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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
8
Index
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Index

Finding the top n ranked topics using Redis

The topology will compute a rolling ranking of the most popular words in the past 5 minutes. The word counts are stored in individual windows of 60 seconds in length. It consists of the following components:

  • Twitter stream spout (twitterstream.py): This reads tweets from the Twitter sample stream. This spout is unchanged from Chapter 4, Example Topology – Twitter.

  • Splitter bolt (splitsentence.py): This receives tweets and splits them into words. This is also identical to the one in Chapter 4, Example Topology – Twitter.

  • Rolling word count bolt (rollingcount.py): This receives words and counts the occurrences. The Redis keys look like twitter_word_count:<start time of current window in seconds>, and the values are stored in a hash using the following simple format:

    {
    "word1": 5,
    "word2", 3,
}

    This bolt uses the Redis expireat command to discard old data after 5 minutes. These lines of code perform the key work:

          self.conn.zincrby(name...
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Building Python Real time Applications with Storm
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