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Home > Data > Data Processing > Learning Real-time Analytics with Storm and Cassandra
Learning Real-time Analytics with Storm and Cassandra
Learning Real-time Analytics with Storm and Cassandra

Learning Real-time Analytics with Storm and Cassandra: Solve real-time analytics problems effectively using Storm and Cassandra

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Learning Real-time Analytics with Storm and Cassandra

Chapter 2. Getting Started with Your First Topology

This chapter is dedicated to guiding you through the steps to set up the environment for the execution of a Storm topology. The intent is to prepare the user sandbox and get you steered toward executing some of the sample code and understanding the working of various components. All the concepts will be accompanied by code snippets and a "try it yourself" section so that you are equipped to understand the components in a practical manner and are ready to explore and harness the power of this wonderful technology.

The topics that will be covered in this chapter are as follows:

  • Storm topology and components
  • Executing the sample Storm topology
  • Executing the topology in distributed mode

By the end of the chapter, you will be able to understand the components and data flow in a topology, understand the simple word count topology, and execute it in the local and distributed modes. You will also be able to tweak the starter project...

Prerequisites for setting up Storm

The prerequisites for executing the setup and execution steps are enlisted here:

  • For a local mode setup, you need Maven, Git, Eclipse, and Java
  • For a distributed setup, you need the following:
    • A Linux or Ubuntu setup or a distributed setup can leverage PowerShell or Cygwin over their Windows systems
    • Having more than one system or virtual machines using the VMware player would help

You can refer to the following links and follow the process laid out to set up the various open source components required to set up Storm and deploy the components explained in this segment of the book:

Components of a Storm topology

A Storm topology consists of two basic components: a spout and one or more bolts. These building blocks are tied together using streams; it is over these streams that endless arrays of tuples flow.

Let's discuss the topology with a simple analogy, as depicted in the diagram and explained thereafter:

Components of a Storm topology

In our example topology, we have a big processing unit for roasted chips where the input, raw potato, is consumed by the spout, and there are various bolts such as a peeler bolt, slicer bolt, and roasting bolt that perform the tasks as their name suggests. There are various assembly lines or workers that move the chips from the peeler unit to the shredder and beyond; in our case, we have streams to link and wire in the spout and bolts with each other. Now the basic unit of exchange between the peeler and shredder is a peeled potato, and between the shredder units and roasting units is a sliced potato. This is analogous to a tuple, the datum of information exchange...

Executing a sample Storm topology – local mode

Before we start this section, the assumption is that you have gone through the prerequisites and installed the expected components.

WordCount topology from the Storm-starter project

To understand the components described in the previous section, let's download the Storm-starter project and execute a sample topology:

  1. The Storm-starter project can be downloaded using the following Git command:
    Linux-command-Prompt $ sudo git clone git://github.com/apache/incubator-storm.git && cd incubator-storm/examples/storm-starter
  2. Next, you need to import the project into your Eclipse workspace:
    1. Start Eclipse.
    2. Click on the File menu and select the Import wizard.
    3. From the Import wizard, select Existing Maven Projects.
      WordCount topology from the Storm-starter project
    4. Select pom.xml in the Storm-starter project and specify it as <download-folder>/starter/incubator-storm/examples/storm-starter.
    5. Once the project has been successfully imported, the Eclipse folder structure will look like the...

Executing the topology in the distributed mode

To set up Storm in distributed mode, we will need to perform the following steps.

Set up Zookeeper (V 3.3.5) for Storm

The coordination of a Storm topology is maintained by a Zookeeper cluster. The utilization of Zookeeper is not very high, as it just maintains the runnable state of the Storm cluster. In most cases, a single Zookeeper node should suffice, but in production scenarios, at least a three-node Zookeeper cluster is recommended so that a single node doesn't become a single point of failure.

For reliable Zookeeper service, deploy Zookeeper in a cluster known as an ensemble. As long as the majority of the ensemble is up, the service will be available. One of the nodes in the ensemble is automatically selected as a leader and others as followers. If the leader goes down, one of the follower nodes becomes the leader.

Perform the following steps on all the machines that will be part of the Zookeeper ensemble to set up the Zookeeper cluster...

Executing the topology from Command Prompt

Once the UI is visible and all the daemons are started, the topology can be submitted on Nimbus using the following command:

storm jar storm-starter-0.0.1-SNAPSHOT-jar-with-dependencies.jar  storm.starter.WordCountTopology WordCount -c nimbus.host=localhost

The Storm UI with the WordCount topology running in distributed mode is shown here. It depicts the topology state, uptime, and other details (we shall discuss the features of the UI in detail in a later chapter). We can kill the topology from the UI.

Executing the topology from Command Prompt

Tweaking the WordCount topology to customize it

Now that we have deployed the WordCount topology in distributed mode, let's tweak the code in the bolts a bit to write WordCount onto a file. To achieve this, we will proceed with the following steps:

  1. We intend to create a new bolt, FileWriterBolt, to achieve this. Open WordCountTopology.java and add the following snippet to WordCountTopology.java:
    public static class FileWriterBolt extends BaseBasicBolt...

Prerequisites for setting up Storm

The prerequisites for executing the setup and execution steps are enlisted here:

  • For a local mode setup, you need Maven, Git, Eclipse, and Java

  • For a distributed setup, you need the following:

    • A Linux or Ubuntu setup or a distributed setup can leverage PowerShell or Cygwin over their Windows systems

    • Having more than one system or virtual machines using the VMware player would help

You can refer to the following links and follow the process laid out to set up the various open source components required to set up Storm and deploy the components explained in this segment of the book:

Components of a Storm topology

A Storm topology consists of two basic components: a spout and one or more bolts. These building blocks are tied together using streams; it is over these streams that endless arrays of tuples flow.

Let's discuss the topology with a simple analogy, as depicted in the diagram and explained thereafter:

In our example topology, we have a big processing unit for roasted chips where the input, raw potato, is consumed by the spout, and there are various bolts such as a peeler bolt, slicer bolt, and roasting bolt that perform the tasks as their name suggests. There are various assembly lines or workers that move the chips from the peeler unit to the shredder and beyond; in our case, we have streams to link and wire in the spout and bolts with each other. Now the basic unit of exchange between the peeler and shredder is a peeled potato, and between the shredder units and roasting units is a sliced potato. This is analogous to a tuple, the datum of information exchange...

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Description

If you want to efficiently use Storm and Cassandra together and excel at developing production-grade, distributed real-time applications, then this book is for you. No prior knowledge of using Storm and Cassandra together is necessary. However, a background in Java is expected.

Who is this book for?

If you want to efficiently use Storm and Cassandra together and excel at developing production-grade, distributed real-time applications, then this book is for you. No prior knowledge of using Storm and Cassandra together is necessary. However, a background in Java is expected.

What you will learn

  • Integrate Storm applications with RabbitMQ for realtime analysis and processing of messages
  • Monitor highly distributed applications using Nagios
  • Integrate the Cassandra data store with Storm
  • Develop and maintain distributed Storm applications in conjunction with Cassandra and In Memory Database (memcache)
  • Build a Trident topology that enables realtime computing with Storm
  • Tune performance for Storm topologies based on the SLA and requirements of the application
  • Use Esper with the Storm framework for rapid development of applications

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Publication date : Mar 27, 2015
Length: 220 pages
Edition : 1st
Language : English
ISBN-13 : 9781784390006
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Category :
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Concepts :
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Publication date : Mar 27, 2015
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Language : English
ISBN-13 : 9781784390006
Vendor :
Apache
Category :
Data
Languages :
Java
Concepts :
Data Processing
Tools :
Storm

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Table of Contents

13 Chapters
1. Let's Understand Storm Chevron down icon Chevron up icon
1. Let's Understand Storm
Distributed computing problems
Solutions for complex distributed use cases
A high-level view of various components of Storm
Delving into the internals of Storm
Quiz time
Summary
2. Getting Started with Your First Topology Chevron down icon Chevron up icon
2. Getting Started with Your First Topology
Prerequisites for setting up Storm
Components of a Storm topology
Executing a sample Storm topology – local mode
Executing the topology in the distributed mode
Executing the topology from Command Prompt
Quiz time
Summary
3. Understanding Storm Internals by Examples Chevron down icon Chevron up icon
3. Understanding Storm Internals by Examples
Customizing Storm spouts
Anchoring and acking
Stream groupings
Quiz time
Summary
4. Storm in a Clustered Mode Chevron down icon Chevron up icon
4. Storm in a Clustered Mode
The Storm cluster setup
Zookeeper configurations
Storm configurations
Storm monitoring tools
Quiz time
Summary
5. Storm High Availability and Failover Chevron down icon Chevron up icon
5. Storm High Availability and Failover
An overview of RabbitMQ
Installing the RabbitMQ cluster
Integrating Storm with RabbitMQ
Building high availability of components
The Storm isolation scheduler
Quiz time
Summary
6. Adding NoSQL Persistence to Storm Chevron down icon Chevron up icon
6. Adding NoSQL Persistence to Storm
The advantages of Cassandra
Columnar database fundamentals
Setting up the Cassandra cluster
Multiple data centers
Introduction to CQLSH
Introduction to CLI
Using different client APIs to access Cassandra
Storm topology wired to the Cassandra store
The best practices for Storm/Cassandra applications
Quiz time
Summary
7. Cassandra Partitioning, High Availability, and Consistency Chevron down icon Chevron up icon
7. Cassandra Partitioning, High Availability, and Consistency
Consistent hashing
Replication in Cassandra and strategies
Cassandra consistency
Quiz time
Summary
8. Cassandra Management and Maintenance Chevron down icon Chevron up icon
8. Cassandra Management and Maintenance
Cassandra – gossip protocol
Cassandra cluster scaling – adding a new node
Cassandra cluster – replacing a dead node
The replication factor
The nodetool commands
Cassandra fault tolerance
Cassandra monitoring systems
Quiz time
Summary
9. Storm Management and Maintenance Chevron down icon Chevron up icon
9. Storm Management and Maintenance
Scaling the Storm cluster – adding new supervisor nodes
Scaling the Storm cluster and rebalancing the topology
Setting up workers and parallelism to enhance processing
Storm troubleshooting
Quiz time
Summary
10. Advance Concepts in Storm Chevron down icon Chevron up icon
10. Advance Concepts in Storm
Building a Trident topology
Understanding the Trident API
Examples and illustrations
Quiz time
Summary
11. Distributed Cache and CEP with Storm Chevron down icon Chevron up icon
11. Distributed Cache and CEP with Storm
The need for distributed caching in Storm
Introduction to memcached
Introduction to the complex event processing engine
Quiz time
Summary
A. Quiz Answers Chevron down icon Chevron up icon
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(2 Ratings)
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Dipankar Dutta Jun 12, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The Book provides valuable insights on the Real time use cases which can be put into production. The language is clear and lucid. It woild have been better if the design principles were discussed.
Amazon Verified review Amazon
rlsarc Jun 16, 2015
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Real-time Analytics with Storm and Cassandra is a good book to learn Storm's and Cassandra's basics as well as various uses cases of both. The book is much more comprehensive than I was expecting and easy to read as well, so it'll serve both as a getting-started guide for inexperienced developers and as a reference to implement more complex use cases.
Amazon Verified review Amazon

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About the author

Profile icon Shilpi Saxena
Shilpi Saxena
Shilpi Saxena is an IT professional and also a technology evangelist. She is an engineer who has had exposure to various domains (machine to machine space, healthcare, telecom, hiring, and manufacturing). She has experience in all the aspects of conception and execution of enterprise solutions. She has been architecting, managing, and delivering solutions in the Big Data space for the last 3 years; she also handles a high-performance and geographically-distributed team of elite engineers. Shilpi has more than 12 years (3 years in the Big Data space) of experience in the development and execution of various facets of enterprise solutions both in the products and services dimensions of the software industry. An engineer by degree and profession, she has worn varied hats, such as developer, technical leader, product owner, tech manager, and so on, and she has seen all the flavors that the industry has to offer. She has architected and worked through some of the pioneers' production implementations in Big Data on Storm and Impala with autoscaling in AWS. Shilpi has also authored Real-time Analytics with Storm and Cassandra (https://www.packtpub.com/big-data-and-business-intelligence/learning-real-time-analytics-storm-and-cassandra) with Packt Publishing.
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