Pyrobuf Library

Introduction

Pyrobuf is an alternative to Google's Python Protobuf library.

It generates lightning-fast Cython code that's 2-4x faster than Google's Python Protobuf library using their C++ backend and 20-40x faster than Google's pure-python implementation.

What's more, Pyrobuf is self-contained and easy to install.

Requirements

Pyrobuf requires Cython, and Jinja2. If you want to contribute to pyrobuf you may also want to install pytest.

Pyrobuf does not require protoc.

Pyrobuf has been tested with Python 2.7 and Python 3.5.

Pyrobuf appears to be working on OSX, Linux and Windows (for the latter getting Cython to work properly is the trickiest bit especially if you are still using 2.7).

Contributing

People use protobuf in many different ways. Pyrobuf handles the use cases of AppNexus and other contributors, but is not yet a 100% drop-in replacement to what protoc would generate.

You can help make it so!

Fork and clone the repository, then run:

$ python setup.py develop

It will generate the platform specific pyrobuf_list then compile the pyrobuf_list and pyrobuf_util modules.

Unit Testing

You can run the test suite (a work in progress) using py.test directly:

$ py.test

Or using the test command (which installs pytest if not already available):

$ python setup.py test

Either method will automatically build all the protobuf message specs in tests/proto and point the PYTHONPATH to the built messages before running the tests.

Re-running the develop or test commands will automatically re-build the pyrobuf_list and pyrobuf_util modules if necessary.

The clean command does the house keeping for you:

$ python setup.py clean

If you find that pyrobuf does not work for one of your proto files, add a minimal proto file to tests/proto that breaks before submitting a pull request.

Pull requests including a breaking test are gold!

Improving testing is in the cards.

Installation

You may very well be able to just use pyrobuf as is... just pip it!

$ pip install pyrobuf

Should do the trick!

To check, you may want to make sure the following command does not raise an exception:

$ python -c "import pyrobuf_list"

If it does raise an exception try:

$ pip install pyrobuf -v -v -v --upgrade --force --no-cache

Compiling

When you pip install pyrobuf you get the pyrobuf CLI tool ...:

$ pyrobuf --help
usage: pyrobuf [-h] [--out-dir OUT_DIR] [--build-dir BUILD_DIR] [--install] source

a Cython based protobuf compiler

positional arguments:
  source                filename.proto or directory containing proto files

optional arguments:
  -h, --help            show this help message and exit
  --out-dir OUT_DIR     cythonize output directory [default: out]
  --build-dir BUILD_DIR
                        C compiler build directory [default: build]
  --install             install the extension [default: False]
  --package             the name of the package to install to

If you do not want to have to deal with setuptools entry_points idiosyncrasies you can also do:

$ python -m pyrobuf --help

Use

Suppose you have installed test_message.proto which contains a spec for the message Test. In Python, you can import your new message class by running:

from test_message_proto import Test

With the message class imported, we can create a new message:

test = Test()

Now that we have instantiated a message test, we can fill individual fields:

>>> test.field = 5
>>> test.req_field = 2
>>> test.string_field = "hello!"
>>> test.list_fieldx.append(12)
>>> test.test_ref.field2 = 3.14

And access those same fields:

>>> test.string_field
'hello!'

Once we have at least filled out any "required" fields, we can serialize to a byte array:

bytearray(b'\x10\x05\x1a\x06hello! \x0c2\t\x19\x1f\x85\xebQ\xb8\x1e\t@P\x02')

We can also deserialize a protobuf message to our message instance:

>>> test.ParseFromString('\x10\x05\x1a\x06hello! \x0c2\t\x19\x1f\x85\xebQ\xb8\x1e\t@P\x02')
25

Note that the ParseFromString method returns the number of bytes consumed.

In addition to serializing and deserializing to and from protobuf messages, Pyrobuf also allows us to serialize and deserialize to and from JSON and native Python dictionaries:

>>> test.SerializeToJson()
'{"field": 5, "req_field": 2, "list_fieldx": [12], "string_field": "hello!", "test_ref": {"field2": 3.14}}'

>>> test.ParseFromJson('{"field": 5, "req_field": 2, "list_fieldx": [12], "string_field": "hello!", "test_ref": {"field2": 3.14}}')

>>> test.SerializeToDict()
{'field': 5,
 'list_fieldx': [12],
 'req_field': 2,
 'string_field': 'hello!',
 'test_ref': {'field2': 3.14}}

Finally, the pyrobuf_util module contains functions for encoding and decoding integers.

>>> import pyrobuf_util
>>> pyrobuf_util.to_varint(2**16-1)
bytearray(b'\xff\xff\x03')
>>> pyrobuf_util.from_varint(b'\xff\xff\x03', offset=0)
(65535L, 3)
>>> pyrobuf_util.to_signed_varint(-2**16)
bytearray(b'\xff\xff\x07')
>>> pyrobuf_util.from_signed_varint(b'\xff\xff\x07', offset=0)
(-65536L, 3)

The from_varint and from_signed_varint functions return both the decoded integer and the offset of the first byte after the encoded integer in the source data.

Packaging

If you are compiling multiple messages or a directory of messages and don't want them all to be built to their own separate package but instead want a single namespace containing all your messages, you can specify a package name:

pyrobuf /path/to/proto/specs --install --package=my_messages

Then you can import your message classes from the my_messages pakcage:

>>> from my_messages import MyMessage1, MyMessage2

Distributing a Python Package with Pyrobuf Modules

Suppose you have a Python package called 'sample' arranged on disk as follows:

sample/
    proto/
        my_message.proto
    sample/
        __init__.py
    setup.py

Pyrobuf adds a new setup keyword pyrobuf_modules which can be used to specify either individual protobuf files or folders containing protobuf files. For example, the setup.py file could look like this:

from setuptools import setup, find_packages

setup(
    name="sample",
    version="0.1",
    packages=find_packages(),
    description="A sample package",
    install_requires=['pyrobuf'],
    setup_requires=['pyrobuf'],
    pyrobuf_modules="proto"
)

In addition to the package "sample", setuptools will also build a package named "sample_proto" which will contain the compiled Protobuf messages.

Once installed this sample package can be used as follows:

>>> from sample_proto import MyMessage
>>> my_message = MyMessage()

Performance

On my development machine (Ubuntu 14.04), Pyrobuf is roughly 2.0x as fast as Google's library for message serialization and 2.3x as fast for message deserialization when using the C++ backend for Google's library:

> python tests/perf_test.py
Google took 1.649168 seconds to serialize
Pyrobuf took 0.825525 seconds to serialize
Google took 1.113041 seconds to deserialize
Pyrobuf took 0.466113 seconds to deserialize

When not using the C++ backend, Pyrobuf is roughly 25x as fast for serialization and 55x as fast for deserialization:

Google took 20.215662 seconds to serialize
Pyrobuf took 0.819555 seconds to serialize
Google took 24.990137 seconds to deserialize
Pyrobuf took 0.455732 seconds to deserialize

Differences from the Google library

If pyrobuf is missing a feature from protoc that you need, let us know! We are trying to make it as easy as possible for you to help make pyrobuf better.

For the most part, Pyrobuf should be a drag-and-drop replacement for the Google protobuf library. There are a few differences, though. First, Pyrobuf does not currently implement the ListFields, WhichOneOf, HasExtension, ClearExtension and ByteSize methods.

Second, Pyrobuf simply assumes that the schema being used for a given message is the same on the send and receive ends, so changing the type of a field on one end without changing it on the other may cause bugs; adding or removing fields will not break anything.