scramjet-framework-py 0.10.1

Scramjet is a simple reactive stream programming framework.

Scramjet in Python

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Scramjet is a simple reactive stream programming framework. The code is written by chaining functions that transform the streamed data, including well known map, filter and reduce.

The main advantage of Scramjet is running asynchronous operations on your data streams concurrently. It allows you to perform the transformations both synchronously and asynchronously by using the same API - so now you can "map" your stream from whatever source and call any number of API's consecutively.

Originally written on top of node.js object streams, Scramjet is now being ported into Python. This is what is happening in this repository.

Tested with Python 3.8.10 and Ubuntu 20.04.

Table of contents

• Installation
• Quick start
• Usage
• Requesting features
• Reporting bugs
• Contributing
• Development Setup

Installation

Scramjet Framework is available on PyPI, You can install it with simple pip command:

pip install scramjet-framework-py

Quick start

Let's say we have a fruits.csv file like this:

orange,sweet,1
lemon,sour,2
pigface,salty,5
banana,sweet,3
cranberries,bitter,6

and we want to write the names of the sweet fruits to a separate file. To do this, write an async function like this:

from scramjet import streams
import asyncio


async def sweet_stream():
    with open("fruits.csv") as file_in, open("sweet.txt", "w") as file_out:
        await (
            streams.Stream
            .read_from(file_in)
            .map(lambda line: line.split(','))
            .filter(lambda record: record[1] == "sweet")
            .map(lambda record: f"{record[0]}\n")
            .write_to(file_out)
        )

asyncio.run(sweet_stream())

output saved in sweet.txt:

orange
banana

and that's it!

Usage

Basic building block of Scramjet is the Stream class. It reads input in chunks, performs operations on these chunks and produces an iterable output that can be collected and written somewhere.

Creating a stream is done using read_from class method. It accepts any iterable or an object implementing .read() method as the input, and returns a Stream instance.

Transforming a stream:

• map - transform each chunk in a stream using specified function.
• filter - keep only chunks for which specified function evaluates to True.
• flatmap - run specified function on each chunk, and return all of its results as separate chunks.
• batch - convert a stream of chunks into a stream of lists of chunks.

Each of these methods return the modified stream, so they can be chained like this: some_stream.map(...).filter(...).batch(...)

Collecting data from the stream (asynchronous):

• write_to - write all resulting stream chunks into a target.
• to_list - return a list with all stream chunks.
• reduce - combine all chunks using specified function.

Examples :books:

You can find more examples in hello_datastream.py file. They don't require any additional dependencies, just the standard library, so you can run them simply with:

python hello_datastream.py

Requesting Features

Anything missing? Or maybe there is something which would make using Scramjet Framework much easier or efficient? Don't hesitate to fill up a new feature request! We really appreciate all feedback.

Reporting bugs

If you have found a bug, inconsistent or confusing behavior please fill up a new bug report.

Contributing

You can contribute to this project by giving us feedback (reporting bugs and requesting features) and also by writing code yourself!

The easiest way is to create a fork of this repository and then create a pull request with all your changes. In most cases, you should branch from and target main branch.

Please refer to Development Setup section on how to setup this project.

Development Setup

1. Install Python3 interpreter on your computer. Refer to official docs.

2. Install git version control system. Refer to official docs.

3. Clone this repository:

git clone [e-mail]:scramjetorg/framework-python.git

4. Create and activate a virtualenv:

sudo apt install python3-virtualenv
virtualenv -p python3 venv
.venv/bin/activate

5. Check Python version:

$ python --version
Python 3.8.10

6. Install dependencies:

pip install -r dev-requirements.txt

7. Run test cases (with activated virtualenv):

pytest

:bulb: HINT: add a filename if you want to limit which tests are run

8. If you want to enable detailed debug logging, set one of the following env variables:

PYFCA_DEBUG=1       # debug pyfca
DATASTREAM_DEBUG=1  # debug datastream
SCRAMJET_DEBUG=1    # debug both