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debian-mirror-gitlab/doc/administration/high_availability/README.md
Sruthi Chandran df6ec68d08 New upstream version 12.8.5
2020-03-09 13:42:32 +05:30

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---
type: reference, concepts
---
# Scaling and High Availability
GitLab supports a number of options for scaling your self-managed instance and configuring high availability (HA).
The solution you choose will be based on the level of scalability and
availability you require. The easiest solutions are scalable, but not necessarily
highly available.
GitLab provides a service that is essential to most organizations: it
enables people to collaborate on code in a timely fashion. Any downtime should
therefore be short and planned. Due to the distributed nature
of Git, developers can continue to commit code locally even when GitLab is not
available. However, some GitLab features such as the issue tracker and
continuous integration are not available when GitLab is down.
If you require all GitLab functionality to be highly available,
consider the options outlined below.
**Keep in mind that all highly-available solutions come with a trade-off between
cost/complexity and uptime**. The more uptime you want, the more complex the
solution. And the more complex the solution, the more work is involved in
setting up and maintaining it. High availability is not free and every HA
solution should balance the costs against the benefits.
There are many options when choosing a highly-available GitLab architecture. We
recommend engaging with GitLab Support to choose the best architecture for your
use case. This page contains recommendations based on
experience with GitLab.com and internal scale testing.
For detailed insight into how GitLab scales and configures GitLab.com, you can
watch [this 1 hour Q&A](https://www.youtube.com/watch?v=uCU8jdYzpac)
with [John Northrup](https://gitlab.com/northrup), and live questions coming in from some of our customers.
## GitLab Components
The following components need to be considered for a scaled or highly-available
environment. In many cases, components can be combined on the same nodes to reduce
complexity.
- GitLab application nodes (Unicorn / Puma, Workhorse) - Web-requests (UI, API, Git over HTTP)
- Sidekiq - Asynchronous/Background jobs
- PostgreSQL - Database
- Consul - Database service discovery and health checks/failover
- PgBouncer - Database pool manager
- Redis - Key/Value store (User sessions, cache, queue for Sidekiq)
- Sentinel - Redis health check/failover manager
- Gitaly - Provides high-level storage and RPC access to Git repositories
- S3 Object Storage service[^4] and / or NFS storage servers[^5] for entities such as Uploads, Artifacts, LFS Objects, etc...
- Load Balancer[^6] - Main entry point and handles load balancing for the GitLab application nodes.
- Monitor - Prometheus and Grafana monitoring with auto discovery.
## Scalable Architecture Examples
When an organization reaches a certain threshold it will be necessary to scale
the GitLab instance. Still, true high availability may not be necessary. There
are options for scaling GitLab instances relatively easily without incurring the
infrastructure and maintenance costs of full high availability.
### Basic Scaling
This is the simplest form of scaling and will work for the majority of
cases. Backend components such as PostgreSQL, Redis, and storage are offloaded
to their own nodes while the remaining GitLab components all run on 2 or more
application nodes.
This form of scaling also works well in a cloud environment when it is more
cost effective to deploy several small nodes rather than a single
larger one.
- 1 PostgreSQL node
- 1 Redis node
- 1 Gitaly node
- 1 or more Object Storage services[^4] and / or NFS storage server[^5]
- 2 or more GitLab application nodes (Unicorn / Puma, Workhorse, Sidekiq)
- 1 or more Load Balancer nodes[^6]
- 1 Monitoring node (Prometheus, Grafana)
#### Installation Instructions
Complete the following installation steps in order. A link at the end of each
section will bring you back to the Scalable Architecture Examples section so
you can continue with the next step.
1. [Load Balancer(s)](load_balancer.md)[^6]
1. [Consul](consul.md)
1. [PostgreSQL](database.md#postgresql-in-a-scaled-environment) with [PgBouncer](pgbouncer.md)
1. [Redis](redis.md#redis-in-a-scaled-environment)
1. [Gitaly](gitaly.md) (recommended) and / or [NFS](nfs.md)[^5]
1. [GitLab application nodes](gitlab.md)
- With [Object Storage service enabled](../gitaly/index.md#eliminating-nfs-altogether)[^4]
1. [Monitoring node (Prometheus and Grafana)](monitoring_node.md)
### Full Scaling
For very large installations, it might be necessary to further split components
for maximum scalability. In a fully-scaled architecture, the application node
is split into separate Sidekiq and Unicorn/Workhorse nodes. One indication that
this architecture is required is if Sidekiq queues begin to periodically increase
in size, indicating that there is contention or there are not enough resources.
- 1 or more PostgreSQL nodes
- 1 or more Redis nodes
- 1 or more Gitaly storage servers
- 1 or more Object Storage services[^4] and / or NFS storage server[^5]
- 2 or more Sidekiq nodes
- 2 or more GitLab application nodes (Unicorn / Puma, Workhorse, Sidekiq)
- 1 or more Load Balancer nodes[^6]
- 1 Monitoring node (Prometheus, Grafana)
## High Availability Architecture Examples
When organizations require scaling *and* high availability, the following
architectures can be utilized. As the introduction section at the top of this
page mentions, there is a tradeoff between cost/complexity and uptime. Be sure
this complexity is absolutely required before taking the step into full
high availability.
For all examples below, we recommend running Consul and Redis Sentinel separately
from the services they monitor. If Consul is running on PostgreSQL nodes or Sentinel on
Redis nodes, there is a potential that high resource usage by PostgreSQL or
Redis could prevent communication between the other Consul and Sentinel nodes.
This may lead to the other nodes believing a failure has occurred and initiating
automated failover. Isolating Consul and Redis Sentinel from the services they monitor
reduces the chances of a false positive that a failure has occurred.
The examples below do not address high availability of NFS for objects. We recommend a
S3 Object Storage service[^4] is used where possible over NFS but it's still required in
certain cases[^5]. Where NFS is to be used some enterprises have access to NFS appliances
that manage availability and this would be best case scenario.
There are many options in between each of these examples. Work with GitLab Support
to understand the best starting point for your workload and adapt from there.
### Horizontal
This is the simplest form of high availability and scaling. It requires the
fewest number of individual servers (virtual or physical) but does have some
trade-offs and limits.
This architecture will work well for many GitLab customers. Larger customers
may begin to notice certain events cause contention/high load - for example,
cloning many large repositories with binary files, high API usage, a large
number of enqueued Sidekiq jobs, and so on. If this happens, you should consider
moving to a hybrid or fully distributed architecture depending on what is causing
the contention.
- 3 PostgreSQL nodes
- 3 Redis nodes
- 3 Consul / Sentinel nodes
- 2 or more GitLab application nodes (Unicorn / Puma, Workhorse, Sidekiq)
- 1 Gitaly storage servers
- 1 Object Storage service[^4] and / or NFS storage server[^5]
- 1 or more Load Balancer nodes[^6]
- 1 Monitoring node (Prometheus, Grafana)
![Horizontal architecture diagram](img/horizontal.png)
### Hybrid
In this architecture, certain components are split on dedicated nodes so high
resource usage of one component does not interfere with others. In larger
environments this is a good architecture to consider if you foresee or do have
contention due to certain workloads.
- 3 PostgreSQL nodes
- 1 PgBouncer node
- 3 Redis nodes
- 3 Consul / Sentinel nodes
- 2 or more Sidekiq nodes
- 2 or more GitLab application nodes (Unicorn / Puma, Workhorse, Sidekiq)
- 1 Gitaly storage servers
- 1 Object Storage service[^4] and / or NFS storage server[^5]
- 1 or more Load Balancer nodes[^6]
- 1 Monitoring node (Prometheus, Grafana)
![Hybrid architecture diagram](img/hybrid.png)
### Fully Distributed
This architecture scales to hundreds of thousands of users and projects and is
the basis of the GitLab.com architecture. While this scales well it also comes
with the added complexity of many more nodes to configure, manage, and monitor.
- 3 PostgreSQL nodes
- 1 or more PgBouncer nodes (with associated internal load balancers)
- 4 or more Redis nodes (2 separate clusters for persistent and cache data)
- 3 Consul nodes
- 3 Sentinel nodes
- Multiple dedicated Sidekiq nodes (Split into real-time, best effort, ASAP,
CI Pipeline and Pull Mirror sets)
- 2 or more Git nodes (Git over SSH/Git over HTTP)
- 2 or more API nodes (All requests to `/api`)
- 2 or more Web nodes (All other web requests)
- 2 or more Gitaly storage servers
- 1 or more Object Storage services[^4] and / or NFS storage servers[^5]
- 1 or more Load Balancer nodes[^6]
- 1 Monitoring node (Prometheus, Grafana)
![Fully Distributed architecture diagram](img/fully-distributed.png)
## Reference Architecture Recommendations
The Support and Quality teams build, performance test, and validate Reference
Architectures that support large numbers of users. The specifications below are
a representation of this work so far and may be adjusted in the future based on
additional testing and iteration.
The architectures have been tested with specific coded workloads, and the
throughputs used for testing were calculated based on sample customer data. We
test each endpoint type with the following number of requests per second (RPS)
per 1000 users:
- API: 20 RPS
- Web: 2 RPS
- Git: 2 RPS
NOTE: **Note:** Note that depending on your workflow the below recommended
reference architectures may need to be adapted accordingly. 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. Additionally the
shown memory values are given directly by [GCP machine types](https://cloud.google.com/compute/docs/machine-types).
On different cloud vendors a best effort like for like can be used.
### 2,000 User Configuration
- **Supported Users (approximate):** 2,000
- **Test RPS Rates:** API: 40 RPS, Web: 4 RPS, Git: 4 RPS
- **Known Issues:** For the latest list of known performance issues head
[here](https://gitlab.com/gitlab-org/gitlab/issues?label_name%5B%5D=Quality%3Aperformance-issues).
| Service | Nodes | Configuration | GCP type |
| ----------------------------|-------|-----------------------|---------------|
| GitLab Rails[^1] | 3 | 8 vCPU, 7.2GB Memory | n1-highcpu-8 |
| PostgreSQL | 3 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| PgBouncer | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Gitaly[^2] [^7] | X | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis[^3] | 3 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Consul + Sentinel[^3] | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Sidekiq | 4 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| S3 Object Storage[^4] | - | - | - |
| NFS Server[^5] [^7] | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| Monitoring node | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| External load balancing node[^6] | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node[^6] | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
### 5,000 User Configuration
- **Supported Users (approximate):** 5,000
- **Test RPS Rates:** API: 100 RPS, Web: 10 RPS, Git: 10 RPS
- **Known Issues:** For the latest list of known performance issues head
[here](https://gitlab.com/gitlab-org/gitlab/issues?label_name%5B%5D=Quality%3Aperformance-issues).
| Service | Nodes | Configuration | GCP type |
| ----------------------------|-------|-----------------------|---------------|
| GitLab Rails[^1] | 3 | 16 vCPU, 14.4GB Memory | n1-highcpu-16 |
| PostgreSQL | 3 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| PgBouncer | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Gitaly[^2] [^7] | X | 8 vCPU, 30GB Memory | n1-standard-8 |
| Redis[^3] | 3 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Consul + Sentinel[^3] | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Sidekiq | 4 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| S3 Object Storage[^4] | - | - | - |
| NFS Server[^5] [^7] | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| Monitoring node | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| External load balancing node[^6] | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node[^6] | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
### 10,000 User Configuration
- **Supported Users (approximate):** 10,000
- **Test RPS Rates:** API: 200 RPS, Web: 20 RPS, Git: 20 RPS
- **Known Issues:** For the latest list of known performance issues head
[here](https://gitlab.com/gitlab-org/gitlab/issues?label_name%5B%5D=Quality%3Aperformance-issues).
| Service | Nodes | Configuration | GCP type |
| ----------------------------|-------|-----------------------|---------------|
| GitLab Rails[^1] | 3 | 32 vCPU, 28.8GB Memory | n1-highcpu-32 |
| PostgreSQL | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| PgBouncer | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Gitaly[^2] [^7] | X | 16 vCPU, 60GB Memory | n1-standard-16 |
| Redis[^3] - Cache | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis[^3] - Queues / Shared State | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis Sentinel[^3] - Cache | 3 | 1 vCPU, 1.7GB Memory | g1-small |
| Redis Sentinel[^3] - Queues / Shared State | 3 | 1 vCPU, 1.7GB Memory | g1-small |
| Consul | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Sidekiq | 4 | 4 vCPU, 15GB Memory | n1-standard-4 |
| S3 Object Storage[^4] | - | - | - |
| NFS Server[^5] [^7] | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| Monitoring node | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| External load balancing node[^6] | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node[^6] | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
### 25,000 User Configuration
- **Supported Users (approximate):** 25,000
- **Test RPS Rates:** API: 500 RPS, Web: 50 RPS, Git: 50 RPS
- **Known Issues:** For the latest list of known performance issues head
[here](https://gitlab.com/gitlab-org/gitlab/issues?label_name%5B%5D=Quality%3Aperformance-issues).
| Service | Nodes | Configuration | GCP type |
| ----------------------------|-------|-----------------------|---------------|
| GitLab Rails[^1] | 7 | 32 vCPU, 28.8GB Memory | n1-highcpu-32 |
| PostgreSQL | 3 | 8 vCPU, 30GB Memory | n1-standard-8 |
| PgBouncer | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Gitaly[^2] [^7] | X | 32 vCPU, 120GB Memory | n1-standard-32 |
| Redis[^3] - Cache | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis[^3] - Queues / Shared State | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis Sentinel[^3] - Cache | 3 | 1 vCPU, 1.7GB Memory | g1-small |
| Redis Sentinel[^3] - Queues / Shared State | 3 | 1 vCPU, 1.7GB Memory | g1-small |
| Consul | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Sidekiq | 4 | 4 vCPU, 15GB Memory | n1-standard-4 |
| S3 Object Storage[^4] | - | - | - |
| NFS Server[^5] [^7] | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| Monitoring node | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| External load balancing node[^6] | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node[^6] | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
### 50,000 User Configuration
- **Supported Users (approximate):** 50,000
- **Test RPS Rates:** API: 1000 RPS, Web: 100 RPS, Git: 100 RPS
- **Known Issues:** For the latest list of known performance issues head
[here](https://gitlab.com/gitlab-org/gitlab/issues?label_name%5B%5D=Quality%3Aperformance-issues).
| Service | Nodes | Configuration | GCP type |
| ----------------------------|-------|-----------------------|---------------|
| GitLab Rails[^1] | 15 | 32 vCPU, 28.8GB Memory | n1-highcpu-32 |
| PostgreSQL | 3 | 16 vCPU, 60GB Memory | n1-standard-16 |
| PgBouncer | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Gitaly[^2] [^7] | X | 64 vCPU, 240GB Memory | n1-standard-64 |
| Redis[^3] - Cache | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis[^3] - Queues / Shared State | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis Sentinel[^3] - Cache | 3 | 1 vCPU, 1.7GB Memory | g1-small |
| Redis Sentinel[^3] - Queues / Shared State | 3 | 1 vCPU, 1.7GB Memory | g1-small |
| Consul | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Sidekiq | 4 | 4 vCPU, 15GB Memory | n1-standard-4 |
| NFS Server[^5] [^7] | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| S3 Object Storage[^4] | - | - | - |
| Monitoring node | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| External load balancing node[^6] | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node[^6] | 1 | 8 vCPU, 7.2GB Memory | n1-highcpu-8 |
[^1]: In our architectures we run each GitLab Rails node using the Puma webserver
and have its number of workers set to 90% of available CPUs along with 4 threads.
[^2]: Gitaly node requirements are dependent on customer data, specifically the number of
projects and their sizes. We recommend 2 nodes as an absolute minimum for HA environments
and at least 4 nodes should be used when supporting 50,000 or more users.
We also recommend that each Gitaly node should store no more than 5TB of data
and have the number of [`gitaly-ruby` workers](../gitaly/index.md#gitaly-ruby)
set to 20% of available CPUs. Additional nodes should be considered in conjunction
with a review of expected data size and spread based on the recommendations above.
[^3]: Recommended Redis setup differs depending on the size of the architecture.
For smaller architectures (up to 5,000 users) we suggest one Redis cluster for all
classes and that Redis Sentinel is hosted alongside Consul.
For larger architectures (10,000 users or more) we suggest running a separate
[Redis Cluster](redis.md#running-multiple-redis-clusters) for the Cache class
and another for the Queues and Shared State classes respectively. We also recommend
that you run the Redis Sentinel clusters separately as well for each Redis Cluster.
[^4]: For data objects such as LFS, Uploads, Artifacts, etc... We recommend a S3 Object Storage
where possible over NFS due to better performance and availability. Several types of objects
are supported for S3 storage - [Job artifacts](../job_artifacts.md#using-object-storage),
[LFS](../lfs/lfs_administration.md#storing-lfs-objects-in-remote-object-storage),
[Uploads](../uploads.md#using-object-storage-core-only),
[Merge Request Diffs](../merge_request_diffs.md#using-object-storage),
[Packages](../packages/index.md#using-object-storage) (Optional Feature),
[Dependency Proxy](../packages/dependency_proxy.md#using-object-storage) (Optional Feature).
[^5]: NFS storage server is still required for [GitLab Pages](https://gitlab.com/gitlab-org/gitlab-pages/issues/196)
and optionally for CI Job Incremental Logging
([can be switched to use Redis instead](../job_logs.md#new-incremental-logging-architecture)).
[^6]: Our architectures have been tested and validated with [HAProxy](https://www.haproxy.org/)
as the load balancer. However other reputable load balancers with similar feature sets
should also work instead but be aware these aren't validated.
[^7]: We strongly recommend that the Gitaly and / or NFS nodes are set up with SSD disks over
HDD with a throughput of at least 8,000 IOPS for read operations and 2,000 IOPS for write
as these components have heavy I/O. These IOPS values are recommended only as a starter
as with time they may be adjusted higher or lower depending on the scale of your
environment's workload. If you're running the environment on a Cloud provider
you may need to refer to their documentation on how configure IOPS correctly.
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