Python SDK for Numaflow

This SDK provides the interface for writing UDFs and UDSinks in Python.

Installation

Install the package using pip.

pip install pynumaflow

Build locally

This project uses Poetry for dependency management and packaging. To build the package locally, run the following command from the root of the project.

make setup

To run unit tests:

make test

To format code style using black and ruff:

make lint

Setup pre-commit hooks:

pre-commit install

Implement a User Defined Function (UDF)

Map

from pynumaflow.mapper import Messages, Message, Datum, Mapper


def my_handler(keys: list[str], datum: Datum) -> Messages:
    val = datum.value
    _ = datum.event_time
    _ = datum.watermark
    return Messages(Message(value=val, keys=keys))


if __name__ == "__main__":
    grpc_server = Mapper(handler=my_handler)
    grpc_server.start()

SourceTransformer - Map with event time assignment capability

In addition to the regular Map function, SourceTransformer supports assigning a new event time to the message. SourceTransformer is only supported at source vertex to enable (a) early data filtering and (b) watermark assignment by extracting new event time from the message payload.

from datetime import datetime
from pynumaflow.sourcetransformer import Messages, Message, Datum, SourceTransformer


def transform_handler(keys: list[str], datum: Datum) -> Messages:
    val = datum.value
    new_event_time = datetime.now()
    _ = datum.watermark
    message_t_s = Messages(Message(val, event_time=new_event_time, keys=keys))
    return message_t_s


if __name__ == "__main__":
    grpc_server = SourceTransformer(handler=transform_handler)
    grpc_server.start()

Reduce

import aiorun
from typing import Iterator, List
from pynumaflow.reducer import Messages, Message, Datum, Metadata, AsyncReducer


async def my_handler(
        keys: List[str], datums: Iterator[Datum], md: Metadata
) -> Messages:
    interval_window = md.interval_window
    counter = 0
    async for _ in datums:
        counter += 1
    msg = (
        f"counter:{counter} interval_window_start:{interval_window.start} "
        f"interval_window_end:{interval_window.end}"
    )
    return Messages(Message(str.encode(msg), keys))


if __name__ == "__main__":
    grpc_server = AsyncReducer(handler=my_handler)
    aiorun.run(grpc_server.start())

Sample Image

A sample UDF Dockerfile is provided under examples.

Implement a User Defined Sink (UDSink)

from typing import Iterator
from pynumaflow.sinker import Datum, Responses, Response, Sinker


def my_handler(datums: Iterator[Datum]) -> Responses:
    responses = Responses()
    for msg in datums:
        print("User Defined Sink", msg.value.decode("utf-8"))
        responses.append(Response.as_success(msg.id))
    return responses


if __name__ == "__main__":
    grpc_server = Sinker(my_handler)
    grpc_server.start()

Sample Image

A sample UDSink Dockerfile is provided under examples.