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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

Summary


In this chapter, we looked at ways to model relationships between objects that go beyond the straightforward parent-child relationships, which are captured elegantly by a compound primary key. We found that query-driven schema design motivated us to create multiple representations of the follow relationship; each representation was optimized to answer a specific question about follows. This led us to a denormalized schema, wherein each follow has multiple representations in our database.

While our denormalized schema requires more write operations than a normalized one and extra care at the application level to ensure the different representations of follows are consistent with one another, we end up with a better overall performance because writing data to Cassandra is cheaper than reading it. By designing our schema to allow Cassandra to efficiently access data in a single partition to answer any question the application needs, we ensure that Cassandra can continue efficiently serving...

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