In a gbase database cluster, many slow queries are not caused by poorly written SQL, but by unbalanced data distribution that overloads certain nodes. The problem is often subtle — tests may pass, yet production slows down dramatically when skewed data arrives. This guide provides a systematic approach to identifying, diagnosing, and fixing data skew.
1. What Is Data Skew?
Data skew occurs when data that should be evenly spread across nodes instead concentrates on a few nodes, turning them into bottlenecks. Common causes include poorly chosen distribution keys with hot values, low‑cardinality columns, partition schemes that don't match real write patterns, and mismatch between join key distribution and the underlying storage layout. The result: a few nodes do most of the work, and the overall response time is dictated by the slowest node.
2. Common Symptoms
Same SQL, varying execution times — fast in some runs, suddenly slow when certain business data enters.
