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Cassandra 3.x High Availability

You're reading from   Cassandra 3.x High Availability Achieve scalability and high availability without compromising on performance

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Product type Paperback
Published in Aug 2016
Publisher
ISBN-13 9781786462107
Length 196 pages
Edition 2nd Edition
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Author (1):
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Robbie Strickland Robbie Strickland
Author Profile Icon Robbie Strickland
Robbie Strickland
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Table of Contents (10) Chapters Close

Preface 1. Cassandras Approach to High Availability FREE CHAPTER 2. Data Distribution 3. Replication 4. Data Centers 5. Scaling Out 6. High Availability Features in the Native Java Client 7. Modeling for Availability 8. Anti-Patterns 9. Failing Gracefully

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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