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flink in action ch1

flink in action manning ch1 .This book is about handling streaming data with Apache Flink. Every business is composed of a series of events. Imagine a large retail store or public news site that is serving customers all over the world; events are constantly being generated. What distinguishes a streaming system from a batch system is that the event stream is unbounded or infinite from a system perspective. Decision-makers need to analyze these streaming events together to make business decisions
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MEAP Edition
Manning Early Access Program
Flink in Action
Version 2
Copyright 2016 Manning Publications
For more information on this and other Manning titles go to
www.manning.com
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and
other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders.
https://forums.manning.com/forums/flink-in-action

welcome
Thank you for purchasing the MEAP for Flink in Action. We are excited to deliver this book as
large-scale stream processing using Flink and Google Data Flow is fast gaining in popularity.
Stream processing is much more than just processing records one at a time as they arrive.
True stream processing needs support for concepts such as event time processing to ensure
stream processing systems are just as accurate as the batch processing system. There is a
need for one system the performs both stream and batch processing. Apache Flink is that
system.
As we started exploring Apache Flink, we discovered the subtle challenges that are
inherent in stream processing. These challenges are intrinsic to how stream processing is
performed. Unlike batch processing, where all data is available when processing begins,
stream processing must be able to handle incomplete data, late arrivals, and out-of-order
arrivals—without compromising performance or accuracy—and be resilient to failure. We
tackle all these challenges in this book.
Writing this book has been a challenge, partly because the technology is changing rapidly
as we write and partly because we want to make this complex topic of streaming easy to
understand in the context of everyday use cases. We believe that eventually streaming
systems will become the norm, because the real world operates in the streaming mode. Real-
world events occur and are captured continuously in transaction systems. The reporting
systems that aggregate these transactions into reports operate in batch-processing mode due
to technology limitations. These limitations are now being addressed by systems such as
Apache Flink. We hope this book helps you develop a strong foundation in the concepts and
the challenges of implementing streaming systems capable of handling high-velocity and high-
volume streaming data.
Please be sure to post any questions, comments, or suggestions you have about the book
in the Author Online forum. Your feedback is essential in developing the best book possible.
— Sameer B. Wadkar and Hari Rajaram
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and
other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders.
https://forums.manning.com/forums/flink-in-action

brief contents
PART 1: STREAM PROCESSING USING FLINK
1 Introducing Apache Flink
2 Getting started with Flink
3 Batch processing using the DataSet API
4 Stream processing using the DataStream API
5 Basics of event time processing
PART 2: ADVANCED STREAM PROCESSING USING FLINK
6 Session windows and custom windows
7 Using the Flink API in practice
8 Using Kafka with Flink
9 Fault tolerance in Flink
PART 3: OUT IN THE WILD
10 Domain-specific libraries in Flink – CEP and Streaming SQL
11 Apache Beam and Flink
APPENDIXES:
A Setting up your local Flink environment
B Installing Apache Kafka
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and
other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders.
https://forums.manning.com/forums/flink-in-action

1
Introducing Apache Flink
This chapter covers
• Why stream processing is important
• What is Apache Flink
• Apache Flink in the context of a real world example
This book is about handling streaming data with Apache Flink. Every business is composed of
a series of events. Imagine a large retail store or public news site that is serving customers all
over the world; events are constantly being generated. What distinguishes a streaming system
from a batch system is that the event stream is unbounded or infinite from a system
perspective. Decision-makers need to analyze these streaming events together to make
business decisions. For example:
• A retail store chain is constantly selling products in various locations. People making
decisions need to know how the various products are selling. Most current systems do
this via nightly extract, transform, and load (ETL) processing, which is common in
enterprise environments, requires decision makers to wait an entire day before reports
become available. Ideally these decision makers would like to be able to inquire in near
real-time the performance of sales across the stores and regions.
• A popular news website is constantly serving user requests. Each request/response can
be considered an event. The stream of events need to be analyzed in near real-time to
understand how the news articles are performing with respect to page-views and to
determine which advertisements should be displayed to the readers as they are
browsing the website.
• Near real-time systems are especially valuable in fraud detection systems. Determining
that a credit card transaction was a fraud within moments of performing it is crucial in
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and
other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders.
https://forums.manning.com/forums/flink-in-action
1
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