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
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.
Example Configuration:
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: 8888This 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.
Key Metrics to Monitor
1. Receiver Metrics
| Metric | Purpose |
|---|---|
| otelcol_receiver_accepted_spans | Spans successfully received |
| otelcol_receiver_refused_spans | Spans rejected or dropped |
| otelcol_receiver_accepted_metric_points | Inbound metric volume |
| otelcol_receiver_accepted_log_records | Logs processed at receiver level |
2. Processor Metrics
| Metric | Purpose |
|---|---|
| otelcol_processor_dropped_spans | Indicates data loss during processing |
| otelcol_processor_batch_send_size | Reveals batch optimization efficiency |
| otelcol_processor_dropped_metric_points | Failed metric transformations |
3. Exporter Metrics
| Metric | Purpose |
|---|---|
| otelcol_exporter_sent_spans | Exported span count |
| otelcol_exporter_send_failed_requests | Failed export operations |
| otelcol_exporter_queue_size | Active items in queue |
| otelcol_exporter_queue_capacity | Max queue size before drops begin |
4. System Metrics
| Metric | Purpose |
|---|---|
| otelcol_process_cpu_seconds_total | Collector CPU usage |
| otelcol_process_resident_memory_bytes | Memory (RSS) footprint |
| otelcol_runtime_heap_alloc_bytes | Heap memory usage |
| otelcol_process_uptime_seconds | Instance uptime duration OpenTelemetry Dashboards |
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