# `protobuf`

The `protobuf` library implements predicates for reading (parsing) and writing (generating) data in the Google Protocol Buffers binary format.

This implementation is based on the Protocol Buffers Language Guide (proto3) and the Protocol Buffers Encoding specification:

- https://protobuf.dev/programming-guides/proto3/

- https://protobuf.dev/programming-guides/encoding/

This library requires a backend supporting unbounded integer arithmetic.

## API documentation

Open the ../../apis/library_index.html#avro link in a web browser.

## Loading

To load all entities in this library, load the `loader.lgt` file:

    | ?- logtalk_load(avro(loader)).

## Testing

To test this library predicates, load the `tester.lgt` file:

    | ?- logtalk_load(avro(tester)).

## Protocol Buffers Overview

Protocol Buffers (protobuf) is a language-neutral, platform-neutral extensible mechanism for serializing structured data. It is widely used for data storage, communication protocols, and more.

## Schema Representation

In this library, Protocol Buffers schemas (normally defined in `.proto` files) are represented as Logtalk terms. The schema representation uses a curly-bracketed syntax similar to JSON representation.

A message schema is represented as:

    {message-MessageName, fields-FieldList}

Where `FieldList` is a list of field definitions, each with:

    {number-FieldNumber, name-FieldName, type-FieldType}

### Supported Types

#### Scalar Value Types

- `int32` - Uses variable-length encoding. Inefficient for negative numbers (use `sint32` instead).

- `int64` - Uses variable-length encoding. Inefficient for negative numbers (use `sint64` instead).

- `uint32` - Uses variable-length encoding.

- `uint64` - Uses variable-length encoding.

- `sint32` - Uses variable-length encoding with ZigZag encoding. More efficient for negative numbers.

- `sint64` - Uses variable-length encoding with ZigZag encoding. More efficient for negative numbers.

- `bool` - Boolean value: `true` or `false`.

- `fixed32` - Always four bytes. More efficient than `uint32` if values are often greater than 2^28.

- `fixed64` - Always eight bytes. More efficient than `uint64` if values are often greater than 2^56.

- `sfixed32` - Always four bytes. Signed fixed-width integer.

- `sfixed64` - Always eight bytes. Signed fixed-width integer.

- `float` - 32-bit IEEE 754 floating point.

- `double` - 64-bit IEEE 754 floating point.

- `string` - UTF-8 encoded text.

- `bytes` - Arbitrary byte sequence.

#### Complex Types

- `{message-Name, fields-Fields}` - Embedded message (nested structure).

### Schema Examples

Simple primitive type schemas:

- `int32`

- `string`

- `bool`

Message schema example:

    {message-'Person', fields-[
        {number-1, name-name, type-string},
        {number-2, name-id, type-int32},
        {number-3, name-email, type-string}
    ]}

Nested message schema:

    {message-'AddressBook', fields-[
        {number-1, name-people, type-{message-'Person', fields-[
            {number-1, name-name, type-string},
            {number-2, name-id, type-int32}
        ]}}
    ]}

## Data Representation

Data to be serialized is represented as a list of field name-value pairs using the `-` operator. For example:

    [name-'John Doe', id-42, email-'john@example.com']

For primitive types, data is represented directly as Logtalk values:

- Integers: `42`, `-17`

- Booleans: `true`, `false`

- Floats: `3.14`, `-273.15`

- Strings: `'Hello World'`, `hello`

- Bytes: `[0x48, 0x65, 0x6c, 0x6c, 0x6f]`

## Encoding

Encoding is accomplished using the `generate/3` or `generate/4` predicates.

### Simple Value Encoding

For example, encoding an integer using the `int32` schema:

    | ?- protobuf::generate(bytes(Bytes), int32, 150).
    Bytes = [150, 1]
    yes

Encoding a string:

    | ?- protobuf::generate(bytes(Bytes), string, 'testing').
    Bytes = [116, 101, 115, 116, 105, 110, 103]
    yes

### Message Encoding

To encode a complete message:

    | ?- Schema = {message-'Person', fields-[
    |        {number-1, name-name, type-string},
    |        {number-2, name-id, type-int32}
    |    ]},
    |    Data = [name-'John', id-42],
    |    protobuf::generate(bytes(Bytes), Schema, Data).

    Schema = {...},
    Data = [name-'John', id-42],
    Bytes = [10, 4, 74, 111, 104, 110, 16, 84]
    yes

### Including Schema in Output

To include the schema in the output (as a custom wrapper message), use the `generate/4` predicate with the second argument set to `true`:

    | ?- protobuf::generate(file('output.pb'), true, Schema, Data).
    yes

This generates a wrapper message with:

- Field 1: schema (as a JSON string)

- Field 2: data (as encoded bytes)

## Decoding

Decoding is accomplished using the `parse/2` or `parse/3` predicates.

### Parsing with Schema Embedded

When parsing a file that includes a schema (wrapper message format), use `parse/2` which returns a `Schema-Data` pair:

    | ?- protobuf::parse(file('input.pb'), Schema-Data).

When the schema is not present in the file, `Schema` is unified with `false`.

### Parsing with Known Schema

When the schema is known and not embedded in the file, use `parse/3`:

    | ?- Schema = {message-'Person', fields-[
    |        {number-1, name-name, type-string},
    |        {number-2, name-id, type-int32}
    |    ]},
    |    protobuf::parse(file('person.pb'), Schema, Data).

    Schema = {...},
    Data = [name-'Alice', id-123]
    yes

### Parsing from Different Sources

The library supports three types of sources:

- `bytes(List)` - Parse from a list of bytes

- `stream(Stream)` - Parse from an open binary stream

- `file(Path)` - Parse from a file

## Wire Format Details

Protocol Buffers uses an efficient binary wire format with the following wire types:

- 0 (Varint): `int32`, `int64`, `uint32`, `uint64`, `sint32`, `sint64`, `bool`

- 1 (64-bit): `fixed64`, `sfixed64`, `double`

- 2 (Length-delimited): `string`, `bytes`, embedded messages

- 5 (32-bit): `fixed32`, `sfixed32`, `float`

Each field is encoded with a tag (field number and wire type) followed by the value.

## Binary Format Compatibility

The binary format produced by this library is compatible with:

- Official Protocol Buffers implementations (C++, Java, Python, Go, etc.)

- Other third-party implementations that follow the protobuf specification

Note: This library implements the core binary encoding format. Advanced features like `oneof`, `map`, `enum`, and `repeated` fields are not yet supported in this initial version.

## Examples and Test Files

The `test_files` subdirectory contains example schemas and data files that demonstrate the library’s capabilities:

### person.proto

A simple Person message schema. This is a classic Protocol Buffers example demonstrating basic field types:

- Source: Adapted from the official Protocol Buffers tutorial

- URL: https://protobuf.dev/getting-started/

- License: Apache License 2.0 (compatible with Protocol Buffers documentation)

### addressbook.proto

An AddressBook schema demonstrating nested messages. Shows how to represent complex hierarchical data structures:

- Source: Adapted from the official Protocol Buffers tutorial

- URL: https://protobuf.dev/getting-started/

- License: Apache License 2.0

### Performance Considerations

Protocol Buffers binary format is designed for:

- Compactness: Variable-length encoding for integers

- Speed: Simple encoding/decoding rules

- Forward/backward compatibility: Unknown fields can be skipped

For optimal performance:

- Use `sint32`/`sint64` for negative numbers

- Use `fixed32`/`fixed64` for large numbers

- Keep field numbers low (1-15 use only 1 byte for tags)

## Further Reading

- Protocol Buffers Language Guide: https://protobuf.dev/programming-guides/proto3/

- Protocol Buffers Encoding Guide: https://protobuf.dev/programming-guides/encoding/

- Protocol Buffers Tutorials: https://protobuf.dev/getting-started/