pyrobuf 0.5.10

# 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 workin 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% shoe-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.

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

$ PYTHONPATH=. py.test

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

$ python setup.py test

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

`test__gen_message` will attempt to process all the proto files in
`tests/proto`.

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 on 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]

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:
```
>>> test.SerializeToString()
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}}

>>> test.ParseFromDict({'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.

### 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"
)
```

Once installed this sample package can be used as follows:

```
>>> import sample
>>> import my_message_proto
```

### 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.