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Learning Apache Cassandra

You're reading from   Learning Apache Cassandra Managing fault-tolerant, scalable data with high performance

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Product type Paperback
Published in Apr 2017
Publisher
ISBN-13 9781787127296
Length 360 pages
Edition 2nd Edition
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Author (1):
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Sandeep Yarabarla Sandeep Yarabarla
Author Profile Icon Sandeep Yarabarla
Sandeep Yarabarla
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Table of Contents (15) Chapters Close

Preface 1. Getting Up and Running with Cassandra FREE CHAPTER 2. The First Table 3. Organizing Related Data 4. Beyond Key-Value Lookup 5. Establishing Relationships 6. Denormalizing Data for Maximum Performance 7. Expanding Your Data Model 8. Collections, Tuples, and User-Defined Types 9. Aggregating Time-Series Data 10. How Cassandra Distributes Data 11. Cassandra Multi-Node Cluster 12. Application Development Using the Java Driver 13. Peeking under the Hood 14. Authentication and Authorization

Denormalization

Our follow tables are also the first example we've seen of denormalization, which is the practice of storing the same data in more than one place. Denormalization is typically frowned upon in relational database schemas, although from a practical standpoint it's often a useful optimization even in that scenario. In non-relational databases, denormalization is often a critical tool in query-driven designs.

The downside of denormalization is exemplified by our preceding insert pattern: we have to make two INSERT statements to fully represent one fundamental fact. From a standpoint of performance, this is acceptable: Cassandra is optimized for efficient write operations, so we're happy to make verbose writes in order to allow efficient reads. This does, of course, add more complexity at the application level: the application is responsible for ensuring that any modification to the user_outbound_follows...

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