Storey

Storey is an asynchronous streaming library for real-time event processing and feature extraction. It is part of the MLRun ecosystem.

Features

• Async event processing with backpressure
• Rich set of built-in transformations: Map, Filter, FlatMap, Batch, Choice, JoinWithTable, and more
• Time-window aggregations with pluggable storage (V3IO, Redis, SQL)
• Sources and targets for files (CSV, Parquet), streams (Kafka, V3IO), and databases (Redis, TimescaleDB)
• Streaming (generator) support in Map steps

Installation

pip install storey

Optional extras:

pip install storey[kafka]       # Kafka support
pip install storey[redis]       # Redis support
pip install storey[psycopg]     # TimescaleDB support

Quick Example

from storey import build_flow, SyncEmitSource, Map, Filter, ParquetTarget

controller = build_flow([
    SyncEmitSource(),
    Filter(lambda event: event["amount"] > 0),
    Map(lambda event: {**event, "amount_cents": int(event["amount"] * 100)}),
    ParquetTarget("output.parquet", columns=["user", "amount", "amount_cents"]),
]).run()

controller.emit({"user": "alice", "amount": 9.99})
controller.emit({"user": "bob", "amount": -1.00})
controller.emit({"user": "carol", "amount": 24.50})

controller.terminate()
controller.await_termination()
# output.parquet now contains the two events with positive amounts,
# each enriched with amount_cents.

Documentation

See the MLRun documentation and the storey transformations API reference.

Contributing

See CONTRIBUTING.md for development setup, testing, and coding conventions.

License

Apache License 2.0