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Cassandra 3.x High Availability

Cassandra 3.x High Availability

By : Strickland
3.8 (6)
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Cassandra 3.x High Availability

Cassandra 3.x High Availability

3.8 (6)
By: Strickland

Overview of this book

Apache Cassandra is a massively scalable, peer-to-peer database designed for 100 percent uptime, with deployments in the tens of thousands of nodes, all supporting petabytes of data. This book offers a practical insight into building highly available, real-world applications using Apache Cassandra. The book starts with the fundamentals, helping you to understand how Apache Cassandra’s architecture allows it to achieve 100 percent uptime when other systems struggle to do so. You’ll get an excellent understanding of data distribution, replication, and Cassandra’s highly tunable consistency model. Then we take an in-depth look at Cassandra's robust support for multiple data centers, and you’ll see how to scale out a cluster. Next, the book explores the domain of application design, with chapters discussing the native driver and data modeling. Lastly, you’ll find out how to steer clear of common anti-patterns and take advantage of Cassandra’s ability to fail gracefully.
Table of Contents (10 chapters)
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When a node goes down


In a cluster of any significant size, nodes are bound to become unresponsive for a variety of reasons. Fortunately, Cassandra has a sophisticated mechanism called the failure detector that is designed to determine when this has occurred and then mark the node as down.

Most node failures result from temporary conditions, such as network issues. Therefore, Cassandra assumes that the node will eventually come back online and that permanent cluster changes will be executed explicitly using nodetool.

Marking a downed node

Each node keeps track of the state of other nodes in the cluster by means of an accrual failure detector (or phi failure detector). This detector evaluates the health of other nodes based on a sliding window of gossip message arrival times. It computes the statistical distribution of those arrival times per node, thus taking into account the current state of the network rather than using naive thresholds or timeouts.

The ultimate result of the failure detection...

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