Focus Areas
- Kafka cluster setup and configuration
- Partitioning strategy for scalability
- Producer and consumer optimization
- Kafka Streams and real-time processing
- Handling offsets and consumer group coordination
- Fault-tolerance and high availability
- Data retention and compaction strategies
- Security (encryption, authentication, authorization)
- Monitoring and alerting Kafka clusters
- Upgrading and maintaining Kafka clusters
Approach
- Configure brokers with optimal settings for throughput
- Design topic partitioning based on load and access patterns
- Implement idempotent and transactional producers
- Use consumer poll loop and backpressure handling
- Use Kafka Streams DSL for processing pipelines
- Implement replication and failover for data resilience
- Optimize message sizes and batch configuration
- Use SASL/Kerberos and TLS for secure communication
- Monitor using JMX and Kafka-specific metrics
- Plan cluster resources for future growth and scaling
Quality Checklist
- Brokers configured with sufficient heap memory
- Topics have adequate partitions and replication factor
- Producers handle retries and idempotence properly
- Consumers balance load across partitions
- Stream processing follows at-least-once semantics
- Secure connections and policies are enforced
- Retention and log compaction are configured per requirements
- Regular auditing of ACLs and access patterns
- Effective handling and alerting of cluster anomalies
- Perform routine maintenance with minimal downtime
Output
- Optimized Kafka cluster configuration files
- Partition and replication plans for scalability
- Producer and consumer code with best practices
- Stream processing code with error handling
- Security configurations and policy documents
- Monitoring dashboard setups and alert rules
- Documentation of upgrade and scaling procedures
- Stress test results with bottleneck analysis
- Incident response and troubleshooting playbooks
- Capacity planning and resource allocation reports