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


In the earlier sections, we tried to understand the Flink architecture and its execution model. Because of its robust architecture, Flink is full of various features.

High performance

Flink is designed to achieve high performance and low latency. Unlike other streaming frameworks such as Spark, you don't need to do many manual configurations to get the best performance. Flink's pipelined data processing gives better performance compared to its counterparts.

Exactly-once stateful computation

As we discussed in the previous section, Flink's distributed checkpoint processing helps to guarantee processing each record exactly once. In the case of high-throughput applications, Flink provides us with a switch to allow at least once processing.

Flexible streaming windows

Flink supports data-driven windows. This means we can design a window based on time, counts, or sessions. A window can also be customized which allows us to detect specific patterns in event streams.

Fault tolerance

Flink's distributed, lightweight snapshot mechanism helps in achieving a great degree of fault tolerance. It allows Flink to provide high-throughput performance with guaranteed delivery.

Memory management

Flink is supplied with its own memory management inside a JVM which makes it independent of Java's default garbage collector. It efficiently does memory management by using hashing, indexing, caching, and sorting.


Flink's batch data processing API is optimized in order to avoid memory-consuming operations such as shuffle, sort, and so on. It also makes sure that caching is used in order to avoid heavy disk IO operations.

Stream and batch in one platform

Flink provides APIs for both batch and stream data processing. So once you set up the Flink environment, it can host stream and batch processing applications easily. In fact Flink works on Streaming first principle and considers batch processing as the special case of streaming.


Flink has a rich set of libraries to do machine learning, graph processing, relational data processing, and so on. Because of its architecture, it is very easy to perform complex event processing and alerting. We are going to see more about these libraries in subsequent chapters.

Event time semantics

Flink supports event time semantics. This helps in processing streams where events arrive out of order. Sometimes events may come delayed. Flink's architecture allows us to define windows based on time, counts, and sessions, which helps in dealing with such scenarios.