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How to use the concise and efficient Python stream processing library Faust. In view of this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and easy way.

Stream processing is a very important technology in distributed systems and real-time data processing. In data-intensive applications, data arrives quickly and is fleeting, so it needs to be processed in time. Streaming processing emphasizes the processing speed of data and events, and has high requirements for performance and reliability.

Stream processing framework includes: Storm,Spark Streaming and Flink, etc., and Kafka is not to be outdone, launched a distributed stream processing platform Kafka Streams. Faust brings Kafka Streams to Python and implements abstraction and optimization, providing an efficient and convenient framework for flow processing of data and events.

Brief introduction

Faust, robinhood's open source Python stream processing library on Github, is currently in version 1.10.4.

Faust brings the concept of Kafka Streams to Python, providing a pattern that includes flow processing and event handling. Faust is implemented in pure Python, which allows developers to use libraries including NumPy, PyTorch, Pandas, etc., for data processing.

Faust has the advantages of simple and elegant implementation, easy to use, excellent performance, high availability, distribution and high flexibility. At present, Faust has been used to build high-performance distributed systems and real-time data pipes.

Use

Faust requires Python 3.6 or above, and requires available Kafka > = 0.10 services. Install using pip:

$pip install-U faust

In addition, some additional features require additional dependencies, such as rocksdb, which can be used as storage for Faust in a production environment, and Redis, which can be used when caching is turned on.

After the installation is complete, you can use it in the project. Let's look at a simple example:

Import faust app = faust.App ('hello-world', broker='kafka://localhost:9092', value_serializer='raw',) greetings_topic = app.topic (' greetings') @ app.agent (greetings_topic) async def greet (greetings): async for greeting in greetings: print (greeting)

First, we use faust.App to create a Faust application and configure the application's name, Kafka broker, and serialization method.

Then, we create a theme, which corresponds to the theme in Kafka.

Using the asynchronous syntax async of Python 3.6 +, Faust defines the asynchronous function greet and registers it as an agent of the Faust application. The function receives a real-time data collection greetings and outputs each item of data asynchronously.

Save the above code as hello_world.py and start the worker on the command line:

$faust-A hello_world worker-l info

The Faust worker will read the data from the Kafka in real time and process it.

We can send some data to observe the effect:

$faust-A hello_world send @ greet "Hello Faust"

The above command sends a message, which we can see on the worker's command line after execution.

Faust also makes full use of the type hint of Python to easily define the data model:

Import faust class Greeting (faust.Record): from_name: str to_name: str app = faust.App ('hello-app', broker='kafka://localhost') topic = app.topic (' hello-topic' Value_type=Greeting) @ app.agent (topic) async def hello (greetings): async for greeting in greetings: print (f'Hello from {greeting.from_name} to {greeting.to_name}') @ app.timer (interval=1.0) async def example_sender (app): await hello.send (value=Greeting (from_name='Faust', to_name='you'),) if _ _ name__ = ='_ main__': app.main ()

Faust brings Kafka Streams to Python to achieve concise and efficient data flow processing. It uses a simple decorator and a type prompt-based data model to define the data processing logic; makes full use of Python's async asynchronous mechanism, and other high-performance asynchronous libraries to achieve efficient performance; it uses Python to implement, using developers can seamlessly dock other data processing and big data-related functions.

This is the answer to the question about how to use Faust, a concise and efficient Python stream processing library. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel for more related knowledge.

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