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 with John 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 service1 and / or NFS storage servers2 for entities such as Uploads, Artifacts, LFS Objects, etc...
- Load Balancer3 - 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 services1 and / or NFS storage server2
- 2 or more GitLab application nodes (Unicorn / Puma, Workhorse, Sidekiq)
- 1 or more Load Balancer nodes3
- 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.
- Load Balancer(s)3
- Consul
- PostgreSQL with PgBouncer
- Redis
- Gitaly (recommended) and / or NFS2
- GitLab application nodes
- Monitoring node (Prometheus and Grafana)
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 services1 and / or NFS storage server2
- 2 or more Sidekiq nodes
- 2 or more GitLab application nodes (Unicorn / Puma, Workhorse, Sidekiq)
- 1 or more Load Balancer nodes3
- 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 on dedicated nodes. 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 Redis and Consul 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 service1 is used where possible over NFS but it's still required in certain cases2. 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
- 2 Redis nodes
- 3 Consul/Sentinel nodes
- 2 or more GitLab application nodes (Unicorn / Puma, Workhorse, Sidekiq)
- 1 Gitaly storage servers
- 1 Object Storage service1 and / or NFS storage server2
- 1 or more Load Balancer nodes3
- 1 Monitoring node (Prometheus, Grafana)
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
- 2 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 service1 and / or NFS storage server2
- 1 or more Load Balancer nodes3
- 1 Monitoring node (Prometheus, Grafana)
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 services1 and / or NFS storage servers2
- 1 or more Load Balancer nodes3
- 1 Monitoring node (Prometheus, Grafana)
Reference Architecture Examples
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 that 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.
2,000 User Configuration
- Supported Users (approximate): 2,000
- Test RPS Rates: API: 40 RPS, Web: 4 RPS, Git: 4 RPS
- Status: Work-in-progress
- Known Issues: For the latest list of known performance issues head here.
NOTE: Note: This architecture is a work-in-progress of the work so far. The Quality team will be certifying this environment in late 2019 or early 2020. The specifications may be adjusted prior to certification based on performance testing.
| Service | Nodes | Configuration | GCP type |
|---|---|---|---|
| GitLab Rails - Puma workers on each node set to 90% of available CPUs with 8 threads |
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 - Gitaly Ruby workers on each node set to 20% of available CPUs |
X4 . | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis Cache + Sentinel - Cache maxmemory set to 90% of available memory |
3 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Redis Persistent + Sentinel | 3 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Sidekiq | 4 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Consul | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| NFS Server2 . | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| S3 Object Storage1 . | - | - | - |
| Monitoring node | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| External load balancing node3 . | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node3 . | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
NOTE: Note: Memory values are given directly by GCP machine sizes. On different cloud vendors a best effort like for like can be used.
5,000 User Configuration
- Supported Users (approximate): 5,000
- Test RPS Rates: API: 100 RPS, Web: 10 RPS, Git: 10 RPS
- Status: Work-in-progress
- Known Issues: For the latest list of known performance issues head here.
NOTE: Note: This architecture is a work-in-progress of the work so far. The Quality team will be certifying this environment in late 2019 or early 2020. The specifications may be adjusted prior to certification based on performance testing.
| Service | Nodes | Configuration | GCP type |
|---|---|---|---|
| GitLab Rails - Puma workers on each node set to 90% of available CPUs with 16 threads |
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 - Gitaly Ruby workers on each node set to 20% of available CPUs |
X4 . | 8 vCPU, 30GB Memory | n1-standard-8 |
| Redis Cache + Sentinel - Cache maxmemory set to 90% of available memory |
3 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Redis Persistent + Sentinel | 3 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Sidekiq | 4 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Consul | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| NFS Server2 . | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| S3 Object Storage1 . | - | - | - |
| Monitoring node | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| External load balancing node3 . | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node3 . | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
NOTE: Note: Memory values are given directly by GCP machine sizes. On different cloud vendors a best effort like for like can be used.
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.
| Service | Nodes | Configuration | GCP type |
|---|---|---|---|
| GitLab Rails - Puma workers on each node set to 90% of available CPUs with 16 threads |
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 - Gitaly Ruby workers on each node set to 20% of available CPUs |
X4 . | 16 vCPU, 60GB Memory | n1-standard-16 |
| Redis Cache + Sentinel - Cache maxmemory set to 90% of available memory |
3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis Persistent + Sentinel | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Sidekiq | 4 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Consul | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| NFS Server2 . | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| S3 Object Storage1 . | - | - | - |
| Monitoring node | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| External load balancing node3 . | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node3 . | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
NOTE: Note: Memory values are given directly by GCP machine sizes. On different cloud vendors a best effort like for like can be used.
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.
| Service | Nodes | Configuration | GCP type |
|---|---|---|---|
| GitLab Rails - Puma workers on each node set to 90% of available CPUs with 16 threads |
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 - Gitaly Ruby workers on each node set to 20% of available CPUs |
X4 . | 32 vCPU, 120GB Memory | n1-standard-32 |
| Redis Cache + Sentinel - Cache maxmemory set to 90% of available memory |
3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis Persistent + Sentinel | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Sidekiq | 4 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Consul | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| NFS Server2 . | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| S3 Object Storage1 . | - | - | - |
| Monitoring node | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| External load balancing node3 . | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node3 . | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
NOTE: Note: Memory values are given directly by GCP machine sizes. On different cloud vendors a best effort like for like can be used.
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.
| Service | Nodes | Configuration | GCP type |
|---|---|---|---|
| GitLab Rails - Puma workers on each node set to 90% of available CPUs with 16 threads |
15 | 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 - Gitaly Ruby workers on each node set to 20% of available CPUs |
X4 . | 64 vCPU, 240GB Memory | n1-standard-64 |
| Redis Cache + Sentinel - Cache maxmemory set to 90% of available memory |
3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis Persistent + Sentinel | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Sidekiq | 4 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Consul | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| NFS Server2 . | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| S3 Object Storage1 . | - | - | - |
| Monitoring node | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| External load balancing node3 . | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node3 . | 1 | 8 vCPU, 7.2GB Memory | n1-highcpu-8 |
NOTE: Note: Memory values are given directly by GCP machine sizes. On different cloud vendors a best effort like for like can be used.
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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, LFS, Uploads, Merge Request Diffs, Packages (Optional Feature), Dependency Proxy (Optional Feature).
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NFS storage server is still required for GitLab Pages and optionally for CI Job Incremental Logging (can be switched to use Redis instead).
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Our architectures have been tested and validated with HAProxy as the load balancer. However other reputable load balancers with similar feature sets should also work instead but be aware these aren't validated.
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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 recommend that each Gitaly node should store no more than 5TB of data. Additional nodes should be considered in conjunction with a review of expected data size and spread based on the recommendations above.
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