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PySpark Cookbook

By : Lee, Drabas

1.7 (3)

PySpark Cookbook

1.7 (3)

By: Lee, Drabas

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. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

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Free Chapter

Installing and Configuring Spark

Installing and Configuring Spark

Introduction

Installing Spark requirements

Installing Spark from sources

Installing Spark from binaries

Configuring a local instance of Spark

Configuring a multi-node instance of Spark

Installing Jupyter

Configuring a session in Jupyter

Working with Cloudera Spark images

Abstracting Data with RDDs

Abstracting Data with RDDs

Introduction

Creating RDDs

Reading data from files

Overview of RDD transformations

Overview of RDD actions

Pitfalls of using RDDs

Abstracting Data with DataFrames

Abstracting Data with DataFrames

Introduction

Creating DataFrames

Accessing underlying RDDs

Performance optimizations

Inferring the schema using reflection

Specifying the schema programmatically

Creating a temporary table

Using SQL to interact with DataFrames

Overview of DataFrame transformations

Overview of DataFrame actions

Preparing Data for Modeling

Preparing Data for Modeling

Introduction

Handling duplicates

Handling missing observations

Handling outliers

Exploring descriptive statistics

Computing correlations

Drawing histograms

Visualizing interactions between features

Machine Learning with MLlib

Machine Learning with MLlib

Loading the data

Exploring the data

Testing the data

Transforming the data

Standardizing the data

Creating an RDD for training

Predicting hours of work for census respondents

Forecasting the income levels of census respondents

Building a clustering models

Computing performance statistics

Machine Learning with the ML Module

Machine Learning with the ML Module

Introducing Transformers

Introducing Estimators

Introducing Pipelines

Selecting the most predictable features

Predicting forest coverage types

Estimating forest elevation

Clustering forest cover types

Tuning hyperparameters

Extracting features from text

Discretizing continuous variables

Standardizing continuous variables

Topic mining

Structured Streaming with PySpark

Structured Streaming with PySpark

Introduction

Understanding DStreams

Understanding global aggregations

Continuous aggregation with structured streaming

GraphFrames – Graph Theory with PySpark

GraphFrames – Graph Theory with PySpark

Introduction

Installing GraphFrames

Preparing the data

Building the graph

Running queries against the graph

Understanding the graph

Using PageRank to determine airport ranking

Finding the fewest number of connections

Visualizing the graph

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Performance optimizations

Starting with Spark 2.0, the performance of PySpark using DataFrames was on apar with that of Scala or Java. However, there was one exception: using User Defined Functions (UDFs); if a user defined a pure Python method and registered it as a UDF, under the hood, PySpark would have to constantly switch runtimes (Python to JVM and back). This was the main reason for an enormous performance hit compared with Scala, which does not need to convert the JVM object to a Python object.

Things have changed significantly in Spark 2.3. First, Spark started using the new Apache project. Arrow creates a single memory space used by all environments, thus removing the need for constant copying and converting between objects.

Source: https://arrow.apache.org/img/shared.png

For an overview of Apache Arrow, go to https://arrow.apache.org.

Second, Arrow...

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