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)

Summary


Collection columns are a powerful feature of CQL that allow us to store multiple values in a single column. Most importantly, it's possible to discretely update single values in a collection without reading the collection's current contents or fully providing the new contents of the collection.

This capability is particularly useful when multiple processes might need to concurrently modify a collection. By avoiding the need to read and then fully overwrite a collection's contents, we avoid situations in which concurrent updates can lead to data loss and can support concurrent updates without resorting to optimistic locking.

Collections are best suited to datasets that are small and bounded. This is both because there is a hard upper limit on the amount of data a collection can hold, and because when a collection is read, it is always read in full. For larger data sets, it is usually most appropriate to create a separate table whose partition key reflects the full primary key of the...