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Learning PySpark

By : Drabas, Lee

3.9 (194)

Learning PySpark

3.9 (194)

By: Drabas, 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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Understanding Spark

1. Understanding Spark

What is Apache Spark?

Spark Jobs and APIs

Spark 2.0 architecture

Summary

2. Resilient Distributed Datasets

2. Resilient Distributed Datasets

Internal workings of an RDD

Creating RDDs

Global versus local scope

Transformations

Actions

Summary

3. DataFrames

3. 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

4. Prepare Data for Modeling

Checking for duplicates, missing observations, and outliers

Getting familiar with your data

Visualization

Summary

5. Introducing MLlib

5. 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

6. 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

7. 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

8. TensorFrames

What is Deep Learning?

What is TensorFlow?

Introducing TensorFrames

TensorFrames – quick start

Summary

9. Polyglot Persistence with Blaze

9. Polyglot Persistence with Blaze

Installing Blaze

Polyglot persistence

Abstracting data

Data operations

Summary

10. Structured Streaming

10. 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

11. Packaging Spark Applications

The spark-submit command

Deploying the app programmatically

Databricks Jobs

Summary

Index

Index

Simple DataFrame queries

Now that you have created the swimmersJSON DataFrame, we will be able to run the DataFrame API, as well as SQL queries against it. Let's start with a simple query showing all the rows within the DataFrame.

DataFrame API query

To do this using the DataFrame API, you can use the show(<n>) method, which prints the first n rows to the console:

Tip

Running the.show() method will default to present the first 10 rows.

# DataFrame API
swimmersJSON.show()

This gives the following output:

SQL query

If you prefer writing SQL statements, you can write the following query:

spark.sql("select * from swimmersJSON").collect()

This will give the following output:

We are using the .collect() method, which returns all the records as a list of Row objects. Note that you can use either the collect() or show() method for both DataFrames and SQL queries. Just make sure that if you use .collect(), this is for a small DataFrame, since it will return all of the rows in the DataFrame and move them...

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