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axiomhq/hyperloglog

HyperLogLog with lots of sugar (Sparse, LogLog-Beta bias correction and TailCut space reduction) brought to you by Axiom observed · 2026-08-28

github.com/axiomhq/hyperloglog · homepage · Go · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

84/100

  • Activity 94
  • Release rhythm 61
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: 18
  • age_days: 3363
  • days_rel: 260
  • days_push: 40
  • n_releases_24m: 6

Full methodology

Adoption not part of the score

1047 stars · 85 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Go library implementing the HyperLogLog algorithm for approximating the number of distinct elements in a multiset, using LogLog-Beta bias correction and sparse representation. It offers configurable precision from 2^4 to 2^18 registers, balancing memory usage against estimation accuracy.

Use cases

  • count distinct visitors to a website without storing every id
  • estimate unique elements in a large data stream with low memory
  • merge cardinality sketches from distributed shards
  • approximate unique ip addresses in access logs
  • count distinct values in a Go application efficiently

When to choose

  • you need fast, memory-efficient approximate distinct counting in Go
  • you need to merge sketches across nodes order-independently
  • you want tunable precision/memory tradeoffs from 16 bytes to 256 KB

When to avoid

  • you need exact distinct counts
  • you are not working in Go
  • your datasets are small enough that exact counting is cheap

Facets

library · maturity active

data-science analytics developer-tools data-science big-data analytics go cross-platform hyperloglog cardinality-estimation count-distinct loglog-beta probabilistic-data-structures sketching algorithms

4 sources

Member repositories

RepositoryRoleHealth v2
axiomhq/hyperloglogmain84

For agents

markdown · JSON · MCP: product_card(name="axiomhq/hyperloglog")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem