Learning PySpark
eBook - ePub

Learning PySpark

Tomasz Drabas, Denny Lee

  1. 274 páginas
  2. English
  3. ePUB (apto para móviles)
  4. Disponible en iOS y Android
eBook - ePub

Learning PySpark

Tomasz Drabas, Denny Lee

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Información del libro

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

About This Book

  • Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0
  • Develop and deploy efficient, scalable real-time Spark solutions
  • Take your understanding of using Spark with Python to the next level with this jump start guide

Who This Book Is For

If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory.

What You Will Learn

  • Learn about Apache Spark and the Spark 2.0 architecture
  • Build and interact with Spark DataFrames using Spark SQL
  • Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively
  • Read, transform, and understand data and use it to train machine learning models
  • Build machine learning models with MLlib and ML
  • Learn how to submit your applications programmatically using spark-submit
  • Deploy locally built applications to a cluster

In Detail

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.

Style and approach

This book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept.

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Información

Año
2017
ISBN
9781786463708
Edición
1
Categoría
Informatique

Learning PySpark


Table of Contents

Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Understanding Spark
What is Apache Spark?
Spark Jobs and APIs
Execution process
Resilient Distributed Dataset
DataFrames
Datasets
Catalyst Optimizer
Project Tungsten
Spark 2.0 architecture
Unifying Datasets and DataFrames
Introducing SparkSession
Tungsten phase 2
Structured Streaming
Continuous applications
Summary
2. Resilient Distributed Datasets
Internal workings of an RDD
Creating RDDs
Schema
Reading from files
Lambda expressions
Global versus local scope
Transformations
The .map(...) transformation
The .filter(...) transformation
The .flatMap(...) transformation
The .distinct(...) transformation
The .sample(...) transformation
The .leftOuterJoin(...) transformation
The .repartition(...) transformation
Actions
The .take(...) method
The .collect(...) method
The .reduce(...) method
The .count(...) method
The .saveAsTextFile(...) method
The .foreach(...) method
Summary
3. DataFrames
Python to RDD communications
Catalyst Optimizer refresh
Speeding up PySpark with DataFrames
Creating DataFrames
Generating our own JSON data
Creating a DataFrame
Creating a temporary table
Simple DataFrame queries
DataFrame API query
SQL query
Interoperating with RDDs
Inferring the schema using reflection
Programmatically specifying the schema
Querying with the DataFrame API
Number of rows
Running filter statements
Querying with SQL
Number of rows
Running filter statements using the where Clauses
DataFrame scenario – on-time flight performance
Preparing the source datasets
Joining flight performance and airports
Visualizing our flight-performance data
Spark Dataset API
Summary
4. Prepare Data for Modeling
Checking for duplicates, missing observations, and outliers
Duplicates
Missing observations
Outliers
Getting familiar with your data
Descriptive statistics
Correlations
Visualization
Histograms
Interactions between features
Summary
5. Introducing MLlib
Overview of the package
Loading and transforming the data
Getting to know your data
Descriptive statistics
Correlations
Statistical testing
Creating the final dataset
Creating an RDD of LabeledPoints
Splitting into training and testing
Predicting infant survival
Logistic regression in MLlib
Selecting only the most predictable features
Random forest in MLlib
Summary
6. Introducing the ML Package
Overview of the package
Transformer
Estimators
Classification
Regression
Clustering
Pipeline
Predicting the chances of infant survival with ML
Loading the data
Creating transformers
Creating an estimator
Creating a pipeline
Fitting the model
Evaluating the performance of the model
Saving the model
Parameter hyper-tuning
Grid search
Train-validation splitting
Other features of PySpark ML in action
Feature extraction
NLP - related feature extractors
Discretizing continuous variables
Standardizing continuous variables
Classification
Clustering
Finding clusters in the births dataset
Topic mining
Regression
Summary
7. GraphFrames
Introducing GraphFrames
Installing GraphFrames
Creating a library
Preparing your flights dataset
Building the graph
Executing simple queries
Determining the number of airports and trips
Determining the longest delay in this dataset
Determining the number of delayed versus on-time/early flights
What flights departing Seattle are most likely to have significant delays?
What states tend to have significant delays departing from Seattle?
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
What is Deep Learning?
The need for neural networks and Deep Learning
What is feature engineering?
Bridging the data and algorithm
What is TensorFlow?
Installing Pip
Installing TensorFlow
Matrix multiplication using constants
Matrix multiplication using placeholders
Running the model
Running another model
Discussion
Introducing TensorFrames
TensorFrames – quick start
Configuration and setup
Launching a Spark cluster
Creating a TensorFrames library
Installing TensorFlow on your cluster
Using TensorFlow to add a constant to an existing column
Executing the Tensor graph
Blockwise reducing operations example
Building a DataFrame of vectors
Analysing the DataFrame
Computing elementwise sum and min of all vectors
Summary
9. Polyglot Persistence with Blaze
Installing Blaze
Polyglot persistence
Abstracting data
Working with NumPy arrays
Working with pandas' DataFrame
Working with files
Working with databases
Interacting with relational databases
Interacting with the MongoDB database
Data operations
Accessing columns
Symbolic transformations
Operations on colum...

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  1. Learning PySpark