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| alpha_database.md | ||
| consul.md | ||
| database.md | ||
| gitaly.md | ||
| gitlab.md | ||
| load_balancer.md | ||
| monitoring_node.md | ||
| nfs.md | ||
| nfs_host_client_setup.md | ||
| object_storage.md | ||
| pgbouncer.md | ||
| README.md | ||
| redis.md | ||
| redis_source.md | ||
| sidekiq.md | ||
| type |
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| reference, concepts |
Scaling and High Availability
GitLab supports a number of options for larger self-managed instances to ensure that they are scalable and highly available. While these needs can be tackled individually, they typically go hand in hand: a performant scalable environment will have availability by default, as its components are separated and pooled.
On this page, we present a maturity model for a progression from simple to complex GitLab installations as your GitLab usage evolves. For larger setups we give several recommended architectures based on experience with GitLab.com and internal scale testing that aim to achieve the right balance between both scalability and availability.
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.
Maturity levels
Level 1: Single-node Omnibus installation
This solution is appropriate for many teams that have a single server at their disposal. With automatic backup of the GitLab repositories, configuration, and the database, this can be an optimal solution if you don't have strict availability requirements.
This configuration is supported in GitLab Starter, Premium and Ultimate.
References:
Level 2: Multiple application servers
By separating components you can see a number of advantages compared to a single-node setup. Namely, you can:
- Increase the number of users
- Enable zero-downtime upgrades
- Increase availability
Additional application nodes will handle frontend traffic, with a load balancer in front to distribute traffic across those nodes. Meanwhile, each application node connects to a shared file server and database systems on the back end. This way, if one of the application servers fails, the workflow is not interrupted.
This configuration is supported in GitLab Starter, Premium and Ultimate.
References:
- High Availability Reference Architectures, without HA components
Level 3: Highly Available
By adding automatic failover for database systems, we can enable higher uptime with an additional layer of complexity.
This configuration is supported in GitLab Premium and Ultimate.
References:
Level 4: GitLab Geo
GitLab Geo allows you to replicate your GitLab instance to other geographical locations as a read-only fully operational instance that can also be promoted in case of disaster.
This configuration is supported in GitLab Premium and Ultimate.
References:
Recommended setups based on number of users
- 1 - 1000 Users: A single-node Omnibus setup with frequent backups. Refer to the requirements page for further details of the specs you will require.
- 1000 - 10000 Users: A scaled environment based on one of our Reference Architectures, without the HA components applied. This can be a reasonable step towards a fully HA environment.
- 2000 - 50000+ Users: A scaled HA environment based on one of our Reference Architectures below.
GitLab components and configuration instructions
The GitLab application depends on the following components. It can also depend on several third party services depending on your environment setup. Here we'll detail both in the order in which you would typically configure them along with our recommendations for their use and configuration.
Third party services
Here's some details of several third party services a typical environment will depend on. The services can be provided by numerous applications or providers and further advice can be given on how best to select. These should be configured first, before the GitLab components.
| Component | Description | Configuration instructions |
|---|---|---|
| Load Balancer(s)1 | Handles load balancing for the GitLab nodes where required | Load balancer HA configuration |
| Cloud Object Storage service2 | Recommended store for shared data objects | Cloud Object Storage configuration |
| NFS3 4 | Shared disk storage service. Can be used as an alternative for Gitaly or Object Storage. Required for GitLab Pages | NFS configuration |
GitLab components
Next are all of the components provided directly by GitLab. As mentioned earlier, they are presented in the typical order you would configure them.
| Component | Description | Configuration instructions |
|---|---|---|
| Consul5 | Service discovery and health checks/failover | Consul HA configuration (PREMIUM ONLY) |
| PostgreSQL | Database | Database HA configuration |
| PgBouncer | Database Pool Manager | PgBouncer HA configuration (PREMIUM ONLY) |
| Redis5 with Redis Sentinel | Key/Value store for shared data with HA watcher service | Redis HA configuration |
| Gitaly6 3 4 | Recommended high-level storage for Git repository data | Gitaly HA configuration |
| Sidekiq | Asynchronous/Background jobs | Sidekiq configuration |
| GitLab application nodes7 | (Unicorn / Puma, Workhorse) - Web-requests (UI, API, Git over HTTP) | GitLab app HA/scaling configuration |
| Prometheus and Grafana | GitLab environment monitoring | Monitoring node for scaling/HA |
In some cases, components can be combined on the same nodes to reduce complexity as well.
Reference architectures
In this section we'll detail the Reference Architectures that can support large numbers of users. These were built, tested and verified by our Quality and Support teams.
Testing was done with our GitLab Performance Tool at 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. 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: List of known performance issues
| Service | Nodes | Configuration8 | GCP type |
|---|---|---|---|
| GitLab Rails7 | 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 |
| Gitaly6 3 4 | X | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis5 | 3 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Consul + Sentinel5 | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Sidekiq | 4 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Cloud Object Storage2 | - | - | - |
| NFS Server3 4 | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| Monitoring node | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| External load balancing node1 | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node1 | 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: List of known performance issues
| Service | Nodes | Configuration8 | GCP type |
|---|---|---|---|
| GitLab Rails7 | 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 |
| Gitaly6 3 4 | X | 8 vCPU, 30GB Memory | n1-standard-8 |
| Redis5 | 3 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Consul + Sentinel5 | 3 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Sidekiq | 4 | 2 vCPU, 7.5GB Memory | n1-standard-2 |
| Cloud Object Storage2 | - | - | - |
| NFS Server3 4 | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| Monitoring node | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| External load balancing node1 | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node1 | 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: List of known performance issues
| Service | Nodes | Configuration8 | GCP type |
|---|---|---|---|
| GitLab Rails7 | 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 |
| Gitaly6 3 4 | X | 16 vCPU, 60GB Memory | n1-standard-16 |
| Redis5 - Cache | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis5 - Queues / Shared State | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis Sentinel5 - Cache | 3 | 1 vCPU, 1.7GB Memory | g1-small |
| Redis Sentinel5 - 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 |
| Cloud Object Storage2 | - | - | - |
| NFS Server3 4 | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| Monitoring node | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| External load balancing node1 | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node1 | 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: List of known performance issues
| Service | Nodes | Configuration8 | GCP type |
|---|---|---|---|
| GitLab Rails7 | 5 | 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 |
| Gitaly6 3 4 | X | 32 vCPU, 120GB Memory | n1-standard-32 |
| Redis5 - Cache | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis5 - Queues / Shared State | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis Sentinel5 - Cache | 3 | 1 vCPU, 1.7GB Memory | g1-small |
| Redis Sentinel5 - 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 |
| Cloud Object Storage2 | - | - | - |
| NFS Server3 4 | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| Monitoring node | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| External load balancing node1 | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node1 | 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: List of known performance issues
| Service | Nodes | Configuration8 | GCP type |
|---|---|---|---|
| GitLab Rails7 | 12 | 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 |
| Gitaly6 3 4 | X | 64 vCPU, 240GB Memory | n1-standard-64 |
| Redis5 - Cache | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis5 - Queues / Shared State | 3 | 4 vCPU, 15GB Memory | n1-standard-4 |
| Redis Sentinel5 - Cache | 3 | 1 vCPU, 1.7GB Memory | g1-small |
| Redis Sentinel5 - 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 Server3 4 | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| Cloud Object Storage2 | - | - | - |
| Monitoring node | 1 | 4 vCPU, 3.6GB Memory | n1-highcpu-4 |
| External load balancing node1 | 1 | 2 vCPU, 1.8GB Memory | n1-highcpu-2 |
| Internal load balancing node1 | 1 | 8 vCPU, 7.2GB Memory | n1-highcpu-8 |
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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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For data objects such as LFS, Uploads, Artifacts, etc. We recommend a Cloud Object Storage service over NFS where possible, due to better performance and availability. ↩︎
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NFS can be used as an alternative for both repository data (replacing Gitaly) and object storage but this isn't typically recommended for performance reasons. Note however it is required for GitLab Pages. ↩︎
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We strongly recommend that any 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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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 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. ↩︎
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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 also recommend that each Gitaly node should store no more than 5TB of data and have the number of
gitaly-rubyworkers 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. ↩︎ -
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. ↩︎
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The architectures were built and tested with the Intel Xeon E5 v3 (Haswell) CPU platform on GCP. On different hardware you may find that adjustments, either lower or higher, are required for your CPU or Node counts accordingly. For more info a Sysbench benchmark of the CPU can be found here. ↩︎