debian-mirror-gitlab/doc/development/database_review.md

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Data Stores	Database	To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/engineering/ux/technical-writing/#assignments

Database Review Guidelines

This page is specific to database reviews. Refer to our code review guide for broader advice and best practices for code review in general.

General process

A database review is required for:

• Changes that touch the database schema or perform data migrations, including files in:
  • db/
  • lib/gitlab/background_migration/
• Changes to the database tooling. For example:
  • migration or ActiveRecord helpers in lib/gitlab/database/
  • load balancing
• Changes that produce SQL queries that are beyond the obvious. It is generally up to the author of a merge request to decide whether or not complex queries are being introduced and if they require a database review.
• Changes in Service Data metrics that use count, distinct_count, estimate_batch_distinct_count and sum. These metrics could have complex queries over large tables. See the Product Intelligence Guide for implementation details.

A database reviewer is expected to look out for overly complex queries in the change and review those closer. If the author does not point out specific queries for review and there are no overly complex queries, it is enough to concentrate on reviewing the migration only.

Required

You must provide the following artifacts when you request a ~database review. If your merge request description does not include these items, the review is reassigned back to the author.

Migrations

If new migrations are introduced, in the MR you are required to provide:

• The output of both migrating (db:migrate) and rolling back (db:rollback) for all migrations.

We have automated tooling for GitLab (provided by the db:check-migrations pipeline job) that provides this output for migrations on ~database merge requests. You do not need to provide this information manually if the bot can do it for you. The bot also checks that migrations are correctly reversible.

Queries

If new queries have been introduced or existing queries have been updated, you are required to provide:

• Query plans for each raw SQL query included in the merge request along with the link to the query plan following each raw SQL snippet.
• Raw SQL for all changed or added queries (as translated from ActiveRecord queries).
  • In case of updating an existing query, the raw SQL of both the old and the new version of the query should be provided together with their query plans.

Refer to Preparation when adding or modifying queries for how to provide this information.

Roles and process

A merge request author's role is to:

• Decide whether a database review is needed.
• If database review is needed, add the ~database label.
• Prepare the merge request for a database review.
• Provide the required artifacts prior to submitting the MR.

A database reviewer's role is to:

• Ensure the required artifacts are provided and in the proper format. If they are not, reassign the merge request back to the author.
• Perform a first-pass review on the MR and suggest improvements to the author.
• Once satisfied, relabel the MR with ~"database::reviewed", approve it, and request a review from the database maintainer suggested by Reviewer Roulette. Remove yourself as a reviewer once this has been done.

A database maintainer's role is to:

• Perform the final database review on the MR.
• Discuss further improvements or other relevant changes with the database reviewer and the MR author.
• Finally approve the MR and relabel the MR with ~"database::approved"
• Merge the MR if no other approvals are pending or pass it on to other maintainers as required (frontend, backend, documentation).
  • If not merging, remove yourself as a reviewer.

Distributing review workload

Review workload is distributed using reviewer roulette (example). The MR author should request a review from the suggested database reviewer. When they sign off, they hand over to the suggested database maintainer.

If reviewer roulette didn't suggest a database reviewer & maintainer, make sure you have applied the ~database label and rerun the danger-review CI job, or pick someone from the @gl-database team.

How to prepare the merge request for a database review

To make reviewing easier and therefore faster, take the following preparations into account.

Preparation when adding migrations

• Ensure db/structure.sql is updated as documented, and additionally ensure that the relevant version files under db/schema_migrations were added or removed.
• Ensure that the Database Dictionary is updated as documented.
• Make migrations reversible by using the change method or include a down method when using up.
  • Include either a rollback procedure or describe how to rollback changes.
• Check that the db:check-migrations pipeline job has run successfully and the migration rollback behaves as expected.
  • Ensure the db:check-schema job has run successfully and no unexpected schema changes are introduced in a rollback. This job may only trigger a warning if the schema was changed.
  • Verify that the previously mentioned jobs continue to succeed whenever you modify the migrations during the review process.
• Add tests for the migration in spec/migrations if necessary. See Testing Rails migrations at GitLab for more details.
• When high-traffic tables are involved in the migration, use the enable_lock_retries method to enable lock-retries. Review the relevant examples in our documentation for use cases and solutions.
• Ensure RuboCop checks are not disabled unless there's a valid reason to.
• When adding an index to a large table, test its execution using CREATE INDEX CONCURRENTLY in the #database-lab Slack channel and add the execution time to the MR description:
  • Execution time largely varies between #database-lab and GitLab.com, but an elevated execution time from #database-lab can give a hint that the execution on GitLab.com is also considerably high.
  • If the execution from #database-lab is longer than 1h, the index should be moved to a post-migration. Keep in mind that in this case you may need to split the migration and the application changes in separate releases to ensure the index is in place when the code that needs it is deployed.
• Manually trigger the database testing job (db:gitlabcom-database-testing) in the test stage.
  • This job runs migrations in a production-like environment (similar to #database_lab) and posts to the MR its findings (queries, runtime, size change).
  • Review migration runtimes and any warnings.

Preparation when adding data migrations

Data migrations are inherently risky. Additional actions are required to reduce the possibility of error that would result in corruption or loss of production data.

Include in the MR description:

• If the migration itself is not reversible, details of how data changes could be reverted in the event of an incident. For example, in the case of a migration that deletes records (an operation that most of the times is not automatically reversible), how could the deleted records be recovered.
• If the migration deletes data, apply the label ~data-deletion.
• Concise descriptions of possible user experience impact of an error; for example, "Issues would unexpectedly go missing from Epics".
• Relevant data from the query plans that indicate the query works as expected; such as the approximate number of records that are modified or deleted.

Preparation when adding or modifying queries

Raw SQL

• Write the raw SQL in the MR description. Preferably formatted nicely with pgFormatter or paste.depesz.com and using regular quotes

(for example, "projects"."id") and avoiding smart quotes (for example, “projects”.“id”).

• In case of queries generated dynamically by using parameters, there should be one raw SQL query for each variation.

  For example, a finder for issues that may take as a parameter an optional filter on projects, should include both the version of the query over issues and the one that joins issues and projects and applies the filter.

  There are finders or other methods that can generate a very large amount of permutations. There is no need to exhaustively add all the possible generated queries, just the one with all the parameters included and one for each type of queries generated.

  For example, if joins or a group by clause are optional, the versions without the group by clause and with less joins should be also included, while keeping the appropriate filters for the remaining tables.

• If a query is always used with a limit and an offset, those should always be included with the maximum allowed limit used and a non 0 offset.

Query Plans

• The query plan for each raw SQL query included in the merge request along with the link to the query plan following each raw SQL snippet.
• Provide a public link to the plan from either:
  • postgres.ai: Follow the link in #database-lab and generate a shareable, public link by clicking Share in the upper right corner.
  • explain.depesz.com or explain.dalibo.com: Paste both the plan and the query used in the form.
• When providing query plans, make sure it hits enough data:
  • You can use a GitLab production replica to test your queries on a large scale, through the #database-lab Slack channel or through ChatOps.
  • To produce a query plan with enough data, you can use the IDs of:
    • The gitlab-org namespace (namespace_id = 9970), for queries involving a group.
    • The gitlab-org/gitlab-foss (project_id = 13083) or the gitlab-org/gitlab (project_id = 278964) projects, for queries involving a project.
    • The gitlab-qa user (user_id = 1614863), for queries involving a user.
      • Optionally, you can also use your own user_id, or the user_id of a user with a long history within the project or group being used to generate the query plan.
  • That means that no query plan should return 0 records or less records than the provided limit (if a limit is included). If a query is used in batching, a proper example batch with adequate included results should be identified and provided.
  • If your queries belong to a new feature in GitLab.com and thus they don't return data in production:
    • You may analyze the query and to provide the plan from a local environment.
    • #database-lab and postgres.ai both allow updates to data (exec UPDATE issues SET ...) and creation of new tables and columns (exec ALTER TABLE issues ADD COLUMN ...).
  • More information on how to find the number of actual returned records in Understanding EXPLAIN plans
• For query changes, it is best to provide both the SQL queries along with the plan before and after the change. This helps spot differences quickly.
• Include data that shows the performance improvement, preferably in the form of a benchmark.

Preparation when adding foreign keys to existing tables

• Include a migration to remove orphaned rows in the source table before adding the foreign key.
• Remove any instances of dependent: ... that may no longer be necessary.

Preparation when adding tables

• Order columns based on the Ordering Table Columns guidelines.
• Add foreign keys to any columns pointing to data in other tables, including an index.
• Add indexes for fields that are used in statements such as WHERE, ORDER BY, GROUP BY, and JOINs.
• New tables and columns are not necessarily risky, but over time some access patterns are inherently difficult to scale. To identify these risky patterns in advance, we must document expectations for access and size. Include in the MR description answers to these questions:
  • What is the anticipated growth for the new table over the next 3 months, 6 months, 1 year? What assumptions are these based on?
  • How many reads and writes per hour would you expect this table to have in 3 months, 6 months, 1 year? Under what circumstances are rows updated? What assumptions are these based on?
  • Based on the anticipated data volume and access patterns, does the new table pose an availability risk to GitLab.com or self-managed instances? Does the proposed design scale to support the needs of GitLab.com and self-managed customers?

Preparation when removing columns, tables, indexes, or other structures

• Follow the guidelines on dropping columns.
• Generally it's best practice (but not a hard rule) to remove indexes and foreign keys in a post-deployment migration.
  • Exceptions include removing indexes and foreign keys for small tables.
• If you're adding a composite index, another index might become redundant, so remove that in the same migration. For example adding index(column_A, column_B, column_C) makes the indexes index(column_A, column_B) and index(column_A) redundant.

How to review for database

• Check migrations
  • Review relational modeling and design choices
    • Consider access patterns and data layout if new tables or columns are added.
  • Review migrations follow database migration style guide, for example
    • Check ordering of columns
    • Check indexes are present for foreign keys
  • Ensure that migrations execute in a transaction or only contain concurrent index/foreign key helpers (with transactions disabled)
  • If an index to a large table is added and its execution time was elevated (more than 1h) on #database-lab:
    • Ensure it was added in a post-migration.
    • Maintainer: After the merge request is merged, notify Release Managers about it on #f_upcoming_release Slack channel.
  • Check consistency with db/structure.sql and that migrations are reversible
  • Check that the relevant version files under db/schema_migrations were added or removed.
  • Check queries timing (If any): In a single transaction, cumulative query time executed in a migration needs to fit comfortably in 15s - preferably much less than that - on GitLab.com.
  • For column removals, make sure the column has been ignored in a previous release
• Check background migrations:
  • Establish a time estimate for execution on GitLab.com. For historical purposes, it's highly recommended to include this estimation on the merge request description.
  • If a single update is below than 1s the query can be placed directly in a regular migration (inside db/migrate).
  • Background migrations are normally used, but not limited to:
    • Migrating data in larger tables.
    • Making numerous SQL queries per record in a dataset.
  • Review queries (for example, make sure batch sizes are fine)
  • Because execution time can be longer than for a regular migration, it's suggested to treat background migrations as post migrations: place them in db/post_migrate instead of db/migrate.
  • If a migration has tracking enabled, ensure mark_all_as_succeeded is called even if no work is done.
• Check timing guidelines for migrations
• Check migrations are reversible and implement a #down method
• Check new table migrations:
  • Are the stated access patterns and volume reasonable? Do the assumptions they're based on seem sound? Do these patterns pose risks to stability?
  • Are the columns ordered to conserve space?
  • Are there foreign keys for references to other tables?
• Check data migrations:
  • Establish a time estimate for execution on GitLab.com.
  • Depending on timing, data migrations can be placed on regular, post-deploy, or background migrations.
  • Data migrations should be reversible too or come with a description of how to reverse, when possible. This applies to all types of migrations (regular, post-deploy, background).
• Query performance
  • Check for any overly complex queries and queries the author specifically points out for review (if any)
  • If not present, ask the author to provide SQL queries and query plans (for example, by using ChatOps or direct database access)
  • For given queries, review parameters regarding data distribution
  • Check query plans and suggest improvements to queries (changing the query, schema or adding indexes and similar)
  • General guideline is for queries to come in below 100ms execution time
  • Avoid N+1 problems and minimize the query count.