Book Image

Learning Apache Flink

By : Tanmay Deshpande
Book Image

Learning Apache Flink

By: Tanmay Deshpande

Overview of this book

<p>With the advent of massive computer systems, organizations in different domains generate large amounts of data on a real-time basis. The latest entrant to big data processing, Apache Flink, is designed to process continuous streams of data at a lightning fast pace.</p> <p>This book will be your definitive guide to batch and stream data processing with Apache Flink. The book begins with introducing the Apache Flink ecosystem, setting it up and using the DataSet and DataStream API for processing batch and streaming datasets. Bringing the power of SQL to Flink, this book will then explore the Table API for querying and manipulating data. In the latter half of the book, readers will get to learn the remaining ecosystem of Apache Flink to achieve complex tasks such as event processing, machine learning, and graph processing. The final part of the book would consist of topics such as scaling Flink solutions, performance optimization and integrating Flink with other tools such as ElasticSearch.</p> <p>Whether you want to dive deeper into Apache Flink, or want to investigate how to get more out of this powerful technology, you’ll find everything you need inside.</p>
Table of Contents (17 chapters)
Learning Apache Flink
About the Author
About the Reviewers
Customer Feedback

Use case - sensor data analytics

Now that we have looked at various aspects of DataStream API, let's try to use these concepts to solve a real world use case. Consider a machine which has sensor installed on it and we wish to collect data from these sensors and calculate average temperature per sensor every five minutes.

Following would be the architecture:

In this scenario, we assume that sensors are sending information to Kafka topic called temp with information as (timestamp, temperature, sensor-ID). Now we need to write code to read data from Kafka topics and processing it using Flink transformation.

Here important thing to consider is as we already have timestamp values coming from sensor, we can use Event Time computations for time factors. This means we would be able to take care of events even if they reach out of order.

We start with simple streaming execution environment which will be reading data from Kafka. Since we have timestamps in events, we will be writing a custom timestamp...