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Learning PySpark

Learning PySpark

By : Drabas, Lee
3.9 (194)
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Learning PySpark

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.
Table of Contents (13 chapters)
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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
1. Understanding Spark
1. Understanding Spark
What is Apache Spark?
Spark Jobs and APIs
Spark 2.0 architecture
Summary
2
2. Resilient Distributed Datasets
2. Resilient Distributed Datasets
Internal workings of an RDD
Creating RDDs
Global versus local scope
Transformations
Actions
Summary
3
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
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
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
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
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
8. TensorFrames
chevron up
8. TensorFrames
What is Deep Learning?
What is TensorFlow?
Introducing TensorFrames
TensorFrames – quick start
Summary
9
9. Polyglot Persistence with Blaze
9. Polyglot Persistence with Blaze
Installing Blaze
Polyglot persistence
Abstracting data
Data operations
Summary
10
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
11. Packaging Spark Applications
11. Packaging Spark Applications
The spark-submit command
Deploying the app programmatically
Databricks Jobs
Summary
12
Index
Index

Introducing TensorFrames

At the time of writing, TensorFrames is an experimental binding for Apache Spark; it was introduced in early 2016, shortly after the release of TensorFlow. With TensorFrames, one can manipulate Spark DataFrames with TensorFlow programs. Referring to the tensor diagrams in the previous section, we have updated the figure to show how Spark DataFrames work with TensorFlow, as shown in the following diagram:

As noted in the preceding diagram, TensorFrames provides a bridge between Spark DataFrames and TensorFlow. This allows you to take your DataFrames and apply them as input into your TensorFlow computation graph. TensorFrames also allows you to take the TensorFlow computation graph output and push it back into DataFrames so you can continue your downstream Spark processing.

In terms of common usage scenarios for TensorFrames, these typically include the following:

Utilize TensorFlow with your data

The integration of TensorFlow and Apache Spark with TensorFrames allows...

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