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Learning Real-time Analytics with Storm and Cassandra

You're reading from   Learning Real-time Analytics with Storm and Cassandra Solve real-time analytics problems effectively using Storm and Cassandra

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Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781784395490
Length 220 pages
Edition 1st Edition
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Author (1):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
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Table of Contents (14) Chapters Close

Preface 1. Let's Understand Storm 2. Getting Started with Your First Topology FREE CHAPTER 3. Understanding Storm Internals by Examples 4. Storm in a Clustered Mode 5. Storm High Availability and Failover 6. Adding NoSQL Persistence to Storm 7. Cassandra Partitioning, High Availability, and Consistency 8. Cassandra Management and Maintenance 9. Storm Management and Maintenance 10. Advance Concepts in Storm 11. Distributed Cache and CEP with Storm A. Quiz Answers Index

Introduction to memcached

Memcached is a very simple in-memory key value store; we can assume it to be an in-memory store for a hash map. This can be used in conjunction with Storm supervisors to serve as a common memory storage, which can be accessed for read/write operations by all the Storm workers on various nodes in the Storm cluster.

Memcached has the following components:

  • The memcached server
  • The memcache client
  • The hashing algorithm (client-based implementation)
  • The server algorithm for data retention

Memcached uses Least Recently Used (LRU) to discard the elements from the cache. This means that the items that have not been referred since the longest time are the first ones to be removed from the cache. These items are said to be expired from the cache, and if they are referred after expiry, they are reloaded from a stable storage.

The following is the flow of how entries are loaded and retrieved from or through a cache:

Introduction to memcached

The preceding figure depicts the scenarios of cache hit and cache...

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