A tool to dynamically create protobuf message classes from JSON Typedef
Project description
JTD To Proto
This library holds utilities for converting JSON Typedef to Protobuf.
Why?
The protobuf
langauge is a powerful tool for defining language-agnostic, composable datastructures. JSON Typedef
(JTD
) is also a powerful tool to accomplish the same task. Both have advantages and disadvantages that make each fit better for certain use cases. For example:
Protobuf
:- Advantages
- Compact serialization
- Auto-generated
grpc
client and service libraries - Client libraries can be used from different programming languages
- Disadvantages
- Learning curve to understand the full ecosystem
- Not a familiar tool outside of service engineering
- Static compilation step required to use in code
- Advantages
JTD
:- Advantages
- Can be learned in 5 minutes
- Can be written inline in the programming language of choice (e.g. as a
dict
inpython
)
- Disadvantages
- No optimized serialization beyond
json
- No automated service implementations
- Static
jtd-codegen
step needed to generate native structures
- No optimized serialization beyond
- Advantages
This project aims to bring them together so that a given project can take advantage of the best of both:
- Define your structures in
JTD
for simplicity - Dynamically create
google.protobuf.Descriptor
objects to allow forprotobuf
serialization and deserialization - Reverse render a
.proto
file from the generatedDescriptor
so that stubs can be generated in other languages - No static compiliation needed!
Usage
The usage of this library can be best understood with a simple example:
import jtd_to_proto
# Declare the Foo protobuf message class
Foo = jtd_to_proto.descriptor_to_message_class(
jtd_to_proto.jtd_to_proto(
name="Foo",
package="foobar",
jtd_def={
"properties": {
# Bool field
"foo": {
"type": "boolean",
},
# Array of nested enum values
"bar": {
"elements": {
"enum": ["EXAM", "JOKE_SETTING"],
}
}
}
},
)
)
# Declare an object that references Foo as the type for a field
Bar = jtd_to_proto.descriptor_to_message_class(
jtd_to_proto.jtd_to_proto(
name="Bar",
package="foobar",
jtd_def={
"properties": {
"baz": {
"type": Foo.DESCRIPTOR,
},
},
},
),
)
def write_protos(proto_dir: str):
"""Write out the .proto files for Foo and Bar to the given directory"""
Foo.write_proto_file(proto_dir)
Bar.write_proto_file(proto_dir)
Similar Projects
There are a number of similar projects in this space that offer slightly diferent value:
jtd-codegen
: This project focuses on statically generating language-native code (includingpython
) to represent the JTD schema.py-json-to-proto
: This project aims to deduce a schema from an instance of ajson
object.pure-protobuf
: This project has a very similar aim tojtd-to-proto
, but it skips the intermediatedescriptor
representation and thus is not able to produce nativemessage.Message
classes.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Hashes for jtd_to_proto-0.10.0-py310-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6fb6ac197de0d66c49d6a1db6853c96fe8b2116e2ad1be0a46de17c505098a36 |
|
MD5 | d67a2ba8788a87d047efe9fd41227059 |
|
BLAKE2b-256 | 6de59b76cffa74b9647ff600e83a91601f9583af26016195a130cf7f808de1af |
Hashes for jtd_to_proto-0.10.0-py39-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ef67025416fd2013f9725896fefcf25af66ad7f3df28d115c41a16e94da2f2cf |
|
MD5 | e26f3c7b29abd64112d29b68cce5cb05 |
|
BLAKE2b-256 | b2f7c5badcf920bc142ca4bbcfc448daf07b1617f8e18d58f841fedb3583d12f |
Hashes for jtd_to_proto-0.10.0-py38-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9bb53ab25a27b9db23d5de71e549eb3c6dd60604f1cc2b10bb6daee393f32a4b |
|
MD5 | 655e69fc61bdb409f5494b1a21878627 |
|
BLAKE2b-256 | 48158653aa98b4d37ff1ffadd3c7a8036ee949cb6e959f8d06c22d13213b226f |
Hashes for jtd_to_proto-0.10.0-py37-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 74e128e895d7a8afbbee5c83b17b89ae8421539ef38b6196a2ecbcf1c85ebb13 |
|
MD5 | 2f622d2b8f6134c661f780abcd68758a |
|
BLAKE2b-256 | 9196f642fad186fdc6225af690dc9e9137a65b406bcd206d488523cafdd07cca |