Apache Kafka is a user-friendly, open-source platform for distributed streaming. It helps you build real-time data pipelines and streaming applications. It’s easy to use and efficiently manages large amounts of data.
Like with any technology, following certain practices with Apache Kafka can save you time and effort. These guidelines make your workflow smoother and help you avoid common mistakes. They also ensure your Kafka setup is secure, scalable, and efficient.
Here are the best practices when using Apache Kafka:
Always Use the Latest Version
When an update is available, download and install it without delay. Always use the latest stable Apache Kafka client library version that matches the Kafka broker version. This ensures maximum performance and compatibility.
Plan Your Data Schema Before Using Kafka
When there’s a plan, you work more efficiently. Plan your data schema. Know how you want to partition data and structure messages. The schema you settle on should remain permanent. This can avoid future compatibility issues.
Implement Connection Pooling
Connection pooling for Kafka producers and consumers reduces the overhead of establishing and closing connections, improving performance and resource utilization.
Configure Kafka with Security In Mind
Secure your Kafka deployment in all ways. Data security must remain a top priority. Use SSL/TLS certificates. Implement authentication and authorization mechanisms. Ensure security settings are turned on and active at all times.
Use Kafka Topics to Organize Data
Topics should be formatted appropriately for your data. Use descriptive topic names, create sufficient partitions for each topic, use the same number across all topics, and ensure partition keys are evenly distributed. This will fine-tune the organization in your Kafka environment.
Consider Data Retention Policies
To maintain organization in Kafka topics, consider data retention policies based on data lifecycle requirements. The default retention period is seven days or 168 hours. A lesser value means data will be available for less time, while a higher value means more disk space will be used on brokers for a particular topic.
Optimize Your Kafka Consumers
When designing your Kafka consumers, optimize them. Use a consumer group to manage multiple consumers. Use a consumer load balancer to distribute work evenly across consumers. Ensure consumer settings are tuned to optimize performance and minimize the risk of data loss.
Readjust Consumers As Needed
You may need to adjust your consumer groups at times. Monitor consumer lag to reduce the time spent processing messages. Consumer groups are how one scales consumers horizontally and achieves parallel processing. As your Kafka environment grows, you can rebalance your consumer groups when needed.
Ensure Partition Strategies Work
For custom partition assignment strategies, always test and evaluate behaviour before committing. Ensure partitions are evenly and efficiently distributed among consumers.
Proper Error Handling and Retries
Implement proper error handling and retries in Kafka client code. Ensure you use backoff strategies to handle transient failures. Errors must be managed and debugged effectively.
Fine-Tune Serialization and Deserialization
Use efficient serialization formats to minimize message size. Use schema evolution techniques to handle schema changes efficiently.
Monitor Apache Kafka Performance
It’s key to monitor Kafka performance to identify issues and optimize usage. Its built-in monitoring tools monitor CPU usage and other real-time metrics. Brokers should use under 60% of their CPUs.
Use External Apache Kafka Monitoring Tools
Kafka Manager, Kafka Monitor, and Kafka Tool are excellent built-in monitoring tools, but you can get more information from external tools, such as Prometheus, Grafana, and Datadog. Consider how comprehensive a view you want to keep track of your Kafka performance.
Optimize Kafka Network Traffic
Kafka messages can be compressed, but be careful when enabling compression, as it increases CPU usage. Even so, you can minimize traffic by batching and compressing messages before sending them, which reduces data transmission and costs.
Optimize Kafka Performance with an Audit
Complete a full review of your Kafka environment. Ensure broker and client configurations are tuned based on hardware resources, workload characteristics, and performance requirements. Consider using dedicated Kafka clusters for different workloads to optimize resource usage. Review all network settings, disk I/O, and JVM garbage collection parameters.
Integrate Kafka with Other Tools
Kafka can do a lot, but more when combined with other tools. Look at how other data processing platforms and tools complement Kafka.
Protect Zookeeper Installation
Zookeeper can be an integral part of Kafka deployment. Keep it secure, ensure it does not face the Internet, and configure ACLs to protect it. Limit the maximum number of nodes to four—one for development and three for production. Secure every connection to your Kafka environment.
Follow Through with Documentation
When designing an application, document the architecture, design decisions, and operational procedures. Write comprehensive unit, integration, and end-to-end tests to ensure application correctness. Consider automating the testing and deployment processes.
