Everything about HyperLogLog
Approximate distinct counting algorithm
HyperLogLog is an algorithm for the count-distinct problem, approximating the number of distinct elements in a multiset. Calculating the exact cardinality of the distinct elements of a multiset requires an amount of memory proportional to the cardinality, which is impractical for very large data sets. Probabilistic cardinality estimators, such as the HyperLogLog algorithm, use significantly less memory than this, but can only approximate the cardinality. The HyperLogLog algorithm is able to estimate cardinalities of > 109 with a typical accuracy (standard error) of 2%, using 1.5 kB of memory. HyperLogLog is an extension of the earlier LogLog algorithm, itself deriving from the 1984 Flajolet–Martin algorithm. Wikipedia →
đź§ Start here: a 6-step reading path
- Read the overview: HyperLogLogA 5-minute orientation
- Skim a recent paper: Using HyperLogLog to Prevent Data Retention in Social Media Streaming Data AnalyticsInfluential work since 2022
- Read the landmark paper: HyperLogLog: the analysis of a near-optimal cardinality estimation algorithmCited 367 times
- Go sideways: Count-distinct problemA closely related topic
- Go sideways: Flajolet–Martin algorithmA closely related topic
đź“– Definitions
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🗂️ The subject at a glance
🏆 Landmark papers (most cited)
HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm
Dashing: fast and accurate genomic distances with HyperLogLog
How can sliding HyperLogLog and EWMA detect port scan attacks in IP traffic?
Security of HyperLogLog (HLL) Cardinality Estimation: Vulnerabilities and Protection
Expected 10-anonymity of HyperLogLog sketches for federated queries of clinical data repositories
Efficient estimation of inclusion coefficient using hyperloglog sketches
UltraLogLog: A Practical and More Space-Efficient Alternative to HyperLogLog for Approximate Distinct Counting
Erasable Virtual HyperLogLog for Approximating Cumulative Distribution over Data Streams
🆕 Influential since 2022
📚 Books worth reading
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đź”— Related topics
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