Metadata-Version: 2.1
Name: heapprof
Version: 1.0.0
Summary: Logging heap profiler
Home-page: UNKNOWN
Author: Yonatan Zunger
Author-email: zunger@humu.com
License: MIT
Project-URL: Documentation, https://humu.github.io/heapprof
Project-URL: Source, https://github.com/humu/heapprof
Project-URL: Tracker, https://github.com/humu/heapprof/issues
Description: [![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-374/)
        [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v1.4%20adopted-ff69b4.svg)](https://humu.github.io/heapprof/code-of-conduct.html)
        [![MIT License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://humu.github.io/heapprof/license.html)
        [![CircleCI](https://circleci.com/gh/humu/heapprof/tree/master.svg?style=svg&circle-token=1557bfcabda0155d6505a45e3f00d4a71a005565)](https://circleci.com/gh/humu/heapprof/tree/master)
        
        # heapprof: A Logging Heap Profiler
        
        heapprof is a logging, sampling heap profiler for Python 3.7+.
        
        * "Logging" means that as the program runs, it steadily generates a log of memory allocation and
            release events. This means that you can easily look at memory usage as a function of time.
        * "Sampling" means that it can record only a statistically random sample of memory events. This
            improves performance dramatically while writing logs, and (with the right parameters) sacrifices
            almost no accuracy.
        
        It comes with a suite of visualization and analysis tools (including time plots, flame graphs, and
        flow graphs), as well as an API for doing your own analyses of the results.
        
        heapprof is complementary to [tracemalloc](https://docs.python.org/3/library/tracemalloc.html),
        which is a snapshotting heap profiler. The difference is that tracemalloc keeps track of live memory
        internally, and only writes snapshots when its snapshot() function is called; this means it has
        slightly lower overhead, but you have to know the moments at which you'll want a snapshot before the
        program starts. This makes it particularly useful for finding leaks (from the snapshot at program
        exit), but not as good for understanding events like memory spikes.
        
        You can install heapprof with `pip install heapprof`. heapprof is released under the
        [MIT License](https://humu.github.io/heapprof/license.html).
        
        You can read all the documentation at [humu.github.io/heapprof](https://humu.github.io/heapprof).
        
        **NOTE:** The 1.0.0 release of heapprof does not include compiled Windows binaries. You will need to
        have a compiler and Python build environment installed in order to use this package. This will be
        fixed in an upcoming maintenance release.
        
        ## Navigating the Repository
        
        If you're trying to find something in the GitHub repository, here's a brief directory (since, like
        most Python packages, this is a maze of twisty subdirectories, all different):
        
        * `heapprof` contains the Python package itself. (The API and visualization logic)
        * `_heapprof` contains the C/C++ package. (The core profiling logic)
        * `docs_src` contains the sources for the documentation, mostly as .md and .rst files.
        * `docs` contains the compiled HTML version of `docs_src`, created with `tools/docs.py` and checked
            in.
        * `tools` contains tools useful when modifying heapprof itself.
        * And then there are the configuration files for all the tools:
            * `setup.py` is the master build configuration for the PIP package.
            * `.flake8` and `.pylintrc` are the configuration for Python linting.
            * `CPPLINT.cfg` is the configuration for C/C++ linting.
            * `mypy.ini` is the configuration for Python type checking.
            * `Gemfile` is for setting up Jekyll for documentation hosting.
            * `_config.yml` is the configuration for Jekyll serving.
            * `docs/Makefile` and `docs/conf.py` are the configuration for building the HTML docs image via
                Sphinx.
            * `.circleci` is the configuration for continuous integration testing.
            * `pyproject.toml` and the root `requirements.txt` make setuptools happy.
        * Additional directories which are .gitignored but which show up during use:
            * `build` contains C/C++ dependencies and their compiled images; it's managed by `setup.py`.
            * `_site` contains the final Jekyll site which is served for documentation; it's created if you
                run `bundle exec jekyll serve` to run the docs web server locally.
        
Keywords: development profiling memory
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.7
Description-Content-Type: text/markdown
