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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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Scaling out versus scaling up


So you know it's time to add more muscle to your cluster, but how do you know whether to scale up or out?

If you're not familiar with the difference, scaling up refers to converting existing infrastructure to better or more robust hardware (or instance types in cloud environments). This could mean adding storage capacity, increasing memory, moving to newer machines with more cores, and so on.

Scaling out simply means adding more machines that roughly match the specifications of the existing machines. Since Cassandra scales linearly with its peer-to-peer architecture, scaling out is often more desirable.

Tip

In general, it is better to replace physical hardware components incrementally rather than all at one time. This is because in large systems failures tend to come after hardware ages to a certain point, which is statistically likely to happen simultaneously for some subset of your nodes. For example, purchasing a large lot of drives from a single source at one...

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