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Building Python Real time Applications with Storm
Building Python Real time Applications with Storm

Building Python Real time Applications with Storm: Learn to process massive real-time data streams using Storm and Python—no Java required!

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Profile Icon Bhatnagar Profile Icon Hart
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€8.99 €19.99
eBook Dec 2015 122 pages 1st Edition
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€8.99 €19.99
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Arrow left icon
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eBook Dec 2015 122 pages 1st Edition
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Building Python Real time Applications with Storm

Chapter 2. The Storm Anatomy

This chapter gives a detailed view of the internal structure and processes of the Storm technology. We will cover the following topics in this chapter:

  • Storm processes
  • Storm-topology-specific terminologies
  • Interprocess communication
  • Fault tolerance in Storm
  • Guaranteed tuple processing
  • Parallelism in Storm—scaling a distributed computation

As we advance through the chapter, you will understand Storm's processes and their role in detail. In this chapter, various Storm-specific terminologies will be explained. You will learn how Storm achieves fault tolerance for different types of failure. We will see what guaranteed message processing is and, most importantly, how to configure parallelism in Storm to achieve fast and reliable processing.

Storm processes

We will start with Nimbus first, which is actually the entry-point daemon in Storm. Just to compare with Hadoop, Nimbus is actually the job tracker of Storm. Nimbus's job is to distribute code to all supervisor daemons of a cluster. So, when topology code is submitted, it actually reaches all physical machines in the cluster. Nimbus also monitors failure of supervisors. If a supervisor continues to fail, then Nimbus reassigns those workers' jobs to other workers of a different physical machine. The current version of Storm allows only one instance of the Nimbus daemon to run. Nimbus is also responsible for assigning tasks to supervisor nodes. If you lose Nimbus, the workers will still continue to compute. Supervisors will continue to restart workers as and when they die. Without Nimbus, a worker's task won't be reassigned to another machine worker within the cluster.

There is no alternative Storm process that will take over if Nimbus dies, and no process...

Storm-topology-specific terminologies

A topology is a logical separation of programming work into many small-scale processing units called spout and bolt, which is similar to MapReduce in Hadoop. A topology can be written in many languages, including Java, Python, and lot more supported languages. In visual depictions, a topology is shown as a graph of connecting spouts and bolts. Spouts and bolts execute tasks across the cluster. Storm has two modes of operation, called local mode and distributed mode:

  • In local mode, all processes of Storm and workers run within your code development environment. This is good for testing and development of topologies.
  • In distributed mode, Storm operates as a cluster of machines. When you submit topology code to the Nimbus, Nimbus takes care of distributing the code and allocating workers to run your topology based on your configuration.

In the following figure, we have purple bolts; these receive a tuple or records from the spout above them. A tuple supports...

A physical view of a Storm cluster

The next figure explains the physical position of each process. There can be only one Nimbus. However, more than one Zookeeper is there to support failover, and per machine, there is one supervisor.

A physical view of a Storm cluster

Stream grouping

A stream grouping controls the flow of tuples between from spout to bolt or bolt to bolt. In Storm, we have four types of groupings. Shuffle and field grouping are most commonly used:

  • Shuffle grouping: Tuple flow between two random tasks in this grouping
  • Field grouping: A tuple with a particular field key is always delivered to the same task of the downstream bolt
  • All grouping: Sends the same tuple to all tasks of the downstream bolt
  • Global grouping: Tuples from all tasks reach one task

The subsequent figure gives a diagrammatic explanation of all the four types of groupings:

Stream grouping

Fault tolerance in Storm

Supervisor runs a synchronization thread to get assignment information (what part of topology I am supposed to run) from Zookeeper and write to the local...

Tuning parallelism in Storm – scaling a distributed computation

To explain parallelism of Storm, we will configure three parameters:

  • The number of workers
  • The number of executors
  • The number of tasks

The following figure gives a diagrammatic explanation of an example where we have a topology with just one spout and one bolt. In this case, we will set different values for the numbers of workers, executors, and tasks at the spout and bolt levels, and see how parallelism works in each case:

Tuning parallelism in Storm – scaling a distributed computation
// assume we have two workers in total for topology.
topology.workers: 2
 // just one executor of spout.
builder.setSpout("spout-sentence", TwitterStreamSpout(),1)

// two executors of bolt.
builder.setBolt("bolt-split", SplitSentenceBolt(),2)
 // four tasks for bolts.
.setNumTasks(4)
.shuffleGrouping("spout-sentence");

For this configuration, we will have two workers, which will run in separate JVMs (worker 1 and worker 2).

For the spout, there is one executor, and the default...

Summary

As this chapter approaches its end, you must have got a brief idea about the Nimbus, supervisor, UI, and Zookeeper processes. This chapter also taught you how to tune parallelism in Storm by playing with the number of workers, executors, and tasks. You became familiar with the important problem of distributing computation, that is, failures and overcoming failures by different kinds of fault tolerance available in the system. And most importantly, you learned how to write a "reliable" spout to achieve guaranteed message processing and linking in bolts.

The next chapter will give you information about how to build a simple topology using a Python library called Petrel. Petrel addresses some limitations of Storm's built-in Python support, providing simpler and more streamlined development.

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Key benefits

  • Learn to use Apache Storm and the Python Petrel library to build distributed applications that process large streams of data
  • Explore sample applications in real-time and analyze them in the popular NoSQL databases MongoDB and Redis
  • Discover how to apply software development best practices to improve performance, productivity, and quality in your Storm projects

Description

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.

Who is this book for?

This book is intended for Python developers who want to benefit from Storm’s real-time data processing capabilities. If you are new to Python, you’ll benefit from the attention to key supporting tools and techniques such as automated testing, virtual environments, and logging. If you’re an experienced Python developer, you’ll appreciate the thorough and detailed examples

What you will learn

  • Install Storm and learn about the prerequisites
  • Get to know the components of a Storm topology and how to control the flow of data between them
  • Ingest Twitter data directly into Storm
  • Use Storm with MongoDB and Redis
  • Build topologies and run them in Storm
  • Use an interactive graphical debugger to debug your topology as it's running in Storm
  • Test your topology components outside of Storm
  • Configure your topology using YAML

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Publication date : Dec 02, 2015
Length: 122 pages
Edition : 1st
Language : English
ISBN-13 : 9781784392871
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Publication date : Dec 02, 2015
Length: 122 pages
Edition : 1st
Language : English
ISBN-13 : 9781784392871
Vendor :
Apache
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Table of Contents

8 Chapters
1. Getting Acquainted with Storm Chevron down icon Chevron up icon
2. The Storm Anatomy Chevron down icon Chevron up icon
3. Introducing Petrel Chevron down icon Chevron up icon
4. Example Topology – Twitter Chevron down icon Chevron up icon
5. Persistence Using Redis and MongoDB Chevron down icon Chevron up icon
6. Petrel in Practice Chevron down icon Chevron up icon
A. Managing Storm Using Supervisord Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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