What this book covers

Chapter 1, Cassandra's Approach to High Availability, is an introduction to concepts related to system availability and the problems that have been encountered historically while trying to make data stores highly available. This chapter outlines Cassandra's solutions to these problems.

Chapter 2, Data Distribution, outlines the core mechanisms that underlie Cassandra's distributed hash table model, including consistent hashing and partitioner implementations.

Chapter 3, Replication, offers an in-depth look at the data replication architecture used in Cassandra, with a focus on the relationship between consistency levels and replication factors.

Chapter 4, Data Centers, enables you to thoroughly understand Cassandra's robust data center replication capabilities, including deployment on EC2 and building separate clusters for analysis using Hadoop or Spark.

Chapter 5, Scaling Out, is a discussion on the tools, processes, and general guidance required to properly increase the size of your cluster.

Chapter 6, High Availability Features in the Native Java Client, covers the new native Java driver and its availability-related features. We'll discuss node discovery, cluster-aware load balancing, automatic failover, and other important concepts.

Chapter 7, Modeling for High Availability, explains the important concepts you need to understand while modeling highly available data in Cassandra. CQL, keys, wide rows, and denormalization are among the topics that will be covered.

Chapter 8, Antipatterns, complements the data modeling chapter by presenting a set of common antipatterns that proliferate among inexperienced Cassandra developers. Some patterns include queues, joins, high delete volumes, and high cardinality secondary indexes among others.

Chapter 9, Failing Gracefully, helps the reader to understand how to deal with various failure cases, as failure in a large distributed system is inevitable. We'll examine a number of possible failure scenarios, and discuss how to detect and resolve them.