--- name: kafka-development-400612fe8de9 description: Best practices and guidelines for Apache Kafka event streaming and distributed messaging --- # Kafka Development You are an expert in Apache Kafka event streaming and distributed messaging systems. Follow these best practices when building Kafka-based applications. ## Core Principles - Kafka is a distributed event streaming platform for high-throughput, fault-tolerant messaging - Unlike traditional pub/sub, Kafka uses a pull model - consumers pull messages from partitions - Design for scalability, durability, and exactly-once semantics where needed - Leave NO todos, placeholders, or missing pieces in the implementation ## Architecture Overview ### Core Components - **Topics**: Categories/feeds for organizing messages - **Partitions**: Ordered, immutable sequences within topics enabling parallelism - **Producers**: Clients that publish messages to topics - **Consumers**: Clients that read messages from topics - **Consumer Groups**: Coordinate consumption across multiple consumers - **Brokers**: Kafka servers that store data and serve clients ### Key Concepts - Messages are appended to partitions in order - Each message has an offset - a unique sequential ID within the partition - Consumers maintain their own cursor (offset) and can read streams repeatedly - Partitions are distributed across brokers for scalability ## Topic Design ### Partitioning Strategy - Use partition keys to place related events in the same partition - Messages with the same key always go to the same partition - This ensures ordering for related events - Choose keys carefully - uneven distribution causes hot partitions ### Partition Count - More partitions = more parallelism but more overhead - Consider: expected throughput, consumer count, broker resources - Start with number of consumers you expect to run concurrently - Partitions can be increased but not decreased ### Topic Configuration - `retention.ms`: How long to keep messages (default 7 days) - `retention.bytes`: Maximum size per partition - `cleanup.policy`: delete (remove old) or compact (keep latest per key) - `min.insync.replicas`: Minimum replicas that must acknowledge ## Producer Best Practices ### Reliability Settings ``` acks=all # Wait for all replicas to acknowledge retries=MAX_INT # Retry on transient failures enable.idempotence=true # Prevent duplicate messages on retry ``` ### Performance Tuning - `batch.size`: Accumulate messages before sending (default 16KB) - `linger.ms`: Wait time for batching (0 = send immediately) - `buffer.memory`: Total memory for buffering unsent messages - `compression.type`: gzip, snappy, lz4, or zstd for bandwidth savings ### Error Handling - Implement retry logic with exponential backoff - Handle retriable vs non-retriable exceptions differently - Log and alert on send failures - Consider dead letter topics for messages that fail repeatedly ### Partitioner - Default: hash of key determines partition (null key = round-robin) - Custom partitioners for specific routing needs - Ensure even distribution to avoid hot partitions ## Consumer Best Practices ### Offset Management - Consumers track which messages they've processed via offsets - `auto.offset.reset`: earliest (start from beginning) or latest (only new messages) - Commit offsets after successful processing, not before - Use `enable.auto.commit=false` for exactly-once semantics ### Consumer Groups - Consumers in a group share partitions (each partition to one consumer) - More consumers than partitions = some consumers idle - Group rebalancing occurs when consumers join/leave - Use `group.instance.id` for static membership to reduce rebalances ### Processing Patterns - Process messages in order within a partition - Handle out-of-order messages across partitions if needed - Implement idempotent processing for at-least-once delivery - Consider transactional processing for exactly-once ### Timeouts and Failures - Implement processing timeout to isolate slow events - When timeout occurs, set event aside and continue to next message - Maintain overall system performance over processing every single event - Use dead letter queues for messages failing all retries ## Error Handling and Retry ### Retry Strategy - Allow multiple runtime retries per processing attempt - Example: 3 runtime retries per redrive, maximum 5 redrives = 15 total retries - Runtime retries typically cover 99% of failures - After exhausting retries, route to dead letter queue ### Dead Letter Topics - Create dedicated DLT for messages that can't be processed - Include original topic, partition, offset, and error details - Monitor DLT for patterns indicating systemic issues - Implement manual or automated retry from DLT ## Schema Management ### Schema Registry - Use Confluent Schema Registry for schema management - Producers validate data against registered schemas during serialization - Schema mismatches throw exceptions, preventing malformed data - Provides common reference for producers and consumers ### Schema Evolution - Design schemas for forward and backward compatibility - Add optional fields with defaults for backward compatibility - Avoid removing or renaming fields - Use schema versioning and migration strategies ## Kafka Streams ### State Management - Implement log compaction to maintain latest version of each key - Periodically purge old data from state stores - Monitor state store size and access patterns - Use appropriate storage backends for your scale ### Windowing Operations - Handle out-of-order events and skewed timestamps - Use appropriate time extraction and watermarking techniques - Configure grace periods for late-arriving data - Choose window types based on use case (tumbling, hopping, sliding, session) ## Security ### Authentication - Use SASL/SSL for client authentication - Support SASL mechanisms: PLAIN, SCRAM, OAUTHBEARER, GSSAPI - Enable SSL for encryption in transit - Rotate credentials regularly ### Authorization - Use Kafka ACLs for fine-grained access control - Grant minimum necessary permissions per principal - Separate read/write permissions by topic - Audit access patterns regularly ## Monitoring and Observability ### Key Metrics - **Producer**: record-send-rate, record-error-rate, batch-size-avg - **Consumer**: records-consumed-rate, records-lag, commit-latency - **Broker**: under-replicated-partitions, request-latency, disk-usage ### Lag Monitoring - Consumer lag = last produced offset - last committed offset - High lag indicates consumers can't keep up - Alert on increasing lag trends - Scale consumers or optimize processing ### Distributed Tracing - Propagate trace context in message headers - Use OpenTelemetry for end-to-end tracing - Correlate producer and consumer spans - Track message journey through the pipeline ## Testing ### Unit Testing - Mock Kafka clients for isolated testing - Test serialization/deserialization logic - Verify partitioning logic - Test error handling paths ### Integration Testing - Use embedded Kafka or Testcontainers - Test full producer-consumer flows - Verify exactly-once semantics if used - Test rebalancing scenarios ### Performance Testing - Load test with production-like message rates - Test consumer throughput and lag behavior - Verify broker resource usage under load - Test failure and recovery scenarios ## Common Patterns ### Event Sourcing - Store all state changes as immutable events - Rebuild state by replaying events - Use log compaction for snapshots - Enable time-travel debugging ### CQRS (Command Query Responsibility Segregation) - Separate write (command) and read (query) models - Use Kafka as the event store - Build read-optimized projections from events - Handle eventual consistency appropriately ### Saga Pattern - Coordinate distributed transactions across services - Each service publishes events for next step - Implement compensating transactions for rollback - Use correlation IDs to track saga instances ### Change Data Capture (CDC) - Capture database changes as Kafka events - Use Debezium or similar CDC tools - Enable real-time data synchronization - Build event-driven integrations --- Source attribution: VRIL-LABS Original project: https://github.com/VRIL-LABS/skill-jam/blob/5b00ad8b9f0787969474ff5e9ec417bb55f23827/skills/ai-ml/skills-main-2/skills-main/kafka-development/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Dave AI. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Dave AI resource metadata Creator / publisher: VRIL-LABS Original project: https://github.com/VRIL-LABS/skill-jam/blob/5b00ad8b9f0787969474ff5e9ec417bb55f23827/skills/ai-ml/skills-main-2/skills-main/kafka-development/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering Roles: Backend Developer, Data Engineer, ML / AI Engineer Specialties: API, Backend Tools: Apache Kafka Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 7baf09ff7e4a674e76cde7e1c38220da2854e8d106c4119f64ae630e5e555138