hdrhistogram 0.1.1

Acknowledgements

The python code was directly ported from the original HDR Histogram Java and C libraries:

• https://github.com/HdrHistogram/HdrHistogram.git

• https://github.com/HdrHistogram/HdrHistogram_c.git

Installation

Pre-requisites:

Make sure you have python 2.7 and pip installed

pip install hdrhistogram

pip install tox
# cd to the proper location to clone the repository
git clone https://github.com/ahothan/hdrhistogram.git
cd hdrhistogram

$ tox
GLOB sdist-make: /openstack/pyhdr/hdrhistogram/setup.py
py27 inst-nodeps: /openstack/pyhdr/hdrhistogram/.tox/dist/hdrhistogram-0.1.0.zip
py27 installed: flake8==2.4.1,hdrhistogram==0.1.0,mccabe==0.3.1,pep8==1.5.7,py==1.4.30,pyflakes==0.8.1,pytest==2.7.2,wsgiref==0.1.2
py27 runtests: PYTHONHASHSEED='40561919'
py27 runtests: commands[0] | py.test -q -s --basetemp=/openstack/pyhdr/hdrhistogram/.tox/py27/tmp

............................
28 passed in 6.40 seconds

pep8 inst-nodeps: /openstack/pyhdr/hdrhistogram/.tox/dist/hdrhistogram-0.1.0.zip
pep8 installed: flake8==2.4.1,hdr-histogram==0.1,hdrhistogram==0.1.0,mccabe==0.3.1,pep8==1.5.7,py==1.4.30,pyflakes==0.8.1,pytest==2.7.2,wsgiref==0.1.2
pep8 runtests: PYTHONHASHSEED='40561919'
pep8 runtests: commands[0] | flake8 hdrh test
________________________________________________________________________ summary _________________________________________________________________________
  py27: commands succeeded
  pep8: commands succeeded
  congratulations :)
$

Aggregation of Distributed Histograms

Aggregation of multiple histograms into 1 is useful in cases where tools that generate these individual histograms have to run in a distributed way in order to scale sufficiently. As an example, the wrk2 tool (https://github.com/giltene/wrk2.git) is a great tool for measuring the latency of HTTP requests with a large number of connections. Although this tool can support thousands of connections per process, some setups require massive scale in the order of hundreds of thousands of connections which require running a large number of instances of wrk processes, possibly on a large number of servers. Given that each instance of wrk can generate a separate histogram, assessing the scale of the entire system requires aggregating all these histograms into 1 in a way that does not impact the accuracy of the results. So there are 2 problems to solve:

• find a way to properly aggregate multiple histograms without losing any detail

• find a way to transport all these histograms into a central place

This library provides a solution for the aggregation part of the problem:

• reuse the HDR histogram compression format version 1 to encode and compress a complete histogram that can be sent over the wire to the aggregator

• provide python APIs to easily and efficiently:

  • compress an histogram instance into a transportable string

  • decompress a compressed histogram and add it to an existing histogram

Refer to the unit test code (test/test_hdrhistogram.py) to see how these APIs can be used.

Limitations and Caveats

The latest features and bug fixes of the original HDR histogram library may not be available in this python port. List of notable features/APIs not implemented:

• concurrency support (AtomicHistogram, ConcurrentHistogram…)

• DoubleHistogram

• histogram auto-resize

• recorder function

Licensing

This code is licensed under Apache License 2.0. The original implementation in Java is licensed under CCO 1.0 (http://creativecommons.org/publicdomain/zero/1.0/)

Contribution

External contribution and forks are welcome.

Changes can be contributed back using preferably GerritHub (https://review.gerrithub.io/#/q/project:ahothan/hdrhistogram)

GitHub pull requests can also be considered.

Links

• Source: https://github.com/ahothan/hdrhistogram.git