Book Image

Learning PySpark

By : Tomasz Drabas, Denny Lee
Book Image

Learning PySpark

By: Tomasz Drabas, Denny Lee

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.

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Table of Contents (20 chapters)
Learning PySpark
Learning PySpark
Credits
Credits
Foreword
Foreword
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Understanding Spark
Understanding Spark
What is Apache Spark?
Spark Jobs and APIs
Spark 2.0 architecture
Summary
2
Resilient Distributed Datasets
Resilient Distributed Datasets
Internal workings of an RDD
Creating RDDs
Global versus local scope
Transformations
Actions
Summary
3
DataFrames
DataFrames
Python to RDD communications
Catalyst Optimizer refresh
Speeding up PySpark with DataFrames
Creating DataFrames
Simple DataFrame queries
Interoperating with RDDs
Querying with the DataFrame API
Querying with SQL
DataFrame scenario – on-time flight performance
Spark Dataset API
Summary
4
Prepare Data for Modeling
Prepare Data for Modeling
Checking for duplicates, missing observations, and outliers
Getting familiar with your data
Visualization
Summary
5
Introducing MLlib
Introducing MLlib
Overview of the package
Loading and transforming the data
Getting to know your data
Creating the final dataset
Predicting infant survival
Summary
6
Introducing the ML Package
Introducing the ML Package
Overview of the package
Predicting the chances of infant survival with ML
Parameter hyper-tuning
Other features of PySpark ML in action
Summary
7
GraphFrames
GraphFrames
Introducing GraphFrames
Installing GraphFrames
Preparing your flights dataset
Building the graph
Executing simple queries
Understanding vertex degrees
Determining the top transfer airports
Understanding motifs
Determining airport ranking using PageRank
Determining the most popular non-stop flights
Using Breadth-First Search
Visualizing flights using D3
Summary
8
TensorFrames
TensorFrames
What is Deep Learning?
What is TensorFlow?
Introducing TensorFrames
TensorFrames – quick start
Summary
9
Polyglot Persistence with Blaze
Polyglot Persistence with Blaze
Installing Blaze
Polyglot persistence
Abstracting data
Data operations
Summary
10
Structured Streaming
Structured Streaming
What is Spark Streaming?
Why do we need Spark Streaming?
What is the Spark Streaming application data flow?
Simple streaming application using DStreams
A quick primer on global aggregations
Introducing Structured Streaming
Summary
11
Packaging Spark Applications
Packaging Spark Applications
The spark-submit command
Deploying the app programmatically
Databricks Jobs
Summary
Index
Index

Catalyst Optimizer refresh

As noted in Chapter 1, Understanding Spark, one of the primary reasons the Spark SQL engine is so fast is because of the Catalyst Optimizer. For readers with a database background, this diagram looks similar to the logical/physical planner and cost model/cost-based optimization of a relational database management system (RDBMS):

The significance of this is that, as opposed to immediately processing the query, the Spark engine's Catalyst Optimizer compiles and optimizes a logical plan and has a cost optimizer that determines the most efficient physical plan generated.

Note

As noted in earlier chapters, while the Spark SQL Engine has both rules-based and cost-based optimizations that include (but are not limited to) predicate push down and column pruning. Targeted for the Apache Spark 2.2 release, the jira item [SPARK-16026] Cost-based Optimizer Framework at https://issues.apache.org/jira/browse/SPARK-16026 is an umbrella ticket to implement a cost-based optimizer framework...

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