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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 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

Handling conflicting data


As we explored above, Cassandra's masterless replication can lead to situations in which multiple versions of the same record exist on different nodes. Since there is no master node containing the canonical copy of a record, Cassandra must use other means to determine which version of the data is correct.

This situation comes into play when reading data at any consistency level other than ONE. When our application requests a row from Cassandra, we will receive a response with that row's data; each column will contain one value. However, if we're reading at a consistency level such as QUORUM or ALL, Cassandra internally will fetch the copies of the data from multiple nodes; it's possible that the different copies will contain conflicting data. It's up to Cassandra to figure out exactly what to return to us.

The problem is most acute when different clients are writing the same piece of data concurrently. Let's return to a scenario we explored in Chapter 7, Expanding...

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