Mastering Kafka Producer Configs: A Guide to Optimizing Performance
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This document serves as a comprehensive guide on optimizing Kafka producer configurations to enhance performance. It outlines essential properties like batch.size, linger.ms, and compression.type, accompanied by performance test results that demonstrate throughput variations based on different settings. Best practices for configuring these parameters are also provided to help users achieve better reliability and efficiency in their Kafka applications.
![Producer
Record
Accumulator
Metadata Cache
Apache Kafka Cluster
Follower Replica Follower Replica
Leader Replica
MyTopic-1
Send Buffer
compression.type
[none,gzip,snappy,lz4,zstd]](https://image.slidesharecdn.com/lt04-20240319-ksl24-confluent-bhardwajravi-240402160923-a2f4cec7/85/Mastering-Kafka-Producer-Configs-A-Guide-to-Optimizing-Performance-8-320.jpg)
![Producer
was it successful?
Record
Accumulator
Metadata Cache
Apache Kafka Cluster
Follower Replica Follower Replica
Leader Replica
Send Buffer
acks=[all,-1,0,1]](https://image.slidesharecdn.com/lt04-20240319-ksl24-confluent-bhardwajravi-240402160923-a2f4cec7/85/Mastering-Kafka-Producer-Configs-A-Guide-to-Optimizing-Performance-10-320.jpg)
Mastering Kafka Producer Configs: A Guide to Optimizing Performance
- 1. Mastering Kafka Producer Configs A Guide to Optimizing Performance
- 2. Agenda 2 01 Introduction An overview on Kafka Producer API 02 Configs walkthrough Understanding different kafka producer configs and test results Best Practices Learnings 03 01 03 02
- 3. Introduction 3 ● Producers are one of the core components of the Kafka ecosystem, responsible for publishing data to Kafka topics. ● Producer configurations are a set of properties that allow developers to customize the behavior of their Kafka producers. ● Understanding producer configurations is important for optimizing performance, ensuring reliability, and achieving specific use cases. ● Explore the most common Kafka producer configurations and how they can be used to fine-tune your Kafka applications.
- 4. MyTopic-1 Producer Apache Kafka Cluster Follower Replica Record Accumulator Metadata Cache Leader Replica Follower Replica ● batch.size ● linger.ms ● max.request.size
- 5. Performance Test Results ● With default configs: Throughput 66.83 MB/sec ● With batch.size=128000 and linger.ms=50 : Throughput 90.33 MB/sec
- 6. MyTopic-1 Producer Send Buffer Record Accumulator Metadata Cache Apache Kafka Cluster Follower Replica Follower Replica Leader Replica ● buffer.memory ● max.block.ms
- 7. Performance Test Results 7 ● With buffer.memory=100MB: Throughput 74.44 MB/sec
- 8. Producer Record Accumulator Metadata Cache Apache Kafka Cluster Follower Replica Follower Replica Leader Replica MyTopic-1 Send Buffer compression.type [none,gzip,snappy,lz4,zstd]
- 9. 9 ● With compression.type=gzip: Throughput 28.24 MB/sec ● With compression.type=zstd and snappy: Throughput 56.25 and 83.93 MB/sec respectively Performance Test Results
- 10. Producer was it successful? Record Accumulator Metadata Cache Apache Kafka Cluster Follower Replica Follower Replica Leader Replica Send Buffer acks=[all,-1,0,1]
- 11. Performance Test Results 11 ● With acks=1: Throughput= 91.84 MB/sec ● With acks=0: Throughput= 96.26 MB/sec
- 12. Best Practices • Adjust batch.size upwards, but avoid setting it excessively high • Determine the optimal value for linger.ms • Choose the compression.type wisely • Configure acknowledgments (acks) according to your specific needs • Increase buffer.memory only when necessary
- 13. Thank You