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You're reading from  Real-time Analytics with Storm and Cassandra

Product typeBook
Published inMar 2015
Reading LevelIntermediate
Publisher
ISBN-139781784395490
Edition1st Edition
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Author (1)
Shilpi Saxena
Shilpi Saxena
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Shilpi Saxena

Shilpi Saxena is an IT professional and also a technology evangelist. She is an engineer who has had exposure to various domains (machine to machine space, healthcare, telecom, hiring, and manufacturing). She has experience in all the aspects of conception and execution of enterprise solutions. She has been architecting, managing, and delivering solutions in the Big Data space for the last 3 years; she also handles a high-performance and geographically-distributed team of elite engineers. Shilpi has more than 12 years (3 years in the Big Data space) of experience in the development and execution of various facets of enterprise solutions both in the products and services dimensions of the software industry. An engineer by degree and profession, she has worn varied hats, such as developer, technical leader, product owner, tech manager, and so on, and she has seen all the flavors that the industry has to offer. She has architected and worked through some of the pioneers' production implementations in Big Data on Storm and Impala with autoscaling in AWS. Shilpi has also authored Real-time Analytics with Storm and Cassandra (https://www.packtpub.com/big-data-and-business-intelligence/learning-real-time-analytics-storm-and-cassandra) with Packt Publishing.
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Consistent hashing


Before you understand its implication and application in Cassandra, let's understand consistent hashing as a concept.

Consistent hashing works on the concept in its name—that is hashing and as we know, for a said hashing algorithm, the same key will always return the same hash code—thus, making the approach pretty deterministic by nature and implementation. When we use this approach for sharding or dividing the keys across the nodes in the cluster, consistent hashing is the technique that determines which node is stored in which node in the cluster.

Have a look at the following diagram to understand the concept of consistent hashing; imagine that the ring depicted in the following diagram represents the Cassandra ring and the nodes are marked here in letters along with the numerals that actually mark the objects (inverted triangles) to be mapped to the ring.

Consistent hashing for the Cassandra cluster

To compute the ownership of the object to the node it belongs to, all...

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Real-time Analytics with Storm and Cassandra
Published in: Mar 2015Publisher: ISBN-13: 9781784395490

Author (1)

author image
Shilpi Saxena

Shilpi Saxena is an IT professional and also a technology evangelist. She is an engineer who has had exposure to various domains (machine to machine space, healthcare, telecom, hiring, and manufacturing). She has experience in all the aspects of conception and execution of enterprise solutions. She has been architecting, managing, and delivering solutions in the Big Data space for the last 3 years; she also handles a high-performance and geographically-distributed team of elite engineers. Shilpi has more than 12 years (3 years in the Big Data space) of experience in the development and execution of various facets of enterprise solutions both in the products and services dimensions of the software industry. An engineer by degree and profession, she has worn varied hats, such as developer, technical leader, product owner, tech manager, and so on, and she has seen all the flavors that the industry has to offer. She has architected and worked through some of the pioneers' production implementations in Big Data on Storm and Impala with autoscaling in AWS. Shilpi has also authored Real-time Analytics with Storm and Cassandra (https://www.packtpub.com/big-data-and-business-intelligence/learning-real-time-analytics-storm-and-cassandra) with Packt Publishing.
Read more about Shilpi Saxena