Kqr Row Cache Contention — Check Gets

In the bustling data center of the e-commerce platform, there lived a tired but loyal piece of infrastructure: a PostgreSQL database named KQR (Key-Query-Resolver).

KQR had a job: cache frequently accessed rows so the main disk could rest. For years, this worked beautifully. Until .

KQR’s cache logic looked like this (pseudocode): kqr row cache contention check gets

CACHE GETS (total): 10,000 CACHE HITS: 0 CACHE MISSES: 10,000 MISSES WHILE LOCK HELD: 10,000 CONTENTION RATIO: 1.00 TOP CONTENDED ROW: item:42 WAITING THREADS: 9,999 LOCK HOLD TIME (avg): 487ms This was a contention storm . The first thread to acquire the cache lock went to the database (487ms). The other 9,999 threads didn’t just wait — they spun, retried, and choked the CPU.

But they didn’t just rush to the database — they collided at the . You see, KQR’s cache was protected by a single, global synchronized block for writes. In the bustling data center of the e-commerce

, the on-call engineer, saw the alert: kqr row cache contention check gets = CRITICAL She’d seen this before. It wasn’t a database problem — it was a thundering herd problem.

At 9:00:00 AM, a surge of traffic hit. Every user, in every time zone, suddenly demanded the same piece of data: the flash sale metadata for item ID #42. The other 9,999 threads didn’t just wait —

— KQR had a little-known diagnostic command: