Kqr Row Cache Contention Check Gets Here

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

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’s row cache for item:42 expired. 9:00:02 — 10,000 concurrent GET requests arrived simultaneously. kqr row cache contention check gets

KQR> ROW CACHE CONTENTION CHECK GETS It printed:

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): From

, 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.

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). the on-call engineer

def get(key): if key in cache: return cache[key] else: value = db.query("SELECT * FROM items WHERE id = ?", key) // slow cache[key] = value return value Because the cache was empty, all 10,000 threads saw a at the exact same moment. They all rushed to the database.