Fast read/write of AVRO files
Project description
fastavro
========
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][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 3.5b2
* PyPy
* PyPy3
[Cython]: http://cython.org/
Usage
=====
Reading
-------
```python
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)
```
Writing
-------
```python
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](http://pypi.python.org/pypi)
pip install fastavro
and on [conda-forge](https://conda-forge.github.io) `conda` channel.
conda install -c conda-forge fastavro
Limitations
===========
* No reader schema
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][venv].
[venv]: http://pypi.python.org/pypi/virtualenv
Builds
======
We're currently using [travis.ci](http://travis-ci.org/#!/tebeka/fastavro)
[](https://travis-ci.org/tebeka/fastavro)
Changes
=======
See the [ChangeLog]
[ChangeLog]: https://github.com/tebeka/fastavro/blob/master/ChangeLog
Contact
=======
[Project Home](https://github.com/tebeka/fastavro)
========
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][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 3.5b2
* PyPy
* PyPy3
[Cython]: http://cython.org/
Usage
=====
Reading
-------
```python
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)
```
Writing
-------
```python
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](http://pypi.python.org/pypi)
pip install fastavro
and on [conda-forge](https://conda-forge.github.io) `conda` channel.
conda install -c conda-forge fastavro
Limitations
===========
* No reader schema
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][venv].
[venv]: http://pypi.python.org/pypi/virtualenv
Builds
======
We're currently using [travis.ci](http://travis-ci.org/#!/tebeka/fastavro)
[](https://travis-ci.org/tebeka/fastavro)
Changes
=======
See the [ChangeLog]
[ChangeLog]: https://github.com/tebeka/fastavro/blob/master/ChangeLog
Contact
=======
[Project Home](https://github.com/tebeka/fastavro)
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