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

Increased performance with good old friends


As in Apache SparkSQL for batch processing and, as Apache Spark structured streaming is part of Apache SparkSQL, the Planner (Catalyst) creates incremental execution plans as well for mini-batches. This means that the whole streaming model is based on batches. This is the reason why a unified API for streams and batch processing could be achieved. The price we pay is that Apache Spark streaming sometimes has drawbacks when it comes to very low latency requirements (sub-second, in the range of tens of ms). As the name Structured Streaming and the usage of DataFrames and Datasets implies, we are also benefiting from performance improvements due to project Tungsten, which has been introduced in a previous chapter. To the Tungsten engine itself, a mini batch doesn't look considerably different from an ordinary batch. Only Catalyst is aware of the incremental nature of streams. Therefore, as of Apache Spark V2.2, the following operations are not (yet...