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  • Book Overview & Buying Building Python Real time Applications with Storm
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Building Python Real time Applications with Storm

Building Python Real time Applications with Storm

By : Bhatnagar, Hart
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Building Python Real time Applications with Storm

Building Python Real time Applications with Storm

By: Bhatnagar, Hart
Buy this Book

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.
Table of Contents (9 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Acquainted with Storm
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1. Getting Acquainted with Storm
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Overview of Storm
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Storm installation
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Summary
2
2. The Storm Anatomy
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2. The Storm Anatomy
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Storm processes
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Storm-topology-specific terminologies
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A physical view of a Storm cluster
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Tuning parallelism in Storm – scaling a distributed computation
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Summary
3
3. Introducing Petrel
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3. Introducing Petrel
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What is Petrel?
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Installing Petrel
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Creating your first topology
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Running the topology
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Troubleshooting
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Productivity tips with Petrel
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Summary
4
4. Example Topology – Twitter
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4. Example Topology – Twitter
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Twitter analysis
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Twitter's Streaming API
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Running the topology
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Summary
5
5. Persistence Using Redis and MongoDB
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5. Persistence Using Redis and MongoDB
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Finding the top n ranked topics using Redis
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Running the topology – the Redis case
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Running the topology – the MongoDB case
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Summary
6
6. Petrel in Practice
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6. Petrel in Practice
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Testing a bolt
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Debugging
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Installing Winpdb
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Profiling your topology's performance
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Summary
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A. Managing Storm Using Supervisord
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A. Managing Storm Using Supervisord
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Storm administration over a cluster
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Summary
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Index
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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...
CONTINUE READING
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Building Python Real time Applications with Storm
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