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Cassandra 3.x High Availability
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Cassandra 3.x High Availability - Second Edition
Copyright © 2016 Packt Publishing
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First published: December 2014
Second edition: August 2016
Production reference: 1250816
Published by Packt Publishing Ltd.
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Birmingham
B3 2PB, UK.
ISBN 978-1-78646-210-7
www.packtpub.com
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Robbie Strickland has been involved in the Apache Cassandra project since 2010, and he initially went to production with the 0.5 release. He has made numerous contributions over the years, including work on drivers for C# and Scala and multiple contributions to the core Cassandra codebase. In 2013 he became the very first certified Cassandra developer, and in 2014 DataStax selected him as an Apache Cassandra MVP.
Robbie has been an active speaker and writer in the Cassandra community and is the founder of the Atlanta Cassandra Users Group. Other examples of his writing can be found on the DataStax blog, and he has presented numerous webinars and conference talks over the years.
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Jimmy Mårdell is a senior software engineer and Cassandra contributor who has worked with Cassandra for more than 5 years. He has been leading the database infrastructure team at Spotify, focusing on improving the Cassandra ecosystem at Spotify and empowering other teams to operate large-scale Cassandra clusters. He has been a speaker at many Cassandra events and in 2015 he was elected by DataStax as an Apache Cassandra MVP. Besides Cassandra, Jimmy likes algorithms and competitive programming and won the programming competition Google Code Jam in 2003.
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Cassandra is a fantastic data store and certainly well suited as the foundation of a highly available system. In fact, it was built just for such a purpose: to handle Facebook’s messaging service. But it hasn’t always been so easy to use, with its early Thrift interface and unfamiliar data model causing many potential users to pause—and in many cases for a good reason.
Fortunately, Cassandra has matured substantially over the last few years. I used to advise people only to use Cassandra if nothing else would do the job because the learning curve was quite steep. Version 3.x continues this trend, with the introduction of features such as materialized views and SASI indexes. These additions reduce developer workload and significantly increase the overall utility of the system.
The flip side is that each new feature further obscures the underlying data structure, making complex operations seem straightforward. The familiarity of a SQL-like interface can lure an unsuspecting new user into dangerous traps. The moral of this story is that it’s still not a relational database, and you still need to know what it’s doing under the hood.
And imparting that knowledge is the core objective of this book. Each chapter attempts to demystify the inner workings of Cassandra so that you’re no longer working blindly against a black box data store. You will learn to configure, design, and build your system based on a fundamentally solid foundation.
The good news is that Cassandra makes the task of building massively scalable and incredibly reliable systems relatively straightforward, presuming you understand how to partner with it to achieve these goals.
Since you are reading this book, I presume you are either already using Cassandra or planning to do so, and that you’re interested in building a highly available system on top of it. If so, I am confident that you will meet with success if you follow the principles and guidelines offered in the chapters that follow.
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 when trying to make data stores highly available. The 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 factor.
Chapter 4, Data Centers, provides you with a thorough understanding of 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 of the tools, processes, and general guidance needed 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 Availability, discusses the important concepts readers need to understand when modeling highly available data in Cassandra. CQL, keys, wide rows, and denormalization are among the topics that will be covered.
Chapter 8, Anti-Patterns, complements the data modeling chapter by presenting a set of common anti-patterns 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 you understand how to deal with the various failure cases, as failure in a large distributed system is inevitable. We’ll examine a number of possible failure scenarios, how to detect them, and how to resolve them.
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