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Logging heap profiler

Project description

Python 3.7 Contributor Covenant MIT License CircleCI

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.

screenshot of split time plot

heapprof is complementary to tracemalloc, 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.

You can read all the documentation at

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/ and checked in.
  • tools contains tools useful when modifying heapprof itself.
  • And then there are the configuration files for all the tools:
    • 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/ 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
    • _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.

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