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openhistogram
libcircllhist
C2019–2021
openhistogram / libcircllhist

libcircllhist

A C implementation of OpenHistogram log-linear histograms

Explore on GitHubRead Hacker News discussions
2Distinct people
3Mentions
0Explicit recommenders
116GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A C implementation of OpenHistogram log-linear histograms

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

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 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 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 month3 dated mentions

Observed mentions: 2019-08-29 16:21:38 UTC to 2021-03-06 12:55:25 UTC.

0122019-08-01 00:00:00 UTC to 2019-09-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsAug 20192019-09-01 00:00:00 UTC to 2019-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-10-01 00:00:00 UTC to 2019-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-11-01 00:00:00 UTC to 2019-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-12-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2020-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-01 00:00:00 UTC to 2020-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-01 00:00:00 UTC to 2020-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-04-01 00:00:00 UTC to 2020-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-05-01 00:00:00 UTC to 2020-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMay 20202020-06-01 00:00:00 UTC to 2020-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-07-01 00:00:00 UTC to 2020-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-08-01 00:00:00 UTC to 2020-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-09-01 00:00:00 UTC to 2020-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-10-01 00:00:00 UTC to 2020-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-11-01 00:00:00 UTC to 2020-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-12-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2021-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-02-01 00:00:00 UTC to 2021-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-03-01 00:00:00 UTC to 2021-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMar 2021

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
Aug 20192019-08-01 00:00:00 UTC2019-09-01 00:00:00 UTC202
Sep 20192019-09-01 00:00:00 UTC2019-10-01 00:00:00 UTC000
Oct 20192019-10-01 00:00:00 UTC2019-11-01 00:00:00 UTC000
Nov 20192019-11-01 00:00:00 UTC2019-12-01 00:00:00 UTC000
Dec 20192019-12-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
Jan 20202020-01-01 00:00:00 UTC2020-02-01 00:00:00 UTC000
Feb 20202020-02-01 00:00:00 UTC2020-03-01 00:00:00 UTC000
Mar 20202020-03-01 00:00:00 UTC2020-04-01 00:00:00 UTC000
Apr 20202020-04-01 00:00:00 UTC2020-05-01 00:00:00 UTC000
May 20202020-05-01 00:00:00 UTC2020-06-01 00:00:00 UTC000
Jun 20202020-06-01 00:00:00 UTC2020-07-01 00:00:00 UTC000
Jul 20202020-07-01 00:00:00 UTC2020-08-01 00:00:00 UTC000
Aug 20202020-08-01 00:00:00 UTC2020-09-01 00:00:00 UTC000
Sep 20202020-09-01 00:00:00 UTC2020-10-01 00:00:00 UTC000
Oct 20202020-10-01 00:00:00 UTC2020-11-01 00:00:00 UTC000
Nov 20202020-11-01 00:00:00 UTC2020-12-01 00:00:00 UTC000
Dec 20202020-12-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
Jan 20212021-01-01 00:00:00 UTC2021-02-01 00:00:00 UTC000
Feb 20212021-02-01 00:00:00 UTC2021-03-01 00:00:00 UTC000
Mar 20212021-03-01 00:00:00 UTC2021-04-01 00:00:00 UTC101

What Hacker News says

3 sources
solarengineer·Mar 6, 2021
Original title "Circonus Contributes its Powerful, Patented Histogram Technology to the Open Source Community" (was a few characters too long). "Major contribution creates vendor-neutral industry standard for the interoperability, mergeability, and seamless analysis of histogram telemetry" "Histograms are a data structure that allows users to model the distribution of a set of samples – for example, the age of every human on earth. But instead of storing each sample as its own record, they are grouped together in “buckets” or “bins” which allows for significant data compression and thus superior economics. This compression of data allows for extraordinary metric transmission and ingestion rates, high frequency, real-time analytics, and economical long-term storage. Histograms are also particularly useful in handling the breadth and depth
Read the rest
of metric data produced by container technologies such as Kubernetes. The key to solving the interoperability and mergeability challenges of histograms is in the definition of the bin sizes or “boundaries.” Without standard bin sizes, there is no way to merge histograms together without introducing significant error that carries into the subsequent analysis of the data." https://openhistogram.io/ https://github.com/circonus-labs/libcircllhist
In Circonus Opensources Its Powerful, Patented Histogram Technology
heinrichhartman·Aug 29, 2019
Sorry, but I don't follow this argument: (1) HDR-Histogram merges are 100% accurate and very fast (few microseconds) (2) The range of HDR-Histogram is bounded in the same way that floating point numbers are bounded. Hence the name "High Definition Range": > For example, a Histogram could be configured to track the counts of observed integer values between 0 and 3,600,000,000,000 while maintaining a value precision of 3 significant digits across that range. http://hdrhistogram.github.io/HdrHistogram/ Circllhist offers a default range of 10^-128 .. 10^+127, which has been more than ample for all use-cases I have seen. https://github.com/circonus-labs/libcircllhist/blob/master/s...
In DDSketch: A fast, fully-mergeable quantile sketch with relative-error guarantees
heinrichhartman·Aug 29, 2019
Nice work! Averaging percentiles is well-known to give terrible results. Glad to see more people, taking this problem serious, and providing viable alternatives! A note on Accuracy: At Circonus, we have been using a version of HDR-Histograms [1] for many years to aggregate latency distributions, and calculate accurate aggregated percentiles. Accuracy was never a problem (worst-case error <5%, usually _much_ better). If I read your evaluation results correctly, you also found HRD-Histograms to be as-accurate or more-accurate, than DDSketches, correct? The differentiator to HDR Histograms seems to be merging speed and size, where DDSketches seem to have an edge. One thing that is not immediately clear to me from reading the paper is, how much of the distribution function can be reconstructed from the sketch? E.g. for SLO calculations one is
Read the rest
often interested in latency bands: "How many requests were faster than 100ms?" [2]. Is it possible to approximate CDF values ("lower counts") from the sketch with low/bounded error? [1] https://github.com/circonus-labs/libcircllhist [2] http://heinrichhartmann.com/pdf/Heinrich%20Hartmann%20-%20La...
In DDSketch: A fast, fully-mergeable quantile sketch with relative-error guarantees

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