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        "author": "Timothy Palpant",
        "author_email": "tim@palpant.us",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Software Development :: Testing"
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        "description": "[![PyPI](https://img.shields.io/pypi/v/mprofile)](https://pypi.org/project/mprofile/)\n[![Build Status](https://travis-ci.org/timpalpant/mprofile.svg?branch=master)](https://travis-ci.org/timpalpant/mprofile)\n![PyPI - License](https://img.shields.io/pypi/l/mprofile)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mprofile)\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/mprofile.svg)](https://pypistats.org/packages/mprofile)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n\n# mprofile\n\nA low-overhead sampling memory profiler for Python, derived from [heapprof](https://github.com/humu/heapprof), with an interface similar to [tracemalloc](https://pytracemalloc.readthedocs.io).\nmprofile attempts to give results comparable to tracemalloc, but uses statistical sampling to lower memory and CPU overhead. The sampling algorithm is the one used by [tcmalloc](https://github.com/gperftools/gperftools) and Golang heap profilers.\n\n## Installation & usage\n\n1.  Install the profiler package using PyPI:\n\n    ```shell\n    pip3 install mprofile\n    ```\n\n2.  Enable the profiler in your application, get a snapshot of (sampled) memory usage:\n\n    ```python\n    import mprofile\n\n    mprofile.start(sample_rate=128 * 1024)\n    snap = mprofile.take_snapshot()\n    ```\n\nSee the [tracemalloc](https://docs.python.org/3/library/tracemalloc.html) for API documentation. The API and objects returned by mprofile are compatible.\n\n## Compatibility\n\nmprofile is compatible with Python >= 3.4.\nIt can also be used with earlier versions of Python, but you must build CPython from source and apply the [pytracemalloc patches](https://pytracemalloc.readthedocs.io/install.html#manual-installation).\n\n## Benchmarks\n\nWe are primarily interested in profiling the memory usage of webservers, so used the `tornado_http` benchmark from pyperformance to estimate overhead.\nmprofile has similar performance to tracemalloc when comprehensively tracing all allocations, but when statistical sampling is used, the overhead is significantly reduced.\nIn addition, mprofile interns call stacks in a tree data structure that reduces memory overhead of storing the traces.\n\nWith the recommended setting of `sample_rate=128kB`, we observe ~5% slow down in the `tornado_http` benchmark.\n\nTODO: Run the full [pyperformance](https://pyperformance.readthedocs.io) suite of benchmarks.\n\n### Baseline\n```\nPython 2.7.16, no profiling:\ntornado_http: Mean +- std dev: 664 ms +- 30 ms\nMaximum resident set size (kbytes): 39176\n```\n\n### tracemalloc\n```\nPython 2.7.16, tracemallocframes=128:\ntornado_http: Mean +- std dev: 1.74 sec +- 0.04 sec\nMaximum resident set size (kbytes): 43752\n\n# Saving only one frame in each stack trace rather than full call stacks.\nPython 2.7.16, tracemallocframes=1:\ntornado_http: Mean +- std dev: 960 ms +- 30 ms\nMaximum resident set size (kbytes): 40000\n```\n\n### mprofile\n```\nPython 2.7.16, mprofileframes=128, mprofilerate=1 (i.e. tracemalloc):\ntornado_http: Mean +- std dev: 1.78 sec +- 0.05 sec\nMaximum resident set size (kbytes): 40588\n\nPython 2.7.16, mprofileframes=128, mprofilerate=1024:\ntornado_http: Mean +- std dev: 888 ms +- 28 ms\nMaximum resident set size (kbytes): 39752\n\nPython 2.7.16, mprofileframes=128, mprofilerate=128 * 1024:\ntornado_http: Mean +- std dev: 700 ms +- 26 ms\nMaximum resident set size (kbytes): 39388\n\n# Saving only one frame in each stack trace rather than full call stacks.\nPython 2.7.16, mprofileframes=1, mprofilerate=1 (i.e. tracemalloc):\ntornado_http: Mean +- std dev: 890 ms +- 19 ms\nMaximum resident set size (kbytes): 40152\n\nPython 2.7.16, mprofileframes=1, mprofilerate=1024:\ntornado_http: Mean +- std dev: 738 ms +- 24 ms\nMaximum resident set size (kbytes): 39568\n\nPython 2.7.16, mprofileframes=1, mprofilerate=128 * 1024:\ntornado_http: Mean +- std dev: 678 ms +- 22 ms\nMaximum resident set size (kbytes): 39328\n```\n\n## Developer notes\n\nRun the unit tests:\n```\nbazel test --test_output=streamed //src:profiler_test\n```\n\nRun the benchmarks:\n```\nbazel test -c opt --test_output=streamed //src:profiler_bench\n```\n\nRun the end-to-end (Python) tests:\n```\nbazel test --config asan --test_output=streamed //test:*\n```\n\nRun tests with ASAN and UBSAN:\n```\nbazel test --config asan --test_output=streamed //src:* //test:*\n```\n\n# Contributing\n\nPull requests and issues are welcomed!\n\n# License\n\nmprofile is released under the [MIT License](https://opensource.org/licenses/MIT) and incorporates code from [heapprof](https://github.com/humu/heapprof), which is also released under the MIT license.\n\n\n",
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