Book Image

Hands-On Big Data Analytics with PySpark

By : Rudy Lai, Bartłomiej Potaczek
Book Image

Hands-On Big Data Analytics with PySpark

By: Rudy Lai, Bartłomiej Potaczek

Overview of this book

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Installing Pyspark and Setting up Your Development Environment
Installing Pyspark and Setting up Your Development Environment
An overview of PySpark
Setting up Spark on Windows and PySpark
Core concepts in Spark and PySpark
Summary
2
Getting Your Big Data into the Spark Environment Using RDDs
Getting Your Big Data into the Spark Environment Using RDDs
Loading data on to Spark RDDs
Parallelization with Spark RDDs
Basics of RDD operation
Summary
3
Big Data Cleaning and Wrangling with Spark Notebooks
Big Data Cleaning and Wrangling with Spark Notebooks
Using Spark Notebooks for quick iteration of ideas
Sampling/filtering RDDs to pick out relevant data points
Splitting datasets and creating some new combinations
Summary
4
Aggregating and Summarizing Data into Useful Reports
Aggregating and Summarizing Data into Useful Reports
Calculating averages with map and reduce
Faster average computations with aggregate
Pivot tabling with key-value paired data points
Summary
5
Powerful Exploratory Data Analysis with MLlib
Powerful Exploratory Data Analysis with MLlib
Computing summary statistics with MLlib
Using Pearson and Spearman correlations to discover correlations
Testing our hypotheses on large datasets
Summary
6
Putting Structure on Your Big Data with SparkSQL
Putting Structure on Your Big Data with SparkSQL
Manipulating DataFrames with Spark SQL schemas
Using Spark DSL to build queries
Summary
7
Transformations and Actions
Transformations and Actions
Using Spark transformations to defer computations to a later time
Avoiding transformations
Using the reduce and reduceByKey methods to calculate the results
Performing actions that trigger computations
Reusing the same rdd for different actions
Summary
8
Immutable Design
Immutable Design
Delving into the Spark RDD's parent/child chain
Using RDD in an immutable way
Using DataFrame operations to transform
Immutability in the highly concurrent environment
Using the Dataset API in an immutable way
Summary
9
Avoiding Shuffle and Reducing Operational Expenses
Avoiding Shuffle and Reducing Operational Expenses
Detecting a shuffle in a process
Testing operations that cause a shuffle in Apache Spark
Changing the design of jobs with wide dependencies
Using keyBy() operations to reduce shuffle
Using a custom partitioner to reduce shuffle
Summary
10
Saving Data in the Correct Format
Saving Data in the Correct Format
Saving data in plain text format
Leveraging JSON as a data format
Tabular formats – CSV
Using Avro with Spark
Columnar formats – Parquet
Summary
11
Working with the Spark Key/Value API
Working with the Spark Key/Value API
Available actions on key/value pairs
Using aggregateByKey instead of groupBy()
Actions on key/value pairs
Available partitioners on key/value data
Implementing a custom partitioner
Summary
12
Testing Apache Spark Jobs
Testing Apache Spark Jobs
Separating logic from Spark engine-unit testing
Integration testing using SparkSession
Mocking data sources using partial functions
Using ScalaCheck for property-based testing
Testing in different versions of Spark
Summary
13
Leveraging the Spark GraphX API
Leveraging the Spark GraphX API
Creating a graph from a data source
Using the Vertex API
Using the Edge API
Calculating the degree of the vertex
Calculating PageRank
Summary
14
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Available partitioners on key/value data

We know that partitioning and partitioners are the key components of Apache Spark. They influence how our data is partitioned, which means they influence where the data actually resides on which executors. If we have a good partitioner, then we will have good data locality, which will reduce shuffle. We know that shuffle is not desirable for processing, so reducing shuffle is crucial, and, therefore, choosing a proper partitioner is also crucial for our systems.

In this section, we will cover the following topics:

  • Examining HashPartitioner
  • Examining RangePartitioner
  • Testing

We will first examine our HashPartitioner and RangePartitioner. We will then compare them and test the code using both the partitioners.

First we will create a UserTransaction array, as per the following example:

 val keysWithValuesList =
 Array(
 UserTransaction(&quot...
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