Book Image

Practical Real-time Data Processing and Analytics

Book Image

Practical Real-time Data Processing and Analytics

Overview of this book

With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you’ll be equipped with a clear understanding of how to solve challenges on your own. We’ll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You’ll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

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...