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)

Materialized views


While we were modeling our follow relationships, we noted that different access patterns required us to store the same data in multiple tables with different schema. To avoid this denormalization, we created a secondary index on one of the columns. But adding a secondary index on a non-partition key column has a performance impact on read latency. This is especially the case when the column on which the index was created has a high cardinality. Thus, secondary indexes should be used in cases of low cardinality columns or you specify the partition key as well within your queries so the queries don't scale across multiple partitions. To avoid client-side denormalization and the use of secondary indexes for high cardinality columns, materialized views were introduced in Cassandra 3.0. Materialized views handle server-side denormalization ensuring eventual consistency between the base and view data. This provides very fast lookups of data in materialized views even when the...