Top Related Projects
gRPC to JSON proxy generator following the gRPC HTTP spec
Clean, modern, Python 3.6+ code generator & library for Protobuf 3 and async gRPC
Evans: more expressive universal gRPC client
The best way of working with Protocol Buffers.
Quick Overview
protoc-gen-doc is a documentation generator plugin for the Protocol Buffers compiler (protoc). It generates documentation from Protocol Buffer files in various formats, including HTML, Markdown, and JSON. This tool simplifies the process of creating and maintaining documentation for Protocol Buffer-based APIs.
Pros
- Supports multiple output formats (HTML, Markdown, JSON)
- Customizable templates for generating documentation
- Integrates seamlessly with the Protocol Buffers compiler
- Supports both proto2 and proto3 syntax
Cons
- Limited styling options for HTML output
- Requires manual integration into build processes
- Documentation may become outdated if not regularly updated with proto file changes
- Limited support for complex nested structures in some output formats
Code Examples
- Generate HTML documentation:
protoc --doc_out=./docs --doc_opt=html,index.html proto/*.proto
This command generates HTML documentation for all .proto files in the proto directory and saves it as index.html in the docs folder.
- Generate Markdown documentation:
protoc --doc_out=./docs --doc_opt=markdown,api.md proto/*.proto
This command generates Markdown documentation for all .proto files in the proto directory and saves it as api.md in the docs folder.
- Generate JSON documentation:
protoc --doc_out=./docs --doc_opt=json,api.json proto/*.proto
This command generates JSON documentation for all .proto files in the proto directory and saves it as api.json in the docs folder.
Getting Started
- Install protoc-gen-doc:
go install github.com/pseudomuto/protoc-gen-doc/cmd/protoc-gen-doc@latest
- Generate documentation:
protoc --doc_out=./docs --doc_opt=html,index.html path/to/your/*.proto
Replace html,index.html
with your desired output format and filename. Adjust the path to your .proto files as needed.
Competitor Comparisons
gRPC to JSON proxy generator following the gRPC HTTP spec
Pros of grpc-gateway
- Provides a RESTful API gateway for gRPC services, allowing easy integration with existing REST ecosystems
- Supports automatic OpenAPI (Swagger) documentation generation
- Offers bidirectional streaming and server-side streaming capabilities
Cons of grpc-gateway
- More complex setup and configuration compared to protoc-gen-doc
- Requires additional runtime dependencies and infrastructure
- May introduce performance overhead due to protocol translation
Code Comparison
grpc-gateway:
service YourService {
rpc CreateUser(CreateUserRequest) returns (User) {
option (google.api.http) = {
post: "/v1/users"
body: "*"
};
}
}
protoc-gen-doc:
service YourService {
// CreateUser creates a new user
rpc CreateUser(CreateUserRequest) returns (User);
}
grpc-gateway focuses on generating a RESTful API gateway and OpenAPI documentation, while protoc-gen-doc is primarily designed for generating documentation from Protocol Buffer files. grpc-gateway requires additional annotations and configuration but offers more functionality in terms of API exposure and integration. protoc-gen-doc provides a simpler approach to documentation generation without the added complexity of API gateway features.
Clean, modern, Python 3.6+ code generator & library for Protobuf 3 and async gRPC
Pros of python-betterproto
- Generates Python-specific code with type hints and dataclasses
- Offers runtime performance improvements over standard protobuf libraries
- Provides a more Pythonic API for working with protobuf messages
Cons of python-betterproto
- Limited to Python language, unlike protoc-gen-doc's language-agnostic documentation
- May require additional setup and integration compared to protoc-gen-doc's simpler documentation generation
- Less focused on documentation generation, which is the primary purpose of protoc-gen-doc
Code Comparison
protoc-gen-doc (documentation generation):
{{range .Files}}
{{range .Messages}}
## {{.LongName}}
{{.Description}}
{{end}}
{{end}}
python-betterproto (Python code generation):
@dataclass
class ExampleMessage(betterproto.Message):
field1: str = betterproto.string_field(1)
field2: int = betterproto.int32_field(2)
The code snippets demonstrate the different focus areas of these projects. protoc-gen-doc generates documentation templates, while python-betterproto produces Python code with type hints and dataclasses for working with protobuf messages.
Evans: more expressive universal gRPC client
Pros of Evans
- Interactive CLI tool for real-time gRPC communication and testing
- Supports both unary and streaming RPCs
- Offers a REPL environment for exploring gRPC services
Cons of Evans
- Limited to gRPC interaction and doesn't generate documentation
- Requires manual input for each request, which can be time-consuming for complex APIs
- Less suitable for generating comprehensive API documentation
Code Comparison
Evans (REPL interaction):
> call SayHello
name (TYPE_STRING) => Alice
message (package.HelloReply) => {
"message": "Hello, Alice!"
}
protoc-gen-doc (generated documentation):
<h2>SayHello</h2>
<p>Sends a greeting</p>
<h3>Request</h3>
<table>
<tr><th>Field</th><th>Type</th><th>Description</th></tr>
<tr><td>name</td><td>string</td><td>The name to greet</td></tr>
</table>
Summary
Evans is an interactive gRPC client tool, while protoc-gen-doc is a documentation generator for Protocol Buffers. Evans excels in real-time API testing and exploration, whereas protoc-gen-doc is better suited for creating comprehensive API documentation. The choice between them depends on whether you need interactive testing capabilities or static documentation generation for your gRPC services.
The best way of working with Protocol Buffers.
Pros of buf
- Comprehensive toolchain for Protocol Buffers, including linting, breaking change detection, and code generation
- Supports remote caching and distributed builds, improving performance for large projects
- Offers a modern CLI interface with intuitive commands and extensive documentation
Cons of buf
- Steeper learning curve due to its broader feature set
- May be overkill for simple projects that only need documentation generation
- Requires additional setup and configuration compared to simpler tools
Code comparison
protoc-gen-doc:
protoc --doc_out=./docs --doc_opt=markdown,docs.md myproto.proto
buf:
buf generate
Additional notes
protoc-gen-doc focuses specifically on documentation generation for Protocol Buffers, while buf is a more comprehensive toolchain. protoc-gen-doc is simpler to use for basic documentation needs, but buf offers more features and scalability for larger projects. buf's code generation is typically configured in a buf.gen.yaml
file, allowing for more flexible and powerful generation options.
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protoc-gen-doc
This is a documentation generator plugin for the Google Protocol Buffers compiler (protoc
). The plugin can generate
HTML, JSON, DocBook, and Markdown documentation from comments in your .proto
files.
It supports proto2 and proto3, and can handle having both in the same context (see examples for proof).
Installation
There is a Docker image available (docker pull pseudomuto/protoc-gen-doc
) that has everything you need to generate
documentation from your protos.
If you'd like to install this locally, you can go get
it.
go install github.com/pseudomuto/protoc-gen-doc/cmd/protoc-gen-doc@latest
Alternatively, you can download a pre-built release for your platform from the releases page.
Finally, this plugin is also available on Maven Central. For details about how to use it, check out the gradle example.
Invoking the Plugin
The plugin is invoked by passing the --doc_out
, and --doc_opt
options to the protoc
compiler. The option has the
following format:
--doc_opt=<FORMAT>|<TEMPLATE_FILENAME>,<OUT_FILENAME>[,default|source_relative]
The format may be one of the built-in ones ( docbook
, html
, markdown
or json
)
or the name of a file containing a custom Go template.
If the source_relative
flag is specified, the output file is written in the same relative directory as the input file.
Using the Docker Image (Recommended)
The docker image has two volumes: /out
and /protos
which are the directory to write the documentation to and the
directory containing your proto files.
You could generate HTML docs for the examples by running the following:
docker run --rm \
-v $(pwd)/examples/doc:/out \
-v $(pwd)/examples/proto:/protos \
pseudomuto/protoc-gen-doc
By default HTML documentation is generated in /out/index.html
for all .proto
files in the /protos
volume. This can
be changed by passing the --doc_opt
parameter to the container.
For example, to generate Markdown for all the examples:
docker run --rm \
-v $(pwd)/examples/doc:/out \
-v $(pwd)/examples/proto:/protos \
pseudomuto/protoc-gen-doc --doc_opt=markdown,docs.md
You can also generate documentation for a single file. This can be done by passing the file(s) to the command:
docker run --rm \
-v $(pwd)/examples/doc:/out \
-v $(pwd)/examples/proto:/protos \
pseudomuto/protoc-gen-doc --doc_opt=markdown,docs.md Booking.proto [OPTIONALLY LIST MORE FILES]
You can also exclude proto files that match specific path expressions. This is done by passing a second option delimited
by :
. For example, you can pass any number of comma separated patterns as the second option:
docker run --rm \
-v $(pwd)/examples/doc:/out \
-v $(pwd)/examples/proto:/protos \
pseudomuto/protoc-gen-doc --doc_opt=:google/*,somepath/*
Remember: Paths should be from within the container, not the host!
NOTE: Due to the way wildcard expansion works with docker you cannot use a wildcard path (e.g.
protos/*.proto
) in the file list. To get around this, if no files are passed, the container will generate docs forprotos/*.proto
, which can be changed by mounting different volumes.
Simple Usage
For example, to generate HTML documentation for all .proto
files in the proto
directory into doc/index.html
, type:
protoc --doc_out=./doc --doc_opt=html,index.html proto/*.proto
The plugin executable must be in PATH
for this to work.
Using a precompiled binary
Alternatively, you can specify a pre-built/not in PATH
binary using the --plugin
option.
protoc \
--plugin=protoc-gen-doc=./protoc-gen-doc \
--doc_out=./doc \
--doc_opt=html,index.html \
proto/*.proto
With a Custom Template
If you'd like to use your own template, simply use the path to the template file rather than the type.
protoc --doc_out=./doc --doc_opt=/path/to/template.tmpl,index.txt proto/*.proto
For information about the available template arguments and functions, see Custom Templates. If you just want
to customize the look of the HTML output, put your CSS in stylesheet.css
next to the output file and it will be picked
up.
Writing Documentation
Messages, Fields, Services (and their methods), Enums (and their values), Extensions, and Files can be documented. Generally speaking, comments come in 2 forms: leading and trailing.
Leading comments
Leading comments can be used everywhere.
/**
* This is a leading comment for a message
*/
message SomeMessage {
// this is another leading comment
string value = 1;
}
NOTE: File level comments should be leading comments on the syntax directive.
Trailing comments
Fields, Service Methods, Enum Values and Extensions support trailing comments.
enum MyEnum {
DEFAULT = 0; // the default value
OTHER = 1; // the other value
}
Excluding comments
If you want to have some comment in your proto files, but don't want them to be part of the docs, you can simply prefix
the comment with @exclude
.
Example: include only the comment for the id
field
/**
* @exclude
* This comment won't be rendered
*/
message ExcludedMessage {
string id = 1; // the id of this message.
string name = 2; // @exclude the name of this message
/* @exclude the value of this message. */
int32 value = 3;
}
Check out the example protos to see all the options.
Output Example
With the input .proto
files
the plugin gives the output
Check out the examples
task in the Makefile to see how these were generated.
Top Related Projects
gRPC to JSON proxy generator following the gRPC HTTP spec
Clean, modern, Python 3.6+ code generator & library for Protobuf 3 and async gRPC
Evans: more expressive universal gRPC client
The best way of working with Protocol Buffers.
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual Copilot