Spin up a local kafka broker using docker-compose.yml to follow along:
docker compose up broker -d
Note: check the logs before sending messages to Kafka, these messages may not be delivered until the broker is ready.
Here's the hello-world application using Kafka:
from time import sleep from snapstream import Topic, snap, stream messages = ('🏆', '📞', '🐟', '👌') t = Topic('emoji', { 'bootstrap.servers': 'localhost:29091', 'auto.offset.reset': 'earliest', 'group.instance.id': 'demo', 'group.id': 'demo', }) @snap(messages, sink=[t]) def produce(msg): sleep(0.5) print(f'producing {msg}') return msg @snap(t, sink=[print]) def consume(msg): val = msg.value().decode() return f'got: {val}' stream()
The principles remain the same:
- Any iterable may act as a source of data
- Any callable can be used as a sink
- When we call
stream()
, each iterable is processed in a separate thread - Elements are published to each handler function (that's decorated using
snap
)
Producing 🏆 got: 🏆 Producing 📞 got: 📞 Producing 🐟 got: 🐟 Producing 👌 got: 👌
These simple concepts offer interesting ways to establish complex arbitrary stateful streams.
Note: out of the box advanced features such as synchronizing streams could be offered in the future, feel free to contribute!