apache-flink-statefun 2.0.0

Python SDK for Apache Flink Stateful functions

Apache Flink Stateful Functions

Stateful Functions is an Apache Flink library for distributed applications and services, based on, well, you guessed it: stateful functions.

The project aims to simplify the development of distributed stateful applications by solving some of the common challenges in those applications: scaling, consistent state management, reliable interaction between distributed services, and resource management.

Stateful Functions uses Apache Flink for distributed coordination, state, and communication.

This description is meant as a brief walkthrough on the core concepts and how to set things up to get yourself started with Stateful Functions.

For a fully detailed documentation, please visit the official docs.

For code examples, please visit the examples in the Github repo.

Table of Contents

• Core Concepts
  • Abstraction
  • Function modules and extensibility
• Python SDK Overview
• Contributing
• License

Core Concepts

Abstraction

A Stateful Functions application consists of the following primitives: stateful functions, ingresses, routers, and egresses.

Stateful functions

• Stateful functions are the building blocks and namesake of the Stateful Functions framework. A function is a small piece of logic that are invoked through a message.

• Each stateful function exist as uniquely invokable virtual instances of a function type. Each instance is addressed by its type, as well as an unique id (a string) within its type.

• Stateful functions may be invoked from ingresses or any other stateful function (including itself). The caller simply needs to know the address of the target function.

• Function instances are virtual, because they are not all active in memory at the same time. At any point in time, only a small set of functions and their state exists as actual objects. When a virtual instance receives a message, one of the objects is configured and loaded with the state of that virtual instance and then processes the message. Similar to virtual memory, the state of many functions might be “swapped out” at any point in time.

• Each virtual instance of a function has its own state, which can be accessed in local variables. That state is private and local to that instance.

If you know Apache Flink’s DataStream API, you can think of stateful functions a bit like a lightweight KeyedProcessFunction. The function type is process function transformation, while the ID is the key. The difference is that functions are not assembled in a directed acyclic graph that defines the flow of data (the streaming topology), but rather send events arbitrarily to all other functions using addresses.

Ingresses and Egresses

• Ingresses are the way that events initially arrive in a Stateful Functions application. Ingresses can be message queues, logs, or HTTP servers - anything that produces an event to be handled by the application.

• Routers are attached to ingresses to determine which function instance should handle an event initially.

• Egresses are a way to send events out from the application in a standardized way. Egresses are optional; it is also possible that no events leave the application and functions sink events or directly make calls to other applications.

Modules and extensibility

A module is the entry point for adding to a Stateful Functions application the core building block primitives, i.e. ingresses, egresses, routers, and stateful functions.

A single application may be a combination of multiple modules, each contributing a part of the whole application. This allows different parts of the application to be contributed by different modules; for example, one module may provide ingresses and egresses, while other modules may individually contribute specific parts of the business logic as stateful functions. This facilitates working in independent teams, but still deploying into the same larger application.

Python SDK Overview

Background

The JVM based stateful functions implementation, has a RequestReply extension (a protocol and an implementation) that allows calling into any HTTP endpoint that implements that protocol.

Although it is possible to implement this protocol independently, this is a minimal library for the Python programing language, that:

• Allows users to define and declare their functions in a convenient way
• Dispatch an invocation request sent from the JVM to the appropriate function previously declared

A Mini Tutorial

Define and Declare a function

from statefun import StatefulFunctions

functions = StatefulFunctions()

@functions.bind("demo/greeter")
def greet(context, message: LoginEvent):
    print("Hey " + message.user_name)

This code, declares a function with a FunctionType("demo", "greeter") and binds the greet Python instance to it.

Expose with a Request Reply Handler

from statefun import RequestReplyHandler

handler = RequestReplyHandler(functions)

Using the Handler with your Favorite HTTP Serving Framework

For example, using Flask:

@app.route('/statefun', methods=['POST'])
def handle():
    response_data = handler(request.data)
    esponse = make_response(response_data)
    response.headers.set('Content-Type', 'application/octet-stream')
    return response

if __name__ == "__main__":
    app.run()

This creates an HTTP server that accepts requests from the Stateful Functions cluster and dispatches it to the handler.

Composing the Module YAML File

The remaining step would be to declare this function type in a module.yaml

functions:
  - function:
    meta:
      kind: http
      type: demo/greeter
    spec:
      endpoint: http://<end point url>/statefun
      states:
        - foo
        - bar
        - baz

Eager State Registration

The request reply protocol, requires that the state names would be registered in the module YAML file under the states section (see the example above). The state values could be absent (None or a google.protobuf.Any) and they can be generally obtained via the context parameter:

@functions.bind("demo/greeter")
def greet(context, message: LoginEvent):
    session = context['session']
    if not session:
       session = start_session(message)
       context['session'] = session
    ...

Testing

1. Create a virtual environment

python3 -m venv venv
source venv/bin/activate

2. Run unit tests

python3 -m unittest tests

Contributing

There are many possible ways to enhance the Stateful Functions API for different types of applications. The runtime and operations will also evolve with the developments in Apache Flink. If you find these ideas interesting or promising, try Stateful Functions out and get involved!

You can learn more about how to contribute in the Apache Flink website. For code contributions, please read carefully the Contributing Code section and check the Stateful Functions component in Jira for an overview of ongoing community work.

License

The code in this repository is licensed under the Apache Software License 2.