# axiomhq/hyperloglog

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

Repository: https://github.com/axiomhq/hyperloglog
Canonical: https://ross.abutalabs.com/products/hyperloglog
Homepage: https://axiom.co
Language: Go
License: MIT
License Family: permissive
Topics: axiom, data-structures, go, golang, hyperloglog
Last push: 2026-07-24T10:21:01+00:00

## Health v2 (maintenance only)
Score: 84/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 94, release rhythm 61, longevity 100
- inputs: {"age_days": 3363, "days_push": 40, "days_rel": 260, "gap_med": 18, "n_releases_24m": 6}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1047, forks 85 (observed 2026-08-28T04:03:22.227454+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: data-science, analytics, developer-tools
- domain: data-science, big-data, analytics
- platform: go, cross-platform
- tags: hyperloglog, cardinality-estimation, count-distinct, loglog-beta, probabilistic-data-structures, sketching, algorithms

## Member repositories
- axiomhq/hyperloglog (main) score 84

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:22.227454+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:01:05.368124+00:00, confidence not recorded.
  - readme: https://github.com/axiomhq/hyperloglog (fetched 2026-08-28T04:03:22.227454+00:00, sha f938b8792552)
  - homepage: https://axiom.co (fetched 2026-08-29T13:02:23.585946+00:00, sha 63470c054065)
  - site_page: https://docs.axiom.co (fetched 2026-08-29T13:02:23.597661+00:00, sha b08b111c0064)
  - site_page: https://axiom.co/pricing (fetched 2026-08-29T13:02:23.595339+00:00, sha f0259f416dd4)
- Data as of 2026-08-30T08:39:29.467469+00:00.