If you're reading this on github, please be aware that it might mention unreleased features! See the latest released README on pypi.
This project aims to provide an improved experience when using Protobuf / gRPC in a modern Python environment by making use of modern language features and generating readable, understandable, idiomatic Python code. It will not support legacy features or environments (e.g. Protobuf 2). The following are supported:
- Protobuf 3 & gRPC code generation
- Both binary & JSON serialization is built-in
- Python 3.6+ making use of:
- Enums
- Dataclasses
async/await- Timezone-aware
datetimeandtimedeltaobjects - Relative imports
- Mypy type checking
This project is heavily inspired by, and borrows functionality from:
- https://github.com/protocolbuffers/protobuf/tree/master/python
- https://github.com/eigenein/protobuf/
- https://github.com/vmagamedov/grpclib
This project exists because I am unhappy with the state of the official Google protoc plugin for Python.
- No
asyncsupport (requires additionalgrpclibplugin) - No typing support or code completion/intelligence (requires additional
mypyplugin) - No
__init__.pymodule files get generated - Output is not importable
- Import paths break in Python 3 unless you mess with
sys.path
- Import paths break in Python 3 unless you mess with
- Bugs when names clash (e.g.
codecspackage) - Generated code is not idiomatic
- Completely unreadable runtime code-generation
- Much code looks like C++ or Java ported 1:1 to Python
- Capitalized function names like
HasField()andSerializeToString() - Uses
SerializeToString()rather than the built-in__bytes__() - Special wrapped types don't use Python's
None - Timestamp/duration types don't use Python's built-in
datetimemodule
This project is a reimplementation from the ground up focused on idiomatic modern Python to help fix some of the above. While it may not be a 1:1 drop-in replacement due to changed method names and call patterns, the wire format is identical.
First, install the package. Note that the [compiler] feature flag tells it to install extra dependencies only needed by the protoc plugin:
# Install both the library and compiler
pip install "betterproto[compiler]"
# Install just the library (to use the generated code output)
pip install betterprotoBetterproto is under active development. To install the latest beta version, use pip install --pre betterproto.
Given you installed the compiler and have a proto file, e.g example.proto:
syntax = "proto3";
package hello;
// Greeting represents a message you can tell a user.
message Greeting {
string message = 1;
}You can run the following to invoke protoc directly:
mkdir lib
protoc -I . --python_betterproto_out=lib example.protoor run the following to invoke protoc via grpcio-tools:
pip install grpcio-tools
python -m grpc_tools.protoc -I . --python_betterproto_out=lib example.protoThis will generate lib/hello/__init__.py which looks like:
# Generated by the protocol buffer compiler. DO NOT EDIT!
# sources: example.proto
# plugin: python-betterproto
from dataclasses import dataclass
import betterproto
@dataclass
class Greeting(betterproto.Message):
"""Greeting represents a message you can tell a user."""
message: str = betterproto.string_field(1)Now you can use it!
>>> from lib.hello import Greeting
>>> test = Greeting()
>>> test
Greeting(message='')
>>> test.message = "Hey!"
>>> test
Greeting(message="Hey!")
>>> serialized = bytes(test)
>>> serialized
b'\n\x04Hey!'
>>> another = Greeting().parse(serialized)
>>> another
Greeting(message="Hey!")
>>> another.to_dict()
{"message": "Hey!"}
>>> another.to_json(indent=2)
'{\n "message": "Hey!"\n}'The generated Protobuf Message classes are compatible with grpclib so you are free to use it if you like. That said, this project also includes support for async gRPC stub generation with better static type checking and code completion support. It is enabled by default.
Given an example service definition:
syntax = "proto3";
package echo;
message EchoRequest {
string value = 1;
// Number of extra times to echo
uint32 extra_times = 2;
}
message EchoResponse {
repeated string values = 1;
}
message EchoStreamResponse {
string value = 1;
}
service Echo {
rpc Echo(EchoRequest) returns (EchoResponse);
rpc EchoStream(EchoRequest) returns (stream EchoStreamResponse);
}Generate echo proto file:
python -m grpc_tools.protoc -I . --python_betterproto_out=. echo.proto
A client can be implemented as follows:
import asyncio
import echo
from grpclib.client import Channel
async def main():
channel = Channel(host="127.0.0.1", port=50051)
service = echo.EchoStub(channel)
response = await service.echo(echo.EchoRequest(value="hello", extra_times=1))
print(response)
async for response in service.echo_stream(echo.EchoRequest(value="hello", extra_times=1)):
print(response)
# don't forget to close the channel when done!
channel.close()
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main())which would output
EchoResponse(values=['hello', 'hello'])
EchoStreamResponse(value='hello')
EchoStreamResponse(value='hello')This project also produces server-facing stubs that can be used to implement a Python gRPC server. To use them, simply subclass the base class in the generated files and override the service methods:
import asyncio
from echo import EchoBase, EchoRequest, EchoResponse, EchoStreamResponse
from grpclib.server import Server
from typing import AsyncIterator
class EchoService(EchoBase):
async def echo(self, echo_request: "EchoRequest") -> "EchoResponse":
return EchoResponse([echo_request.value for _ in range(echo_request.extra_times)])
async def echo_stream(self, echo_request: "EchoRequest") -> AsyncIterator["EchoStreamResponse"]:
for _ in range(echo_request.extra_times):
yield EchoStreamResponse(echo_request.value)
async def main():
server = Server([EchoService()])
await server.start("127.0.0.1", 50051)
await server.wait_closed()
if __name__ == '__main__':
loop = asyncio.get_event_loop()
loop.run_until_complete(main())Both serializing and parsing are supported to/from JSON and Python dictionaries using the following methods:
- Dicts:
Message().to_dict(),Message().from_dict(...) - JSON:
Message().to_json(),Message().from_json(...)
For compatibility the default is to convert field names to camelCase. You can control this behavior by passing a casing value, e.g:
MyMessage().to_dict(casing=betterproto.Casing.SNAKE)Sometimes it is useful to be able to determine whether a message has been sent on the wire. This is how the Google wrapper types work to let you know whether a value is unset, set as the default (zero value), or set as something else, for example.
Use betterproto.serialized_on_wire(message) to determine if it was sent. This is a little bit different from the official Google generated Python code, and it lives outside the generated Message class to prevent name clashes. Note that it only supports Proto 3 and thus can only be used to check if Message fields are set. You cannot check if a scalar was sent on the wire.
# Old way (official Google Protobuf package)
>>> mymessage.HasField('myfield')
# New way (this project)
>>> betterproto.serialized_on_wire(mymessage.myfield)Protobuf supports grouping fields in a oneof clause. Only one of the fields in the group may be set at a given time. For example, given the proto:
syntax = "proto3";
message Test {
oneof foo {
bool on = 1;
int32 count = 2;
string name = 3;
}
}You can use betterproto.which_one_of(message, group_name) to determine which of the fields was set. It returns a tuple of the field name and value, or a blank string and None if unset.
>>> test = Test()
>>> betterproto.which_one_of(test, "
8000
foo")
["", None]
>>> test.on = True
>>> betterproto.which_one_of(test, "foo")
["on", True]
# Setting one member of the group resets the others.
>>> test.count = 57
>>> betterproto.which_one_of(test, "foo")
["count", 57]
>>> test.on
False
# Default (zero) values also work.
>>> test.name = ""
>>> betterproto.which_one_of(test, "foo")
["name", ""]
>>> test.count
0
>>> test.on
FalseAgain this is a little different than the official Google code generator:
# Old way (official Google protobuf package)
>>> message.WhichOneof("group")
"foo"
# New way (this project)
>>> betterproto.which_one_of(message, "group")
["foo", "foo's value"]Google provides several well-known message types like a timestamp, duration, and several wrappers used to provide optional zero value support. Each of these has a special JSON representation and is handled a little differently from normal messages. The Python mapping for these is as follows:
| Google Message | Python Type | Default |
|---|---|---|
google.protobuf.duration |
datetime.timedelta |
0 |
google.protobuf.timestamp |
Timezone-aware datetime.datetime |
1970-01-01T00:00:00Z |
google.protobuf.*Value |
Optional[...] |
None |
google.protobuf.* |
betterproto.lib.google.protobuf.* |
None |
For the wrapper types, the Python type corresponds to the wrapped type, e.g. google.protobuf.BoolValue becomes Optional[bool] while google.protobuf.Int32Value becomes Optional[int]. All of the optional values default to None, so don't forget to check for that possible state. Given:
syntax = "proto3";
import "google/protobuf/duration.proto";
import "google/protobuf/timestamp.proto";
import "google/protobuf/wrappers.proto";
message Test {
google.protobuf.BoolValue maybe = 1;
google.protobuf.Timestamp ts = 2;
google.protobuf.Duration duration = 3;
}You can do stuff like:
>>> t = Test().from_dict({"maybe": True, "ts": "2019-01-01T12:00:00Z", "duration": "1.200s"})
>>> t
Test(maybe=True, ts=datetime.datetime(2019, 1, 1, 12, 0, tzinfo=datetime.timezone.utc), duration=datetime.timedelta(seconds=1, microseconds=200000))
>>> t.ts - t.duration
datetime.datetime(2019, 1, 1, 11, 59, 58, 800000, tzinfo=datetime.timezone.utc)
>>> t.ts.isoformat()
'2019-01-01T12:00:00+00:00'
>>> t.maybe = None
>>> t.to_dict()
{'ts': '2019-01-01T12:00:00Z', 'duration': '1.200s'}- Join us on Slack!
- See how you can help → Contributing
-
Python (3.6 or higher)
-
poetry Needed to install dependencies in a virtual environment
-
poethepoet for running development tasks as defined in pyproject.toml
- Can be installed to your host environment via
pip install poethepoetthen executed as simplepoe - or run from the poetry venv as
poetry run poe
- Can be installed to your host environment via
# Get set up with the virtual env & dependencies
poetry install -E compiler
# Activate the poetry environment
poetry shellThis project enforces black python code formatting.
Before committing changes run:
poe formatTo avoid merge conflicts later, non-black formatted python code will fail in CI.
There are two types of tests:
- Standard tests
- Custom tests
Adding a standard test case is easy.
- Create a new directory
betterproto/tests/inputs/<name>- add
<name>.protowith a message calledTest - add
<name>.jsonwith some test data (optional)
- add
It will be picked up automatically when you run the tests.
- See also: Standard Tests Development Guide
Custom tests are found in tests/test_*.py and are run with pytest.
Here's how to run the tests.
# Generate assets from sample .proto files required by the tests
poe generate
# Run the tests
poe testTo run tests as they are run in CI (with tox) run:
poe full-testBetterproto includes compiled versions for Google's well-known types at betterproto/lib/google. Be sure to regenerate these files when modifying the plugin output format, and validate by running the tests.
Normally, the plugin does not compile any references to google.protobuf, since they are pre-compiled. To force compilation of google.protobuf, use the option --custom_opt=INCLUDE_GOOGLE.
Assuming your google.protobuf source files (included with all releases of protoc) are located in /usr/local/include, you can regenerate them as follows:
protoc \
--plugin=protoc-gen-custom=src/betterproto/plugin/main.py \
--custom_opt=INCLUDE_GOOGLE \
--custom_out=src/betterproto/lib \
-I /usr/local/include/ \
/usr/local/include/google/protobuf/*.proto- Fixed length fields
- Packed fixed-length
- Zig-zag signed fields (sint32, sint64)
- Don't encode zero values for nested types
- Enums
- Repeated message fields
- Maps
- Maps of message fields
- Support passthrough of unknown fields
- Refs to nested types
- Imports in proto files
- Well-known Google types
- Support as request input
- Support as response output
- Automatically wrap/unwrap responses
- OneOf support
- Basic support on the wire
- Check which was set from the group
- Setting one unsets the others
- JSON that isn't completely naive.
- 64-bit ints as strings
- Maps
- Lists
- Bytes as base64
- Any support
- Enum strings
- Well known types support (timestamp, duration, wrappers)
- Support different casing (orig vs. camel vs. others?)
- Async service stubs
- Unary-unary
- Server streaming response
- Client streaming request
- Renaming messages and fields to conform to Python name standards
- Renaming clashes with language keywords
- Python package
- Automate running tests
- Cleanup!
Join us on Slack!
Copyright © 2019 Daniel G. Taylor