Python API

Users of this library can generally play one of 2 roles (sometimes both):

• record values into 1 or more histograms (histogram provisioning)

• analyze and display histogram content and characteristics (histogram query)

In distributed cases, histogram provisioning can be be done remotely (and possibly in multiple locations) then aggregated in a central place for analysis.

A histogram instance can be created using the HdrHistogram class and specifying the minimum and maximum trackable value and the number of precision digits desired. For example to create a histogram that can count values in the [1..3600000] range and 1% precision (this could be for example to track latencies in the range [1 msec..1 hour]):

histogram = HdrHistogram(1, 60 * 60 * 1000, 2)

By default counters are 64-bit while 16 or 32-bit counters can be specified (word_size option set to 2 or 4 bytes). Note that counter overflow is not tested in this version so be careful when using smaller counter sizes.

Once created it is easy to add values to a histogram:

histogram.record_value(latency)

If the code that generates the values is subject to Coordinated Omission, use the corrected version of that method (example when the expected interval is 10 msec):

histogram.record_corrected_value(latency, 10)

At any time, the histogram can be queried to return any property, such as getting the total number of values recorded or the value at a given percentile:

count = histogram.get_total_count()
value = histogram.get_value_at_percentile(99.9)

Recorded values can be iterated over using the recorded iterator:

for item in histogram.get_recorded_iterator():
    print('value=%f count=%d percentile=%f' %
            item.value_iterated_to,
            item.count_added_in_this_iter_step,
            item.percentile)

A histoblob (base64 encoded/compressed histogram) is a convenient way to serialize and store a histogram instance without losing precision (lossless). The resulting base 64 string can then be stored inside a standard container such as a JSON document, XML, CSV…

The histoblob for a histogram instance can be generated by calling the compress method:

histoblob = histogram.encode()

A histoblob can be decoded into a histogram instance with the decode method:

decoded_histogram = HdrHistogram.decode(histoblob)
count = decoded_histogram.get_total_count()

In the case of aggregation, the decode_and_add method can be used:

aggregation_histogram.decode_and_add(histoblob)

If you want to print the histogram in a plotter-friendly percentile tabular format (.hgrm):

histogram.output_percentile_distribution(file, scaling_ratio)

For additional help on how to use the API:

• browse through the python code and check the API documentation in the comment section for each method (where available)

• the best documentation is by looking at the test code under the test directory

The test code (https://github.com/HdrHistogram/HdrHistogram_py/blob/master/test/test_hdrhistogram.py) pretty much covers every API.

Installation

Pre-requisites:

Make sure you have Python 3.10 or later, and pip installed

pip install hdrhistogram

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

$ tox
...
py313: commands[0]> pytest --cov=hdrh --cov-report=term-missing -vv test
...
====================== 39 passed in 5.11s ======================
lint: commands[0]> pylint --rcfile pylint.rc hdrh test

--------------------------------------------------------------------
Your code has been rated at 10.00/10

  py310: OK
  py311: OK
  py312: OK
  py313: OK
  py314: OK
  lint: OK
  congratulations :)

Display percentile table (.hgrm) from a histoblob (dump_hdrh)

To print the .hgrm percentile table of any histoblob, use the dump_hdrh tool (installed along with the package).

$ dump_hdrh

Usage: dump_hdrh [<string encoded hdr histogram>]*

You can pass one or more histoblobs to the tool:

$ dump_hdrh 'HISTFAAAACl4nJNpmSzMwMDAxQABzFCaEUzOmNZg/wEi0NzIyPSYlWmpGBMAh4gG4A=='

Dumping histogram: HISTFAAAACl4nJNpmSzMwMDAxQABzFCaEUzOmNZg/wEi0NzIyPSYlWmpGBMAh4gG4A==

      Value     Percentile TotalCount 1/(1-Percentile)

139647.000 0.000000000000          1           1.00
139647.000 0.100000000000          1           1.11
139647.000 0.190000000000          1           1.23
139647.000 0.271000000000          1           1.37
187135.000 0.343900000000          2           1.52
187135.000 0.409510000000          2           1.69
187135.000 0.468559000000          2           1.88
187135.000 0.521703100000          2           2.09
187135.000 0.569532790000          2           2.32
187135.000 0.612579511000          2           2.58
187135.000 0.651321559900          2           2.87
477695.000 0.686189403910          3           3.19
477695.000 1.000000000000          3
#[Mean    =   268074.667, StdDeviation   =   149397.390]
#[Max     =   477695.000, TotalCount     =        3.000]
#[Buckets =           14, SubBuckets     =         2048]

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.

Histogram wire encoding and size

Histograms are encoded using the HdrHistogram V2 format which is based on an adapted ZigZag LEB128 encoding where:

• consecutive zero counters are encoded as a negative number representing the count of consecutive zeros

• non zero counter values are encoded as a positive number

An empty histogram (all zeros counters) is encoded in exactly 48 bytes regardless of the counter size. A typical histogram (2 digits precision 1 usec to 1 day range) can be encoded in less than the typical MTU size of 1500 bytes.

This format is compatible with the HdrHistogram Java and C implementations.

Performance

Histogram value recording has the same cost characteristics than the original Java version since it is a direct port (fixed cost for CPU and reduced memory usage). Encoding and decoding in the python version is very fast and close to native performance thanks to the use of:

• integrated C extensions (native C code called from python) that have been developed to handle the low-level byte encoding/decoding/addition work at native speed

• native compression library (zlib and base64)

On a macbook pro (2019 Intel Core i7 @ 2.6GHz) and Linux server (Intel(R) Xeon(R) Gold 5118 CPU @ 2.30GHz):

The typical histogram is defined as one that has 30% of 64-bit buckets filled with sequential values starting at 20% of the array, for a range of 1 usec to 24 hours and 2 digits precision. This represents a total of 3968 buckets, of which the first 793 are zeros, the next 1190 buckets have a sequential/unique value and all remaining buckets are zeros, for an encoded length of 3116 bytes. Most real-world histograms have a much sparser pattern that will yield a lower encoding and decoding time. Decode and add will decode the encoded histogram and add its content to an existing histogram.

To measure the performance of encoding and decoding and get the profiling, you must clone the github repository with git, install it (in a virtual environment if needed) and call pytest with the –runperf option. The 2 profiling functions will provide the profiling information for encoding and decoding the typical histogram 1000 times (so the time values shown are seconds for 1000 decodes/decodes).

Example of run on Linux:

# pytest -s -k test_cod_perf --runperf
=============================================================================== test session starts ================================================================================
platform linux -- Python 3.13.3, pytest-8.3.5, pluggy-1.5.0
rootdir: /root/HdrHistogram_py, configfile: tox.ini
collected 39 items / 38 deselected / 1 selected

test_hdrhistogram.py 0:00:00.061559
         35305 function calls in 0.068 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
      1    0.000    0.000    0.068    0.068 <string>:1(<module>)
   2000    0.002    0.000    0.002    0.000 __init__.py:483(string_at)
   1000    0.000    0.000    0.004    0.000 base64.py:51(b64encode)
      1    0.000    0.000    0.000    0.000 codec.py:119(__init__)
      1    0.000    0.000    0.000    0.000 codec.py:154(_init_counts)
      1    0.000    0.000    0.000    0.000 codec.py:172(get_counts)
   1000    0.004    0.000    0.050    0.000 codec.py:214(compress)
      1    0.000    0.000    0.000    0.000 codec.py:256(__init__)
      1    0.000    0.000    0.000    0.000 codec.py:285(get_counts)
   1000    0.002    0.000    0.061    0.000 codec.py:291(encode)
      1    0.000    0.000    0.000    0.000 codec.py:65(get_encoding_cookie)
      1    0.000    0.000    0.000    0.000 codec.py:69(get_compression_cookie)
   2190    0.001    0.000    0.001    0.000 histogram.py:142(_clz)
   2190    0.002    0.000    0.003    0.000 histogram.py:153(_get_bucket_index)
   2190    0.001    0.000    0.001    0.000 histogram.py:159(_get_sub_bucket_index)
   1190    0.000    0.000    0.000    0.000 histogram.py:162(_counts_index)
   1190    0.001    0.000    0.003    0.000 histogram.py:172(_counts_index_for)
   1190    0.001    0.000    0.005    0.000 histogram.py:177(record_value)
   1190    0.000    0.000    0.000    0.000 histogram.py:232(get_value_from_sub_bucket)
   1190    0.001    0.000    0.001    0.000 histogram.py:235(get_value_from_index)
      1    0.000    0.000    0.000    0.000 histogram.py:34(get_bucket_count)
   1000    0.000    0.000    0.061    0.000 histogram.py:419(encode)
   1000    0.001    0.000    0.003    0.000 histogram.py:462(get_counts_array_index)
      1    0.000    0.000    0.000    0.000 histogram.py:65(__init__)
      1    0.001    0.001    0.006    0.006 test_hdrhistogram.py:408(fill_hist_counts)
      1    0.000    0.000    0.068    0.068 test_hdrhistogram.py:526(check_cod_perf)
   5000    0.000    0.000    0.000    0.000 {built-in method _ctypes.addressof}
   1000    0.004    0.000    0.004    0.000 {built-in method binascii.b2a_base64}
   2190    0.000    0.000    0.000    0.000 {built-in method builtins.bin}
      1    0.000    0.000    0.068    0.068 {built-in method builtins.exec}
   3190    0.000    0.000    0.000    0.000 {built-in method builtins.len}
   1190    0.000    0.000    0.000    0.000 {built-in method builtins.max}
   1190    0.000    0.000    0.000    0.000 {built-in method builtins.min}
      1    0.000    0.000    0.000    0.000 {built-in method builtins.print}
      1    0.000    0.000    0.000    0.000 {built-in method math.ceil}
      1    0.000    0.000    0.000    0.000 {built-in method math.floor}
      4    0.000    0.000    0.000    0.000 {built-in method math.log}
      2    0.000    0.000    0.000    0.000 {built-in method math.pow}
      2    0.000    0.000    0.000    0.000 {built-in method now}
   1000    0.006    0.000    0.006    0.000 {built-in method pyhdrh.encode}
   1000    0.039    0.000    0.039    0.000 {built-in method zlib.compress}
      1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

And for decoding:

# pytest -s -k test_dec_perf --runperf
=============================================================================== test session starts ================================================================================
platform linux -- Python 3.13.3, pytest-8.3.5, pluggy-1.5.0
rootdir: /root/HdrHistogram_py, configfile: tox.ini
collected 39 items / 38 deselected / 1 selected

test_hdrhistogram.py 0:00:00.106705
         118327 function calls in 0.113 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
      1    0.000    0.000    0.113    0.113 <string>:1(<module>)
      2    0.000    0.000    0.000    0.000 __init__.py:483(string_at)
   1000    0.001    0.000    0.001    0.000 base64.py:34(_bytes_from_decode_data)
      1    0.000    0.000    0.000    0.000 base64.py:51(b64encode)
   1000    0.001    0.000    0.010    0.000 base64.py:65(b64decode)
   1001    0.001    0.000    0.019    0.000 codec.py:119(__init__)
   1001    0.004    0.000    0.004    0.000 codec.py:154(_init_counts)
   1000    0.002    0.000    0.012    0.000 codec.py:157(init_counts)
   3001    0.000    0.000    0.000    0.000 codec.py:172(get_counts)
   1000    0.002    0.000    0.018    0.000 codec.py:175(_decompress)
      1    0.000    0.000    0.000    0.000 codec.py:214(compress)
   1001    0.002    0.000    0.002    0.000 codec.py:256(__init__)
   3001    0.001    0.000    0.001    0.000 codec.py:285(get_counts)
      1    0.000    0.000    0.000    0.000 codec.py:291(encode)
   1000    0.003    0.000    0.032    0.000 codec.py:313(decode)
   1000    0.001    0.000    0.011    0.000 codec.py:359(add)
   3000    0.001    0.000    0.001    0.000 codec.py:56(get_cookie_base)
   1000    0.000    0.000    0.001    0.000 codec.py:59(get_word_size_in_bytes_from_cookie)
      1    0.000    0.000    0.000    0.000 codec.py:65(get_encoding_cookie)
   1001    0.000    0.000    0.000    0.000 codec.py:69(get_compression_cookie)
      1    0.000    0.000    0.000    0.000 expression.py:81(lex)
   7191    0.003    0.000    0.005    0.000 histogram.py:142(_clz)
   7191    0.006    0.000    0.011    0.000 histogram.py:153(_get_bucket_index)
   7191    0.002    0.000    0.002    0.000 histogram.py:159(_get_sub_bucket_index)
   1190    0.000    0.000    0.000    0.000 histogram.py:162(_counts_index)
   1190    0.001    0.000    0.003    0.000 histogram.py:172(_counts_index_for)
   1190    0.001    0.000    0.005    0.000 histogram.py:177(record_value)
   10190   0.002    0.000    0.002    0.000 histogram.py:232(get_value_from_sub_bucket)
   4190    0.002    0.000    0.003    0.000 histogram.py:235(get_value_from_index)
   2000    0.002    0.000    0.005    0.000 histogram.py:244(get_lowest_equivalent_value)
   4000    0.004    0.000    0.013    0.000 histogram.py:252(get_highest_equivalent_value)
   1000    0.000    0.000    0.000    0.000 histogram.py:330(get_total_count)
   1001    0.007    0.000    0.007    0.000 histogram.py:34(get_bucket_count)
   2000    0.001    0.000    0.007    0.000 histogram.py:346(get_max_value)
   2000    0.001    0.000    0.007    0.000 histogram.py:351(get_min_value)
      1    0.000    0.000    0.000    0.000 histogram.py:419(encode)
   1000    0.001    0.000    0.006    0.000 histogram.py:445(set_internal_tacking_values)
      1    0.000    0.000    0.000    0.000 histogram.py:462(get_counts_array_index)
   1000    0.005    0.000    0.035    0.000 histogram.py:513(add)
   1000    0.001    0.000    0.106    0.000 histogram.py:544(decode_and_add)
   1000    0.002    0.000    0.071    0.000 histogram.py:563(decode)
   1001    0.008    0.000    0.037    0.000 histogram.py:65(__init__)
      1    0.001    0.001    0.006    0.006 test_hdrhistogram.py:408(fill_hist_counts)
      1    0.000    0.000    0.113    0.113 test_hdrhistogram.py:539(check_dec_perf)
   3005    0.000    0.000    0.000    0.000 {built-in method _ctypes.addressof}
   1000    0.008    0.000    0.008    0.000 {built-in method binascii.a2b_base64}
      1    0.000    0.000    0.000    0.000 {built-in method binascii.b2a_base64}
   7191    0.001    0.000    0.001    0.000 {built-in method builtins.bin}
      1    0.000    0.000    0.113    0.113 {built-in method builtins.exec}
   2000    0.000    0.000    0.000    0.000 {built-in method builtins.isinstance}
   9192    0.001    0.000    0.001    0.000 {built-in method builtins.len}
   3190    0.001    0.000    0.001    0.000 {built-in method builtins.max}
   3190    0.001    0.000    0.001    0.000 {built-in method builtins.min}
      1    0.000    0.000    0.000    0.000 {built-in method builtins.print}
   1001    0.000    0.000    0.000    0.000 {built-in method math.ceil}
   1001    0.000    0.000    0.000    0.000 {built-in method math.floor}
   4004    0.001    0.000    0.001    0.000 {built-in method math.log}
   2002    0.000    0.000    0.000    0.000 {built-in method math.pow}
      2    0.000    0.000    0.000    0.000 {built-in method now}
   1000    0.008    0.000    0.008    0.000 {built-in method pyhdrh.add_array}
   1000    0.007    0.000    0.007    0.000 {built-in method pyhdrh.decode}
      1    0.000    0.000    0.000    0.000 {built-in method pyhdrh.encode}
      1    0.000    0.000    0.000    0.000 {built-in method zlib.compress}
   1000    0.014    0.000    0.014    0.000 {built-in method zlib.decompress}
      1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
   2000    0.001    0.000    0.001    0.000 {method 'from_buffer_copy' of '_ctypes.PyCStructType' objects}

Limitations, Caveats and Known Issues

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

• concurrency support (AtomicHistogram, ConcurrentHistogram…)

• DoubleHistogram

• histogram auto-resize

• recorder function

This implementation has byte endianess encoding issues when used with PyPy due to a limitation of the PyPy code (see https://github.com/HdrHistogram/HdrHistogram_py/issues/13).

The current implementation has issues running on Windows 32-bit systems (library crashing during decode).

Dependencies

The only dependency (outside of using pytest and tox for the unit testing) is the small pbr python package which takes care of the versioning (among other things).

Publishing a New Release to PyPI

To create a new release, apply a new release tag then create a new Release using GitHub (this requires the appropriate permissions). Publishing a Release triggers the Upload Python Package GitHub Actions workflow (.github/workflows/python-publish.yml), which builds the source distribution and binary wheels for Linux, macOS and Windows (CPython 3.10 to 3.14) and uploads them to PyPI using Trusted Publishing (no API token required).

Licensing

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Contribution

External contribution, forks and GitHub pull requests are welcome. For any discussion, head to the gitter HdrHistogram space at https://gitter.im/HdrHistogram/HdrHistogram

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

Links

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