Book Image

Mastering Apache Spark 2.x - Second Edition

Book Image

Mastering Apache Spark 2.x - Second Edition

Overview of this book

Apache Spark is an in-memory, cluster-based Big Data processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and more. This book will take your knowledge of Apache Spark to the next level by teaching you how to expand Spark’s functionality and build your data flows and machine/deep learning programs on top of the platform. The book starts with a quick overview of the Apache Spark ecosystem, and introduces you to the new features and capabilities in Apache Spark 2.x. You will then work with the different modules in Apache Spark such as interactive querying with Spark SQL, using DataFrames and DataSets effectively, streaming analytics with Spark Streaming, and performing machine learning and deep learning on Spark using MLlib and external tools such as H20 and Deeplearning4j. The book also contains chapters on efficient graph processing, memory management and using Apache Spark on the cloud. By the end of this book, you will have all the necessary information to master Apache Spark, and use it efficiently for Big Data processing and analytics.
Table of Contents (21 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
10
Deep Learning on Apache Spark with DeepLearning4j and H2O

Windowing


Open source and commercial streaming engines such as IBM Streams, Apache Storm, or Apache Flink are using the concept of windows.

Note

Windows specify the granularity or number of subsequent records, which are taken into account when executing aggregation functions on streams.

How streaming engines use windowing

There exist five different properties in two dimensions, which is how windows can be defined, where each window definition needs to use one property of each dimension.

The first property is the mode in which subsequent windows of a continuous stream of tuples can be created: sliding and tumbling.

The second is that the number of tuples that fall into a window has to be specified: either count-based, time-based or session-based.

Let's take a look at what they mean:

  • Sliding windows: A sliding window removes a tuple from it whenever a new tuple is eligible to be included.
  • Tumbling windows: A tumbling window removes all tuples from it whenever there are enough tuples arriving to create...