Files
team/docs/main/administration-guide/configure/calls-metrics-monitoring.mdx
b5f007d35e docs: fix broken path links, anchors, and tab rendering from RST-to-MDX migration (#37433)
* docs: resolve cross-file anchor mismatches from RST-to-MDX migration

The Sphinx-to-Docusaurus migration converted `:ref:`/`{ref}` links using
naive text-slugification, so anchors that pointed to headings in a
different file (e.g. release-summary tables linking into their
respective changelog pages) were emitted as same-page fragments and
silently resolved to nothing.

Built a label registry from the original Sphinx `.. _label:` / `(label)=`
targets to recover the true source file for each cross-reference, then
rewrote the affected links to point at the correct file + heading
anchor. Fixes 282 broken cross-file anchors, primarily in the mobile/
desktop/server release-summary pages, plus assorted same-page anchor
mismatches caught by re-auditing after the prior admonition/anchor fix.

* docs: manually resolve remaining anchor mismatches after cross-file audit

Hand-verified fixes for anchors that fuzzy-matching alone couldn't
safely resolve (confirmed against actual heading text/context, and in a
few cases against the original RST source, before applying):

- Typos and dropped words carried over from the original Sphinx docs
  (e.g. "bot-acocunts", "auto-follow-all-new-channel-threads" ->
  "follow-all-new-channel-threads").
- Sphinx auto-generated duplicate-heading IDs (#id1/#id3/#id4) resolved
  to their real target headings.
- Numbered-step TOC links in enterprise-roll-out-checklist.mdx that
  dropped the leading "N. " used in the actual heading slugs.
- Push-notification docs pointing at the wrong file entirely
  (environment-configuration-settings instead of
  push-notification-server-configuration-settings).
- SAML "saml-enterprise" label resolved to its true cross-file target
  (authentication-configuration-settings#saml-2-0), recovered from the
  original RST label definition that the automated registry missed
  because it sat after an RST transition marker.
- sso-saml-technical references converted from broken same-page/
  malformed-path anchors to correct page links.

* docs: fix SAML 2.0 anchor slug (saml-2-0 -> saml-20)

github-slugger strips periods rather than hyphenating them, so "SAML
2.0" slugifies to saml-20, not saml-2-0 as previously fixed.

* docs: fix broken path links found during docs/main migration cleanup

* docs: convert div.tab blocks to Docusaurus Tabs/TabItem components

* docs: restore missing Kubernetes deployment architecture diagrams

The RST-to-MDX migration never copied over the AWS/Azure/Oracle
architecture diagrams referenced by the deployment-architecture-at-scale
page, leaving broken images in each Tabs/TabItem.

* docs: fix 7 anchor mismatches from the fragment/heading audit

Covers three root causes from the anchor mismatch breakdown:
- Fragment repeated the target page's own title instead of a real
  heading, so the link is simplified to point at the bare page.
- Heading exists verbatim but lives on a sibling settings page
  (experimental-configuration-settings, push-notification-server-
  configuration-settings) rather than environment-configuration-settings.
- A page path was mistakenly pasted into a same-page #fragment instead
  of being used as a real cross-page link.

* docs: fix 8 anchor slugs that were close but didn't match the real heading

Repoints links whose fragment was a plausible guess (typo, reworded
heading, or wrong section) at the actual auto-generated slug of the
target heading, confirmed by inspecting each target page directly.

* docs: merge split Tabs blocks so tabs render horizontally

The div.tab conversion script split some sibling tab groups into
separate single-item Tabs blocks whenever a stray blockquote marker
sat between them, causing them to render stacked instead of as one
horizontal tab bar. Merges the MySQL/PostgreSQL and Team/Enterprise
Edition tab groups back into single Tabs blocks.

* docs: remove Azure tab and restore Kubernetes deploy content

The Mattermost Operator, Azure, and Oracle platform tabs on the
Kubernetes deployment guide were split across two separate Tabs
blocks (rendering stacked instead of as one tab bar) and all three
tab bodies were empty because the migration never flattened the
original RST include directives. Removes the unsupported Azure tab
and restores the Mattermost Operator and Oracle content from the
pre-migration RST sources as a single merged Tabs block.

* addresscoderabbitreview

* docs: unify Linux/Kubernetes/Containers deploy pages and fix FIPS/STIG placement

Kubernetes and Containers duplicated full install content across a top-level
page (Tabs) and orphaned sidebar subpages that nothing linked to. Reworked
both to follow the Linux page's pattern: shared content on the landing page,
platform-specific instructions live once in their own subpage.

Also removed configure-fips-at-install-time.mdx, which claimed FIPS is
available for native Linux installs — FIPS/STIG in Mattermost is a container
image property (Chainguard-based build), not available outside Docker/
Kubernetes. Consolidated all FIPS/STIG references onto
containers/fips-stig.mdx and pointed the Mattermost Operator subpage (not
Oracle OKE, which doesn't expose image selection) at it.

* docs: fix review findings — image alt text, Important callout markup, typo

- Replace generic alt="image" on scaling diagrams with descriptive text
  identifying cloud provider and user scale.
- Convert raw div.important markup in deploy-containers.mdx to the
  repo's <Important> component, matching usage elsewhere in the file.
- Fix "Matermost" typo in manage-your-display-options.mdx.

---------

Co-authored-by: Hanzo Dev <[email protected]>
2026-07-10 14:50:13 +03:00

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---
title: "Calls Metrics and Monitoring"
---
<PlanAvailability slug="ent-plus" />
This guide provides detailed information on monitoring Mattermost Calls performance and health through metrics and observability tools. Effective monitoring is essential for maintaining optimal call quality and quickly addressing any issues that arise.
- [Metrics overview](#metrics-overview)
- [Setting up monitoring](#setting-up-monitoring)
- [Key metrics to monitor](#key-metrics-to-monitor)
- [Performance baselines](#performance-baselines)
- [Troubleshooting metrics collection](#troubleshooting-metrics-collection)
## Metrics Overview
Mattermost Calls provides metrics through Prometheus for both the Calls plugin and the RTCD service. These metrics help track:
- Active call sessions and participants
- Media track statistics
- Connection states and errors
- Resource utilization (CPU, memory, network)
- WebSocket connections and events
The metrics are exposed through HTTP endpoints:
- **Calls Plugin**: `/plugins/com.mattermost.calls/metrics`
- **RTCD Service**: `/metrics` (default) or a configured endpoint
Resource utilization metrics (CPU, memory, network) are mainly provided by an external service ([`node-exporter`](https://prometheus.io/docs/guides/node-exporter/)).
> Metrics for the calls plugin are exposed through the `/plugins/com.mattermost.calls/metrics` subpath under the existing Mattermost server metrics endpoint. This is controlled by the [Listen address for performance](https://docs.mattermost.com/configure/environment-configuration-settings.html#listen-address-for-performance) configuration setting. It defaults to port 8067. For example: `http://localhost:8067/plugins/com.mattermost.calls/metrics`
> The RTCD Service `/metrics` endpoint is exposed on the HTTP API (e.g. `http://localhost:8045/metrics`).
## Setting Up Monitoring
For instructions on deploying Prometheus and Grafana for Mattermost, please refer to the [Deploy Prometheus and Grafana for Performance Monitoring](https://docs.mattermost.com/scale/deploy-prometheus-grafana-for-performance-monitoring.html) guide.
Once Prometheus and Grafana are set up, you will need to configure Prometheus to scrape metrics from the Calls-related services.
### Prometheus Scrape Configuration
Add the following jobs to your `prometheus.yml` configuration:
```yaml
scrape_configs:
- job_name: 'calls-plugin'
metrics_path: /plugins/com.mattermost.calls/metrics
static_configs:
- targets: ['MATTERMOST_SERVER_IP:8067']
labels:
service_name: 'calls-plugin'
- job_name: 'rtcd'
metrics_path: /metrics
static_configs:
- targets: ['RTCD_SERVER_IP:8045']
labels:
service_name: 'rtcd'
- job_name: 'rtcd-node-exporter'
metrics_path: /metrics
static_configs:
- targets: ['RTCD_SERVER_IP:9100']
labels:
service_name: 'rtcd'
- job_name: 'calls-offloader-node-exporter'
metrics_path: /metrics
static_configs:
- targets: ['CALLS_OFFLOADER_SERVER_IP:9100']
labels:
service_name: 'offloader'
```
Replace the placeholder IP addresses with your actual server addresses:
- `MATTERMOST_SERVER_IP`: IP address of your Mattermost server
- `RTCD_SERVER_IP`: IP address of your RTCD server
- `CALLS_OFFLOADER_SERVER_IP`: IP address of your calls-offloader server (if deployed)
<Important>
**Metrics Configuration Notice**: Use the `service_name` labels as shown in the configuration above. These labels help organize metrics in dashboards and enable proper service identification.
</Important>
<Note>
- **node_exporter**: Optional but recommended for system-level metrics (CPU, memory, disk, network). See [node_exporter setup guide](https://prometheus.io/docs/guides/node-exporter/) for installation instructions.
- **calls-offloader**: Only needed if you have call recording/transcription enabled.
</Note>
### Mattermost Calls Grafana Dashboard
You can use the official [Mattermost Calls Performance Monitoring](https://grafana.com/grafana/dashboards/23225-mattermost-calls-performance-monitoring/) dashboard to visualize these metrics.
- To import it directly into Grafana, use dashboard ID: `23225`.
- The dashboard is also available as JSON source from the [Mattermost performance assets repository](https://github.com/mattermost/mattermost-performance-assets/blob/master/grafana/mattermost-calls-performance-monitoring.json) for manual import or customization.
## Key Metrics to Monitor
### RTCD Metrics
#### Process Metrics
These metrics help monitor the health and resource usage of the RTCD process:
- `rtcd_process_cpu_seconds_total`: Total CPU time spent
- `rtcd_process_open_fds`: Number of open file descriptors
- `rtcd_process_max_fds`: Maximum number of file descriptors
- `rtcd_process_resident_memory_bytes`: Memory usage in bytes
- `rtcd_process_virtual_memory_bytes`: Virtual memory used
#### WebRTC Connection Metrics
These metrics track the WebRTC connections and media flow:
- `rtcd_rtc_conn_states_total&#123;state="X"&#125;`: Count of connections in different states
- `rtcd_rtc_errors_total&#123;type="X"&#125;`: Count of RTC errors by type
- `rtcd_rtc_rtp_tracks_total&#123;direction="X"&#125;`: Count of RTP tracks (incoming/outgoing)
- `rtcd_rtc_sessions_total`: Total number of active RTC sessions
#### WebSocket Metrics
These metrics track the signaling channel:
- `rtcd_ws_connections_total`: Total number of active WebSocket connections. This is about RTCD &lt;-> MM, so the connection count should match the number of MM nodes.
- `rtcd_ws_messages_total&#123;direction="X"&#125;`: Count of WebSocket messages (sent/received)
### Calls Plugin Metrics
Similar metrics are available for the Calls plugin with the following prefixes:
- Process metrics: `mattermost_plugin_calls_process_*`
- WebRTC connection metrics: `mattermost_plugin_calls_rtc_*`
- WebSocket metrics: `mattermost_plugin_calls_websocket_*`
- Store metrics: `mattermost_plugin_calls_store_ops_total`
## Performance Baselines
The following performance benchmarks provide baseline metrics for RTCD deployments under various load conditions and configurations.
**Deployment specifications**
- 1x r6i.large nginx proxy
- 3x c5.large MM app nodes (HA)
- 2x db.x2g.xlarge RDS Aurora MySQL v8 (one writer, one reader)
- 1x (c7i.xlarge, c7i.2xlarge, c7i.4xlarge) RTCD
- 2x c7i.2xlarge load-test agents
**App specifications**
- Mattermost v9.6
- Mattermost Calls v0.28.0
- RTCD v0.16.0
- load-test agent v0.28.0
**Media specifications**
- Speech sample bitrate: 80Kbps
- Screen sharing sample bitrate: 1.6Mbps
**Results**
Below are the detailed benchmarks based on internal performance testing:
<table border="1" class="network-requirements">
<thead>
<tr>
<th>Calls</th>
<th>Participants/call</th>
<th>Unmuted/call</th>
<th>Screen sharing</th>
<th>CPU (avg)</th>
<th>Memory (avg)</th>
<th>Bandwidth (in/out)</th>
<th>Instance type (RTCD)</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>1000</td>
<td>2</td>
<td>no</td>
<td>47%</td>
<td>1.46GB</td>
<td>1Mbps / 194Mbps</td>
<td>c7i.xlarge</td>
</tr>
<tr>
<td>1</td>
<td>800</td>
<td>1</td>
<td>yes</td>
<td>64%</td>
<td>1.43GB</td>
<td>2.7Mbps / 1.36Gbps</td>
<td>c7i.xlarge</td>
</tr>
<tr>
<td>1</td>
<td>1000</td>
<td>1</td>
<td>yes</td>
<td>79%</td>
<td>1.54GB</td>
<td>2.9Mbps / 1.68Gbps</td>
<td>c7i.xlarge</td>
</tr>
<tr>
<td>10</td>
<td>100</td>
<td>1</td>
<td>yes</td>
<td>74%</td>
<td>1.56GB</td>
<td>18.2Mbps / 1.68Gbps</td>
<td>c7i.xlarge</td>
</tr>
<tr>
<td>100</td>
<td>10</td>
<td>2</td>
<td>no</td>
<td>49%</td>
<td>1.46GB</td>
<td>18.7Mbps / 175Mbps</td>
<td>c7i.xlarge</td>
</tr>
<tr>
<td>100</td>
<td>10</td>
<td>1</td>
<td>yes</td>
<td>84%</td>
<td>1.73GB</td>
<td>171Mbps / 1.53Gbps</td>
<td>c7i.xlarge</td>
</tr>
<tr>
<td>1</td>
<td>1000</td>
<td>2</td>
<td>no</td>
<td>20%</td>
<td>1.44GB</td>
<td>1.4Mbps / 194Mbps</td>
<td>c7i.2xlarge</td>
</tr>
<tr>
<td>1</td>
<td>1000</td>
<td>2</td>
<td>yes</td>
<td>49%</td>
<td>1.53GB</td>
<td>3.6Mbps / 1.79Gbps</td>
<td>c7i.2xlarge</td>
</tr>
<tr>
<td>2</td>
<td>1000</td>
<td>1</td>
<td>yes</td>
<td>73%</td>
<td>2.38GB</td>
<td>5.7Mbps / 3.06Gbps</td>
<td>c7i.2xlarge</td>
</tr>
<tr>
<td>100</td>
<td>10</td>
<td>2</td>
<td>yes</td>
<td>60%</td>
<td>1.74GB</td>
<td>181Mbps / 1.62Gbps</td>
<td>c7i.2xlarge</td>
</tr>
<tr>
<td>150</td>
<td>10</td>
<td>1</td>
<td>yes</td>
<td>72%</td>
<td>2.26GB</td>
<td>257Mbps / 2.30Gbps</td>
<td>c7i.2xlarge</td>
</tr>
<tr>
<td>150</td>
<td>10</td>
<td>2</td>
<td>yes</td>
<td>79%</td>
<td>2.34GB</td>
<td>271Mbps / 2.41Gbps</td>
<td>c7i.2xlarge</td>
</tr>
<tr>
<td>250</td>
<td>10</td>
<td>2</td>
<td>no</td>
<td>58%</td>
<td>2.66GB</td>
<td>47Mbps / 439Mbps</td>
<td>c7i.2xlarge</td>
</tr>
<tr>
<td>1000</td>
<td>2</td>
<td>2</td>
<td>no</td>
<td>78%</td>
<td>2.31GB</td>
<td>178Mbps / 195Mbps</td>
<td>c7i.2xlarge</td>
</tr>
<tr>
<td>2</td>
<td>1000</td>
<td>2</td>
<td>yes</td>
<td>41%</td>
<td>2.6GB</td>
<td>7.23Mbps / 3.60Gbps</td>
<td>c7i.4xlarge</td>
</tr>
<tr>
<td>3</td>
<td>1000</td>
<td>2</td>
<td>yes</td>
<td>63%</td>
<td>3.53GB</td>
<td>10.9Mbps / 5.38Gbps</td>
<td>c7i.4xlarge</td>
</tr>
<tr>
<td>4</td>
<td>1000</td>
<td>2</td>
<td>yes</td>
<td>83%</td>
<td>4.40GB</td>
<td>14.5Mbps / 7.17Gbps</td>
<td>c7i.4xlarge</td>
</tr>
<tr>
<td>250</td>
<td>10</td>
<td>2</td>
<td>yes</td>
<td>79%</td>
<td>3.49GB</td>
<td>431Mbps / 3.73Gbps</td>
<td>c7i.4xlarge</td>
</tr>
<tr>
<td>500</td>
<td>2</td>
<td>2</td>
<td>yes</td>
<td>71%</td>
<td>2.54GB</td>
<td>896Mbps / 919Mbps</td>
<td>c7i.4xlarge</td>
</tr>
</tbody>
</table>
## Troubleshooting Metrics Collection
### Verify RTCD Metrics are Being Collected
To verify that Prometheus is successfully collecting RTCD metrics, use this command:
```bash
curl http://PROMETHEUS_IP:9090/api/v1/label/__name__/values | jq '.' | grep rtcd
```
This command queries Prometheus for all available metric names and filters for RTCD-related metrics.
If no RTCD metrics appear, check:
1. RTCD is running
2. Prometheus is configured to scrape the RTCD metrics endpoint
3. RTCD metrics port is accessible from Prometheus (default: 8045)
### Check Prometheus Scrape Targets
To verify all Calls-related services are being scraped successfully:
1. Open the Prometheus web interface (typically `http://PROMETHEUS_IP:9090`)
2. Navigate to **Status > Targets**
3. Look for your configured Calls services:
- Mattermost server (for Calls plugin metrics)
- RTCD service
Each target should show status "UP" in green. If a target shows "DOWN" or errors:
- Verify the service is running
- Check network connectivity between Prometheus and the target
- Verify the metrics endpoint is accessible
## Other Calls Documentation
- [Calls Deployment Guide](calls-deployment-guide.mdx): Overview of deployment options and architecture
- [RTCD Setup and Configuration](calls-rtcd-setup.mdx): Comprehensive guide for setting up the dedicated RTCD service
- [Calls Offloader Setup and Configuration](calls-offloader-setup.mdx): Setup guide for call recording and transcription
- [Calls Deployment on Kubernetes](calls-kubernetes.mdx): Detailed guide for deploying Calls in Kubernetes environments
- [Calls Logging](calls-logging.mdx): Detailed guidance for collecting Calls logs and client diagnostics
**Note:**
Configure Prometheus storage accordingly to balance disk usage with retention needs. If you need to be tight on storage, you can use a short retention period. If you have lots of storage you can keep the retention length longer.