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tdunning
t-digest
Java2016–2024
tdunning / t-digest

t-digest

A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means

Data & DatabasesScience & EngineeringLibraries & SDKs
Explore on GitHubRead Hacker News discussions
14Distinct people
17Mentions
1Explicit recommenders
2.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

Great article! One of my favorite data-structures lately is the https://github.com/tdunning/t-digest. Would be great to see how it compares and what additional accuracy we get…

superyesh used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 8:03:49 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year17 dated mentions

Observed mentions: 2016-12-16 16:55:04 UTC to 2024-07-26 19:15:42 UTC.

012342016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20162017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC110
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC202
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC413
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC303
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC202
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC202

What Hacker News says

2 sources
vvern·Oct 11, 2018
I agree with the premise but it seems that there are more solutions out there. As other commenters noted, you can collect histograms or hdrhistograms. Those have the problem of needing to be precofiguring and of not being able to be merged unless they are configured the same way. Instead you can use the t-digest (https://github.com/tdunning/t-digest), a very cool online quantile estimation data structure from Ted Dunning (which he has recently improved with the Merging approach). There are a number of implementations out there. It is not unreasonable to serialize them and merge them. Unfortunately there’s no easy way to set this up in Prometheus but making that easy could be a fun project
In We can do better than percentile latencies

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