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Practical Real-time Data Processing and Analytics

You're reading from  Practical Real-time Data Processing and Analytics

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Pages 360 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Introducing Real-Time Analytics 2. Real Time Applications – The Basic Ingredients 3. Understanding and Tailing Data Streams 4. Setting up the Infrastructure for Storm 5. Configuring Apache Spark and Flink 6. Integrating Storm with a Data Source 7. From Storm to Sink 8. Storm Trident 9. Working with Spark 10. Working with Spark Operations 11. Spark Streaming 12. Working with Apache Flink 13. Case Study

Trident internals


Every Trident flow is a Storm flow. The concept of executors and workers is exactly the same as Storm. Trident topology is nothing but a Storm bolt. Trident operations such as spouts, each, and aggregations are actually implemented in Storm bolt.

Trident turns your topology into a dataflow (acyclic directed) graph that it uses to assign operations to bolts and then to assign those bolts to workers. It's smart enough to optimize that assignment: it combines operations into bolts so that, as much as possible, tuples are handed off with simple method cause and it arranges bolts among workers so that, as much as possible, tuples are handed off to local executors.

The actual spout of a Trident topology is called the Master Batch Coordinator (MBC). All it does is emit a tuple describing itself as batch 1 and then a tuple describing itself as batch 2, and so forth. Also deciding when to emit those batches, retry them, and so on, is quite exciting, but Storm doesn't know anything...

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