# Pipeline Metrics

{% hint style="info" %}
To access the Pipeline Metrics page, you must belong to a group assigned to the [**metrics viewer** role](/documentation/developer-guide/platform-administration/administration/new-access-control/access-control-roles.md#metrics-viewer), such as the support default group. 
{% endhint %}

On the Pipeline Metrics page, you can analyze charts about the performance of deployed pipelines.

The Digibee Platform provides two pipeline status filters:

* **Active**: Displays only active pipelines.
* **All**: Displays all pipelines, including those that have not yet been deployed.

### Viewing charts

Follow the steps below to view pipeline metrics charts:

1. Select the environment in the upper left corner. The page updates to reflect the selected environment.
2. Select a **Pipeline Status** from the dropdown on the left: **Active** or **All**.
3. Click the pipeline dropdown to search and select the desired pipeline.
4. Choose a reporting period. The platform displays the **15-minute** interval by default. You can change it to **1 hour**, **6 hours**, **1 day**, **7 days**, **30 days**, or set a custom time range.

To learn more about deployment size, concurrent executions, and replicas, see the [Deployments documentation](/documentation/developer-guide/development-cycle/overview/deployment.md).

### Types of charts

<figure><img src="/files/K9ujuWTLgNi6m2nF7JFt" alt=""><figcaption></figcaption></figure>

#### Trigger invocations × Pipeline executions × Errors

This chart combines three metrics:

* **Trigger invocations**: The number of times something triggered the pipeline.
* **Pipeline executions**: The number of times the pipeline actually ran.
* **Errors**: The number of pipeline runs resulted in failures.

Use this chart to identify discrepancies between the number of times the pipeline was triggered, the number of times it ran, and how many of those runs failed.

#### Pipeline messages in queue × Inflight messages

This chart shows the number of messages in the queue compared to the number of concurrent executions in progress. Use it to identify processing delays, which can occur when the number of replicas is not sufficient to keep up with consumer demand.

#### Total response time × Running replicas (autoscaling only)

This chart shows the time the system takes to process a message, considering the trigger invocation time, the time in the request queue, and the processing time — relative to how many replicas are running in the selected period.

Use these metrics to configure autoscaling replicas to increase capacity only when needed, improving workflow efficiency and maintaining optimal total response times for your integrations.

#### Total response time × Pipeline response time

This chart compares the pipeline processing time (pipeline response time) with the total pipeline response time, which includes the trigger invocation time and the time spent in the request queue.

Use it as a diagnostic tool to identify whether the primary contributor to a message's total latency is the pipeline's processing time or queue time, both of which can be influenced by deployment configuration.

#### Request queue time

This chart shows the average time a request spends in the queue (RabbitMQ) before being processed by the pipeline, making it easier to visualize queue latency.

#### Pipeline memory usage × Pipeline message sizes

This chart illustrates the minimum, average, and maximum memory usage of the pipeline relative to the request and response message sizes.

#### Pipeline CPU usage

This chart shows the minimum, average, and maximum CPU percentage usage for each pipeline replica, based on the chosen deployment size. Use it to evaluate whether the pipeline size selected during deployment is appropriate, since higher CPU usage can lead to slower processing.

#### Top offenders in connector latency

This chart shows the [latency of the connectors](/documentation/developer-guide/development-cycle/dashboards/pipeline-metrics/connector-latency.md) in your pipelines, representing the time each step took during pipeline execution.


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