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Learning Apache Cassandra

You're reading from   Learning Apache Cassandra Managing fault-tolerant, scalable data with high performance

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Product type Paperback
Published in Apr 2017
Publisher
ISBN-13 9781787127296
Length 360 pages
Edition 2nd Edition
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Author (1):
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Sandeep Yarabarla Sandeep Yarabarla
Author Profile Icon Sandeep Yarabarla
Sandeep Yarabarla
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Table of Contents (15) Chapters Close

Preface 1. Getting Up and Running with Cassandra FREE CHAPTER 2. The First Table 3. Organizing Related Data 4. Beyond Key-Value Lookup 5. Establishing Relationships 6. Denormalizing Data for Maximum Performance 7. Expanding Your Data Model 8. Collections, Tuples, and User-Defined Types 9. Aggregating Time-Series Data 10. How Cassandra Distributes Data 11. Cassandra Multi-Node Cluster 12. Application Development Using the Java Driver 13. Peeking under the Hood 14. Authentication and Authorization

Application Development Using the Java Driver

Since the beginning of Chapter 1, Getting Up and Running with Cassandra, we have been using cqlsh to execute all queries. Whether it is reading or writing data, changing consistency level, or enabling tracing, cqlsh facilitates performing all these tasks with ease. When developing applications that work with Cassandra, we use various drivers, supported either by DataStax or by the open source community. During the course of this chapter, we will be focusing on the Java driver in particular, which is used widely, and most new changes go into this first. Cqlsh uses the Python driver underneath.

The Java driver is based on CQL3 and Cassandra binary protocol. It uses Netty-based transport, which provides high performance and is optimized for Cassandra. Prior to the Java driver, which is based on native protocol, thrift protocol was more popular. As newer versions of...

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