries executed using the table. This significantly hinders
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the development velocity and contributes to incidents on the production
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environment.
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We also expect a significant, exponential growth in the upcoming years.
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One of the forecasts done using [Facebook's Prophet](https://facebook.github.io/prophet/)
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shows that in the first half of 2024 we expect seeing 20M builds created on
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GitLab.com each day. In comparison to around 5M we see created today. This is
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10x growth from numbers we saw in 2021.
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**Status**: As of October 2021 we reduced the growth rate of `ci_builds` table
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by writing build options and variables to `ci_builds_metadata` table. We are
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also working on partitioning the largest CI/CD database tables using
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[time decay pattern](../ci_data_decay/index.md).
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### Queuing mechanisms were using the large table: DONE
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Because of how large the table is, mechanisms that we used to build queues of
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pending builds (there is more than one queue), were not very efficient. Pending
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builds represented a small fraction of what we store in the `ci_builds` table,
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yet we needed to find them in this big dataset to determine an order in which we
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wanted to process them.
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This mechanism was very inefficient, and it had been causing problems on the
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production environment frequently. This usually resulted in a significant drop
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of the CI/CD Apdex score, and sometimes even caused a significant performance
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degradation in the production environment.
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There were multiple other strategies that we considered to improve performance and
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reliability. We evaluated using [Redis queuing](https://gitlab.com/gitlab-org/gitlab/-/issues/322972), or
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[a separate table that would accelerate SQL queries used to build queues](https://gitlab.com/gitlab-org/gitlab/-/issues/322766).
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We decided to proceed with the latter.
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In October 2021 we finished shipping the new architecture of builds queuing
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[on GitLab.com](https://gitlab.com/groups/gitlab-org/-/epics/5909#note_680407908).
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We then made the new architecture [generally available](https://gitlab.com/groups/gitlab-org/-/epics/6954).
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### Moving big amounts of data is challenging: IN PROGRESS
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We store a significant amount of data in `ci_builds` table. Some of the columns
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in that table store a serialized user-provided data. Column `ci_builds.options`
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stores more than 600 gigabytes of data, and `ci_builds.yaml_variables` more
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than 300 gigabytes (as of February 2021).
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It is a lot of data that needs to be reliably moved to a different place.
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Unfortunately, right now, our [background migrations](../../../development/database/background_migrations.md)
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are not reliable enough to migrate this amount of data at scale. We need to
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build mechanisms that will give us confidence in moving this data between
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columns, tables, partitions or database shards.
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Effort to improve background migrations will be owned by our Database Team.
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**Status**: In progress. We plan to ship further improvements that will be
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described in a separate architectural blueprint.
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## Proposal
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Below you can find the original proposal made in early 2021 about how we want
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to move forward with CI Scaling effort:
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> Making GitLab CI/CD product ready for the scale we expect to see in the
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> upcoming years is a multi-phase effort.
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>
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> First, we want to focus on things that are urgently needed right now. We need
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> to fix primary keys overflow risk and unblock other teams that are working on
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> database partitioning and sharding.
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>
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> We want to improve known bottlenecks, like
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> builds queuing mechanisms that is using the large table, and other things that
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> are holding other teams back.
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>
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> Extending CI/CD metrics is important to get a better sense of how the system
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> performs and to what growth should we expect. This will make it easier for us
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> to identify bottlenecks and perform more advanced capacity planning.
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>
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> Next step is to better understand how we can leverage strong time-decay
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> characteristic of CI/CD data. This might help us to partition CI/CD dataset to
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> reduce the size of CI/CD database tables.
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## Iterations
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Work required to achieve our next CI/CD scaling target is tracked in the
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[CI/CD Scaling](https://gitlab.com/groups/gitlab-org/-/epics/5745) epic.
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1. ✓ Migrate primary keys to big integers on GitLab.com.
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1. ✓ Implement the new architecture of builds queuing on GitLab.com.
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1. ✓ [Make the new builds queuing architecture generally available](https://gitlab.com/groups/gitlab-org/-/epics/6954).
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1. [Partition CI/CD data using time-decay pattern](../ci_data_decay/index.md).
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## Status
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Created at 21.01.2021, approved at 26.04.2021.
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Status: In progress.
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## Who
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Proposal:
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<!-- vale gitlab.Spelling = NO -->
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| Role | Who
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|------------------------------|-------------------------|
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| Author | Grzegorz Bizon |
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| Architecture Evolution Coach | Kamil Trzciński |
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| Engineering Leader | Cheryl Li |
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| Product Manager | Jackie Porter |
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| Domain Expert / Verify | Fabio Pitino |
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| Domain Expert / Database | Jose Finotto |
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| Domain Expert / PostgreSQL | Nikolay Samokhvalov |
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DRIs:
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| Role | Who
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|------------------------------|------------------------|
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| Leadership | Cheryl Li |
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| Product | Jackie Porter |
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| Engineering | Grzegorz Bizon |
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Domain experts:
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| Area | Who
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|------------------------------|------------------------|
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| Domain Expert / Verify | Fabio Pitino |
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| Domain Expert / Verify | Marius Bobin |
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| Domain Expert / Database | Jose Finotto |
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| Domain Expert / PostgreSQL | Nikolay Samokhvalov |
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<!-- vale gitlab.Spelling = YES -->
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