---
title: Troubleshoot an out-of-memory EDOT Collector
description: Diagnose and resolve out-of-memory issues in the EDOT Collector using Go’s Performance Profiler.
url: https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/5399/troubleshoot/ingest/opentelemetry/edot-collector/collector-oomkilled
products:
  - Elastic Cloud Serverless
  - Elastic Distribution of OpenTelemetry Collector
  - Elastic Observability
applies_to:
  - Serverless Observability projects: Generally available
  - Elastic Stack: Generally available
  - Elastic Distribution of OpenTelemetry Collector: Generally available
---

# Troubleshoot an out-of-memory EDOT Collector
If your EDOT Collector pods terminate with an `OOMKilled` status, this usually indicates sustained memory pressure or potentially a memory leak due to an introduced regression or a bug. You can use the Performance Profiler (`pprof`) extension to collect and analyze memory profiles, helping you identify the root cause of the issue.
If you're running the Collector in Kubernetes and experiencing resource allocation issues, refer to [Insufficient resources in Kubernetes](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/5399/troubleshoot/ingest/opentelemetry/edot-collector/insufficient-resources-kubestack) for troubleshooting steps.

## Symptoms

These symptoms typically indicate that the EDOT Collector is experiencing a memory-related failure:
- EDOT Collector pod restarts with an `OOMKilled` status in Kubernetes.
- Memory usage steadily increases before the crash.
- The Collector's logs don't show clear errors before termination.

For more detailed diagnostics, refer to [Enable debug logging](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/5399/troubleshoot/ingest/opentelemetry/edot-collector/enable-debug-logging).

## Resolution

Turn on runtime profiling using the `pprof` extension and then gather memory heap profiles from the affected pod:
<stepper>
  <step title="Enable `pprof` in the Collector">
    Edit the EDOT Collector Daemonset configuration and include the `pprof` extension:
    ```yaml
    exporters:
      ...
    processors:
      ...
    receivers:
      ...
    extensions:
      pprof:

    service:
      extensions:
       - pprof
       - ...
      pipelines:
        metrics:
          receivers: [ ... ]
          processors: [ ... ]
          exporters: [ ... ]
    ```
    Restart the Collector after applying these changes. When the Daemonset is deployed again, spot the pod that is getting restarted.
  </step>

  <step title="Access the affected pod and collect a heap dump">
    When a pod starts exhibiting high memory usage or restarts due to OOM, run the following to enter a debug shell:
    ```json
    kubectl debug -it <collector-pod-name> --image=ubuntu:latest
    ```
    In the debug container:
    ```json
    apt update
    apt install -y curl
    curl http://localhost:1777/debug/pprof/heap > heap.out
    ```
  </step>

  <step title="Copy the heap file from the pod">
    From your local machine, copy the heap file using:
    ```bash
    kubectl cp <collector-pod-name>:heap.out ./heap.out -c <debug-container-name>
    ```

    <note>
      Replace `<debug-container-name>` with the name assigned to the debug container. Without the `-c` flag, Kubernetes will show the list of available containers.
    </note>
  </step>

  <step title="Convert the heap profile for analysis">
    You can now generate a visual representation, for example PNG:
    ```bash
    go tool pprof -png heap.out > heap.png
    ```
  </step>
</stepper>


## Best practices

To improve the effectiveness of memory diagnostics and reduce investigation time, consider the following:
- Collect multiple heap profiles over time (for example, every few minutes) to observe memory trends before the crash.
- Automate heap profile collection at intervals to observe trends over time.


## Resources

- [Go's pprof documentation](https://pkg.go.dev/net/http/pprof)
- [OpenTelemetry Collector troubleshooting documentation](https://opentelemetry.io/docs/collector/troubleshooting/#performance-profiler-pprof)