How Pinterest Runs Kafka at Scale

What this post added

This post details Pinterest's operational experience running Kafka at scale, focusing on challenges and solutions related to performance and cost control. Key technical aspects include the transition from magnetic disks to SSDs for improved IOPS and reduced latency during broker recovery, the adoption of a static assignment model ('brokerset's) instead of dynamic rebalancing, and efforts to manage message format versions to reduce CPU load. Cost control measures include rack-aware data transfer strategies, enforcing compression on the producer side, and optimizing topic retention and replication factors. The post also touches upon the use of Singer for log ingestion and Maxwell for database changelogs.