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...
Mastering Apache Spark 2.x - Second Edition
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
Free Chapter
A First Taste and What’s New in Apache Spark V2
Apache Spark SQL
The Catalyst Optimizer
Project Tungsten
Apache Spark Streaming
Structured Streaming
Apache Spark MLlib
Apache SparkML
Apache SystemML
Deep Learning on Apache Spark with DeepLearning4j and H2O
Apache Spark GraphX
Apache Spark GraphFrames
Apache Spark with Jupyter Notebooks on IBM DataScience Experience
Customer Reviews