debian-mirror-gitlab/doc/administration/operations/puma.md

---
stage: Enablement
group: Distribution
info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/engineering/ux/technical-writing/#assignments
---

# Puma **(FREE SELF)**

Puma is a simple, fast, multi-threaded, and highly concurrent HTTP 1.1 server for
Ruby applications. It's the default GitLab web server since GitLab 13.0
and has replaced Unicorn. From GitLab 14.0, Unicorn is no longer supported.

NOTE:
Starting with GitLab 13.0, Puma is the default web server and Unicorn has been disabled.
In GitLab 14.0, Unicorn was removed from the Linux package and only Puma is available.

## Configure Puma

To configure Puma:

1. Determine suitable Puma worker and thread [settings](../../install/requirements.md#puma-settings).
1. If you're switching from Unicorn, [convert any custom settings to Puma](#convert-unicorn-settings-to-puma).
1. For multi-node deployments, configure the load balancer to use the
   [readiness check](../load_balancer.md#readiness-check).
1. Reconfigure GitLab so the above changes take effect:

   ```shell
   sudo gitlab-ctl reconfigure
   ```

For Helm-based deployments, see the
[`webservice` chart documentation](https://docs.gitlab.com/charts/charts/gitlab/webservice/index.html).

For more details about the Puma configuration, see the
[Puma documentation](https://github.com/puma/puma#configuration).

## Puma Worker Killer

Puma forks worker processes as part of a strategy to reduce memory use.

Each time a worker is created, it shares memory with the primary process and
only uses additional memory when it makes changes or additions to its memory pages.

Memory use by workers therefore increases over time, and Puma Worker Killer is the
mechanism that recovers this memory.

By default:

- The [Puma Worker Killer](https://github.com/schneems/puma_worker_killer) restarts a worker if it
  exceeds a [memory limit](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/cluster/puma_worker_killer_initializer.rb).
- Rolling restarts of Puma workers are performed every 12 hours.

To change the memory limit setting:

1. Edit `/etc/gitlab/gitlab.rb`:

   ```ruby
   puma['per_worker_max_memory_mb'] = 1024
   ```

1. Reconfigure GitLab for the changes to take effect:

   ```shell
   sudo gitlab-ctl reconfigure
   ```

There are costs associated with killing and replacing workers including
reduced capacity to run GitLab, and CPU that is consumed
restarting the workers. `per_worker_max_memory_mb` should be set to a
higher value if the worker killer is replacing workers too often.

Worker count is calculated based on CPU cores, so a small GitLab deployment
with 4-8 workers may experience performance issues if workers are being restarted
frequently, once or more per minute. This is too often.

A higher value of `1200` or more would be beneficial if the server has free memory.

The worker killer checks every 20 seconds, and can be monitored using
[the Puma log](../logs.md#puma_stdoutlog) `/var/log/gitlab/puma/puma_stdout.log`.
For example, for GitLab 13.5:

```plaintext
PumaWorkerKiller: Out of memory. 4 workers consuming total: 4871.23828125 MB
out of max: 4798.08 MB. Sending TERM to pid 26668 consuming 1001.00390625 MB.
```

From this output:

- The formula that calculates the maximum memory value results in workers
  being killed before they reach the `per_worker_max_memory_mb` value.
- The default values for the formula before GitLab 13.5 were 550MB for the primary
  and `per_worker_max_memory_mb` specified 850MB for each worker.
- As of GitLab 13.5 the values are primary: 800MB, worker: 1024MB.
- The threshold for workers to be killed is set at 98% of the limit:

  ```plaintext
  0.98 * ( 800 + ( worker_processes * 1024MB ) )
  ```

- In the log output above, `0.98 * ( 800 + ( 4 * 1024 ) )` returns the
  `max: 4798.08 MB` value.

Increasing the maximum to `1200`, for example, would set a `max: 5488 MB` value.

Workers use additional memory on top of the shared memory, how much
depends on a site's use of GitLab.

## Worker timeout

A [timeout of 60 seconds](https://gitlab.com/gitlab-org/gitlab/-/blob/master/config/initializers/rack_timeout.rb)
is used when Puma is enabled.

NOTE:
Unlike Unicorn, the `puma['worker_timeout']` setting does not set the maximum request duration.

To change the worker timeout:

1. Edit `/etc/gitlab/gitlab.rb`:

   ```ruby
   gitlab_rails['env'] = {
      'GITLAB_RAILS_RACK_TIMEOUT' => 600
    }
   ```

1. Reconfigure GitLab for the changes to take effect:

   ```shell
   sudo gitlab-ctl reconfigure
   ```

## Memory-constrained environments

In a memory-constrained environment with less than 4GB of RAM available, consider disabling Puma
[Clustered mode](https://github.com/puma/puma#clustered-mode).

Configuring Puma by setting the amount of `workers` to `0` could reduce memory usage by hundreds of MB.
For details on Puma worker and thread settings, see the [Puma requirements](../../install/requirements.md#puma-settings).

Unlike in a Clustered mode, which is set up by default, only a single Puma process would serve the application.

The downside of running Puma with such configuration is the reduced throughput, which could be
considered as a fair tradeoff in a memory-constraint environment.

When running Puma in Single mode, some features are not supported:

- [Phased restart](https://gitlab.com/gitlab-org/gitlab/-/issues/300665)
- [Puma Worker Killer](https://gitlab.com/gitlab-org/gitlab/-/issues/300664)

To learn more, visit [epic 5303](https://gitlab.com/groups/gitlab-org/-/epics/5303).

## Performance caveat when using Puma with Rugged

For deployments where NFS is used to store Git repository, we allow GitLab to use
[direct Git access](../gitaly/index.md#direct-access-to-git-in-gitlab) to improve performance using
[Rugged](https://github.com/libgit2/rugged).

Rugged usage is automatically enabled if direct Git access
[is available](../gitaly/index.md#how-it-works)
and Puma is running single threaded, unless it is disabled by
[feature flags](../../development/gitaly.md#legacy-rugged-code).

MRI Ruby uses a GVL. This allows MRI Ruby to be multi-threaded, but running at
most on a single core. Since Rugged can use a thread for long periods of
time (due to intensive I/O operations of Git access), this can starve other threads
that might be processing requests. This is not a case for Unicorn or Puma running
in a single thread mode, as concurrently at most one request is being processed.

We are actively working on removing Rugged usage. Even though performance without Rugged
is acceptable today, in some cases it might be still beneficial to run with it.

Given the caveat of running Rugged with multi-threaded Puma, and acceptable
performance of Gitaly, we disable Rugged usage if Puma multi-threaded is
used (when Puma is configured to run with more than one thread).

This default behavior may not be the optimal configuration in some situations. If Rugged
plays an important role in your deployment, we suggest you benchmark to find the
optimal configuration:

- The safest option is to start with single-threaded Puma. When working with
  Rugged, single-threaded Puma works the same as Unicorn.
- To force Rugged to be used with multi-threaded Puma, you can use
  [feature flags](../../development/gitaly.md#legacy-rugged-code).

## Convert Unicorn settings to Puma

NOTE:
Starting with GitLab 13.0, Puma is the default web server and Unicorn has been
disabled by default. In GitLab 14.0, Unicorn was removed from the Linux package
and only Puma is available.

Puma has a multi-thread architecture which uses less memory than a multi-process
application server like Unicorn. On GitLab.com, we saw a 40% reduction in memory
consumption. Most Rails applications requests normally include a proportion of I/O wait time.

During I/O wait time MRI Ruby releases the GVL (Global VM Lock) to other threads.
Multi-threaded Puma can therefore still serve more requests than a single process.

When switching to Puma, any Unicorn server configuration will _not_ carry over
automatically, due to differences between the two application servers.

The table below summarizes which Unicorn configuration keys correspond to those
in Puma when using the Linux package, and which ones have no corresponding counterpart.

| Unicorn                              | Puma                               |
| ------------------------------------ | ---------------------------------- |
| `unicorn['enable']`                  | `puma['enable']`                   |
| `unicorn['worker_timeout']`          | `puma['worker_timeout']`           |
| `unicorn['worker_processes']`        | `puma['worker_processes']`         |
| n/a                                  | `puma['ha']`                       |
| n/a                                  | `puma['min_threads']`              |
| n/a                                  | `puma['max_threads']`              |
| `unicorn['listen']`                  | `puma['listen']`                   |
| `unicorn['port']`                    | `puma['port']`                     |
| `unicorn['socket']`                  | `puma['socket']`                   |
| `unicorn['pidfile']`                 | `puma['pidfile']`                  |
| `unicorn['tcp_nopush']`              | n/a                                |
| `unicorn['backlog_socket']`          | n/a                                |
| `unicorn['somaxconn']`               | `puma['somaxconn']`                |
| n/a                                  | `puma['state_path']`               |
| `unicorn['log_directory']`           | `puma['log_directory']`            |
| `unicorn['worker_memory_limit_min']` | n/a                                |
| `unicorn['worker_memory_limit_max']` | `puma['per_worker_max_memory_mb']` |
| `unicorn['exporter_enabled']`        | `puma['exporter_enabled']`         |
| `unicorn['exporter_address']`        | `puma['exporter_address']`         |
| `unicorn['exporter_port']`           | `puma['exporter_port']`            |