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)

Chapter 6. Denormalizing Data for Maximum Performance

In the previous chapter, we created a structure that allows a user to follow other users. The goal of the follow system was to allow users to see all of their followed users' status updates in one place, which we'll call the home timeline. In this chapter, we will build a table to store users' home timelines.

The follow structures in Chapter 5, Establishing Relationships, introduced the concept of denormalization, the practice of storing the same piece of data in more than one place in order to optimize read performance. The denormalization we used for follows was fairly mild; however, each follow relationship is stored in exactly two places. For home timelines, we will create a much more aggressively denormalized data structure: a given piece of data will be stored in an arbitrary number of places.

While this highly denormalized structure will be the end result of our work in this chapter, we'll explore several approaches along the way...