Data compression in Kafka is essential for improving system efficiency and performance by reducing message size, which lowers network and storage needs and enhances disk I/O. The study benchmarks various algorithms like Gzip, Zstd, Lz4, and Snappy, highlighting their trade-offs in terms of compression ratio, speed, and resource consumption. Zstd at level 3 was found to be the most optimal for balancing compression efficiency and resource usage. Implementing the right compression strategy can significantly optimize Kafka's handling of large datasets, reduce costs, and maintain high performance under heavy loads.
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