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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
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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 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 A. Peeking Under the Hood B. Authentication and Authorization Index

Lightweight transactions have a cost


Lightweight transactions allow us to maintain data integrity in the face of concurrent updates but they don't do it for free. Because of Cassandra's distributed architecture, it's actually quite involved to guarantee that the data is in a certain state before modifying it, as all the machines that store that piece of data need to be in agreement. Accordingly, there's a performance penalty in using lightweight transactions; thus, you shouldn't use them in situations where you don't need to.

When lightweight transactions aren't necessary

As we discussed earlier, concurrent insertions aren't a concern for any table that uses UUIDs that are generated by the application or by Cassandra at row creation time. Simply using UUIDs guarantees we'll never have a row key collision.

Another scenario in which we can skip conditional inserts is when we have a globally unique natural key. For instance, if you're building an RSS (short for Rich Site Summary) reader, you might...

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