How to Monitor Open Telemetry Collector Performance: A Complete, Production -Grade Guide

Lency Korien
19 hours ago
2 min read

In modern distributed systems, observability is not a luxury—it’s a necessity. At the center of this landscape stands the Open Telemetry Collector, acting as the critical data pipeline responsible for receiving, processing, and exporting telemetry signals (traces, metrics, logs).

However, monitoring the monitor itself presents unique challenges. When your OpenTelemetry Collector becomes a bottleneck or fails silently, your entire observability stack suffers. This comprehensive guide will walk you through production-tested strategies for monitoring your OpenTelemetry Collector’s performance, ensuring your observability infrastructure remains robust and reliable.

Why Monitor the Open Telemetry Collector

Without active monitoring, the Open Telemetry Collector can silently drop telemetry data, over-consume resources, or fail to export traces and metrics. Its failure undermines visibility into the system it’s meant to observe.

Monitoring ensures:

Proactive issue detection (e.g., telemetry drops, high CPU usage)
Resource usage awareness (CPU, memory, queue sizes)
SLA enforcement and capacity planning
Debugging efficiency across distributed systems

[ Good Read: What Are The Benefits Of Using AWS DMS For Enterprise Data Migration? ]

How to Enable Open Telemetry Collector Monitoring

Monitoring the Open Telemetry Collector involves enabling metrics scraping and exposing internal metrics through supported protocols.

a. Pull-Based Metrics Collection

In development or small-scale environments, the simplest approach is to scrape internal metrics using Prometheus.

receivers:
  prometheus:
    config:
      scrape_configs:
      - job_name: otel-collector
        scrape_interval: 10s
        static_configs:
          - targets: ['127.0.0.1:8888']

service:
  pipelines:
    metrics:
      receivers: [prometheus]
      exporters: []

  telemetry:
    metrics:
      level: detailed
      readers:
        - pull:
            exporter:
              prometheus:
                host: 127.0.0.1
                port: 8888

This configuration exposes internal collector metrics at

http://localhost:8888/metrics.

b. Self-Monitoring Configuration

For production environments, it’s recommended to enable self-monitoring pipelines that scrape the collector’s internal state and forward it to external observability platforms.

Production-Grade Remote Export Example:

exporters:   
   prometheusremotewrite:     
     endpoint: ${PROMETHEUS_ENDPOINT}     
     retry_on_failure:       enabled: true  

service:   
   pipelines:     
     metrics:       
       receivers: [prometheus]       
       exporters: [prometheusremotewrite]

Key Considerations:

Use prometheusremotewrite for Prometheus-compatible backends (e.g., AWS Managed Prometheus, Grafana Cloud).
Set level: detailed in telemetry settings to expose granular metrics.
Secure endpoint access with authentication extensions such as sigv4auth, basicauth, or oauth2

You can check more info: How to Monitor Open Telemetry Collector Performance.