--- type: reference, dev stage: Data Stores group: Database info: "See the Technical Writers assigned to Development Guidelines: https://about.gitlab.com/handbook/engineering/ux/technical-writing/#assignments-to-development-guidelines" --- # Batched background migrations Batched Background Migrations should be used to perform data migrations whenever a migration exceeds [the time limits](../migration_style_guide.md#how-long-a-migration-should-take) in our guidelines. For example, you can use batched background migrations to migrate data that's stored in a single JSON column to a separate table instead. ## When to use batched background migrations Use a batched background migration when you migrate _data_ in tables containing so many rows that the process would exceed [the time limits in our guidelines](../migration_style_guide.md#how-long-a-migration-should-take) if performed using a regular Rails migration. - Batched background migrations should be used when migrating data in [high-traffic tables](../migration_style_guide.md#high-traffic-tables). - Batched background migrations may also be used when executing numerous single-row queries for every item on a large dataset. Typically, for single-record patterns, runtime is largely dependent on the size of the dataset. Split the dataset accordingly, and put it into background migrations. - Don't use batched background migrations to perform schema migrations. Background migrations can help when: - Migrating events from one table to multiple separate tables. - Populating one column based on JSON stored in another column. - Migrating data that depends on the output of external services. (For example, an API.) NOTE: If the batched background migration is part of an important upgrade, it must be announced in the release post. Discuss with your Project Manager if you're unsure if the migration falls into this category. ## Isolation Batched background migrations must be isolated and can not use application code (for example, models defined in `app/models` except the `ApplicationRecord` classes). Because these migrations can take a long time to run, it's possible for new versions to deploy while the migrations are still running. ## Accessing data for multiple databases Background Migration contrary to regular migrations does have access to multiple databases and can be used to efficiently access and update data across them. To properly indicate a database to be used it is desired to create ActiveRecord model inline the migration code. Such model should use a correct [`ApplicationRecord`](multiple_databases.md#gitlab-schema) depending on which database the table is located. As such usage of `ActiveRecord::Base` is disallowed as it does not describe a explicitly database to be used to access given table. ```ruby # good class Gitlab::BackgroundMigration::ExtractIntegrationsUrl class Project < ::ApplicationRecord self.table_name = 'projects' end class Build < ::Ci::ApplicationRecord self.table_name = 'ci_builds' end end # bad class Gitlab::BackgroundMigration::ExtractIntegrationsUrl class Project < ActiveRecord::Base self.table_name = 'projects' end class Build < ActiveRecord::Base self.table_name = 'ci_builds' end end ``` Similarly the usage of `ActiveRecord::Base.connection` is disallowed and needs to be replaced preferably with the usage of model connection. ```ruby # good Project.connection.execute("SELECT * FROM projects") # acceptable ApplicationRecord.connection.execute("SELECT * FROM projects") # bad ActiveRecord::Base.connection.execute("SELECT * FROM projects") ``` ## Idempotence Batched background migrations are executed in a context of a Sidekiq process. The usual Sidekiq rules apply, especially the rule that jobs should be small and idempotent. Make sure that in case that your migration job is retried, data integrity is guaranteed. See [Sidekiq best practices guidelines](https://github.com/mperham/sidekiq/wiki/Best-Practices) for more details. ## Batched background migrations for EE-only features All the background migration classes for EE-only features should be present in GitLab CE. For this purpose, create an empty class for GitLab CE, and extend it for GitLab EE as explained in the guidelines for [implementing Enterprise Edition features](../ee_features.md#code-in-libgitlabbackground_migration). Batched Background migrations are simple classes that define a `perform` method. A Sidekiq worker then executes such a class, passing any arguments to it. All migration classes must be defined in the namespace `Gitlab::BackgroundMigration`. Place the files in the directory `lib/gitlab/background_migration/`. ## Queueing Queueing a batched background migration should be done in a post-deployment migration. Use this `queue_batched_background_migration` example, queueing the migration to be executed in batches. Replace the class name and arguments with the values from your migration: ```ruby queue_batched_background_migration( JOB_CLASS_NAME, TABLE_NAME, JOB_ARGUMENTS, JOB_INTERVAL ) ``` Make sure the newly-created data is either migrated, or saved in both the old and new version upon creation. Removals in turn can be handled by defining foreign keys with cascading deletes. ### Requeuing batched background migrations If one of the batched background migrations contains a bug that is fixed in a patch release, you must requeue the batched background migration so the migration repeats on systems that already performed the initial migration. When you requeue the batched background migration, turn the original queuing into a no-op by clearing up the `#up` and `#down` methods of the migration performing the requeuing. Otherwise, the batched background migration is queued multiple times on systems that are upgrading multiple patch releases at once. When you start the second post-deployment migration, delete the previously batched migration with the provided code: ```ruby delete_batched_background_migration(MIGRATION_NAME, TABLE_NAME, COLUMN, JOB_ARGUMENTS) ``` ## Cleaning up NOTE: Cleaning up any remaining background migrations must be done in either a major or minor release. You must not do this in a patch release. Because background migrations can take a long time, you can't immediately clean things up after queueing them. For example, you can't drop a column used in the migration process, as jobs would fail. You must add a separate _post-deployment_ migration in a future release that finishes any remaining jobs before cleaning things up. (For example, removing a column.) To migrate the data from column `foo` (containing a big JSON blob) to column `bar` (containing a string), you would: 1. Release A: 1. Create a migration class that performs the migration for a row with a given ID. 1. Update new rows using one of these techniques: - Create a new trigger for simple copy operations that don't need application logic. - Handle this operation in the model/service as the records are created or updated. - Create a new custom background job that updates the records. 1. Queue the batched background migration for all existing rows in a post-deployment migration. 1. Release B: 1. Add a post-deployment migration that checks if the batched background migration is completed. 1. Deploy code so that the application starts using the new column and stops to update new records. 1. Remove the old column. Bump to the [import/export version](../../user/project/settings/import_export.md) may be required, if importing a project from a prior version of GitLab requires the data to be in the new format. ## Example The `routes` table has a `source_type` field that's used for a polymorphic relationship. As part of a database redesign, we're removing the polymorphic relationship. One step of the work is migrating data from the `source_id` column into a new singular foreign key. Because we intend to delete old rows later, there's no need to update them as part of the background migration. 1. Start by defining our migration class, which should inherit from `Gitlab::BackgroundMigration::BatchedMigrationJob`: ```ruby class Gitlab::BackgroundMigration::BackfillRouteNamespaceId < BatchedMigrationJob # For illustration purposes, if we were to use a local model we could # define it like below, using an `ApplicationRecord` as the base class # class Route < ::ApplicationRecord # self.table_name = 'routes' # end def perform each_sub_batch( operation_name: :update_all, batching_scope: -> (relation) { relation.where("source_type <> 'UnusedType'") } ) do |sub_batch| sub_batch.update_all('namespace_id = source_id') end end end ``` NOTE: Job classes must be subclasses of `BatchedMigrationJob` to be correctly handled by the batched migration framework. Any subclass of `BatchedMigrationJob` is initialized with necessary arguments to execute the batch, as well as a connection to the tracking database. Additional `job_arguments` set on the migration are passed to the job's `perform` method. 1. Add a new trigger to the database to update newly created and updated routes, similar to this example: ```ruby execute(<<~SQL) CREATE OR REPLACE FUNCTION example() RETURNS trigger LANGUAGE plpgsql AS $$ BEGIN NEW."namespace_id" = NEW."source_id" RETURN NEW; END; $$; SQL ``` 1. Create a post-deployment migration that queues the migration for existing data: ```ruby class QueueBackfillRoutesNamespaceId < Gitlab::Database::Migration[2.0] disable_ddl_transaction! MIGRATION = 'BackfillRouteNamespaceId' DELAY_INTERVAL = 2.minutes restrict_gitlab_migration gitlab_schema: :gitlab_main def up queue_batched_background_migration( MIGRATION, :routes, :id, job_interval: DELAY_INTERVAL ) end def down delete_batched_background_migration(MIGRATION, :routes, :id, []) end end ``` NOTE: When queuing a batched background migration, you need to restrict the schema to the database where you make the actual changes. In this case, we are updating `routes` records, so we set `restrict_gitlab_migration gitlab_schema: :gitlab_main`. If, however, you need to perform a CI data migration, you would set `restrict_gitlab_migration gitlab_schema: :gitlab_ci`. After deployment, our application: - Continues using the data as before. - Ensures that both existing and new data are migrated. 1. In the next release, remove the trigger. We must also add a new post-deployment migration that checks that the batched background migration is completed. For example: ```ruby class FinalizeBackfillRouteNamespaceId < Gitlab::Database::Migration[2.0] MIGRATION = 'BackfillRouteNamespaceId' disable_ddl_transaction! restrict_gitlab_migration gitlab_schema: :gitlab_main def up ensure_batched_background_migration_is_finished( job_class_name: MIGRATION, table_name: :routes, column_name: :id, job_arguments: [], finalize: true ) end def down # no-op end end ``` NOTE: If the batched background migration is not finished, the system will execute the batched background migration inline. If you don't want to see this behavior, you need to pass `finalize: false`. If the application does not depend on the data being 100% migrated (for instance, the data is advisory, and not mission-critical), then you can skip this final step. This step confirms that the migration is completed, and all of the rows were migrated. After the batched migration is completed, you can safely depend on the data in `routes.namespace_id` being populated. ## Testing Writing tests is required for: - The batched background migrations' queueing migration. - The batched background migration itself. - A cleanup migration. The `:migration` and `schema: :latest` RSpec tags are automatically set for background migration specs. Refer to the [Testing Rails migrations](../testing_guide/testing_migrations_guide.md#testing-a-non-activerecordmigration-class) style guide. Remember that `before` and `after` RSpec hooks migrate your database down and up. These hooks can result in other batched background migrations being called. Using `spy` test doubles with `have_received` is encouraged, instead of using regular test doubles, because your expectations defined in a `it` block can conflict with what is called in RSpec hooks. Refer to [issue #35351](https://gitlab.com/gitlab-org/gitlab/-/issues/18839) for more details. ## Best practices 1. Know how much data you're dealing with. 1. Make sure the batched background migration jobs are idempotent. 1. Confirm the tests you write are not false positives. 1. If the data being migrated is critical and cannot be lost, the clean-up migration must also check the final state of the data before completing. 1. Discuss the numbers with a database specialist. The migration may add more pressure on DB than you expect. Measure on staging, or ask someone to measure on production. 1. Know how much time is required to run the batched background migration. ## Additional tips and strategies ### Viewing failure error logs You can view failures in two ways: - Via GitLab logs: 1. After running a batched background migration, if any jobs fail, view the logs in [Kibana](https://log.gprd.gitlab.net/goto/5f06a57f768c6025e1c65aefb4075694). View the production Sidekiq log and filter for: - `json.new_state: failed` - `json.job_class_name: ` - `json.job_arguments: ` 1. Review the `json.exception_class` and `json.exception_message` values to help understand why the jobs failed. 1. Remember the retry mechanism. Having a failure does not mean the job failed. Always check the last status of the job. - Via database: 1. Get the batched background migration `CLASS_NAME`. 1. Execute the following query in the PostgreSQL console: ```sql SELECT migration.id, migration.job_class_name, transition_logs.exception_class, transition_logs.exception_message FROM batched_background_migrations as migration INNER JOIN batched_background_migration_jobs as jobs ON jobs.batched_background_migration_id = migration.id INNER JOIN batched_background_migration_job_transition_logs as transition_logs ON transition_logs.batched_background_migration_job_id = jobs.id WHERE transition_logs.next_status = '2' AND migration.job_class_name = "CLASS_NAME"; ```