Part 3 · Module 23

Probabilistic data structures

Trade a little accuracy for huge savings in memory: Bloom filters for membership, HyperLogLog for counting unique items, and count-min sketches for frequencies and top-K.

Start

0 of 4 lessons27 min

23.1Bloom filtersUp next7m
23.2HyperLogLog7m
23.3Count-min sketch and top-K7m
23.4Sampling and percentile sketches6m
LabHave we sent this coupon?Finish “A region goes down at lunch” first35m

Practice what this module teaches

Web Crawler HLDSchedule a bounded crawl with URL deduplication, per-host politeness and crash recovery.Advanced55 min API Usage Metering Pipeline HLDCount billable-like API units with deduplication, corrections and reproducible usage statements.Advanced60 min Metrics Ingestion

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