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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

  1. Read the overview: HyperLogLogA 5-minute orientation
  2. Skim a recent paper: Using HyperLogLog to Prevent Data Retention in Social Media Streaming Data AnalyticsInfluential work since 2022
  3. Read the landmark paper: HyperLogLog: the analysis of a near-optimal cardinality estimation algorithmCited 367 times
  4. Go sideways: Count-distinct problemA closely related topic
  5. Go sideways: Flajolet–Martin algorithmA closely related topic

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🗂️ The subject at a glance

  1. Terminology
  2. Algorithm
  3. Operations
  4. Complexity
  5. HLL++
  6. Streaming HLL

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

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đź”— Related topics