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  • Learning PySpark
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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
8. TensorFrames
What is Deep Learning?
What is TensorFlow?
Introducing TensorFrames
TensorFrames – quick start
Summary
9
9. Polyglot Persistence with Blaze
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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

Abstracting data

Blaze can abstract many different data structures and expose a single, easy-to-use API. This helps to get a consistent behavior and reduce the need to learn multiple interfaces to handle data. If you know pandas, there is not really that much to learn, as the differences in the syntax are subtle. We will go through some examples to illustrate this.

Working with NumPy arrays

Getting data from a NumPy array into the DataShape object of Blaze is extremely easy. First, let's create a simple NumPy array: we first load NumPy and then create a matrix with two rows and three columns:

import numpy as np
simpleArray = np.array([
        [1,2,3],
        [4,5,6]
    ])

Now that we have an array, we can abstract it with Blaze's DataShape structure:

simpleData_np = bl.Data(simpleArray)

That's it! Simple enough.

In order to peek inside the structure you can use the .peek() method:

simpleData_np.peek()

You should see an output similar to what is shown in the following screenshot...

End of Section 4
Next Section
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