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Cassandra 3.x High Availability

You're reading from   Cassandra 3.x High Availability Achieve scalability and high availability without compromising on performance

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Product type Paperback
Published in Aug 2016
Publisher
ISBN-13 9781786462107
Length 196 pages
Edition 2nd Edition
Languages
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Author (1):
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Robbie Strickland Robbie Strickland
Author Profile Icon Robbie Strickland
Robbie Strickland
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Table of Contents (10) Chapters Close

Preface 1. Cassandras Approach to High Availability FREE CHAPTER 2. Data Distribution 3. Replication 4. Data Centers 5. Scaling Out 6. High Availability Features in the Native Java Client 7. Modeling for Availability 8. Anti-Patterns 9. Failing Gracefully

Consistent hashing

The solution is consistent hashing. Introduced as a term in 1997, consistent hashing was originally used as a means of routing requests among a large number of web servers. It's easy to see how the web could benefit from a hash mechanism that allows any node in the network to efficiently determine the location of an object, in spite of the constant shifting of nodes in and out of the network. This is the fundamental objective of consistent hashing.

How it works

With consistent hashing, the buckets are arranged in a ring with a predefined range. The exact range depends on the partitioner being used. Keys are then hashed to produce a value that lies somewhere along the ring. Nodes are assigned a range, which is computed as follows:

Range start

Token value

Range end

Next token value -1

Tip

The following examples assume the default Murmur3Partitioner is used. For more information on this partitioner, take a look at the documentation, which can be found here: http...

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