Awesome
This product is not actively maintained. See streem/pbandk for a more actively maintained fork.
PBAndK
PBAndK is a Kotlin code generator and runtime for Protocol Buffers. It is built to work across multiple Kotlin platforms.
Features
- Clean data class generation
- Works for JVM and JS
- Support for proto2 and proto3 syntaxes
- Oneof's are properly handled as sealed classes
- JVM platform leverages Protobuf's Java library for best performance
- JS platform leverages protobuf.js for best performance
- Support for custom service/gRPC code generator
Not Yet Implemented
- Kotlin Native runtime support
- Protobuf code generator in Kotlin Native for easier importing
- Specialized support for well known types instead of just referencing them
- Code comments on generated code
- JSON support
Read below for more information and see the examples.
Beta
This project is currently in beta yet has the features needed to solve the author's original goals. The "Not Yet Implemented" features above will be implemented only if this project garners reasonable interest. If the features are implemented, it is possible they may be done in a backwards incompatible way.
NOTE: This product is not actively maintained. See https://github.com/streem/pbandk for a more actively maintained fork.
Generated Code
For the following addressbook.proto
file:
syntax = "proto3";
package tutorial;
import "google/protobuf/timestamp.proto";
message Person {
string name = 1;
int32 id = 2;
string email = 3;
enum PhoneType {
MOBILE = 0;
HOME = 1;
WORK = 2;
}
message PhoneNumber {
string number = 1;
PhoneType type = 2;
}
repeated PhoneNumber phones = 4;
google.protobuf.Timestamp last_updated = 5;
}
message AddressBook {
repeated Person people = 1;
}
The following file will be generated at tutorial/addressbook.kt
:
package tutorial
data class Person(
val name: String = "",
val id: Int = 0,
val email: String = "",
val phones: List<tutorial.Person.PhoneNumber> = emptyList(),
val lastUpdated: pbandk.wkt.Timestamp? = null,
val unknownFields: Map<Int, pbandk.UnknownField> = emptyMap()
) : pbandk.Message<Person> {
override operator fun plus(other: Person?) = protoMergeImpl(other)
override val protoSize by lazy { protoSizeImpl() }
override fun protoMarshal(m: pbandk.Marshaller) = protoMarshalImpl(m)
companion object : pbandk.Message.Companion<Person> {
override fun protoUnmarshal(u: pbandk.Unmarshaller) = Person.protoUnmarshalImpl(u)
}
data class PhoneType(override val value: Int) : pbandk.Message.Enum {
companion object : pbandk.Message.Enum.Companion<PhoneType> {
val MOBILE = PhoneType(0)
val HOME = PhoneType(1)
val WORK = PhoneType(2)
override fun fromValue(value: Int) = when (value) {
0 -> MOBILE
1 -> HOME
2 -> WORK
else -> PhoneType(value)
}
}
}
data class PhoneNumber(
val number: String = "",
val type: tutorial.Person.PhoneType = tutorial.Person.PhoneType.fromValue(0),
val unknownFields: Map<Int, pbandk.UnknownField> = emptyMap()
) : pbandk.Message<PhoneNumber> {
override operator fun plus(other: PhoneNumber?) = protoMergeImpl(other)
override val protoSize by lazy { protoSizeImpl() }
override fun protoMarshal(m: pbandk.Marshaller) = protoMarshalImpl(m)
companion object : pbandk.Message.Companion<PhoneNumber> {
override fun protoUnmarshal(u: pbandk.Unmarshaller) = PhoneNumber.protoUnmarshalImpl(u)
}
}
}
data class AddressBook(
val people: List<tutorial.Person> = emptyList(),
val unknownFields: Map<Int, pbandk.UnknownField> = emptyMap()
) : pbandk.Message<AddressBook> {
override operator fun plus(other: AddressBook?) = protoMergeImpl(other)
override val protoSize by lazy { protoSizeImpl() }
override fun protoMarshal(m: pbandk.Marshaller) = protoMarshalImpl(m)
companion object : pbandk.Message.Companion<AddressBook> {
override fun protoUnmarshal(u: pbandk.Unmarshaller) = AddressBook.protoUnmarshalImpl(u)
}
}
// Omitted multiple supporting private extension methods
To see a full version of the file, see here. See the "Generated Code" section below under "Usage" for more details.
Usage
Generating Code
PBAndK's code generator leverages protoc
. Download the
latest protoc and make sure protoc
is on the PATH
. Then
download the latest protoc-gen-kotlin and make sure
protoc-gen-kotlin
is on the PATH
. To generate code from sample.proto
and put in src/main/kotlin
, run:
protoc --kotlin_out=src/main/kotlin sample.proto
For Windows however, protoc
doesn't support finding protoc-gen-kotlin.bat
on the PATH
. So it has to be specified
explicitly as a plugin:
protoc --kotlin_out=src/main/kotlin --plugin=protoc-gen-kotlin=path/to/protoc-gen-kotlin.bat sample.proto
The file is generated as sample.kt
in the subdirectories specified by the package. Like other X_out
arguments,
comma-separated options can be added to --kotlin_out
before the colon and out dir path. To explicitly set the Kotlin
package to my.pkg
, use the kotlin_package
option like so:
protoc --kotlin_out=kotlin_package=my.pkg:src/main/kotlin sample.proto
To log debug logs during generation, log=debug
can be set as well.
In addition to running protoc
manually, the
Protobuf Plugin for Gradle can be used. See
this example to see how.
Runtime Library
PBAndK's runtime library is a thin layer over the preferred Protobuf library for each platform. The libraries are
present on Maven Central. Using Gradle
and assuming mavenCentral()
is one of the repositories
, the dependency can be added for JVM libraries:
dependencies {
compile 'com.github.cretz.pbandk:pbandk-runtime-jvm:0.3.0'
}
It has a dependency on the Google Protobuf Java library. The code targets Java 1.6 to be Android friendly. For Kotlin
JS, change pbandk-runtime-jvm
to pbandk-runtime-js
and for common multiplatform code, change pbandk-runtime-jvm
to
pbandk-runtime-common
.
Service Code Generation
PBAndK does not generate gRPC code itself, but offers a pbandk.gen.ServiceGenerator
interface in the
protoc-gen-kotlin-jvm
project (really in the protoc-gen-kotlin-common
project and inherited) with a single method
that can be implemented to generate the code.
To do this, first depend on the project but it will only be needed at compile time because it's already there at runtime:
dependencies {
compileOnly 'com.github.cretz.pbandk:protoc-gen-kotlin-jvm:0.3.0'
}
Then, the kotlin_service_gen
option can be given to protoc
to use the generator. The option is a path-separated
collection of JAR files to put on the classpath. It can end with a pipe (i.e. |
) following by the fully-qualified
class name of the implementation of the ServiceGenerator
to use. If the last part is not present, it will use the
ServiceLoader
mechanism to find the first implementation to use. For example, to gen with my.Generator
from
gen.jar
, it might look like:
protoc --kotlin_out=kotlin_service_gen=gen.jar|my.Generator,kotlin_package=my.pkg:src/main/kotlin some.proto
For more details, see the custom-service-gen example.
Generated Code
Package
The package is either the kotlin_package
plugin option or the package set in the message. If the google.protobuf
package is referenced, it is assumed to be a well-known type and is changed to reference pbandk.wkt
.
Message
Each Protobuf message extends pbandk.Message
and has two overloaded protoMarshal
methods, the most useful of which
marshals to a byte array. The companion object of every message has two overloaded protoUnmarshal
methods, the most
useful of which accepts a byte array and returns an instance of the class. The other protoMarshal
and protoUnmarshal
methods accept Marshaller
and Unmarshaller
instances respectively which are different for each platform. For
example, the JVM Marshaller
uses com.google.protobuf.CodedOutputStream
.
Messages are immutable Kotlin data classes. This means they automatically implement hashCode
, equals
, and
toString
. Each class has an unknownFields
map which contains information about extra fields the unmarshaller didn't
recognize. If there are values in this map, they will be marshalled on output. The Unmarshaller
instances have a
constructor option to discard unknown fields when reading.
For proto3, the only nullable fields are other messages and oneof fields. Other values have defaults. For proto2,
optional fields are nullable and defaulted as such. Types are basically the same as they would be in Java. However,
bytes
fields actually use a pbandk.ByteArr
class which is a simple wrapper around a byte array. This was done
because Kotlin does not handle array fields in data classes predictably and it wasn't worth overriding equals
and
hashCode
every time.
Regardless of optimize_for
options, the generated code is always the same. Each message has a protoSize
field that
lazily calculates the size of the message when first invoked. Also, each message has the plus
operator defined which
follows protobuf merge semantics.
Oneof
Oneof fields are generated as nested classes of a common sealed base class. Each oneof inner field is a data class that wraps a single value.
Enum
Enum fields are generated as single-integer-value immutable data classes with known values as vals on the companion
object. This is preferred over traditional enum classes because enums in protobuf are open ended and may not be one of
the specific known values. Traditional enum classes would not be able to capture this state and using data classes this
way requires the user to do explicit checks for unknown ordinals during exhaustive when clauses. This does come at very
negligible cost during equality checks. Although there is the normal single-integer-value constructor on the data class,
developers should use the fromValue
method present on the companion object. This will return an existing val if known.
It is possible in future versions that the generated data class constructor will become private.
Note, there is no reflection outside of normal Kotlin reflection. This means Kotlin reflection has to be used to get string values of the enums currently. This may change as the JSON format is developed which requires the string form.
Repeated and Map
Repeated fields are normal lists. Developers should make no assumptions about which list implementation is used. Maps are represented by Kotlin maps. In proto2, due to how map entries are serialized, both the key and the value are considered nullable.
Well-Known Types
Well known types (i.e. those in the google/protobuf
imports) are shipped with the runtime. At this early stage,
specialized support (e.g. using Kotlin Any
for google.protobuf.Any
) is not implemented.
Services
Services can be handled with a custom service generator. See the "Service Code Generation" section above and the custom-service-gen example.
Building
The project is built with Gradle and has several sub projects. In alphabetical order, they are:
conformance/conformance-common
- Multiplatform common code for conformance testsconformance/conformance-js
- JS code for conformance testsconformance/conformance-jvm
- JVM code for conformance testsconformance/conformance-native
- Native code for conformance tests (not built yet)protoc-gen-kotlin/protoc-gen-kotlin-common
- Multiplatform common code for the code generatorprotoc-gen-kotlin/protoc-gen-kotlin-jvm
- JVM code for the code generatorprotoc-gen-kotlin/protoc-gen-kotlin-native
- Native code for the code generator (not built yet)runtime/runtime-common
- Multiplatform common code for runtime Protobuf supportruntime/runtime-js
- JS code for runtime Protobuf supportruntime/runtime-jvm
- JVM code for runtime Protobuf supportruntime/runtime-native
- Native code for runtime Protobuf support
Due to current issues, Gradle must be version 4.7 in the steps below.
Code Generator
To generate the protoc-gen-kotlin
distribution, run:
path/to/gradle :protoc-gen-kotlin:protoc-gen-kotlin-jvm:assembleDist
Or use installDist
to just put it locally and it will be in the build/install
folder.
Runtime Library
To build the runtime library for both JS and the JVM, run:
path/to/gradle :runtime:runtime-js:assemble
path/to/gradle :runtime:runtime-jvm:assemble
Conformance Tests
To run conformance tests, the conformance-test-runner must be built (does not work on Windows). Then both the JS and JVM projects must be built via:
path/to/gradle :conformance:conformance-js:assemble
path/to/gradle :conformance:conformance-jvm:installDist
Set the CONF_TEST_PATH
environment variable to the full path to
path/to/protobuf/conformance/conformance-test-runner
. The JS tests need to have npm install
run in
conformance/conformance-js
. Then simply run conformance/test-conformance.sh
.