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Apache Spark 2.x Cookbook

By : Yadav

3.3 (3)

Apache Spark 2.x Cookbook

3.3 (3)

By: Yadav

Overview of this book

While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark. Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Free Chapter

Getting Started with Apache Spark

Getting Started with Apache Spark

Introduction

Leveraging Databricks Cloud

Deploying Spark using Amazon EMR

Installing Spark from binaries

Building the Spark source code with Maven

Launching Spark on Amazon EC2

Deploying Spark on a cluster in standalone mode

Deploying Spark on a cluster with Mesos

Deploying Spark on a cluster with YARN

Understanding SparkContext and SparkSession

Understanding resilient distributed dataset - RDD

Developing Applications with Spark

Developing Applications with Spark

Introduction

Exploring the Spark shell

Developing a Spark applications in Eclipse with Maven

Developing a Spark applications in Eclipse with SBT

Developing a Spark application in IntelliJ IDEA with Maven

Developing a Spark application in IntelliJ IDEA with SBT

Developing applications using the Zeppelin notebook

Setting up Kerberos to do authentication

Enabling Kerberos authentication for Spark

Spark SQL

Spark SQL

Understanding the evolution of schema awareness

Understanding the Catalyst optimizer

Inferring schema using case classes

Programmatically specifying the schema

Understanding 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

Understanding joins

Analyzing nested structures

Working with External Data Sources

Working with External Data Sources

Introduction

Loading data from the local filesystem

Loading data from HDFS

Loading data from Amazon S3

Loading data from Apache Cassandra

Spark Streaming

Spark Streaming

Introduction

WordCount using Structured Streaming

Taking a closer look at Structured Streaming

Streaming Twitter data

Streaming using Kafka

Understanding streaming challenges

Getting Started with Machine Learning

Getting Started with Machine Learning

Introduction

Creating vectors

Calculating correlation

Understanding feature engineering

Understanding Spark ML

Understanding hyperparameter tuning

Supervised Learning with MLlib — Regression

Supervised Learning with MLlib — Regression

Introduction

Using linear regression

Understanding the cost function

Doing linear regression with lasso

Doing ridge regression

Supervised Learning with MLlib — Classification

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 forest

Doing classification using gradient boosted trees

Doing classification with Naïve Bayes

Unsupervised Learning

Unsupervised Learning

Introduction

Clustering using k-means

Dimensionality reduction with principal component analysis

Dimensionality reduction with singular value decomposition

Recommendations Using Collaborative Filtering

Recommendations Using Collaborative Filtering

Introduction

Collaborative filtering using explicit feedback

Collaborative filtering using implicit feedback

Graph Processing Using GraphX and GraphFrames

Graph Processing Using GraphX and GraphFrames

Introduction

Fundamental operations on graphs

Using PageRank

Finding connected components

Performing neighborhood aggregation

Understanding GraphFrames

Optimizations and Performance Tuning

Optimizations and Performance Tuning

Optimizing memory

Leveraging speculation

Optimizing joins

Using compression to improve performance

Using serialization to improve performance

Optimizing the level of parallelism

Understanding project Tungsten

Collaborative filtering using implicit feedback

Sometimes, the feedback available is not in the form of ratings but in the form of audio tracks played, movies watched, and so on. This data, at first glance, may not look as good as explicit ratings by users, but this is much more exhaustive.

How to do it...

We are going to use the million song data from http://www.kaggle.com/c/msdchallenge/data. You need to download three files:

kaggle_visible_evaluation_triplets
kaggle_users.txt
kaggle_songs.txt

We still need to do some more preprocessing. ALS in MLlib takes both user and product IDs as integers. The Kaggle_songs.txt file has song IDs and a sequence number next to it. The Kaggle_users.txt file does not have a sequence number. Our goal is to replace the userid and songid in the triplets data with the corresponding integer sequence numbers. To do this, follow these steps:

Start Spark shell or Databricks Cloud (preferred):

        $ spark-shell

Do the necessary imports:

        import org.apache.spark...

CONTINUE READING

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