Scaling & Performance

Scaling & Performance

Last Updated: 2026-02-05

Performance and scaling are workload-dependent. Use the benchmark suites in benchmarks/ and validate on your target hardware and network before making production commitments.

Benchmarking

• See benchmarks/README.md for available benchmarks and how to run them.
• Run benchmarks on the same class of hardware you plan to deploy.
• Capture results with -benchmem and keep baselines in version control if needed.

Practical Tuning Levers

• server.tcp.plain.max_connections: protect the broker from excess concurrent connections
• session.max_sessions: cap active MQTT sessions
• broker.max_message_size: limit payload size
• session.max_offline_queue_size and session.max_inflight_messages: control per-client memory usage
• queues.*.limits: bound queue depth, message size, and TTL

OS and Runtime Considerations

• Ensure file descriptor limits are high enough for your target connection count.
• Tune TCP keepalive and timeouts based on your workload (IoT vs low-latency systems).
• Measure with production-like TLS settings if TLS is enabled in production.

Cluster Scaling

• Clustering spreads sessions and subscriptions across nodes but requires etcd coordination.
• Use multiple nodes to improve availability and distribute connections.
• Queue replication is optional and configured via cluster.raft.

For configuration details, see docs/configuration.md.