Kqr Row Cache Contention Check Gets Now

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

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 row cache contention check gets

From that day on, KQR’s monitoring dashboard had a new rule: If row cache contention check gets > 1000 per second — flip on single-flight mode. And the team learned a valuable lesson: sometimes, the most dangerous lock isn’t in your database — it’s in your cache’s eagerness to help . KQR had a job: cache frequently accessed rows

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. In the bustling data center of the e-commerce

KQR> ROW CACHE CONTENTION CHECK GETS It printed:

def get(key): if key in cache: return cache[key] else: // Only one thread goes to DB; others wait for its result return cache.load_or_wait(key) Within 30 seconds, the contention ratio dropped from 1.00 to 0.001.

KQR’s cache logic looked like this (pseudocode):