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Building Python Real time Applications with Storm

Book Image

Building Python Real time Applications with Storm

Overview of this book

Big data is a trending concept that everyone wants to learn about. With its ability to process all kinds of data in real time, Storm is an important addition to your big data “bag of tricks.” At the same time, Python is one of the fastest-growing programming languages today. It has become a top choice for both data science and everyday application development. Together, Storm and Python enable you to build and deploy real-time big data applications quickly and easily. You will begin with some basic command tutorials to set up storm and learn about its configurations in detail. You will then go through the requirement scenarios to create a Storm cluster. Next, you’ll be provided with an overview of Petrel, followed by an example of Twitter topology and persistence using Redis and MongoDB. Finally, you will build a production-quality Storm topology using development best practices.

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Building Python Real time Applications with Storm
Dec, 2015 | 122 pages
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Table of Contents (14 chapters)
Building Python Real-Time Applications with Storm
Building Python Real-Time Applications with Storm
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Acquainted with Storm
Getting Acquainted with Storm
Overview of Storm
Storm installation
Summary
2
The Storm Anatomy
The Storm Anatomy
Storm processes
Storm-topology-specific terminologies
A physical view of a Storm cluster
Tuning parallelism in Storm – scaling a distributed computation
Summary
3
Introducing Petrel
Introducing Petrel
What is Petrel?
Installing Petrel
Creating your first topology
Running the topology
Troubleshooting
Productivity tips with Petrel
Summary
4
Example Topology – Twitter
Example Topology – Twitter
Twitter analysis
Twitter's Streaming API
Running the topology
Summary
5
Persistence Using Redis and MongoDB
Persistence Using Redis and MongoDB
Finding the top n ranked topics using Redis
Running the topology – the Redis case
Running the topology – the MongoDB case
Summary
6
Petrel in Practice
Petrel in Practice
Testing a bolt
Debugging
Installing Winpdb
Profiling your topology's performance
Summary
Managing Storm Using Supervisord
Managing Storm Using Supervisord
Storm administration over a cluster
Summary
Index
Index

Creating your first topology

Now, we'll create a Storm topology that breaks sentences into words and then counts the number of occurrences of each word. Implementing this topology in Storm requires the following components:

  • Sentence spout (randomsentence.py): A topology always begins with a spout; that's how data gets into Storm. The sentence spout will emit an infinite stream of sentences.

  • Splitter bolt (splitsentence.py): This receives sentences and splits them into words.

  • Word count bolt (wordcount.py): This receives words and counts the occurrences. For each word processed, output the word along with the number of occurrences.

The following figure shows how data flows through the topology:

Word count topology

Now that we've seen the basic data flow, let's implement the topology and see how it works.

Sentence spout

In this section, we implement a spout that generates random sentences. Enter this code in a file called randomsentence.py:

import time
import random

from petrel import storm
from petrel...
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