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Mastering Apache Storm

You're reading from   Mastering Apache Storm Real-time big data streaming using Kafka, Hbase and Redis

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Product type Paperback
Published in Aug 2017
Publisher
ISBN-13 9781787125636
Length 284 pages
Edition 1st Edition
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Author (1):
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Ankit Jain Ankit Jain
Author Profile Icon Ankit Jain
Ankit Jain
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Table of Contents (13) Chapters Close

Preface 1. Real-Time Processing and Storm Introduction FREE CHAPTER 2. Storm Deployment, Topology Development, and Topology Options 3. Storm Parallelism and Data Partitioning 4. Trident Introduction 5. Trident Topology and Uses 6. Storm Scheduler 7. Monitoring of Storm Cluster 8. Integration of Storm and Kafka 9. Storm and Hadoop Integration 10. Storm Integration with Redis, Elasticsearch, and HBase 11. Apache Log Processing with Storm 12. Twitter Tweet Collection and Machine Learning

Guaranteed message processing

In a Storm topology, a single tuple being emitted by a spout can result in a number of tuples being generated in the later stages of the topology. For example, consider the following topology:

Here, Spout A emits a tuple T(A), which is processed by bolt B and bolt C, which emit tuple T(AB) and T(AC) respectively. So, when all the tuples produced as a result of tuple T(A)--namely, the tuple tree T(A), T(AB), and T(AC)--are processed, we say that the tuple has been processed completely.

When some of the tuples in a tuple tree fail to process either due to some runtime error or a timeout that is configurable for each topology, then Storm considers that to be a failed tuple.

Here are the six steps that are required by Storm to guarantee message processing:

  1. Tag each tuple emitted by a spout with a unique message ID. This can be done by using the org...
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