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KafkaDistributed SystemsBackendData Engineering

Kafka Best Practices

8 min read

A practical guide to designing and operating Kafka at scale — covering partitioning strategies, consumer group management, exactly-once semantics, and observability.

1. Partitioning Strategy

Choose partition counts intentionally. A good rule of thumb is to target throughput divided by consumer throughput per partition. Avoid over-partitioning early.

2. Consumer Group Design

Match the number of consumers to the number of partitions. Offload heavy work to a thread pool and commit offsets manually after successful processing.

3. Exactly-Once Semantics (EOS)

Enable idempotent producers (enable.idempotence=true) and use transactional APIs when your pipeline demands exactly-once guarantees.

4. Retention and Compaction

Use time-based retention for event streams and log compaction for changelog topics to keep only the latest value per key.

5. Observability

Track consumer lag (records-lag-max) as your primary SLI. Instrument producers with error-rate and batch-size metrics.

Key Takeaways

  • Right-size partitions based on throughput.
  • Manual offset commits give you control over delivery.
  • EOS is powerful but adds latency — evaluate carefully.
  • Consumer lag rate-of-change is the best alert signal.