Docker Logs Monitoring & Observability

Explore the nuances of Docker Logging, offering a comprehensive approach to efficiently troubleshoot Docker, trace issues, and optimize cluster performance.

Monitor Docker container logs across hosts and production environments

Collect Docker container logs

Capture logs written to stdout and stderr by Docker containers using the default logging drivers across running and stopped containers.

Support multiple logging drivers

Ingest logs produced via Docker logging drivers such as json-file, journald, and syslog without altering container images.

Track container lifecycle events

Analyze log output generated during container start, stop, restart, and failure events to troubleshoot runtime issues.

Monitor Docker daemon activity

Collect Docker daemon logs to investigate image pulls, container scheduling failures, and resource constraints on hosts.

Detect container crashes and exits

Identify abnormal container exits, crash loops, and application failures using runtime log patterns.

Enrich logs with container metadata

Attach container identifiers, image names, labels, and host information to each log entry for precise filtering and analysis.

Handle high-volume container output

Centralize Docker logs from high-throughput containers while maintaining performance during spikes and rolling restarts.

Support standalone and swarm setups

Aggregate Docker logs from standalone hosts and Docker Swarm clusters into a unified logging view.

Core Platform Capabilities

Centralize Your Docker Container Logs for Real-Time Insight

Send logs from your Docker containers into Atatus so you can search, parse, and analyze events across all services without juggling local files or isolated streams.

Real-Time Log IngestionStructured ParsingSearch & FiltersLive TailCorrelation With Metrics & Traces

Logs Scattered Across Containers

Docker logs generated by many containers are difficult to unify, and centralized ingestion brings them together to reveal patterns across your stack.

Raw Streams Are Hard to Analyze

Default stdout and stderr streams make trend analysis difficult, and structured parsing turns raw output into searchable fields.

High Volume Buries Useful Data

Continuous container output can drown important events, and search with filters helps narrow down to relevant log entries quickly.

Real-Time Issues Can Be Missed

Manually tailing logs across containers misses context, and live log ingestion allows you to view events as they happen.

Logs Do Not Stand Alone

Standalone log files lack performance context, and correlating Docker logs with traces and metrics shows how events impact latency and errors.

Unified Logs Monitoring & Observability Across Different Platforms

Frequently Asked Questions

Find answers to common questions about our platform