This guide explains why Kafka backlog occurs when production outpaces consumption and provides practical steps—such as increasing consumer instances, optimizing processing, expanding partitions, applying flow‑control, and managing message capacity—to eliminate the backlog and keep the cluster healthy.
StarRocks 3.5 introduces a cluster‑level Snapshot backup for fast recovery, a bulk‑load optimization that reduces small files and compaction cost, smarter partition management with time‑based merging and TTL, multi‑statement transactions with full ACID guarantees, low‑cardinality dictionary support for lake tables, and several security and performance upgrades.
This article explains what Kafka message backlog is, its symptoms such as consumer latency and broker disk growth, and presents three practical solutions—expanding consumer instances, optimizing consumption logic, and increasing partition count—to effectively eliminate backlog and keep systems stable.
