实时数据分析：Storm与Cassandra实战

Storm

Cassandra

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[ vii ]

Preface

Storm, initially a project from the house of Twitter, has graduated to the league of

Apache and thus rechristened from Twitter Storm. It is the brainchild of Nathan

Marz that's now adopted by leagues of Cloudera's Distribution Including Apache

Hadoop (CDH) and the Hortonworks Data Platform (HDP), and so on.

Apache Storm is a highly scalable, distributed, fast, and reliable real-time computing

system designed to process very high velocity data. Cassandra complements the

computing capability by providing lightning-fast read and writes, and this is the

best combination currently available for data storage with Storm.

The combination of the Storm computing and Cassandra storage is helping

technology evangelists to solve various business problems involving complex

and high data volume situations such as real-time customer service, dashboards,

security, sensor data analysis, data monetization, and so on.

This book will equip users with the capability to harness the processing power

of Storm in combination with the speed and reliability of Cassandra to develop

production-grade enterprise solutions on real-time use cases.

What this book covers

Chapter 1, Let's Understand Storm, gets you acquainted with the problems that need

distributed computing solutions. It will take you through the journey of Storm and

its advent.

Chapter 2, Getting Started with Your First Topology, teaches you to set up the developer's

environment—sandbox and execute some of the code samples.

Chapter 3, Understanding Storm Internals by Examples, teaches you how to prepare

Storm spouts and custom spouts. You will understand various kinds of groupings

provided by Storm and their application to practical problems.

Preface

[ viii ]

Chapter 4, Storm in a Clustered Mode, teaches you how to set up a multi-node Storm

cluster to get the user acquainted with the distributed Storm setup and its components.

This chapter will also get your acquainted with the Storm UI and various monitoring

tools for Storm.

Chapter 5, Storm High Availability and Failover, conjugates the Storm topology with the

RabbitMQ broker service and explores the high availability and failover scenarios of

Storm with the help of various practical examples.

Chapter 6, Adding NoSQL Persistence to Storm, introduces you to Cassandra and

explores various wrapper API's available to work with Cassandra. We will use

the Hector API to connect Storm and Cassandra.

Chapter 7, Cassandra Partitioning, High Availability, and Consistency, walks you

through the Cassandra internals. You will understand and apply the concepts of

high availability, hinted handoff, and eventual consistency in context to Cassandra.

Chapter 8, Cassandra Management and Maintenance, gets you acquainted with the

management aspects of Cassandra, such as scaling the cluster, node replacement,

and so on, thus equipping you with all the experience required to handle real-life

situations with Cassandra.

Chapter 9, Storm Management and Maintenance, gets you acquainted with the

management aspects of Storm, such as scaling the cluster, setting up parallelism,

and troubleshooting Storm.

Chapter 10, Advance Concepts in Storm, gives you an understanding of the Trident

API. You will be building the Trident API with certain examples and illustrations

around Trident.

Chapter 11, Distributed Cache and CEP with Storm, gets you acquainted with distributed

cache, its need, and applicability to solve real-life use cases with Storm. It will also

educate you about Esper as a CEP in combination with Storm.

Appendix, Quiz Answers, contains all the answers to the questions of the true or false

statements and the ll in the blanks section.

Bonus Chapter, Using Storm and Cassandra for Real Life Use Cases, explains a few

real-life use cases and blueprints to solve these cases using the technologies such as

Storm and Cassandra. This chapter can be found online at https://www.packtpub.

com/sites/default/files/downloads/Bonus_Chapter.pdf.

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