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Pack logtalk -- logtalk-3.98.0/library/protobuf/NOTES.md

This file is part of Logtalk https://logtalk.org/ SPDX-FileCopyrightText: 1998-2026 Paulo Moura <pmoura@logtalk.org> SPDX-License-Identifier: Apache-2.0

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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:

API documentation

Open the [../../apis/library_index.html#avro](../../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:

[105, 110, 116, 51, 50]
[115, 116, 114, 105, 110, 103]
[98, 111, 111, 108]
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:

addressbook.proto

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

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