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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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Toc

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

Anchoring and acking

We have talked about DAG that is created for the execution of a Storm topology. Now when you are designing your topologies to cater to reliability, there are two items that needs to be added to Storm:

  • Whenever a new link, that is, a new stream is being added to the DAG, it is called anchoring
  • When the tuple is processed in entirety, it is called acking

When Storm knows these preceding facts, then during the processing of tuples it can gauge them and accordingly fail or acknowledge the tuples depending upon whether they are completely processed or not.

Let's take a look at the following WordCount topology bolts to understand the Storm API anchoring and acking better:

  • SplitSentenceBolt: The purpose of this bolt was to split the sentence into different words and emit it. Now let's examine the output declarer and the execute methods of this bolt in detail (specially the highlighted sections) as shown in the following code:
      public void execute(Tuple tuple) {
         ...
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