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 9. Aggregating Time-Series Data

In the preceding chapters, you learned how to use Cassandra as a primary data store for the MyStatus application, with a focus on modeling the data that drives the main user experience on the site. In this chapter, we'll shift focus to another popular use of Cassandra: aggregating data that we observe over time. In particular, we'll build a small analytics component into our schema, which will allow us to keep track of how many times a given status update was viewed on a given day.

In order to do this, we'll introduce a new type of column, the counter column, which is a special numeric column type that can be discretely incremented or decremented. Counter columns have a lot in common with collection columns, which we explored in the previous chapter: you can make discrete changes to them without reading their current value, and they're good for scenarios in which many threads or processes might need to update the same piece of data at the same time...