gitlab-org--gitlab-foss/doc/administration/operations/puma.md

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Configure the bundled Puma instance of the GitLab package (FREE SELF)

Puma is a fast, multi-threaded, and highly concurrent HTTP 1.1 server for Ruby applications. It runs the core Rails application that provides the user-facing features of GitLab.

Reducing memory use

To reduce memory use, Puma forks worker processes. Each time a worker is created, it shares memory with the primary process. The worker uses additional memory only when it changes or adds to its memory pages.

Memory use increases over time, but you can use Puma Worker Killer to recover memory.

By default:

• The Puma Worker Killer restarts a worker if it exceeds a memory limit.
• Rolling restarts of Puma workers are performed every 12 hours.

Change the memory limit setting

To change the memory limit setting:

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

   puma['per_worker_max_memory_mb'] = 1024
   

2. Reconfigure GitLab:

   sudo gitlab-ctl reconfigure
   

When workers are killed and replaced, capacity to run GitLab is reduced, and CPU is consumed. Set per_worker_max_memory_mb to a higher value if the worker killer is replacing workers too often.

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

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

Monitor worker memory

The worker killer checks memory every 20 seconds.

To monitor the worker killer, use the Puma log /var/log/gitlab/puma/puma_stdout.log. For example:

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.

• In GitLab 13.4 and earlier, the default values for the formula were 550MB for the primary and 850MB for each worker.

• In GitLab 13.5 and later, the values are primary: 800MB, worker: 1024MB.

• The threshold for workers to be killed is set at 98% of the limit:

  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. The amount of memory depends on a site's use of GitLab.

Change the worker timeout

The default Puma timeout is 60 seconds.

NOTE: The puma['worker_timeout'] setting does not set the maximum request duration.

To change the worker timeout to 600 seconds:

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

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

2. Reconfigure GitLab:

   sudo gitlab-ctl reconfigure
   

Disable Puma clustered mode in memory-constrained environments

WARNING: This is an experimental Alpha feature and subject to change without notice. The feature is not ready for production use. If you want to use this feature, we recommend testing with non-production data first. See the known issues for additional details.

In a memory-constrained environment with less than 4GB of RAM available, consider disabling Puma clustered mode.

Set the number of workers to 0 to reduce memory usage by hundreds of MB:

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

   puma['worker_processes'] = 0
   

2. Reconfigure GitLab:

   sudo gitlab-ctl reconfigure
   

Unlike in a clustered mode, which is set up by default, only a single Puma process would serve the application. For details on Puma worker and thread settings, see the Puma requirements.

The downside of running Puma in this configuration is the reduced throughput, which can be considered a fair tradeoff in a memory-constrained environment.

Puma single mode known issues

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

• Phased restart
• Puma Worker Killer

To learn more, visit epic 5303.

Performance caveat when using Puma with Rugged

For deployments where NFS is used to store Git repositories, GitLab uses direct Git access to improve performance by using Rugged.

Rugged usage is automatically enabled if direct Git access is available and Puma is running single threaded, unless it is disabled by a feature flag.

MRI Ruby uses a Global VM Lock (GVL). GVL allows MRI Ruby to be multi-threaded, but running at most on a single core.

Git includes intensive I/O operations. When Rugged uses a thread for a long period of time, other threads that might be processing requests can starve. Puma running in single thread mode does not have this issue, because concurrently at most one request is being processed.

GitLab is working to remove 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.
• To force Rugged to be used with multi-threaded Puma, you can use a feature flag.

Switch from Unicorn to Puma

NOTE: For Helm-based deployments, see the webservice chart documentation.

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 is no longer supported.

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

During I/O wait time, MRI Ruby releases the GVL 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.

To switch from Unicorn to Puma:

1. Determine suitable Puma worker and thread settings.

2. Convert any custom Unicorn settings to Puma.

   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']

3. Reconfigure GitLab:

   sudo gitlab-ctl reconfigure
   

4. Optional. For multi-node deployments, configure the load balancer to use the readiness check.

Related topics

• Use the Puma exporter to measure various Puma metrics