golang-github-spacejam-loghisto 0.0~git20150819.0.3233097-2 source package in Ubuntu

Changelog

golang-github-spacejam-loghisto (0.0~git20150819.0.3233097-2) unstable; urgency=medium

  [ Tim Potter ]
  * Add B-D on newer version dh-golang to build in jessie-backports
  * Add me as uploader

  [ Michael Stapelberg ]
  * Fix Vcs-Git URL

 -- Michael Stapelberg <email address hidden>  Sun, 28 Jan 2018 20:07:29 +0100

Upload details

Uploaded by:
Debian Go Packaging Team
Uploaded to:
Sid
Original maintainer:
Debian Go Packaging Team
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Jammy release universe misc
Impish release universe misc
Hirsute release universe misc
Groovy release universe misc
Focal release universe misc
Bionic release universe misc

Builds

Bionic: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
golang-github-spacejam-loghisto_0.0~git20150819.0.3233097-2.dsc 2.4 KiB 57a5be1d5241d26c669a6f26f48caff85c015f98768850124647a3b55beceebb
golang-github-spacejam-loghisto_0.0~git20150819.0.3233097.orig.tar.xz 11.1 KiB cc50f34239e9f066dcffaecd924c336f5511047751ff92fc38755b64aa204374
golang-github-spacejam-loghisto_0.0~git20150819.0.3233097-2.debian.tar.xz 2.0 KiB 4212e10c13993d5a8e694d5a91dbbc90cd489fe31bb68bd0c8950b0d3acf8c96

No changes file available.

Binary packages built by this source

golang-github-spacejam-loghisto-dev: counters and logarithmically bucketed histograms for distributed systems

 A metric system for high performance counters and histograms. Unlike
 popular metric systems today, this does not destroy the accuracy of
 histograms by sampling. Instead, a logarithmic bucketing function
 compresses values, generally within 1% of their true value (although
 between 0 and 1 the precision loss may not be within this boundary). This
 allows for extreme compression, which allows us to calculate arbitrarily
 high percentiles with no loss of accuracy - just a small amount of
 precision. This is particularly useful for highly-clustered events that
 are tolerant of a small precision loss, but for which you REALLY care
 about what the tail looks like, such as measuring latency across a
 distributed system.