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Home > Data > Big Data > Mastering Apache Spark 2.x
Mastering Apache Spark 2.x
Mastering Apache Spark 2.x

Mastering Apache Spark 2.x: Advanced techniques in complex Big Data processing, streaming analytics and machine learning , Second Edition

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Mastering Apache Spark 2.x

Apache Spark SQL

In this chapter, we will examine ApacheSparkSQL, SQL, DataFrames, and Datasets on top of Resilient Distributed Datasets (RDDs). DataFrames were introduced in Spark 1.3, basically replacing SchemaRDDs, and are columnar data storage structures roughly equivalent to relational database tables, whereas Datasets were introduced as experimental in Spark 1.6 and have become an additional component in Spark 2.0.

We have tried to reduce the dependency between individual chapters as much as possible in order to give you the opportunity to work through them as you like. However, we do recommend that you read this chapter because the other chapters are dependent on the knowledge of DataFrames and Datasets.

This chapter will cover the following topics:

  • SparkSession
  • Importing and saving data
  • Processing the text files
  • Processing the JSON files
  • Processing the Parquet files
  • DataSource...

The SparkSession--your gateway to structured data processing

The SparkSession is the starting point for working with columnar data in Apache Spark. It replaces SQLContext used in previous versions of Apache Spark. It was created from the Spark context and provides the means to load and save data files of different types using DataFrames and Datasets and manipulate columnar data with SQL, among other things. It can be used for the following functions:

  • Executing SQL via the sql method
  • Registering user-defined functions via the udf method
  • Caching
  • Creating DataFrames
  • Creating Datasets
The examples in this chapter are written in Scala as we prefer the language, but you can develop in Python, R, and Java as well. As stated previously, the SparkSession is created from the Spark context.

Using the SparkSession allows you to implicitly convert RDDs into DataFrames or Datasets. For instance...

Importing and saving data

We wanted to add this section about importing and saving data here, even though it is not purely about Spark SQL, so that concepts such as Parquet and JSON file formats could be introduced. This section also allows us to cover how to access saved data in loose text as well as the CSV, Parquet, and JSON formats conveniently in one place.

Processing the text files

Using SparkContext, it is possible to load a text file in RDD using the textFile method. Additionally, the wholeTextFile method can read the contents of a directory to RDD. The following examples show you how a file, based on the local filesystem (file://) or HDFS (hdfs://), can be read to a Spark RDD. These examples show you that the data will...

Understanding the DataSource API

The DataSource API was introduced in Apache Spark 1.1, but is constantly being extended. You have already used the DataSource API without knowing when reading and writing data using SparkSession or DataFrames.

The DataSource API provides an extensible framework to read and write data to and from an abundance of different data sources in various formats. There is built-in support for Hive, Avro, JSON, JDBC, Parquet, and CSV and a nearly infinite number of third-party plugins to support, for example, MongoDB, Cassandra, ApacheCouchDB, Cloudant, or Redis.

Usually, you never directly use classes from the DataSource API as they are wrapped behind the read method of SparkSession or the write method of the DataFrame or Dataset. Another thing that is hidden from the user is schema discovery.

...

DataFrames

We have already used DataFrames in previous examples; it is based on a columnar format. Temporary tables can be created from it but we will expand on this in the next section. There are many methods available to the data frame that allow data manipulation and processing.

Let's start with a simple example and load some JSON data coming from an IoT sensor on a washing machine. We are again using the Apache Spark DataSource API under the hood to read and parse JSON data. The result of the parser is a data frame. It is possible to display a data frame schema as shown here:

As you can see, this is a nested data structure. So, the doc field contains all the information that we are interested in, and we want to get rid of the meta information that Cloudant/ApacheCouchDB added to the original JSON file. This can be accomplished by a call to the select method on the DataFrame...

Using SQL

After using the previous Scala example to create a data frame from a JSON input file on HDFS, we can now define a temporary table based on the data frame and run SQL against it.

The following example shows you the temporary table called washing_flat being defined and a row count being created using count(*):

The schema for this data was created on the fly (inferred). This is a very nice function of the Apache Spark DataSource API that has been used when reading the JSON file from HDFS using the SparkSession object. However, if you want to specify the schema on your own, you can do so.

Defining schemas manually

So first, we have to import some classes. Follow the code to do this:

import org.apache.spark.sql.types._

So...

Using Datasets

This API as been introduced since Apache Spark 1.6 as experimental and finally became a first-class citizen in Apache Spark 2.0. It is basically a strongly typed version of DataFrames.

DataFrames are kept for backward compatibility and are not going to be deprecated for two reasons. First, a DataFrame since Apache Spark 2.0 is nothing else but a Dataset where the type is set to Row. This means that you actually lose the strongly static typing and fall back to a dynamic typing. This is also the second reason why DataFrames are going to stay. Dynamically typed languages such as Python or R are not capable of using Datasets because there isn't a concept of strong, static types in the language.

So what are Datasets exactly? Let's create one:

import spark.implicits._
case class Person(id: Long, name: String)
 val caseClassDS = Seq(Person(1,"Name1")...

The SparkSession--your gateway to structured data processing

The SparkSession is the starting point for working with columnar data in Apache Spark. It replaces SQLContext used in previous versions of Apache Spark. It was created from the Spark context and provides the means to load and save data files of different types using DataFrames and Datasets and manipulate columnar data with SQL, among other things. It can be used for the following functions:

  • Executing SQL via the sql method
  • Registering user-defined functions via the udf method
  • Caching
  • Creating DataFrames
  • Creating Datasets

Note

The examples in this chapter are written in Scala as we prefer the language, but you can develop in Python, R, and Java as well. As stated previously, the SparkSession is created from the Spark context.

Using the SparkSession allows you to implicitly convert RDDs into DataFrames or Datasets. For instance, you can convert RDD into a DataFrame or Dataset by calling the toDF or toDS methods:

 import spark.implicits._...

Importing and saving data

We wanted to add this section about importing and saving data here, even though it is not purely about Spark SQL, so that concepts such as Parquet and JSON file formats could be introduced. This section also allows us to cover how to access saved data in loose text as well as the CSV, Parquet, and JSON formats conveniently in one place.

Processing the text files

Using SparkContext, it is possible to load a text file in RDD using the textFile method. Additionally, the wholeTextFile method can read the contents of a directory to RDD. The following examples show you how a file, based on the local filesystem (file://) or HDFS (hdfs://), can be read to a Spark RDD. These examples show you that the data will be divided into six partitions for increased performance. The first two examples are the same as they both load a file from the Linux filesystem, whereas the last one resides in HDFS:

sc.textFile("/data/spark/tweets.txt",6)
sc.textFile("file:///data/spark/tweets.txt...

Understanding the DataSource API

The DataSource API was introduced in Apache Spark 1.1, but is constantly being extended. You have already used the DataSource API without knowing when reading and writing data using SparkSession or DataFrames.

The DataSource API provides an extensible framework to read and write data to and from an abundance of different data sources in various formats. There is built-in support for Hive, Avro, JSON, JDBC, Parquet, and CSV and a nearly infinite number of third-party plugins to support, for example, MongoDB, Cassandra, ApacheCouchDB, Cloudant, or Redis.

Usually, you never directly use classes from the DataSource API as they are wrapped behind the read method of SparkSession or the write method of the DataFrame or Dataset. Another thing that is hidden from the user is schema discovery.

Implicit schema discovery

One important aspect of the DataSource API is implicit schema discovery. For a subset of data sources, implicit schema discovery is possible. This means...

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Key benefits

  • Master the art of real-time Big Data processing using Apache Spark 2.x
  • Perform machine learning, deep learning and streaming data analytics by extending the most up-to-date functionalities of Apache Spark
  • An advanced guide with a unique combination of tips, instructions and practical examples on using Apache Spark effectively

Description

Apache Spark is an in-memory, cluster-based Big Data processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and more. This book will take your knowledge of Apache Spark to the next level by teaching you how to expand Spark’s functionality and build your data flows and machine/deep learning programs on top of the platform. The book starts with a quick overview of the Apache Spark ecosystem, and introduces you to the new features and capabilities in Apache Spark 2.x. You will then work with the different modules in Apache Spark such as interactive querying with Spark SQL, using DataFrames and DataSets effectively, streaming analytics with Spark Streaming, and performing machine learning and deep learning on Spark using MLlib and external tools such as H20 and Deeplearning4j. The book also contains chapters on efficient graph processing, memory management and using Apache Spark on the cloud. By the end of this book, you will have all the necessary information to master Apache Spark, and use it efficiently for Big Data processing and analytics.

Who is this book for?

If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this book is for you. Big Data professionals who wish to know how to integrate and use the features of Apache Spark to build a strong Big Data pipeline will also find this book to be a useful resource. A fundamental knowledge of Apache Spark and the Scala programming language is assumed.

What you will learn

  • • Get to grips with the newly introduced features in Apache Spark 2.x
  • • Perform highly optimised unified batch and real-time data processing using
  • SparkSQL and Structured Streaming
  • • Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames
  • • Perform advanced machine learning and deep learning with Spark MLlib, SparkML, SystemML, H2O and DeepLearning4J
  • • Learn how specific parameter settings affect overall performance of an
  • Apache Spark cluster
  • • Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud
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Publication date : Jul 26, 2017
Length: 354 pages
Edition : 2nd
Language : English
ISBN-13 : 9781786462749
Vendor :
Apache
Category :
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Concepts :
Big Data
Tools :
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Publication date : Jul 26, 2017
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Language : English
ISBN-13 : 9781786462749
Vendor :
Apache
Category :
Data
Languages :
Scala
Concepts :
Big Data
Tools :
Apache Spark

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Table of Contents

14 Chapters
A First Taste and What’s New in Apache Spark V2 Chevron down icon Chevron up icon
A First Taste and What’s New in Apache Spark V2
Spark machine learning
Spark Streaming
Spark SQL
Spark graph processing
Extended ecosystem
What's new in Apache Spark V2?
Cluster design
Cluster management
Cloud-based deployments
Performance
Cloud
Summary
Apache Spark SQL Chevron down icon Chevron up icon
Apache Spark SQL
The SparkSession--your gateway to structured data processing
Importing and saving data
Understanding the DataSource API
DataFrames
Using SQL
Using Datasets
User-defined functions
RDDs versus DataFrames versus Datasets
Summary
The Catalyst Optimizer Chevron down icon Chevron up icon
The Catalyst Optimizer
Understanding the workings of the Catalyst Optimizer
Managing temporary views with the catalog API
The SQL abstract syntax tree
How to go from Unresolved Logical Execution Plan to Resolved Logical Execution Plan
Code generation
Summary
Project Tungsten Chevron down icon Chevron up icon
Project Tungsten
Memory management beyond the Java Virtual Machine Garbage Collector
Cache-friendly layout of data in memory
Code generation
Summary
Apache Spark Streaming Chevron down icon Chevron up icon
Apache Spark Streaming
Overview
Errors and recovery
Streaming sources
Summary
Structured Streaming Chevron down icon Chevron up icon
Structured Streaming
The concept of continuous applications
Windowing
Increased performance with good old friends
How transparent fault tolerance and exactly-once delivery guarantee is achieved
Example - connection to a MQTT message broker
Summary
Apache Spark MLlib Chevron down icon Chevron up icon
Apache Spark MLlib
Architecture
Classification with Naive Bayes
Clustering with K-Means
Artificial neural networks
Summary
Apache SparkML Chevron down icon Chevron up icon
Apache SparkML
What does the new API look like?
The concept of pipelines
Model evaluation
CrossValidation and hyperparameter tuning
Winning a Kaggle competition with Apache SparkML
Summary
Apache SystemML Chevron down icon Chevron up icon
Apache SystemML
Why do we need just another library?
A cost-based optimizer for machine learning algorithms
Performance measurements
Apache SystemML in action
Summary
Deep Learning on Apache Spark with DeepLearning4j and H2O Chevron down icon Chevron up icon
Deep Learning on Apache Spark with DeepLearning4j and H2O
H2O
Deeplearning4j
Summary
Apache Spark GraphX Chevron down icon Chevron up icon
Apache Spark GraphX
Overview
Graph analytics/processing with GraphX
Summary
Apache Spark GraphFrames Chevron down icon Chevron up icon
Apache Spark GraphFrames
Architecture
Examples
Summary
Apache Spark with Jupyter Notebooks on IBM DataScience Experience Chevron down icon Chevron up icon
Apache Spark with Jupyter Notebooks on IBM DataScience Experience
Why notebooks are the new standard
Learning by example
Summary
Apache Spark on Kubernetes Chevron down icon Chevron up icon
Apache Spark on Kubernetes
Bare metal, virtual machines, and containers
Understanding the core concepts of Docker
Understanding Kubernetes
Using Kubernetes for provisioning containerized Spark applications
Example--Apache Spark on Kubernetes
Summary

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(2 Ratings)
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1 star 0%
M.R. Oct 14, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Romeo Kienzler takes the reader on a big and detailed tour through significant Spark topics and exercises, which occur in the practical usage of Spark in Big Data, Analytics, Data Science and Analytic Data Warehouse ("ADW") projects. In his book topics like the new Spark V2 Ecosystem, Machine Learning, Spark Streaming, Graph Processing, Cluster Design and Management (Yarn and Mesos), Cloud based deployments, Performance topics around HDFS, Date importing and handling, Spark Data Source API, Spark Dataframes and Datasets API, Code Generation for expression evaluation, Project Tungsten, Spark error handling and much more are covered. If you have taken one or more of the well done Spark courses from Databricks before, the topics might familiar but the book covers even some more enhanced topics as well it can be taken as a good comprehension or as in-depth notes. Additionally the book focus on very specific details and problems in parallel programming with Spark, derived from practical use cases.As well the book contains links and references on papers, literature and web forums. To summarize I would recommend this book as an excellent starting point and Spark reference guide.
Amazon Verified review Amazon
Dr. Raj Kamal . Aug 30, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Good book to start Spark. Helped me greatly to finish my upcoming book from McGraw-Hill on Big Data Anaytics. My students work and do Big Data data sets analysis using Spark
Amazon Verified review Amazon

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About the author

Profile icon Romeo Kienzler
Romeo Kienzler
Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics.
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  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact [email protected] with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at [email protected] using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on [email protected] with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on [email protected] within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on [email protected] who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on [email protected] within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela