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Practical Real-time Data Processing and Analytics

You're reading from   Practical Real-time Data Processing and Analytics Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Length 360 pages
Edition 1st Edition
Languages
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Authors (2):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
Saurabh Gupta Saurabh Gupta
Author Profile Icon Saurabh Gupta
Saurabh Gupta
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introducing Real-Time Analytics FREE CHAPTER 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

State retention and the need for Trident


Trident is a distributed real-time analytics framework. Trident maintains its state either internally for example, in-memory, or externally for example, Hazelcast, in a fault-tolerant way. It is similar to processing an event exactly once. Trident fits for micro batch processing use cases such as aggregation, filtration, and so on.

Let's take an example that explains how to achieve exactly-once semantics. Suppose that you're doing a count of how many people visited your blog and also storing the running count in a database. Now suppose you store a single value representing the count in the database, and every time you process a new tuple you increment the count.

Now, if failures happen, tuples will be replayed by Storm topology. Here the problem is whether or not the tuple has been processed and the count has already been updated in the database—if so, then you should not update it again or if the tuple did not process successfully then you have to...

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