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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

Write complexity and data integrity

The amount of work we need to do to write data in the fully normalized strategy is basically equal to what we needed to do with a partially normalized layout. Our storage needs to increase by a bit; now we're storing one full copy of each status update for every follower the author has. However, storage is cheap, and writing data in Cassandra is cheap, so we've managed to make our timeline read pattern far more efficient at a low cost.

One concern in any sort of denormalized scenario is data integrity. At the Cassandra level, the only thing stopping us from adding a status update to the user_status_updates table is forgetting to add copies as appropriate to the home_status_updates table, or vice versa. Even worse, if a user deletes a status update and we don't properly remove copies from all the home_status_updates table, the user's followers might see status...

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