Book Image

Learning Apache Cassandra - Second Edition

Book Image

Learning Apache Cassandra - Second Edition

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 (14 chapters)

Multidata center cluster


One of the nice features of Cassandra that makes it a popular NoSQL database in the industry is its native support for multidata center clusters. There are no manual steps required to clone data from one data center to another. Once the cluster is properly configured with two data centers, all the requests (read or write) are forwarded to both data centers. For this setup to work, a few configuration changes, as well as schema changes, are required.

Here is an illustration of a multidata center cluster:

Here, we have two datacenters named DC1 and DC2, each with four nodes. For simplicity purposes, I am assuming a token range of 0-100. We will notice that the entire token range is split into four ranges on both the data centers rather than eight ranges. This means the same data is written to both the data centers. Even though they are part of the same cluster, both of them are considered separate entities when it comes to topology. Also, there has to be connectivity...