How to Tune Elastic Beats Performance: A Practical Example with Batch Size, Worker Count, and More

The blog post provides a practical guide on tuning the performance of Elastic Beats, specifically focusing on optimizing Filebeat's ingestion rates into Elasticsearch by adjusting batch size and worker count. The author highlights that performance tuning is an iterative process, often revealing bottlenecks not in Beats but in subsequent stages. Using a controlled experiment setup with an Elasticsearch cluster and a consistent log file, various batch sizes and worker counts were tested, demonstrating that increasing these parameters does not always yield better performance and that optimal settings depend on specific conditions. The post emphasizes the importance of methodical testing in a stable, repeatable environment and acknowledges that while tuning these parameters can significantly improve performance, it may also increase resource consumption. The article concludes with a preview of more advanced tuning options, suggesting that further tuning can lead to even higher throughput but with additional resource trade-offs.