295 lines
13 KiB
Markdown
295 lines
13 KiB
Markdown
# GitLab scalability
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This section describes the current architecture of GitLab as it relates to
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scalability and reliability.
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## Reference Architecture Overview
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_[diagram source - GitLab employees only](https://docs.google.com/drawings/d/1RTGtuoUrE0bDT-9smoHbFruhEMI4Ys6uNrufe5IA-VI/edit)_
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The diagram above shows a GitLab reference architecture scaled up for 50,000
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users. We will discuss each component below.
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## Components
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### PostgreSQL
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The PostgreSQL database holds all metadata for projects, issues, merge
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requests, users, etc. The schema is managed by the Rails application
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[db/schema.rb](https://gitlab.com/gitlab-org/gitlab/blob/master/db/schema.rb).
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GitLab Web/API servers and Sidekiq nodes talk directly to the database via a
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Rails object relational model (ORM). Most SQL queries are accessed via this
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ORM, although some custom SQL is also written for performance or for
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exploiting advanced PostgreSQL features (e.g. recursive CTEs, LATERAL JOINs,
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etc.).
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The application has a tight coupling to the database schema. When the
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application starts, Rails queries the database schema, caching the tables and
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column types for the data requested. Because of this schema cache, dropping a
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column or table while the application is running can produce 500 errors to the
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user. This is why we have a [process for dropping columns and other
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no-downtime changes](what_requires_downtime.md).
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#### Multi-tenancy
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A single database is used to store all customer data. Each user can belong to
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many groups or projects, and the access level (e.g. guest, developer,
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maintainer, etc.) to groups and projects determines what users can see and
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what they can access.
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Users with admin access can access all projects and even impersonate
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users.
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#### Sharding and partitioning
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The database is not divided up in any way; currently all data lives in
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one database in many different tables. This works for simple
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applications, but as the data set grows, it becomes more challenging to
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maintain and support one database with tables with many rows.
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There are two ways to deal with this:
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- Partioning. Locally split up tables data.
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- Sharding. Distribute data across multiple databases.
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Partioning is a built-in PostgreSQL feature and requires minimal changes
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in the application. However, it [requires PostgreSQL
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11](https://www.2ndquadrant.com/en/blog/partitioning-evolution-postgresql-11/).
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For example, a natural way to partition is to [partition tables by
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dates](https://gitlab.com/groups/gitlab-org/-/epics/2023). For example,
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the `events` and `audit_events` table are natural candidates for this
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kind of partitioning.
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Sharding is likely more difficult and will require significant changes
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to the schema and application. For example, if we have to store projects
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in many different databases, we immediately run into the question, "How
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can we retrieve data across different projects?" One answer to this is
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to abstract data access into API calls that abstract the database from
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the application, but this is a significant amount of work.
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There are solutions that may help abstract the sharding to some extent
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from the application. For example, we will want to look at [Citus
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Data](https://www.citusdata.com/product/community) closely. Citus Data
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provides a Rails plugin that adds a [tenant ID to ActiveRecord
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models](https://www.citusdata.com/blog/2017/01/05/easily-scale-out-multi-tenant-apps/).
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Sharding can also be done based on feature verticals. This is the
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microservice approach to sharding, where each service represents a
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bounded context and operates on its own service-specific database
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cluster. In that model data wouldn't be distributed according to some
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internal key (such as tenant IDs) but based on team and product
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ownership. It shares a lot of challenges with traditional, data-oriented
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sharding, however. For instance, joining data has to happen in the
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application itself rather than on the query layer (although additional
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layers like GraphQL might mitigate that) and it requires true
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parallelism to run efficiently (i.e. a scatter-gather model to collect,
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then zip up data records), which is a challenge in itself in Ruby based
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systems.
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#### Database size
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A recent [database checkup shows a breakdown of the table sizes on
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GitLab.com](https://gitlab.com/gitlab-com/gl-infra/infrastructure/issues/8022#master-1022016101-8).
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Since `merge_request_diff_files` contains over 1 TB of data, we will want to
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reduce/eliminate this table first. GitLab has support for [storing diffs in
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object storage](../administration/merge_request_diffs.md), which we [will
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want to do on
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GitLab.com](https://gitlab.com/gitlab-com/gl-infra/infrastructure/issues/7356).
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#### High availability
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There are several strategies to provide high-availability and redundancy:
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- Write-ahead logs (WAL) streamed to object storage (e.g. S3, Google Cloud
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Storage).
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- Read-replicas (hot backups).
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- Delayed replicas.
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To restore a database from a point in time, a base backup needs to have
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been taken prior to that incident. Once a database has restored from
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that backup, the database can apply the WAL logs in order until the
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database has reached the target time.
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On GitLab.com, Consul and Patroni work together to coordinate failovers with
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the read replicas. [Omnibus ships with repmgr instead of
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Consul](../administration/high_availability/database.md).
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#### Load-balancing
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GitLab EE has [application support for load balancing using read
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replicas](../administration/database_load_balancing.md). This load
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balancer does some smart things that are not traditionally available in
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standard load balancers. For example, the application will only consider a
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replica if its replication lag is low (e.g. WAL data behind by < 100
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megabytes).
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More [details are in a blog
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post](https://about.gitlab.com/2017/10/02/scaling-the-gitlab-database/).
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### PgBouncer
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As PostgreSQL forks a backend process for each request, PostgreSQL has a
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finite limit of connections that it can support, typically around 300 by
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default. Without a connection pooler like PgBouncer, it's quite possible to
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hit connection limits. Once the limits are reached, then GitLab will generate
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errors or slow down as it waits for a connection to be available.
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#### High availability
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PgBouncer is a single-threaded process. Under heavy traffic, PgBouncer can
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saturate a single core, which can result in slower response times for
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background job and/or Web requests. There are two ways to address this
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limitation:
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- Run multiple PgBouncer instances.
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- Use a multi-threaded connection pooler (e.g.
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[Odyssey](https://gitlab.com/gitlab-com/gl-infra/infrastructure/issues/7776).
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On some Linux systems, it's possible to run [multiple PgBouncer instances on
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the same port](https://gitlab.com/gitlab-org/omnibus-gitlab/issues/4796).
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On GitLab.com, we run multiple PgBouncer instances on different ports to
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avoid saturating a single core.
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In addition, the PgBouncer instances that communicate with the primary
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and secondaries are set up a bit differently:
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- Multiple PgBouncer instances in different availability zones talk to the
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PostgreSQL primary.
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- Multiple PgBouncer processes are colocated with PostgreSQL read replicas.
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For replicas, colocating is advantageous because it reduces network hops
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and hence latency. However, for the primary, colocating is
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disadvantageous because PgBouncer would become a single point of failure
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and cause errors. When a failover occurs, one of two things could
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happen:
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- The primary disappears from the network.
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- The primary becomes a replica.
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In the first case, if PgBouncer is colocated with the primary, database
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connections would time out or fail to connect, and downtime would
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occur. Having multiple PgBouncer instances in front of a load balancer
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talking to the primary can mitigate this.
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In the second case, existing connections to the newly-demoted replica
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may execute a write query, which would fail. During a failover, it may
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be advantageous to shut down the PgBouncer talking to the primary to
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ensure no more traffic arrives for it. The alternative would be to make
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the application aware of the failover event and terminate its
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connections gracefully.
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### Redis
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There are three ways Redis is used in GitLab:
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- Queues. Sidekiq jobs marshal jobs into JSON payloads.
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- Persistent state. Session data, exclusive leases, etc.
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- Cache. Repository data (e.g. Branch and tag names), view partials, etc.
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For GitLab instances running at scale, splitting Redis usage into
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separate Redis clusters helps for two reasons:
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- Each has different persistence requirements.
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- Load isolation.
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For example, the cache instance can behave like an least-recently used
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(LRU) cache by setting the `maxmemory` configuration option. That option
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should not be set for the queues or persistent clusters because data
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would be evicted from memory at random times. This would cause jobs to
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be dropped on the floor, which would cause many problems (e.g. merges
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not running, builds not updating, etc.).
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Sidekiq also polls its queues quite frequently, and this activity can
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slow down other queries. For this reason, having a dedicated Redis
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cluster for Sidekiq can help improve performance and reduce load on the
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Redis process.
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#### High availability/Risks
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Single-core: Like PgBouncer, a single Redis process can only use one
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core. It does not support multi-threading.
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Dumb secondaries: Redis secondaries (aka slaves) don't actually
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handle any load. Unlike PostgreSQL secondaries, they don't even serve
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read queries. They simply replicate data from the primary and take over
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only when the primary fails.
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### Redis Sentinels
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[Redis Sentinel](https://redis.io/topics/sentinel) provides high
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availability for Redis by watching the primary. If multiple Sentinels
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detect that the primary has gone away, the Sentinels performs an
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election to determine a new leader.
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#### Failure Modes
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No leader: A Redis cluster can get into a mode where there are no
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primaries. For example, this can happen if Redis nodes are misconfigured
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to follow the wrong node. Sometimes this requires forcing one node to
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become a primary via the [`SLAVEOF NO ONE`
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command](https://redis.io/commands/slaveof).
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### Sidekiq
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Sidekiq is a multi-threaded, background job processing system used in
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Ruby on Rails applications. In GitLab, Sidekiq performs the heavy
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lifting of many activities, including:
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- Updating merge requests after a push.
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- Sending e-mails.
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- Updating user authorizations.
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- Processing CI builds and pipelines.
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The full list of jobs can be found in the
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[app/workers](https://gitlab.com/gitlab-org/gitlab/tree/master/app/workers)
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and
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[ee/app/workers](https://gitlab.com/gitlab-org/gitlab/tree/master/ee/app/workers)
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directories in the GitLab code base.
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#### Runaway Queues
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As jobs are added to the Sidekiq queue, Sidekiq worker threads need to
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pull these jobs from the queue and finish them at a rate faster than
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they are added. When an imbalance occurs (e.g. delays in the database,
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slow jobs, etc.), Sidekiq queues can balloon and lead to runaway queues.
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In recent months, many of these queues have ballooned due to delays in
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PostgreSQL, PgBouncer, and Redis. For example, PgBouncer saturation can
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cause jobs to wait a few seconds before obtaining a database connection,
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which can cascade into a large slowdown. Optimizing these basic
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interconnections comes first.
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However, there are a number of strategies to ensure queues get drained
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in a timely manner:
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- Add more processing capacity. This can be done by spinning up more
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instances of Sidekiq or [Sidekiq Cluster](../administration/operations/extra_sidekiq_processes.md).
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- Split jobs into smaller units of work. For example, `PostReceive`
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used to process each commit message in the push, but now it farms out
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this to `ProcessCommitWorker`.
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- Redistribute/gerrymander Sidekiq processes by queue
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types. Long-running jobs (e.g. relating to project import) can often
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squeeze out jobs that run fast (e.g. delivering e-mail). [This technique
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was used in to optimize our existing Sidekiq deployment](https://gitlab.com/gitlab-com/gl-infra/infrastructure/issues/7219#note_218019483).
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- Optimize jobs. Eliminating unnecessary work, reducing network calls
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(e.g. SQL, Gitaly, etc.), and optimizing processor time can yield significant
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benefits.
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From the Sidekiq logs, it's possible to see which jobs run the most
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frequently and/or take the longest. For example, theis Kibana
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visualizations show the jobs that consume the most total time:
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_[visualization source - GitLab employees only](https://log.gitlab.net/goto/2c036582dfc3219eeaa49a76eab2564b)_
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This shows the jobs that had the longest durations:
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_[visualization source - GitLab employees only](https://log.gitlab.net/goto/494f6c8afb61d98c4ff264520d184416)_
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