Debezium is a powerful open-source Change Data Capture (CDC) platform that streams database changes into messaging systems like Kafka. As CDC pipelines grow in complexity, monitoring and troubleshooting become critical to maintaining data integrity and system performance. Leveraging industry-standard tools like Prometheus and Grafana enables intermediate and advanced users to gain deep observability into Debezium pipelines, catch anomalies early, and diagnose issues efficiently.

This post explores how to integrate Prometheus metrics exporters with Debezium connectors and create Grafana dashboards tailored for CDC pipeline health monitoring. We will also cover best practices for troubleshooting common pipeline issues using these insights.

Why Monitor Debezium Pipelines

Debezium connectors track database transaction logs and stream changes downstream. Without visibility into connector health, lag, and error rates, silent failures or performance degradations can lead to data loss or stale streams. Key reasons to monitor include:

  • Connector health: Detect connector failures or restarts promptly.
  • Lag monitoring: Ensure Debezium is keeping pace with database changes.
  • Error tracking: Identify serialization or deserialization errors that block events.
  • Throughput and latency: Measure pipeline performance and detect bottlenecks.
  • Resource utilization: Monitor CPU, memory, and network usage impacting connectors.

Prometheus, with its pull-based metrics model and rich querying capabilities, combined with Grafana’s visualization and alerting features, forms a robust observability stack for Debezium pipelines.

Exposing Debezium Metrics to Prometheus

Debezium connectors expose metrics via Micrometer and JMX interfaces. To integrate with Prometheus:

  1. Enable JMX Exporter: Use the Prometheus JMX Exporter as a Java agent attached to the Debezium Connect worker JVM. This exposes a /metrics HTTP endpoint with JMX metrics in Prometheus format.

  2. Configure Micrometer Metrics: If using Debezium on Kafka Connect, configure micrometer-registry-prometheus to export metrics directly.

  3. Key Metrics to Monitor:

    • kafka_connect_connector_task_status: Task state (RUNNING, FAILED, etc.)
    • debezium_connector_source_record_poll_total: Number of records polled from source
    • debezium_connector_source_record_emit_total: Number of records emitted downstream
    • debezium_connector_source_record_error_total: Number of errors encountered
    • connect_worker_task_lag: Task lag indicating delay in processing

  4. Prometheus Scrape Configuration: Add the Debezium Connect REST endpoint to Prometheus scrape targets to collect these metrics at regular intervals.

Designing Effective Grafana Dashboards for Debezium

Once metrics are ingested by Prometheus, Grafana dashboards provide a visual overview of pipeline health and performance:

  • Connector and Task Status Panels: Visualize task states and connector availability using single stat or table panels to quickly identify failures.

  • Throughput and Latency Graphs: Use time series panels to track records polled and emitted per second, and calculate processing latency trends.

  • Error Rate Heatmaps: Highlight spikes in error counts, such as serialization or deserialization failures.

  • Lag Monitoring: Plot lag metrics for each connector task to detect backpressure or processing delays.

  • Resource Utilization: Integrate host metrics (CPU, memory) to correlate system health with connector performance.

Here is an example Prometheus query to display connector task lag:

connect_worker_task_lag{connector="mysql-connector-1"}

Grafana alerting rules can be set on critical metrics such as task failures or lag thresholds to notify DevOps teams proactively.

Troubleshooting Common Debezium Pipeline Issues

With detailed metrics and dashboards, troubleshooting becomes systematic:

  • Connector Task Failures: Inspect kafka_connect_connector_task_status and error logs from the Connect REST API. Frequent restarts or FAILED states indicate configuration or resource issues.

  • High Lag: Sustained lag suggests downstream consumer slowness or resource bottlenecks. Verify Kafka consumer group status and increase worker resources as needed.

  • Serialization Errors: Elevated source_record_error_total often points to schema compatibility issues. Check Debezium schema registry integration and Kafka topic schemas.

  • Network or Broker Issues: Drops in emitted records but stable polling can indicate Kafka broker connectivity problems. Correlate with network metrics and Kafka cluster health.

  • Backpressure Effects: Monitor CPU and memory utilization alongside lag to detect backpressure causing connector stalls.

Using Grafana’s drill-down panels combined with Prometheus’s query flexibility, root causes can be isolated quickly to minimize downtime.

Best Practices for Scaling Observability in Debezium Architectures

  • Centralized Metrics Collection: Aggregate metrics from multiple Debezium connectors and Kafka Connect workers into a single Prometheus instance.

  • Labeling and Tagging: Use consistent labels (e.g., connector name, task ID) to filter and group metrics effectively.

  • Retention and Storage: Configure Prometheus retention policies to balance historical data needs and storage costs.

  • Custom Exporters: Extend metric collection with custom exporters if additional business or pipeline-specific metrics are required.

  • Automated Alerting: Define actionable alert rules in Grafana to reduce alert fatigue and focus on critical issues.

  • Security: Secure the Prometheus and Grafana endpoints with authentication and encrypted communication to protect sensitive metadata.

Conclusion

Monitoring and troubleshooting Debezium pipelines with Prometheus and Grafana empowers data engineers and platform operators to maintain reliable, high-throughput CDC streams. By exposing rich metrics, designing insightful dashboards, and applying systematic troubleshooting techniques, teams can proactively address issues before they impact downstream applications. Integrating these observability tools into your data pipeline infrastructure is essential for scaling modern event-driven architectures with confidence.

Boost your Debezium deployment’s resilience and performance today by implementing the monitoring strategies outlined in this guide.