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Learning Apache Cassandra

Learning Apache Cassandra

By : Yarabarla
4 (26)
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Learning Apache Cassandra

Learning Apache Cassandra

4 (26)
By: Yarabarla

Overview of this book

Cassandra is a distributed database that stands out thanks to its robust feature set and intuitive interface, while providing high availability and scalability of a distributed data store. This book will introduce you to the rich feature set offered by Cassandra, and empower you to create and manage a highly scalable, performant and fault-tolerant database layer. The book starts by explaining the new features implemented in Cassandra 3.x and get you set up with Cassandra. Then you’ll walk through data modeling in Cassandra and the rich feature set available to design a flexible schema. Next you’ll learn to create tables with composite partition keys, collections and user-defined types and get to know different methods to avoid denormalization of data. You will then proceed to create user-defined functions and aggregates in Cassandra. Then, you will set up a multi node cluster and see how the dynamics of Cassandra change with it. Finally, you will implement some application-level optimizations using a Java client. By the end of this book, you'll be fully equipped to build powerful, scalable Cassandra database layers for your applications.
Table of Contents (15 chapters)
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Cassandra internals

In this section, we will take a look at some of the internals of Cassandra including read and write paths along with some of the mechanisms used to optimize both read and write operations. The write path is fairly straightforward which appends records to a log structure providing really high throughput. The read path consists of several data structures which are used to reduce the amount of disk seeks and optimize reads.

The write path

When Cassandra initially came out, it was widely considered a write-optimized database. Whenever a client makes a write request, a coordinator node receives it. The coordinator node forwards the request to the relevant nodes based on the partition key and replication factor. The reason local writes are fast in Cassandra...

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