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diff --git a/doc/architecture/blueprints/ci_data_decay/index.md b/doc/architecture/blueprints/ci_data_decay/index.md new file mode 100644 index 00000000000..155c781b04a --- /dev/null +++ b/doc/architecture/blueprints/ci_data_decay/index.md @@ -0,0 +1,255 @@ +--- +stage: none +group: unassigned +comments: false +description: 'CI/CD data time decay' +--- + +# CI/CD data time decay + +## Summary + +GitLab CI/CD is one of the most data and compute intensive components of GitLab. +Since its [initial release in November 2012](https://about.gitlab.com/blog/2012/11/13/continuous-integration-server-from-gitlab/), +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/) +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/). + +On February 1st, 2021, GitLab.com surpassed 1 billion CI/CD builds, and the number of +builds [continues to grow exponentially](../ci_scale/index.md). + +GitLab CI/CD has come a long way since the initial release, but the design of +the data storage for pipeline builds remains almost the same since 2012. In +2021 we started working on database decomposition and extracting CI/CD data to +ia separate database. Now we want to improve the architecture of GitLab CI/CD +product to enable further scaling. + +_Disclaimer: The following contains information related to upcoming products, +features, and functionality._ + +_It is important to note that the information presented is for informational +purposes only. Please do not rely on this information for purchasing or +planning purposes._ + +_As with all projects, the items mentioned in this document and linked pages are +subject to change or delay. The development, release and timing of any +products, features, or functionality remain at the sole discretion of GitLab +Inc._ + +## Goals + +**Implement a new architecture of CI/CD data storage to enable scaling.** + +## Challenges + +There are more than two billion rows describing CI/CD builds in GitLab.com's +database. This data represents a sizable portion of the whole data stored in +PostgreSQL database running on GitLab.com. + +This volume contributes to significant performance problems, development +challenges and is often related to production incidents. + +We also expect a [significant growth in the number of builds executed on +GitLab.com](../ci_scale/index.md) in the upcoming years. + +## Opportunity + +CI/CD data is subject to +[time-decay](https://about.gitlab.com/company/team/structure/working-groups/database-scalability/time-decay.html) +because, usually, pipelines that are a few months old are not frequently +accessed or are even not relevant anymore. Restricting access to processing +pipelines that are older than a few months might help us to move this data out +of the primary database, to a different storage, that is more performant and +cost effective. + +It is already possible to prevent processing builds [that have been +archived](../../../user/admin_area/settings/continuous_integration.md#archive-jobs). +When a build gets archived it will not be possible to retry it, but we still do +keep all the processing metadata in the database, and it consumes resources +that are scarce in the primary database. + +In order to improve performance and make it easier to scale CI/CD data storage +we might want to follow these three tracks described below. + + + +<!-- markdownlint-disable MD029 --> + +1. Partition builds queuing tables +2. Archive CI/CD data into partitioned database schema +3. Migrate archived builds metadata out of primary database + +<!-- markdownlint-enable MD029 --> + +### Migrate archived builds metadata out of primary database + +Once a build (or a pipeline) gets archived, it is no longer possible to resume +pipeline processing in such pipeline. It means that all the metadata, we store +in PostgreSQL, that is needed to efficiently and reliably process builds can be +safely moved to a different data store. + +Currently, storing pipeline processing data is expensive as this kind of CI/CD +data represents a significant portion of data stored in CI/CD tables. Once we +restrict access to processing archived pipelines, we can move this metadata to +a different place - preferably object storage - and make it accessible on +demand, when it is really needed again (for example for compliance or auditing purposes). + +We need to evaluate whether moving data is the most optimal solution. We might +be able to use de-duplication of metadata entries and other normalization +strategies to consume less storage while retaining ability to query this +dataset. Technical evaluation will be required to find the best solution here. + +Epic: [Migrate archived builds metadata out of primary database](https://gitlab.com/groups/gitlab-org/-/epics/7216). + +### Archive CI/CD data into partitioned database schema + +After we move CI/CD metadata to a different store, the problem of having +billions of rows describing pipelines, builds and artifacts, remains. We still +need to keep reference to the metadata we store in object storage and we still +do need to be able to retrieve this information reliably in bulk (or search +through it). + +It means that by moving data to object storage we might not be able to reduce +the number of rows in CI/CD tables. Moving data to object storage should help +with reducing the data size, but not the quantity of entries describing this +data. Because of this limitation, we still want to partition CI/CD data to +reduce the impact on the database (indices size, auto-vacuum time and +frequency). + +Our intent here is not to move this data out of our primary database elsewhere. +What want to divide very large database tables, that store CI/CD data, into +multiple smaller ones, using PostgreSQL partitioning features. + +There are a few approaches we can take to partition CI/CD data. A promising one +is using list-based partitioning where a partition number is assigned a +pipeline, and gets propagated to all resources that are related to this +pipeline. We assign the partition number based on when the pipeline was created +or when we observed the last processing activity in it. This is very flexible +because we can extend this partitioning strategy at will; for example with this +strategy we can assign an arbitrary partition number based on multiple +partitioning keys, combining time-decay-based partitioning with tenant-based +partitioning on the application level. + +Partitioning rarely accessed data should also follow the policy defined for +builds archival, to make it consistent and reliable. + +Epic: [Archive CI/CD data into partitioned database schema](https://gitlab.com/groups/gitlab-org/-/epics/5417). + +### Partition builds queuing tables + +While working on the [CI/CD Scale](../ci_scale/index.md) blueprint, we have +introduced a [new architecture for queuing CI/CD builds](https://gitlab.com/groups/gitlab-org/-/epics/5909#note_680407908) +for execution. + +This allowed us to significantly improve performance. We still consider the new +solution as an intermediate mechanism, needed before we start working on the +next iteration. The following iteration that should improve the architecture of +builds queuing even more (it might require moving off the PostgreSQL fully or +partially). + +In the meantime we want to ship another iteration, an intermediate step towards +more flexible and reliable solution. We want to partition the new queuing +tables, to reduce the impact on the database, to improve reliability and +database health. + +Partitioning of CI/CD queuing tables does not need to follow the policy defined +for builds archival. Instead we should leverage a long-standing policy saying +that builds created more 24 hours ago need to be removed from the queue. This +business rule is present in the product since the inception of GitLab CI. + +Epic: [Partition builds queuing tables](https://gitlab.com/gitlab-org/gitlab/-/issues/347027). + +## Principles + +All the three tracks we will use to implement CI/CD time decay pattern are +associated with some challenges. As we progress with the implementation we will +need to solve many problems and devise many implementation details to make this +successful. + +Below, we documented a few foundational principles to make it easier for +everyone to understand the vision described in this architectural blueprint. + +### Removing pipeline data + +While it might be tempting to simply remove old or archived data from our +databases this should be avoided. It is usually not desired to permanently +remove user data unless consent is given to do so. We can, however, move data +to a different data store, like object storage. + +Archived data can still be needed sometimes (for example for compliance or +auditing reasons). We want to be able to retrieve this data if needed, as long +as permanent removal has not been requested or approved by a user. + +### Accessing pipeline data in the UI + +Implementing CI/CD data time-decay through partitioning might be challenging +when we still want to make it possible for users to access data stored in many +partitions. + +We want to retain simplicity of accessing pipeline data in the UI. It will +require some backstage changes in how we reference pipeline data from other +resources, but we don't want to make it more difficult for users to find their +pipelines in the UI. + +We may need to add "Archived" tab on the pipelines / builds list pages, but we +should be able to avoid additional steps / clicks when someone wants to view +pipeline status or builds associated with a merge request or a deployment. + +We also may need to disable search in the "Archived" tab on pipelines / builds +list pages. + +### Accessing pipeline data through the API + +We accept the possible necessity of building a separate API endpoint / +endpoints needed to access pipeline data through the API. + +In the new API users might need to provide a time range in which the data has +been created to search through their pipelines / builds. In order to make it +efficient it might be necessary to restrict access to querying data residing in +more than two partitions at once. We can do that by supporting time ranges +spanning the duration that equals to the builds archival policy. + +It is possible to still allow users to use the old API to access archived +pipelines data, although a user provided partition identifier may be required. + +## Iterations + +All three tracks can be worked on in parallel: + +1. [Migrate archived build metadata to object storage](https://gitlab.com/groups/gitlab-org/-/epics/7216). +1. [Partition CI/CD data that have been archived](https://gitlab.com/groups/gitlab-org/-/epics/5417). +1. [Partition CI/CD queuing tables using list partitioning](https://gitlab.com/gitlab-org/gitlab/-/issues/347027) + +## Status + +In progress. + +## Who + +Proposal: + +<!-- vale gitlab.Spelling = NO --> + +| Role | Who +|------------------------------|-------------------------| +| Author | Grzegorz Bizon | +| Engineering Leader | Cheryl Li | +| Product Manager | Jackie Porter | +| Architecture Evolution Coach | Kamil Trzciński | + +DRIs: + +| Role | Who +|------------------------------|------------------------| +| Leadership | Cheryl Li | +| Product | Jackie Porter | +| Engineering | Grzegorz Bizon | + +Domain experts: + +| Area | Who +|------------------------------|------------------------| +| Verify / Pipeline execution | Fabio Pitino | +| Verify / Pipeline execution | Marius Bobin | +| PostgreSQL Database | Andreas Brandl | + +<!-- vale gitlab.Spelling = YES --> |