216 lines
9.5 KiB
Markdown
216 lines
9.5 KiB
Markdown
---
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type: reference, concepts
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---
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# Scaling and High Availability
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GitLab supports several different types of clustering and high-availability.
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The solution you choose will be based on the level of scalability and
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availability you require. The easiest solutions are scalable, but not necessarily
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highly available.
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GitLab provides a service that is usually essential to most organizations: it
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enables people to collaborate on code in a timely fashion. Any downtime should
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therefore be short and planned. Luckily, GitLab provides a solid setup even on
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a single server without special measures. Due to the distributed nature
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of Git, developers can still commit code locally even when GitLab is not
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available. However, some GitLab features such as the issue tracker and
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Continuous Integration are not available when GitLab is down.
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**Keep in mind that all highly-available solutions come with a trade-off between
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cost/complexity and uptime**. The more uptime you want, the more complex the
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solution. And the more complex the solution, the more work is involved in
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setting up and maintaining it. High availability is not free and every HA
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solution should balance the costs against the benefits.
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There are many options when choosing a highly-available GitLab architecture. We
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recommend engaging with GitLab Support to choose the best architecture for your
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use-case. This page contains some various options and guidelines based on
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experience with GitLab.com and Enterprise Edition on-premises customers.
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For a detailed insight into how GitLab scales and configures GitLab.com, you can
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watch [this 1 hour Q&A](https://www.youtube.com/watch?v=uCU8jdYzpac)
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with [John Northrup](https://gitlab.com/northrup), and live questions coming in from some of our customers.
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## GitLab Components
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The following components need to be considered for a scaled or highly-available
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environment. In many cases components can be combined on the same nodes to reduce
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complexity.
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- Unicorn/Workhorse - Web-requests (UI, API, Git over HTTP)
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- Sidekiq - Asynchronous/Background jobs
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- PostgreSQL - Database
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- Consul - Database service discovery and health checks/failover
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- PGBouncer - Database pool manager
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- Redis - Key/Value store (User sessions, cache, queue for Sidekiq)
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- Sentinel - Redis health check/failover manager
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- Gitaly - Provides high-level RPC access to Git repositories
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## Scalable Architecture Examples
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When an organization reaches a certain threshold it will be necessary to scale
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the GitLab instance. Still, true high availability may not be necessary. There
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are options for scaling GitLab instances relatively easily without incurring the
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infrastructure and maintenance costs of full high availability.
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### Basic Scaling
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This is the simplest form of scaling and will work for the majority of
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cases. Backend components such as PostgreSQL, Redis and storage are offloaded
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to their own nodes while the remaining GitLab components all run on 2 or more
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application nodes.
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This form of scaling also works well in a cloud environment when it is more
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cost-effective to deploy several small nodes rather than a single
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larger one.
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- 1 PostgreSQL node
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- 1 Redis node
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- 1 NFS/Gitaly storage server
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- 2 or more GitLab application nodes (Unicorn, Workhorse, Sidekiq)
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- 1 Monitoring node (Prometheus, Grafana)
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#### Installation Instructions
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Complete the following installation steps in order. A link at the end of each
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section will bring you back to the Scalable Architecture Examples section so
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you can continue with the next step.
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1. [PostgreSQL](database.md#postgresql-in-a-scaled-environment)
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1. [Redis](redis.md#redis-in-a-scaled-environment)
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1. [Gitaly](gitaly.md) (recommended) or [NFS](nfs.md)
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1. [GitLab application nodes](gitlab.md)
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1. [Monitoring node (Prometheus and Grafana)](monitoring_node.md)
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### Full Scaling
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For very large installations it may be necessary to further split components
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for maximum scalability. In a fully-scaled architecture the application node
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is split into separate Sidekiq and Unicorn/Workhorse nodes. One indication that
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this architecture is required is if Sidekiq queues begin to periodically increase
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in size, indicating that there is contention or not enough resources.
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- 1 PostgreSQL node
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- 1 Redis node
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- 2 or more NFS/Gitaly storage servers
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- 2 or more Sidekiq nodes
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- 2 or more GitLab application nodes (Unicorn, Workhorse)
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- 1 Monitoring node (Prometheus, Grafana)
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## High Availability Architecture Examples
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When organizations require scaling *and* high availability the following
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architectures can be utilized. As the introduction section at the top of this
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page mentions, there is a tradeoff between cost/complexity and uptime. Be sure
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this complexity is absolutely required before taking the step into full
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high availability.
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For all examples below, we recommend running Consul and Redis Sentinel on
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dedicated nodes. If Consul is running on PostgreSQL nodes or Sentinel on
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Redis nodes there is a potential that high resource usage by PostgreSQL or
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Redis could prevent communication between the other Consul and Sentinel nodes.
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This may lead to the other nodes believing a failure has occurred and automated
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failover is necessary. Isolating them from the services they monitor reduces
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the chances of split-brain.
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The examples below do not really address high availability of NFS. Some enterprises
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have access to NFS appliances that manage availability. This is the best case
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scenario. In the future, GitLab may offer a more user-friendly solution to
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[GitLab HA Storage](https://gitlab.com/gitlab-org/omnibus-gitlab/issues/2472).
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There are many options in between each of these examples. Work with GitLab Support
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to understand the best starting point for your workload and adapt from there.
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### Horizontal
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This is the simplest form of high availability and scaling. It requires the
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fewest number of individual servers (virtual or physical) but does have some
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trade-offs and limits.
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This architecture will work well for many GitLab customers. Larger customers
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may begin to notice certain events cause contention/high load - for example,
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cloning many large repositories with binary files, high API usage, a large
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number of enqueued Sidekiq jobs, etc. If this happens you should consider
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moving to a hybrid or fully distributed architecture depending on what is causing
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the contention.
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- 3 PostgreSQL nodes
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- 2 Redis nodes
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- 3 Consul/Sentinel nodes
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- 2 or more GitLab application nodes (Unicorn, Workhorse, Sidekiq, PGBouncer)
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- 1 NFS/Gitaly server
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- 1 Monitoring node (Prometheus, Grafana)
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### Hybrid
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In this architecture, certain components are split on dedicated nodes so high
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resource usage of one component does not interfere with others. In larger
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environments this is a good architecture to consider if you foresee or do have
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contention due to certain workloads.
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- 3 PostgreSQL nodes
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- 1 PgBouncer node
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- 2 Redis nodes
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- 3 Consul/Sentinel nodes
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- 2 or more Sidekiq nodes
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- 2 or more GitLab application nodes (Unicorn, Workhorse)
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- 1 or more NFS/Gitaly servers
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- 1 Monitoring node (Prometheus, Grafana)
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#### Reference Architecture
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- **Supported Users (approximate):** 10,000
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- **Known Issues:** While validating the reference architecture, slow endpoints were discovered and are being investigated. [gitlab-org/gitlab-ce/issues/64335](https://gitlab.com/gitlab-org/gitlab-foss/issues/64335)
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The Support and Quality teams built, performance tested, and validated an
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environment that supports about 10,000 users. The specifications below are a
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representation of the work so far. The specifications may be adjusted in the
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future based on additional testing and iteration.
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NOTE: **Note:** The specifications here were performance tested against a specific coded workload. Your exact needs may be more, depending on your workload. Your workload is influenced by factors such as - but not limited to - how active your users are, how much automation you use, mirroring, and repo/change size.
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- 3 PostgreSQL - 4 CPU, 16GiB memory per node
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- 1 PgBouncer - 2 CPU, 4GiB memory
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- 2 Redis - 2 CPU, 8GiB memory per node
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- 3 Consul/Sentinel - 2 CPU, 2GiB memory per node
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- 4 Sidekiq - 4 CPU, 16GiB memory per node
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- 5 GitLab application nodes - 16 CPU, 64GiB memory per node
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- 1 Gitaly - 16 CPU, 64GiB memory
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- 1 Monitoring node - 2 CPU, 8GiB memory, 100GiB local storage
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### Fully Distributed
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This architecture scales to hundreds of thousands of users and projects and is
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the basis of the GitLab.com architecture. While this scales well it also comes
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with the added complexity of many more nodes to configure, manage and monitor.
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- 3 PostgreSQL nodes
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- 4 or more Redis nodes (2 separate clusters for persistent and cache data)
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- 3 Consul nodes
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- 3 Sentinel nodes
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- Multiple dedicated Sidekiq nodes (Split into real-time, best effort, ASAP,
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CI Pipeline and Pull Mirror sets)
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- 2 or more Git nodes (Git over SSH/Git over HTTP)
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- 2 or more API nodes (All requests to `/api`)
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- 2 or more Web nodes (All other web requests)
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- 2 or more NFS/Gitaly servers
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- 1 Monitoring node (Prometheus, Grafana)
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The following pages outline the steps necessary to configure each component
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separately:
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1. [Configure the database](database.md)
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1. [Configure Redis](redis.md)
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1. [Configure Redis for GitLab source installations](redis_source.md)
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1. [Configure NFS](nfs.md)
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1. [NFS Client and Host setup](nfs_host_client_setup.md)
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1. [Configure the GitLab application servers](gitlab.md)
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1. [Configure the load balancers](load_balancer.md)
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1. [Monitoring node (Prometheus and Grafana)](monitoring_node.md)
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