Real-time Data Processing in Python: Using Apache Storm

Introduction
Prerequisites
Setting Up Apache Storm
Creating a Storm Topology
Running the Storm Topology
Conclusion

Introduction

In today’s world, real-time data processing has become a necessity for many businesses and organizations. Apache Storm is a powerful distributed real-time computation system that allows you to process and analyze large volumes of data in a highly scalable and fault-tolerant manner. In this tutorial, you will learn how to use Apache Storm in Python to create a real-time data processing application. By the end of this tutorial, you will have a solid understanding of Apache Storm’s concepts and how to implement them in a Python environment.

Prerequisites

Before you proceed with this tutorial, it is recommended to have the following knowledge and software installed:

Basic understanding of Python programming language.
Familiarity with command-line tools.
Python 3.x installed on your machine.
Apache Storm installed and running on your system.

Setting Up Apache Storm

To begin, we need to set up Apache Storm on our local machine.

Download and install Apache Storm from the official website.
Extract the downloaded archive to a desired location on your machine.
Set up the necessary configuration files (e.g., storm.yaml) according to your cluster setup and preferences.
Start the Apache Storm Nimbus daemon by running the following command in the Storm installation directory:
```
bin/storm nimbus
```
Start the Apache Storm Supervisor daemon by running the following command in the Storm installation directory:
```
bin/storm supervisor
```
Verify that both the Nimbus and Supervisor daemons are running without any errors.

Creating a Storm Topology

A Storm topology is responsible for defining the data flow and processing logic of a real-time data processing application. In this section, we will create a simple Storm topology that reads data from a spout and processes it through a bolt.

Create a new directory for your Storm topology project.
Inside the project directory, create a new Python file named topology.py.

Import the necessary Storm classes and modules:

from streamparse import Topology, Grouping, Tuple

Define a class for your Storm topology by subclassing the Topology class:

class MyTopology(Topology):
    def spout(self):
        pass  # Define your spout logic here
       
    def bolt(self, p):
        pass  # Define your bolt logic here

Implement the spout method to define the logic for your data source. A spout generates or fetches data and emits it as tuples:
```
class MyTopology(Topology):
    def spout(self):
        return MySpout.spec()
```

Implement the bolt method to define the logic for processing the data. A bolt receives tuples from spouts, performs operations on them, and emits the results:

class MyTopology(Topology):
    def spout(self):
        return MySpout.spec()
       
    def bolt(self, p):
        return MyBolt.spec(inputs={self.spout: Grouping.fields(['field1'])}, par=p)

Define the main method to create an instance of your topology and run it:
```
if __name__ == '__main__':
    MyTopology().run()
```
Save the topology.py file.

Running the Storm Topology

Now that we have created our Storm topology, we can run it and see the real-time data processing in action.

Open a terminal or command prompt.
Navigate to your Storm topology project directory.
Run the following command to deploy and run your Storm topology:
```
sparse run
```
Watch the terminal output to see the logs and progress of your topology.

Congratulations! You have successfully set up and run a Storm topology in Python. You can now explore more advanced features of Apache Storm and build more complex real-time data processing applications.

Conclusion

In this tutorial, you learned how to use Apache Storm in Python for real-time data processing. We started by setting up Apache Storm on our local machine and then created a simple Storm topology that demonstrates the flow of data and processing logic. By following the step-by-step instructions, you should now have a solid understanding of Apache Storm’s concepts and be able to create your own real-time data processing applications using Python.

Keep exploring the vast capabilities of Apache Storm and experiment with different data sources, transformations, and processing logic to unleash the full potential of real-time data processing in Python.

Hope you found this tutorial helpful and have a great time exploring real-time data processing with Apache Storm in Python!

Published: 21 June 2020