Because the Apache Python avro package is written in pure Python, it is relatively slow. In one test case, it takes about 14 seconds to iterate through a file of 10,000 records. By comparison, the JAVA avro SDK reads the same file in 1.9 seconds.

The fastavro library was written to offer performance comparable to the Java library. With regular CPython, fastavro uses C extensions which allow it to iterate the same 10,000 record file in 1.7 seconds. With PyPy, this drops to 1.5 seconds (to be fair, the JAVA benchmark is doing some extra JSON encoding/decoding).

fastavro supports the following Python versions:

• Python 2.7
• Python 3.5
• Python 3.6
• Python 3.7
• Python 3.8
• PyPy
• PyPy3

• File Writer
• File Reader (iterating via records or blocks)
• Schemaless Writer
• Schemaless Reader
• JSON Writer
• JSON Reader
• Codecs (Snappy, Deflate, Zstandard, Bzip2)
• Schema resolution
• Aliases
• Logical Types

• Anything involving Avro's RPC features
• Parsing schemas into the canonical form
• Schema fingerprinting

Documentation is available at http://fastavro.readthedocs.io/en/latest/

fastavro is available both on PyPi

pip install fastavro

and on conda-forge conda channel.

conda install -c conda-forge fastavro

• Bugs and new feature requests typically start as github issues where they can be discussed. I try to resolve these as time affords, but PRs are welcome from all.
• Get approval from discussing on the github issue before opening the pull request
• Tests must be passing for pull request to be considered

Developer requirements can be installed with pip install -r developer_requirements.txt. If those are installed, you can run the tests with ./run-tests.sh. If you have trouble installing those dependencies, you can run docker build . to run the tests inside a docker container. This won't test on all versions of python or on pypy, so it's possible to still get CI failures after making a pull request, but we can work through those errors if/when they happen.

We release both to pypi and to conda-forge.

We assume you have twine installed and that you've created your own fork of fastavro-feedstock.

• Make sure the tests pass
• Run make tag
• Copy the windows build artifacts for the new version from https://ci.appveyor.com/project/scottbelden/fastavro to the dist folder
• Copy the linux build artifacts for the new version from https://github.com/fastavro/fastavro/releases/tag/ to the dist folder
• Run make publish
• Note the sha signature emitted at the above
• Switch to feedstock directory and edit recipe/meta.yaml
  • Update version and sha256 variables at the top of the file
  • Run python recipe/test_recipe.py
  • Submit a PR

See the ChangeLog

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