- Embedded Prometheus metrics
- Available metrics
- Configuration of the metrics HTTP server
GitLab Runner can be monitored using Prometheus.
The embedded HTTP Statistics Server with Prometheus metrics was introduced in GitLab Runner 1.8.0.
GitLab Runner is instrumented with native Prometheus
metrics, which can be exposed via an embedded HTTP server on the
path. The server - if enabled - can be scraped by the Prometheus monitoring
system or accessed with any other HTTP client.
The exposed information includes:
- Runner business logic metrics (e.g., the number of currently running jobs)
- Go-specific process metrics (garbage collection stats, goroutines, memstats, etc.)
- general process metrics (memory usage, CPU usage, file descriptor usage, etc.)
- build version information
The metrics format is documented in Prometheus’ Exposition formats specification.
These metrics are meant as a way for operators to monitor and gain insight into your runners. For example, you may be interested if the load average increase on your runner’s host is related to an increase in processed jobs or not. Or you are running a cluster of machines to be used for the jobs and you want to track build trends to plan changes in your infrastructure.
To learn how to set up a Prometheus server to scrape this HTTP endpoint and make use of the collected metrics, see Prometheus’s Getting started guide. Also see the Configuration section for more details on how to configure Prometheus, as well as the section on Alerting rules and setting up an Alertmanager to dispatch alert notifications.
To find a full list of all available metrics,
curl the metrics endpoint after it is configured and enabled. For example, for a local runner configured with listening port
$ curl -s "http://localhost:9252/metrics" | grep -E "# HELP" # HELP gitlab_runner_api_request_statuses_total The total number of api requests, partitioned by runner, endpoint and status. # HELP gitlab_runner_autoscaling_machine_creation_duration_seconds Histogram of machine creation time. # HELP gitlab_runner_autoscaling_machine_states The current number of machines per state in this provider. # HELP gitlab_runner_concurrent The current value of concurrent setting # HELP gitlab_runner_errors_total The number of caught errors. # HELP gitlab_runner_limit The current value of limit setting # HELP gitlab_runner_request_concurrency The current number of concurrent requests for a new job # HELP gitlab_runner_request_concurrency_exceeded_total Counter tracking exceeding of request concurrency # HELP gitlab_runner_version_info A metric with a constant '1' value labeled by different build stats fields. ...
pprof integration was introduced in GitLab Runner 1.9.0.
While having metrics about the internal state of the GitLab Runner process is useful,
we’ve found that in some cases it would be good to check what is happening
inside of the Running process in real time. That’s why we’ve introduced
pprof HTTP endpoints.
pprof endpoints will be available via an embedded HTTP server on
You can read more about using
pprof in its documentation.
The metrics HTTP server can be configured in two ways:
- with a
listen_addressglobal configuration option in
- with a
--listen-addresscommand line option for the
If you add the address to your
config.toml file, to start the metrics HTTP server,
you must restart the runner process.
In both cases the option accepts a string with the format
hostcan be an IP address or a hostname,
portis a valid TCP port or symbolic service name (like
http). We recommend using port
9252which is already allocated in Prometheus.
If the listen address does not contain a port, it will default to
Examples of addresses:
:9252- will listen on all IPs of all interfaces on port
localhost:9252- will only listen on the loopback interface on port
[2001:db8::1]:http- will listen on IPv6 address
[2001:db8::1]on the HTTP port
Remember that for listening on ports below
1024 - at least on Linux/Unix
systems - you need to have root/administrator rights.
The HTTP server is opened on the selected
without any authorization. If you plan to bind the metrics server
to a public interface then you should consider to use your firewall to
limit access to this server or add an HTTP proxy which will add the
authorization and access control layer.