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Learning Apache Cassandra

You're reading from   Learning Apache Cassandra Build an efficient, scalable, fault-tolerant, and highly-available data layer into your application using Cassandra

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Product type Paperback
Published in Feb 2015
Publisher
ISBN-13 9781783989201
Length 246 pages
Edition 1st Edition
Languages
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Author (1):
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Matthew Brown Matthew Brown
Author Profile Icon Matthew Brown
Matthew Brown
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Table of Contents (14) Chapters Close

Preface 1. Getting Up and Running with Cassandra 2. The First Table FREE CHAPTER 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 A. Peeking Under the Hood B. Authentication and Authorization Index

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 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 updates that they aren't supposed to.

For the most part, the responsibility for maintaining data integrity...

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