Spark Cookbook

封面

版权信息

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Chapter 1. Getting Started with Apache Spark

Introduction

Installing Spark from binaries

Building the Spark source code with Maven

Launching Spark on Amazon EC2

Deploying on a cluster in standalone mode

Deploying on a cluster with Mesos

Deploying on a cluster with YARN

Using Tachyon as an off-heap storage layer

Chapter 2. Developing Applications with Spark

Introduction

Exploring the Spark shell

Developing Spark applications in Eclipse with Maven

Developing Spark applications in Eclipse with SBT

Developing a Spark application in IntelliJ IDEA with Maven

Developing a Spark application in IntelliJ IDEA with SBT

Chapter 3. External Data Sources

Introduction

Loading data from the local filesystem

Loading data from HDFS

Loading data from HDFS using a custom InputFormat

Loading data from Amazon S3

Loading data from Apache Cassandra

Loading data from relational databases

Chapter 4. Spark SQL

Introduction

Understanding the Catalyst optimizer

Creating HiveContext

Inferring schema using case classes

Programmatically specifying the schema

Loading and saving data using the Parquet format

Loading and saving data using the JSON format

Loading and saving data from relational databases

Loading and saving data from an arbitrary source

Chapter 5. Spark Streaming

Introduction

Word count using Streaming

Streaming Twitter data

Streaming using Kafka

Chapter 6. Getting Started with Machine Learning Using MLlib

Introduction

Creating vectors

Creating a labeled point

Creating matrices

Calculating summary statistics

Calculating correlation

Doing hypothesis testing

Creating machine learning pipelines using ML

Chapter 7. Supervised Learning with MLlib – Regression

Introduction

Using linear regression

Understanding cost function

Doing linear regression with lasso

Doing ridge regression

Chapter 8. Supervised Learning with MLlib – Classification

Introduction

Doing classification using logistic regression

Doing binary classification using SVM

Doing classification using decision trees

Doing classification using Random Forests

Doing classification using Gradient Boosted Trees

Doing classification with Naïve Bayes

Chapter 9. Unsupervised Learning with MLlib

Introduction

Clustering using k-means

Dimensionality reduction with principal component analysis

Dimensionality reduction with singular value decomposition

Chapter 10. Recommender Systems

Introduction

Collaborative filtering using explicit feedback

Collaborative filtering using implicit feedback

Chapter 11. Graph Processing Using GraphX

Introduction

Fundamental operations on graphs

Using PageRank

Finding connected components

Performing neighborhood aggregation

Chapter 12. Optimizations and Performance Tuning

Introduction

Optimizing memory

Using compression to improve performance

Using serialization to improve performance

Optimizing garbage collection

Optimizing the level of parallelism

Understanding the future of optimization – project Tungsten