fastavro

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The current Python avro package is packed with features but dog slow.

On a test case of about 10K records, it takes about 14sec to iterate over all of them. In comparison the JAVA avro SDK does it in about 1.9sec.

fastavro is less feature complete than avro, however it's much faster. It iterates over the same 10K records in 2.9sec, and if you use it with PyPy it'll do it in 1.5sec (to be fair, the JAVA benchmark is doing some extra JSON encoding/decoding).

If the optional C extension (generated by Cython) is available, then fastavro will be even faster. For the same 10K records it'll run in about 1.7sec.

fastavro supports the following Python versions:

• Python 2.6
• Python 2.7
• Python 3.4
• Python 3.5
• Python 3.6
• PyPy
• PyPy3

Usage

Reading

import fastavro as avro

with open('weather.avro', 'rb') as fo:
    reader = avro.reader(fo)
    schema = reader.schema

    for record in reader:
        process_record(record)

You may also explicitly specify reader schema to perform schema validation:

import fastavro as avro

schema = {
    'doc': 'A weather reading.',
    'name': 'Weather',
    'namespace': 'test',
    'type': 'record',
    'fields': [
        {'name': 'station', 'type': 'string'},
        {'name': 'time', 'type': 'long'},
        {'name': 'temp', 'type': 'int'},
    ],
}


with open('weather.avro', 'rb') as fo:
    reader = avro.reader(fo, reader_schema=schema)

    # will raise a fastavro.reader.SchemaResolutionError in case of
    # incompatible schema
    for record in reader:
        process_record(record)

Writing

from fastavro import writer

schema = {
    'doc': 'A weather reading.',
    'name': 'Weather',
    'namespace': 'test',
    'type': 'record',
    'fields': [
        {'name': 'station', 'type': 'string'},
        {'name': 'time', 'type': 'long'},
        {'name': 'temp', 'type': 'int'},
    ],
}

# 'records' can be any iterable (including a generator)
records = [
    {u'station': u'011990-99999', u'temp': 0, u'time': 1433269388},
    {u'station': u'011990-99999', u'temp': 22, u'time': 1433270389},
    {u'station': u'011990-99999', u'temp': -11, u'time': 1433273379},
    {u'station': u'012650-99999', u'temp': 111, u'time': 1433275478},
]

with open('weather.avro', 'wb') as out:
    writer(out, schema, records)

You can also use the fastavro script from the command line to dump avro files.

fastavro weather.avro

By default fastavro prints one JSON object per line, you can use the --pretty flag to change this.

You can also dump the avro schema

fastavro --schema weather.avro

Here's the full command line help

usage: fastavro [-h] [--schema] [--codecs] [--version] [-p] [file [file ...]]

iter over avro file, emit records as JSON

positional arguments:
  file          file(s) to parse

optional arguments:
  -h, --help    show this help message and exit
  --schema      dump schema instead of records
  --codecs      print supported codecs
  --version     show program's version number and exit
  -p, --pretty  pretty print json

Installing

fastavro is available both on PyPi

pip install fastavro

and on conda-forge conda channel.

conda install -c conda-forge fastavro

Hacking

As recommended by Cython, the C files output is distributed. This has the advantage that the end user does not need to have Cython installed. However it means that every time you change fastavro/pyfastavro.py you need to run make.

For make to succeed you need both python and Python 3 installed, Cython on both of them. For ./test-install.sh you'll need virtualenv.

Builds

We're currently using travis.ci

Changes

See the ChangeLog

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