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A better Protobuf / gRPC generator & library

Project description

Better Protobuf / gRPC Support for Python

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 datetime and timedelta objects
    • Relative imports
    • Mypy type checking

This project is heavily inspired by, and borrows functionality from:


This project exists because I am unhappy with the state of the official Google protoc plugin for Python.

  • No async support (requires additional grpclib plugin)
  • No typing support or code completion/intelligence (requires additional mypy plugin)
  • No module files get generated
  • Output is not importable
    • Import paths break in Python 3 unless you mess with sys.path
  • Bugs when names clash (e.g. codecs package)
  • 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() and SerializeToString()
    • 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 datetime module

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.

Installation & Getting Started

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 betterproto

Now, 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:

$ protoc -I . --python_betterproto_out=. example.proto

This will generate which looks like:

# Generated by the protocol buffer compiler.  DO NOT EDIT!
# sources: hello.proto
# plugin: python-betterproto
from dataclasses import dataclass

import betterproto

class Hello(betterproto.Message):
    """Greeting represents a message you can tell a user."""

    message: str = betterproto.string_field(1)

Now you can use it!

>>> from hello import Hello
>>> test = Hello()
>>> test

>>> test.message = "Hey!"
>>> test

>>> serialized = bytes(test)
>>> serialized

>>> another = Hello().parse(serialized)
>>> another

>>> another.to_dict()
{"message": "Hey!"}
>>> another.to_json(indent=2)
'{\n  "message": "Hey!"\n}'

Async gRPC Support

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 like:

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);

You can use it like so (enable async in the interactive shell first):

>>> import echo
>>> from grpclib.client import Channel

>>> channel = Channel(host="", port=1234)
>>> service = echo.EchoStub(channel)
>>> await service.echo(value="hello", extra_times=1)
EchoResponse(values=["hello", "hello"])

>>> async for response in service.echo_stream(value="hello", extra_times=1)



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)

Determining if a message was sent

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)

One-of Support

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, "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

# Default (zero) values also work.
>>> = ""
>>> betterproto.which_one_of(test, "foo")
["name", ""]
>>> test.count
>>> test.on

Again this is a little different than the official Google code generator:

# Old way (official Google protobuf package)
>>> message.WhichOneof("group")

# New way (this project)
>>> betterproto.which_one_of(message, "group")
["foo", "foo's value"]

Well-Known Google Types

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

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()

>>> t.maybe = None
>>> t.to_dict()
{'ts': '2019-01-01T12:00:00Z', 'duration': '1.200s'}


First, make sure you have Python 3.6+ and pipenv installed, along with the official Protobuf Compiler for your platform. Then:

# Get set up with the virtual env & dependencies
$ pipenv install --dev

# Link the local package
$ pipenv shell
$ pip install -e .

Code style

This project enforces black python code formatting.

Before commiting changes run:

pipenv run black .

To avoid merge conflicts later, non-black formatted python code will fail in CI.


There are two types of tests:

  1. Standard tests
  2. Custom tests

Standard tests

Adding a standard test case is easy.

  • Create a new directory betterproto/tests/inputs/<name>
    • add <name>.proto with a message called Test
    • add <name>.json with some test data

It will be picked up automatically when you run the tests.

Custom tests

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
$ pipenv run generate

# Run all tests
$ pipenv run test


  • 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!


Copyright © 2019 Daniel G. Taylor

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