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)

Handling conflicting data


As we explored earlier, Cassandra's masterless replication can lead to situations in which multiple versions of the same record exist on different nodes. Since there is no master node containing the canonical copy of a record, Cassandra must use other means to determine which version of the data is correct.

This situation comes into play when reading data at any consistency level other than ONE. When our application requests a row from Cassandra, we will receive a response with that row's data; each column will contain one value. However, if we're reading at a consistency level such as QUORUM or ALL, Cassandra internally will fetch the copies of the data from multiple nodes; it's possible that the different copies will contain conflicting data. It's up to Cassandra to figure out exactly what to return to us.

The problem is most acute when different clients are writing the same piece of data concurrently. Let's return to a scenario we explored in Chapter 7, Expanding...