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)

Refining our mental model


In the previous chapter, we began to develop a mental model of a Cassandra table that looked like a key-value store where each value is a collection of columns with values. Now that we have seen compound primary keys, we can refine that mental model to take into account the more complex structures we now know how to build, as follows:

We can envision the user_status_updates table as a more robust key-value structure. Our keys are still usernames, but the values are now ordered collections of rows, both identified and ordered by the id clustering column. As with our earlier model, each partition key's data stands alone; to get data from multiple partitions, we have to go looking in multiple places.