tdunning/t-digest

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2166 stars · 232 forks observed · 2026-08-28

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

A Java library implementing the t-digest data structure for accurate online accumulation of rank-based statistics such as quantiles and trimmed means. Digests are compact, fast, and mergeable, making them suitable for map-reduce and parallel streaming workloads.

Use cases

• estimate quantiles of a large data stream in constant memory
• compute median and percentile statistics in Spark or map-reduce jobs
• monitor latency percentiles for high-throughput services
• compute trimmed means over streaming data
• merge distributed sketch summaries into one accurate summary

When to choose

• you need highly accurate extreme quantiles (tails) from streaming or distributed data
• you need compact, mergeable summaries for parallel processing
• you work on the JVM and want a dependency-free, well-tested implementation

When to avoid

• you need exact quantile values, not approximations
• you need histograms or other non-rank-based statistics
• you are not on the JVM and cannot use a Java port

Facets

library · maturity stable

data-science analytics streaming serialization benchmarking data-science analytics big-data performance jvm cross-platform t-digest quantile-estimation sketching streaming-statistics mergeable-summaries approximate-algorithms algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="tdunning/t-digest")