Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By : Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei
Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By: Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei

Overview of this book

Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: • Mastering Apache Spark 2.x by Romeo Kienzler • Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla • Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook
Table of Contents (23 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

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...