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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

Consistency

Masterless replication offers 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 earlier. 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 writes 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...

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