https://github.com/maki-nage/makinage

Stream Processing Made Easy
https://github.com/maki-nage/makinage

distributed-systems kafka machine-learning python reactive-machine-learning reactive-programming reactive-systems stream-processing streaming streaming-data

Last synced: 5 months ago
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Stream Processing Made Easy

• Host: GitHub
• URL: https://github.com/maki-nage/makinage
• Owner: maki-nage
• License: mit
• Created: 2020-03-02T22:18:22.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-04-24T20:17:10.000Z (almost 3 years ago)
• Last Synced: 2024-11-12T07:56:01.746Z (5 months ago)
• Topics: distributed-systems, kafka, machine-learning, python, reactive-machine-learning, reactive-programming, reactive-systems, stream-processing, streaming, streaming-data
• Language: Python
• Homepage: https://www.makinage.org
• Size: 227 KB
• Stars: 39
• Watchers: 4
• Forks: 1
• Open Issues: 8
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-streaming - Maki Nage - A stream processing framework for data scientists, based on Kafka and ReactiveX. (Table of Contents / Streaming Engine)

README

        
===========================

|makinage-logo| Maki Nage

===========================

.. |makinage-logo| image:: https://github.com/maki-nage/makinage/raw/master/asset/makinage_logo.png

Stream Processing Made Easy

.. image:: https://badge.fury.io/py/makinage.svg

    :target: https://badge.fury.io/py/makinage

.. image:: https://github.com/maki-nage/makinage/workflows/Python%20package/badge.svg

    :target: https://github.com/maki-nage/makinage/actions?query=workflow%3A%22Python+package%22

    :alt: Github WorkFlows

.. image:: https://github.com/maki-nage/makinage/raw/master/asset/docs_download.svg

    :target: https://www.makinage.org/doc/makinage-book/latest/index.html

    :alt: Documentation

Maki Nage is a Python stream processing library and framework. It provides

**expressive** and **extensible** APIs. Maki Nage speeds up the development of

stream applications. It can be used to process **stream** and **batch** data.

More than that, it allows to develop an application with batch data, and deploy

it as a **Kafka micro-service**.

`Read the doc `_

to learn more.

.. image:: https://github.com/maki-nage/makinage/raw/master/asset/graph.png

    :width: 50%

Main Features

==============

* **Expressive** and **Extensible** APIs: Maki-Nage is based on `ReactiveX `_.

* Deployment Ready: Maki-Nage uses **Kafka** to scale the workload, and be resilient to errors.

* **Unifies** Batch and Stream processing: The same APIs work on both sources of data.

* Flexible: Start working on your laptop, continue on a server, deploy on a cluster.

* **ML Streaming Serving**: Serve your machine learning model as a Kafka micro-service.

Installation

==============

Maki Nage is available on PyPI:

.. code:: console

    pip install makinage

Getting started

===============

Write your data transforms

---------------------------

.. code:: Python

    import rx

    import rxsci as rs

    def rolling_mean():

        return rx.pipe(            

            rs.data.roll(window=3, stride=3, pipeline=rx.pipe(

                rs.math.mean(reduce=True),

            )),

        )

Test your code on batch data

-------------------------------

You can test your code from any python data or CSV file.

.. code:: Python

    data = [1, 2, 3, 4, 5, 6, 7]

    rx.from_(data).pipe(

        rs.state.with_memory_store(rx.pipe(

            rolling_mean(),

        )),

    ).subscribe(

        on_next=print

    )

.. code:: console

    2.0

    5.0

Deploy your code as a Kafka micro-service

-------------------------------------------

To deploy the code, package it as a function:

.. code:: Python

    def my_app(config, data):

        roll_mean = rx.from_(data).pipe(

            rs.state.with_memory_store(rx.pipe(

                rolling_mean(),

            )),

        )

        return roll_mean,

Create a configuration file:

.. code:: yaml

    application:

        name: my_app

    kafka:

        endpoint: "localhost"

    topics:

        - name: data

        - name: features

    operators:

        compute_features:

            factory: my_app:my_app

            sources:

                - data

            sinks:

                - features

And start it!

.. code:: console

    makinage --config myconfig.yaml

Serve Machine Learning Models

===============================

Maki Nage contains a model serving tool. With it, serving a machine

learning model in streaming mode just requires a configuration file:

.. code:: yaml

    application:

        name: my_model_serving

    Kafka:

        endpoint: "localhost"

    topics:

    - name: data

      encoder: makinage.encoding.json

    - name: model

      encoder: makinage.encoding.none

      start_from: last

    - name: predict

      encoder: makinage.encoding.json

    operators:

      serve:

        factory: makinage.serve:serve

        sources:

          - model

          - data

        sinks:

          - predict

    config:

      serve: {}

And then serving the model it done the same way than any makinage application:

.. code:: console

    makinage --config config.serve.yaml

Some pre and post processing steps are possible if input features or predictions

must be modified before/after the inference:

.. image:: https://github.com/maki-nage/makinage/raw/master/asset/serve.png

`Read the book `_

to learn more.

Publications

===============

* Toward Data Science: `Stream Processing Made Easy `_

* KDnuggets: `Real-Time Histogram Plots on Unbounded Data `_

License

=========

Maki Nage is publised under the MIT License.