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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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Denormalizing with materialized views


There are times when your use case requires you to read data using an alternate key entirely. In order to be able to read your data by partition key, and in sorted order, it is often necessary to write data in more than one way. Prior to version 3.0, we would accomplish this by literally creating and writing to multiple tables, one for each query type.

Fortunately Cassandra now provides an alternative, called materialized views. This new feature handles the administrative task of populating these alternate table views, removing the burden from our application and reducing the risk of orphaned data.

Creating a materialized view is straightforward. As an example, let's say we need to query for all authors in a given year, which is not possible with the authors table introduced earlier. To accomplish this, we need a view that specifies a primary key starting with the year column, followed by clustering columns for both name and title:

CREATE MATERIALIZED VIEW...

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