Kafka Logs Monitoring & Observability

Effortlessly track Kafka logs, gaining instant insights into errors and refining logging for a more efficient and reliable application.

Monitor Kafka logs to troubleshoot brokers, topics, and message delivery issues

Broker startup and shutdown logs

Analyze Kafka broker logs to detect configuration errors, JVM startup failures, listener binding issues, and unclean shutdown events.

Track partition and leader events

Monitor Kafka log entries related to leader elections, partition reassignments, ISR shrinkage, and replica state changes.

Detect producer and consumer errors

Capture Kafka logs reporting producer send failures, consumer group rebalances, offset commit errors, and deserialization issues.

Monitor replication health

Inspect Kafka replication logs to identify under-replicated partitions, fetcher lag, and broker communication failures.

Track log retention and cleanup

Analyze Kafka log cleaner and retention-related messages to verify segment deletion, compaction behavior, and disk usage patterns.

Identify controller warnings

Detect controller log warnings related to Zookeeper or KRaft metadata inconsistencies affecting cluster stability.

Observe disk and I O issues

Capture Kafka logs reporting disk failures, log directory errors, and write latency affecting message persistence.

Correlate streaming and application logs

Link Kafka log events with application logs to trace message delivery failures back to producing or consuming services.

Core Platform Capabilities

Unify Kafka Log Streams for Real-Time Operational Insight

Send Kafka broker, producer, and consumer logs into Atatus so you can parse key fields, refine log data, and explore events across your streaming environment without scattered files.

Real-Time Log IngestionStructured ParsingCustom PipelinesSaved ViewsFocused Filters

Logs Spread Across Brokers and Clients

Kafka logs originate from brokers, producers, and consumers across the cluster, and central collection brings them together for unified exploration.

Unstructured Messages Mask Important Events

Raw Kafka log messages mix information and context, and structured parsing converts them into searchable fields you can query efficiently.

Key Signals Hidden in High Volume

Continuous Kafka log output can bury meaningful events, and custom pipelines help surface only the data that matters most.

Context Lost Without Saved Views

Manually rebuilding filters slows investigation, and saved views let you recall focused log contexts instantly.

Filtering at Scale Is Resource-Intensive

Sifting through Kafka logs without refined filters is slow, and applying focused filters across ingested streams helps narrow results quickly.

Unified Logs Monitoring & Observability Across Different Platforms

Frequently Asked Questions

Find answers to common questions about our platform