A Python library for automatically optimizing Kafka producer configurations based on topic-specific recommendations.
Superstream Clients works as a Python import hook that intercepts Kafka producer creation and applies optimized configurations without requiring any code changes in your application. It dynamically retrieves optimization recommendations from Superstream and applies them based on impact analysis.
- kafka-python
- aiokafka
- confluent-kafka
- Faust
- FastAPI event publishers
- Celery Kafka backends
- Any custom wrapper around these Kafka clients
- Zero-code integration: No code changes required in your application
- Dynamic configuration: Applies optimized settings based on topic-specific recommendations
- Intelligent optimization: Identifies the most impactful topics to optimize
- Graceful fallback: Falls back to default settings if optimization fails
pip install superstream-clients && python -m superclient install_pthThat's it! Superclient will now automatically load and optimize all Kafka producers in your Python environment.
After installation, superclient works automatically. Just use your Kafka clients as usual.
When using Superstream Clients with containerized applications, include the package in your Dockerfile:
FROM python:3.8-slim
# Install superclient
RUN pip install superstream-clients
RUN python -m superclient install_pth
# Your application code
COPY . /app
WORKDIR /app
# Run your application
CMD ["python", "your_app.py"]SUPERSTREAM_TOPICS_LIST: Comma-separated list of topics your application produces to
SUPERSTREAM_LATENCY_SENSITIVE: Set to "true" to prevent any modification to linger.ms valuesSUPERSTREAM_DISABLED: Set to "true" to disable optimizationSUPERSTREAM_DEBUG: Set to "true" to enable debug logs
Example:
export SUPERSTREAM_TOPICS_LIST=orders,payments,user-events
export SUPERSTREAM_LATENCY_SENSITIVE=true- Python 3.8 or higher
- Kafka cluster that is connected to the Superstream's console
- Read and write permissions to the
superstream.*topics
Apache License 2.0