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.
, 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. kqr row cache contention check gets
— KQR had a little-known diagnostic command: def get(key): if key in cache: return cache[key]
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. — KQR had a little-known diagnostic command: def
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.
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).
She hot-patched KQR’s logic to use :