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  • Book Overview & Buying Learning Real-time Analytics with Storm and Cassandra
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Learning Real-time Analytics with Storm and Cassandra

Learning Real-time Analytics with Storm and Cassandra

By : Shilpi Saxena
3.5 (2)
Buy this Book
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Learning Real-time Analytics with Storm and Cassandra

Learning Real-time Analytics with Storm and Cassandra

3.5 (2)
By: Shilpi Saxena
Buy this Book

Overview of this book

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.
Table of Contents (14 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. Let's Understand Storm
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1. Let's Understand Storm
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Distributed computing problems
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Solutions for complex distributed use cases
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A high-level view of various components of Storm
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Delving into the internals of Storm
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Quiz time
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Summary
2
2. Getting Started with Your First Topology
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2. Getting Started with Your First Topology
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Prerequisites for setting up Storm
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Components of a Storm topology
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Executing a sample Storm topology – local mode
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Executing the topology in the distributed mode
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Executing the topology from Command Prompt
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Quiz time
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Summary
3
3. Understanding Storm Internals by Examples
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3. Understanding Storm Internals by Examples
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Customizing Storm spouts
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Anchoring and acking
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Stream groupings
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Quiz time
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Summary
4
4. Storm in a Clustered Mode
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4. Storm in a Clustered Mode
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The Storm cluster setup
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Zookeeper configurations
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Storm configurations
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Storm monitoring tools
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Quiz time
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Summary
5
5. Storm High Availability and Failover
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5. Storm High Availability and Failover
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An overview of RabbitMQ
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Installing the RabbitMQ cluster
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Integrating Storm with RabbitMQ
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Building high availability of components
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The Storm isolation scheduler
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Quiz time
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Summary
6
6. Adding NoSQL Persistence to Storm
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6. Adding NoSQL Persistence to Storm
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The advantages of Cassandra
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Columnar database fundamentals
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Setting up the Cassandra cluster
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Multiple data centers
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Introduction to CQLSH
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Introduction to CLI
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Using different client APIs to access Cassandra
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Storm topology wired to the Cassandra store
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The best practices for Storm/Cassandra applications
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Quiz time
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Summary
7
7. Cassandra Partitioning, High Availability, and Consistency
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7. Cassandra Partitioning, High Availability, and Consistency
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Consistent hashing
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Replication in Cassandra and strategies
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Cassandra consistency
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Quiz time
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Summary
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8. Cassandra Management and Maintenance
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8. Cassandra Management and Maintenance
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Cassandra – gossip protocol
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Cassandra cluster scaling – adding a new node
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Cassandra cluster – replacing a dead node
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The replication factor
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The nodetool commands
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Cassandra fault tolerance
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Cassandra monitoring systems
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Quiz time
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Summary
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9. Storm Management and Maintenance
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9. Storm Management and Maintenance
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Scaling the Storm cluster – adding new supervisor nodes
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Scaling the Storm cluster and rebalancing the topology
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Setting up workers and parallelism to enhance processing
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Storm troubleshooting
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Quiz time
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Summary
10
10. Advance Concepts in Storm
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10. Advance Concepts in Storm
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Building a Trident topology
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Understanding the Trident API
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Examples and illustrations
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Quiz time
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Summary
11
11. Distributed Cache and CEP with Storm
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11. Distributed Cache and CEP with Storm
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The need for distributed caching in Storm
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Introduction to memcached
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Introduction to the complex event processing engine
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Quiz time
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Summary
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A. Quiz Answers
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Chapter 1
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Chapter 2
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Chapter 3
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Chapter 4
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Chapter 5
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Chapter 6
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Chapter 7
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Chapter 8
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Chapter 9
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Chapter 10
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Chapter 11
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Index
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Index

Examples and illustrations

One of the other out-of-the-box and popular implementations of Trident is reach topology, which is a pure DRPC topology that finds the reach of a URL on demand. Let's first understand some of the jargon before we delve deeper.

Reach is basically a sum total of the count of Twitter users exposed to a URL.

Reach computation is a multistep process that can be attained by the following examples:

  • Get all the users who have ever tweeted a URL

  • Fetch the follower tree of each of these users

  • Assemble the huge follower sets fetched previously

  • Count the set

Well, looking at the skeletal algorithm entailed previously, you can make out that it is beyond the capability of a single machine and we'd need a distributed compute engine to achieve it. It's an ideal candidate of the Storm Trident framework, as you have the capability to execute highly parallel computations at each step across the cluster.

  • Our Trident reach topology would be sucking data from two large data banks

  • Bank A is the...

CONTINUE READING
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Tech Concepts
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Programming languages
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Learning Real-time Analytics with Storm and Cassandra
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