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Fast read/write of AVRO files

Project description

# fastavro
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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. 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

`fastavro` supports the following Python versions:

* 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:

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:

## 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`

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/` you need to run

For `make` to succeed you need both python and Python 3 installed, Cython on both
of them. For `./` you'll need [virtualenv][venv].


### Releasing

We release both to [pypi][pypi] and to [conda-forge][conda-forge].

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

* Make sure the tests pass
* Copy the windows build artifacts for the new version from 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/`
- Submit a [PR][pr]


# Changes

See the [ChangeLog]


# Contact

[Project Home](

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