ds-event-stream-python-sdk 0.1.1

A modern Python SDK for Kafka event streaming (Grasp Labs)

ds-event-stream-python-sdk

A Python SDK for producing and consuming events on a Kafka-based event stream, following Grasp Labs' event stream conventions.

Features

• Dataclass Models: Auto-generated from JSON schemas for type-safe event handling
• Kafka Producer/Consumer Services: Simple interfaces for sending and receiving events
• Service Principal Configuration: Centralized config via KafkaConfig for authentication and connection settings
• Mockable for Testing: All Kafka interactions can be mocked for CI and local testing

Installation

1. Clone the repository:

   git clone https://github.com/grasp-labs/ds-event-stream-python-sdk.git
   cd ds-event-stream-python-sdk
   

2. Set up a Python virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install the package:

   pip install -e .
   

4. (Optional) Generate models from schemas:

   make generate-models
   

Quick Start

1. Configure Kafka Connection

from dseventstream.kafka.kafka_config import KafkaConfig

config = KafkaConfig(
    bootstrap_servers="kafka-prod:9092",
    username="service-principal",
    password="secret"
)

2. Create Producer/Consumer

from dseventstream.kafka.kafka_service import KafkaProducerService, KafkaConsumerService

producer = KafkaProducerService(config=config)
consumer = KafkaConsumerService(config=config, group_id="my-group")

3. Send an Event

from dseventstream.models.event import Event

event = Event(
    id="event-id",
    session_id="session-id",
    request_id="request-id",
    tenant_id="tenant-id",
    event_type="type",
    event_source="source",
    metadata={"key": "value"},
    timestamp="2025-09-18T00:00:00Z",
    created_by="user",
    md5_hash="..."
)

producer.send("topic-name", event)

4. Consume Events

def on_message(event_dict):
    print(f"Received event: {event_dict}")

consumer.consume("topic-name", on_message)

Package Structure

dseventstream/
├── models/
│   ├── event.py           # Event dataclass (auto-generated)
│   └── system_topics.py   # System topics enum (auto-generated)
└── kafka/
    ├── kafka_service.py   # Producer/Consumer service classes
    └── kafka_config.py    # Shared config for Kafka principals

docs/                      # Documentation source files
├── README.md             # Main documentation
├── usage.md              # Usage examples
├── api.md                # API reference
├── testing.md            # Testing guide
└── license.md            # License information

tests/                    # Test suite
├── test_kafka_producer.py
├── test_kafka_consumer.py
└── test_kafka_mock.py

schemas/                  # JSON schemas for model generation
├── event.json
└── system-topics.json

.github/workflows/        # CI/CD pipelines
├── deploy-docs.yml       # Documentation deployment
└── deploy-pypi.yml       # PyPI release automation

API Reference

Core Classes

KafkaConfig

Centralized configuration for Kafka connections.

from dseventstream.kafka.kafka_config import KafkaConfig

# Production configuration
config = KafkaConfig(
    bootstrap_servers="kafka-prod:9092",
    username="service-principal",
    password="secret"
)

# Development configuration
config = KafkaConfig(bootstrap_servers="localhost:9092")

KafkaProducerService

Service for publishing events to Kafka topics.

from dseventstream.kafka.kafka_service import KafkaProducerService

producer = KafkaProducerService(config=config)
producer.send("topic-name", event, key="optional-key")

KafkaConsumerService

Service for consuming events from Kafka topics.

from dseventstream.kafka.kafka_service import KafkaConsumerService

consumer = KafkaConsumerService(config=config, group_id="my-group")

def handle_message(event_dict):
    print(f"Received: {event_dict}")

consumer.consume("topic-name", handle_message)

Event

Auto-generated dataclass for event structure.

from dseventstream.models.event import Event

event = Event(
    id="unique-event-id",
    session_id="session-123",
    request_id="request-456",
    tenant_id="tenant-789",
    event_type="user.action",
    event_source="web-app",
    metadata={"key": "value"},
    timestamp="2025-01-01T00:00:00Z",
    created_by="user-id",
    md5_hash="computed-hash"
)

Development

Prerequisites

• Python 3.13+
• Make (for code generation)

Setup Development Environment

# Install development dependencies
pip install -e ".[dev]"

# Generate models from schemas
make generate-models

# Run tests
make test

Testing

All Kafka interactions are mockable using unittest.mock. Example tests are provided in the tests/ folder.

# Run all tests
python -m unittest discover -s tests

# Run specific test
python -m unittest tests.test_kafka_producer

Security

• SASL_PLAINTEXT and SCRAM-SHA-512 are used for authentication (see KafkaConfig)
• For security issues, please see our Security Policy

Contributing

We welcome contributions! Here's how to get started:

Development Workflow

1. Fork and Clone

   git clone https://github.com/your-username/ds-event-stream-py-sdk.git
   cd ds-event-stream-py-sdk
   

2. Set up Development Environment

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -e ".[dev]"
   

3. Make Changes and Test

   # Generate models if needed
   make generate-models
   
   # Run tests
   make test
   # or
   python -m unittest discover -s tests
   

4. Create Pull Request

   • Create a feature branch: git checkout -b feature/your-feature-name
   • Make your changes and commit them
   • Push to your fork and create a PR

Automated Release Process

This project uses automated releases when PRs are merged to main. The version bump type is determined by your PR title:

Version Bump Types

PR Title Contains	Version Bump	Example
major, breaking, BREAKING CHANGE	Major	0.1.0 → 1.0.0
feat, feature, minor	Minor	0.1.0 → 0.2.0
Default (bug fixes, docs, etc.)	Patch	0.1.0 → 0.1.1

Examples

# Patch release (bug fix)
PR Title: "Fix kafka connection timeout issue"
→ Version: 0.1.0 → 0.1.1

# Minor release (new feature)
PR Title: "feat: Add event filtering capabilities"
→ Version: 0.1.0 → 0.2.0

# Major release (breaking change)
PR Title: "BREAKING CHANGE: Redesign API interface"
→ Version: 0.1.0 → 1.0.0

What Happens When PR is Merged

1. ✅ Version automatically bumped in pyproject.toml
2. ✅ Git tag created (v{new_version})
3. ✅ GitHub release created with changelog
4. ✅ Package published to PyPI automatically

Code Style and Standards

• Follow PEP 8 for Python code style
• Add type hints where appropriate
• Write tests for new functionality
• Update documentation for API changes
• Use descriptive commit messages

Testing

# Run all tests
python -m unittest discover -s tests

# Run specific test file
python -m unittest tests.test_kafka_producer

# Run with coverage (if installed)
coverage run -m unittest discover -s tests
coverage report

Documentation

Full documentation is available at: https://grasp-labs.github.io/ds-event-stream-python-sdk/

Installation for End Users

From PyPI (Recommended)

pip install ds-event-stream-python-sdk

From Source

git clone https://github.com/grasp-labs/ds-event-stream-py-sdk.git
cd ds-event-stream-py-sdk
pip install -e .

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.