When monitoring data streaming systems, the key metrics fall into three categories: throughput and latency, error handling, and system health. These metrics help ensure data is processed efficiently, reliably, and without overloading infrastructure. Let’s break them down with specific examples.
First, track throughput and latency to understand performance. Throughput measures how much data is processed per second (e.g., 100,000 events/second in Apache Kafka). Low throughput might indicate bottlenecks in producers, brokers, or consumers. Latency measures the time from data production to consumption. For instance, if a Kafka broker takes 50ms to deliver a message but the consumer takes 200ms to process it, end-to-end latency spikes. Tools like Prometheus can track these metrics, and dashboards (e.g., Grafana) help visualize trends. If latency increases while throughput drops, it might signal network issues or resource constraints.
Second, monitor error rates and consumer lag. Error rates include failed messages (e.g., deserialization errors in Apache Flink jobs) or retries due to network timeouts. High error rates suggest misconfigured clients or unstable dependencies. Consumer lag (e.g., Kafka’s consumer_lag metric) shows how far behind consumers are compared to producers. A growing lag might mean consumers are underpowered or logic is inefficient—like a Spark Streaming job stuck processing large batches. Backpressure metrics (e.g., in Apache Pulsar) also matter: if producers slow down because consumers can’t keep up, it indicates a scalability issue. Addressing these prevents data loss or stale results.
Finally, check system resource usage and delivery guarantees. Track CPU, memory, and disk usage on brokers (e.g., Kafka nodes) and workers (e.g., Flink task managers). High CPU usage on a broker might require partitioning topics or scaling the cluster. For stateful stream processors, disk usage for checkpoints (e.g., Flink’s savepoints) must stay within limits. Also, verify delivery semantics: at-least-once (no data loss) or exactly-once (no duplicates) require monitoring idempotent writes or transactional commits. For example, if a Kafka producer’s error_rate spikes, it could break exactly-once guarantees. Regular audits of these metrics ensure the system meets reliability requirements without unexpected failures.