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Home > Data > Big Data > Learning Apache Spark 2
Learning Apache Spark 2
Learning Apache Spark 2

Learning Apache Spark 2: A beginner's guide to real-time Big Data processing using the Apache Spark framework

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Learning Apache Spark 2

Chapter 2. Transformations and Actions with Spark RDDs

Now that we have had a basic overview of the architecture of Spark and key software components, we will cover Spark RDD's in this chapter. During the course of this chapter, we'll walk through the following topics:

  • How to construct RDDs
  • Operations on RDDs, such as transformations and actions
  • Passing functions to Spark (Scala, Java, and Python)
  • Transformations such as map, filter, flatMap, and sample
  • Set operations such as distinct, intersection, and union
  • Actions such as reduce, collect, count, take, and first
  • PairRDDs
  • Shared and broadcast variables

Let's get cracking!

What is an RDD?

What's in a name might be true for a rose, but perhaps not for Resilient Distributed Datasets (RDD) which, in essence, describes what an RDD is.

They are basically datasets, which are distributed across a cluster (remember the Spark framework is inherently based on an MPP architecture), and provide resilience (automatic failover) by nature.

Before we go into any further detail, let's try to understand this a little bit, and again we are trying to be as abstract as possible. Let us assume that you have a sensor data from aircraft sensors and you want to analyze the data irrespective of its size and locality. For example, an Airbus A350 has roughly 6000 sensors across the entire plane and generates 2.5 TB data per day, while the newer model expected to launch in 2020 will generate roughly 7.5 TB per day. From a data engineering point of view, it might be important to understand the data pipeline, but from an analyst and a data scientist point of view, the major concern...

Operations on RDD

Two major operation types can be performed on an RDD. They are called:

  • Transformations
  • Actions

Transformations

Transformations are operations that create a new dataset, as RDDs are immutable. They are used to transform data from one to another, which could result in amplification of the data, reduction of the data, or a totally different shape altogether. These operations do not return any value back to the driver program, and hence are lazily evaluated, which is one of the main benefits of Spark.

An example of a transformation would be a map function that will pass through each element of the RDD and return a totally new RDD representing the results of application of the function on the original dataset.

Actions

Actions are operations that return a value to the driver program. As previously discussed, all transformations in Spark are lazy, which essentially means that Spark remembers all the transformations carried out on an RDD, and applies them in the most optimal fashion...

Passing functions to Spark (Scala)

As you have seen in the previous example, passing functions is a critical functionality provided by Spark. From a user's point of view you would pass the function in your driver program, and Spark would figure out the location of the data partitions across the cluster memory, running it in parallel. The exact syntax of passing functions differs by the programming language. Since Spark has been written in Scala, we'll discuss Scala first.

In Scala, the recommended ways to pass functions to the Spark framework are as follows:

  • Anonymous functions
  • Static singleton methods

Anonymous functions

Anonymous functions are used for short pieces of code. They are also referred to as lambda expressions, and are a cool and elegant feature of the programming language. The reason they are called anonymous functions is because you can give any name to the input argument and the result would be the same.

For example, the following code examples would produce the same...

Passing functions to Spark (Java)

In Java, to create a function you will have to implement the interfaces available in the org.apache.spark.api.java function package. There are two popular ways to create such functions:

  • Implement the interface in your own class, and pass the instance to Spark.
  • Starting Java 8, you can use Lambda expressions to pass off the functions to the Spark framework.

Let's implement the preceding word count examples in Java:

Passing functions to Spark (Java)

Figure 2.13: Code example of Java implementation of word count (inline functions)

If you belong to a group of programmers who feel that writing inline functions makes the code complex and unreadable (a lot of people do agree to that assertion), you may want to create separate functions and call them as follows:

Passing functions to Spark (Java)

Figure 2.14: Code example of Java implementation of word count

Passing functions to Spark (Python)

Python provides a simple way to pass functions to Spark. The Spark programming guide available at spark.apache.org suggests there are three recommended ways to do this:

  • Lambda expressions is the ideal way for short functions that can be written inside a single expression
  • Local defs inside the function calling into Spark for longer code
  • Top-level functions in a module

While we have already looked at the lambda functions in some of the previous examples, let's look at local definitions of the functions. We can encapsulate our business logic which is splitting of words, and counting into two separate functions as shown below.

def splitter(lineOfText): 
     words = lineOfText.split(" ") 
     return len(words) 
def aggregate(numWordsLine1, numWordsLineNext): 
     totalWords = numWordsLine1 + numWordsLineNext 
     return totalWords

Let's see the working code example:

Passing functions to Spark (Python)

Figure 2.15: Code example of Python word count (local definition of...

What is an RDD?

What's in a name might be true for a rose, but perhaps not for Resilient Distributed Datasets (RDD) which, in essence, describes what an RDD is.

They are basically datasets, which are distributed across a cluster (remember the Spark framework is inherently based on an MPP architecture), and provide resilience (automatic failover) by nature.

Before we go into any further detail, let's try to understand this a little bit, and again we are trying to be as abstract as possible. Let us assume that you have a sensor data from aircraft sensors and you want to analyze the data irrespective of its size and locality. For example, an Airbus A350 has roughly 6000 sensors across the entire plane and generates 2.5 TB data per day, while the newer model expected to launch in 2020 will generate roughly 7.5 TB per day. From a data engineering point of view, it might be important to understand the data pipeline, but from an analyst and a data scientist point of view, the major concern is to...

Operations on RDD

Two major operation types can be performed on an RDD. They are called:

  • Transformations
  • Actions

Transformations

Transformations are operations that create a new dataset, as RDDs are immutable. They are used to transform data from one to another, which could result in amplification of the data, reduction of the data, or a totally different shape altogether. These operations do not return any value back to the driver program, and hence are lazily evaluated, which is one of the main benefits of Spark.

An example of a transformation would be a map function that will pass through each element of the RDD and return a totally new RDD representing the results of application of the function on the original dataset.

Actions

Actions are operations that return a value to the driver program. As previously discussed, all transformations in Spark are lazy, which essentially means that Spark remembers all the transformations carried out on an RDD, and applies them in the most optimal fashion...

Passing functions to Spark (Scala)

As you have seen in the previous example, passing functions is a critical functionality provided by Spark. From a user's point of view you would pass the function in your driver program, and Spark would figure out the location of the data partitions across the cluster memory, running it in parallel. The exact syntax of passing functions differs by the programming language. Since Spark has been written in Scala, we'll discuss Scala first.

In Scala, the recommended ways to pass functions to the Spark framework are as follows:

  • Anonymous functions
  • Static singleton methods

Anonymous functions

Anonymous functions are used for short pieces of code. They are also referred to as lambda expressions, and are a cool and elegant feature of the programming language. The reason they are called anonymous functions is because you can give any name to the input argument and the result would be the same.

For example, the following code examples would produce the same output:

val...

Passing functions to Spark (Java)

In Java, to create a function you will have to implement the interfaces available in the org.apache.spark.api.java function package. There are two popular ways to create such functions:

  • Implement the interface in your own class, and pass the instance to Spark.
  • Starting Java 8, you can use Lambda expressions to pass off the functions to the Spark framework.

Let's implement the preceding word count examples in Java:

Figure 2.13: Code example of Java implementation of word count (inline functions)

If you belong to a group of programmers who feel that writing inline functions makes the code complex and unreadable (a lot of people do agree to that assertion), you may want to create separate functions and call them as follows:

Figure 2.14: Code example of Java implementation of word count

Passing functions to Spark (Python)

Python provides a simple way to pass functions to Spark. The Spark programming guide available at spark.apache.org suggests there are three recommended ways to do this:

  • Lambda expressions is the ideal way for short functions that can be written inside a single expression
  • Local defs inside the function calling into Spark for longer code
  • Top-level functions in a module

While we have already looked at the lambda functions in some of the previous examples, let's look at local definitions of the functions. We can encapsulate our business logic which is splitting of words, and counting into two separate functions as shown below.

def splitter(lineOfText): 
     words = lineOfText.split(" ") 
     return len(words) 
def aggregate(numWordsLine1, numWordsLineNext): 
     totalWords = numWordsLine1 + numWordsLineNext 
     return totalWords

Let's see the working code example:

Figure 2.15: Code example of Python word count (local definition...

Transformations

We've used few transformation functions in the examples in this chapter, but I would like to share with you a list of the most commonly used transformation functions in Apache Spark. You can find a complete list of functions in the official documentation http://bit.ly/RDDTransformations.

Most Common Transformations

 

map(func)

coalesce(numPartitions)

filter(func)

repartition(numPartitions)

flatMap(func)

repartitionAndSortWithinPartitions(partitioner)

mapPartitions(func)

join(otherDataset, [numTasks])

mapPartitionsWithIndex(func)

cogroup(otherDataset, [numTasks])

sample(withReplacement, fraction, seed)

cartesian(otherDataset)

Map(func)

The map transformation is the most commonly used and the simplest of transformations on an RDD. The map transformation applies the function passed in the arguments to each of the elements of the source RDD. In the previous examples, we have seen the usage of map() transformation where we have passed the split() function...

Set operations in Spark

For those of you who are from the database world and have now ventured into the world of big data, you're probably looking at how you can possibly apply set operations on Spark datasets. You might have realized that an RDD can be a representation of any sort of data, but it does not necessarily represent a set based data. The typical set operations in a database world include the following operations, and we'll see how some of these apply to Spark. However, it is important to remember that while Spark offers some of the ways to mimic these operations, spark doesn't allow you to apply conditions to these operations, which is common in SQL operations:

  • Distinct: Distinct operation provides you a non-duplicated set of data from the dataset
  • Intersection: The intersection operations returns only those elements that are available in both datasets
  • Union: A union operation returns the elements from both datasets
  • Subtract: A subtract operation returns the elements from one dataset...
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Key benefits

  • Familiarize yourself with the new features introduced in Apache Spark 2, as well as its components for Big Data processing and analytics
  • Manipulate your data, perform stream analytics and machine learning, and deploy your Spark models to production using practical examples
  • If you are new to Apache Spark and want to quickly get started with it, this book will help you

Description

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.

Who is this book for?

This book is intended for aspiring Big Data professionals and anyone who wants to get started with Apache Spark for Big Data processing and analytics. If you’ve worked with Apache Spark before and want to get familiarized with the new features introduced in Spark 2, this book will also help you. Some fundamental understanding of Big Data concepts and knowledge of Scala programming is required to get the best out of this book.

What you will learn

  • Get a thorough overview of Big Data processing and analytics, and its importance to organizations and data professionals
  • Get familiarized with the Apache Spark ecosystem, and the new features released in Spark 2 for data processing and analysis
  • Get a thorough understanding of different modules of Apache Spark such as Spark SQL, Spark RDD, Spark Streaming, Spark MLlib and GraphX
  • Work with data of different file formats, and learn how to process it with Apache Spark
  • Introduce yourself to SparkR and walk through the details of data munging including selecting, aggregating and grouping data using R studio
  • Realize how to deploy Spark with YARN, MESOS or a Stand-alone cluster manager
  • Build effective recommendation engines with Spark using collaborative filtering

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 28, 2017
Length: 356 pages
Edition : 1st
Language : English
ISBN-13 : 9781785885136
Category :
Data
Languages :
Python
Concepts :
Big Data
Tools :
Apache Spark

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Product Details

Publication date : Mar 28, 2017
Length: 356 pages
Edition : 1st
Language : English
ISBN-13 : 9781785885136
Category :
Data
Languages :
Python
Concepts :
Big Data
Tools :
Apache Spark

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Table of Contents

11 Chapters
1. Architecture and Installation Chevron down icon Chevron up icon
1. Architecture and Installation
Apache Spark architecture overview
Installing Apache Spark
Writing your first Spark program
Spark architecture
Apache Spark cluster manager types
Running Spark examples
Brain teasers
References
Summary
2. Transformations and Actions with Spark RDDs Chevron down icon Chevron up icon
2. Transformations and Actions with Spark RDDs
What is an RDD?
Operations on RDD
Passing functions to Spark (Scala)
Passing functions to Spark (Java)
Passing functions to Spark (Python)
Transformations
Set operations in Spark
Actions
PairRDDs
Shared variables
References
Summary
3. ETL with Spark Chevron down icon Chevron up icon
3. ETL with Spark
What is ETL?
How is Spark being used?
Commonly Supported File Formats
Commonly supported file systems
Structured Data sources and Databases
References
Summary
4. Spark SQL Chevron down icon Chevron up icon
4. Spark SQL
What is Spark SQL?
What is DataFrame API?
What is DataSet API?
What's new in Spark 2.0?
The Sparksession
Creating a DataFrame
Parquet files
Working with Hive
SparkSQL CLI
References
Summary
5. Spark Streaming Chevron down icon Chevron up icon
5. Spark Streaming
What is Spark Streaming?
Steps involved in a streaming app
Architecture of Spark Streaming
Caching and persistence
Checkpointing
DStream best practices
Fault tolerance
What is Structured Streaming?
References
Summary
6. Machine Learning with Spark Chevron down icon Chevron up icon
6. Machine Learning with Spark
What is machine learning?
Why machine learning?
Types of machine learning
Introduction to Spark MLLib
Why do we need the Pipeline API?
How does it work?
Feature engineering
Classification and regression
Clustering
Collaborative filtering
ML-tuning - model selection and hyperparameter tuning
References
Summary
7. GraphX Chevron down icon Chevron up icon
7. GraphX
Graphs in everyday life
What is a graph?
Why are Graphs elegant?
What is GraphX?
Creating your first Graph (RDD API)
Basic graph operators (RDD API)
Caching and uncaching of graphs
Graph algorithms in GraphX
GraphFrames
Comparison between GraphFrames and GraphX
References
Summary
8. Operating in Clustered Mode Chevron down icon Chevron up icon
8. Operating in Clustered Mode
Clusters, nodes and daemons
Running Spark in standalone mode
Using the Cluster Launch Scripts to Start a Standalone Cluster
Running Spark in YARN
Running Spark in Mesos
References:
Summary
9. Building a Recommendation System Chevron down icon Chevron up icon
9. Building a Recommendation System
What is a recommendation system?
User specific recommendations
Key issues with recommendation systems
Recommendation system in Spark
References
Summary
10. Customer Churn Prediction Chevron down icon Chevron up icon
10. Customer Churn Prediction
Overview of customer churn
Why is predicting customer churn important?
How do we predict customer churn with Spark?
Exploring customer service calls
References
Summary
Theres More with Spark Chevron down icon Chevron up icon
Theres More with Spark
Performance tuning
I/O tuning
Sizing up your executors
The skew problem
Security configuration in Spark
Setting up Jupyter Notebook with Spark
Shared variables
References
Summary

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Shambhu Nath Mar 13, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Delivery is awesome ! this book is used simple english language, So good for beginner, also explanations is good but I felt screenshot print is not so good !Thanks,Shambhu Nath
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Ivan Falcão Oct 17, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excelente livro. Apresenta uma base teórica considerável, além de diversos exemplos práticos. Certamente uma das melhores opções pra quem quer aprender mais spark
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Kalaiselvan Dec 25, 2017
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Simple language, good book for hands on development
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Deepak May 20, 2019
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Not much in details. Tells only on high level and gives the link to refer for further details. Returned it. Ordered learning spark from orielly.
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Jose VL Jun 21, 2017
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Nice reading to learn about Spark. I'd have liked to see more information for developers ... maybe next edition :-)
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Generative AI with LangChain
Generative AI with LangChain
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Dec 2023 368 pages
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Causal Inference and Discovery in Python
Causal Inference and Discovery in Python
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May 2023 456 pages
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About the author

Profile icon Abbasi
Abbasi
Muhammad Asif Abbasi has worked in the industry for over 15 years in a variety of roles from engineering solutions to selling solutions and everything in between. Asif is currently working with SAS a market leader in Analytic Solutions as a Principal Business Solutions Manager for the Global Technologies Practice. Based in London, Asif has vast experience in consulting for major organizations and industries across the globe, and running proof-of-concepts across various industries including but not limited to telecommunications, manufacturing, retail, finance, services, utilities and government. Asif is an Oracle Certified Java EE 5 Enterprise architect, Teradata Certified Master, PMP, Hortonworks Hadoop Certified developer, and administrator. Asif also holds a Master's degree in Computer Science and Business Administration.
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