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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

Consistency


Masterless replication confers huge advantages in maintaining full availability in the face of hardware failure, but it also creates a thorny, if subtle, problem: how can we be sure that the data we're reading is the most recent version of that data?

Let's first consider the master-follower architecture we discussed above. When our application issues a write request to the master instance, that request only returns a successful response once the data has been successfully written to the master instance. A successful response does not, however, guarantee that the data has been replicated to all of the followers: any master-follower system will involve some delay before write are replicated to the followers.

Still, in this system, there is a simple way to guarantee that, when we read a piece of data, we're reading the most up-to-date version of it: simply read from the master. Of course, if we only ever read from the master, the followers don't do much for us other than providing...

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