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Home > Data > Data Processing > Learning PySpark
Learning PySpark
Learning PySpark

Learning PySpark: Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

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Learning PySpark

Chapter 2. Resilient Distributed Datasets

Resilient Distributed Datasets (RDDs) are a distributed collection of immutable JVM objects that allow you to perform calculations very quickly, and they are the backbone of Apache Spark.

As the name suggests, the dataset is distributed; it is split into chunks based on some key and distributed to executor nodes. Doing so allows for running calculations against such datasets very quickly. Also, as already mentioned in Chapter 1, Understanding Spark, RDDs keep track (log) of all the transformations applied to each chunk to speed up the computations and provide a fallback if things go wrong and that portion of the data is lost; in such cases, RDDs can recompute the data. This data lineage is another line of defense against data loss, a complement to data replication.

The following topics are covered in this chapter:

  • Internal workings of an RDD
  • Creating RDDs
  • Global versus local scopes
  • Transformations
  • Actions

Internal workings of an RDD

RDDs operate in parallel. This is the strongest advantage of working in Spark: Each transformation is executed in parallel for enormous increase in speed.

The transformations to the dataset are lazy. This means that any transformation is only executed when an action on a dataset is called. This helps Spark to optimize the execution. For instance, consider the following very common steps that an analyst would normally do to get familiar with a dataset:

  1. Count the occurrence of distinct values in a certain column.
  2. Select those that start with an A.
  3. Print the results to the screen.

As simple as the previously mentioned steps sound, if only items that start with the letter A are of interest, there is no point in counting distinct values for all the other items. Thus, instead of following the execution as outlined in the preceding points, Spark could only count the items that start with A, and then print the results to the screen.

Let's break this example down in code...

Creating RDDs

There are two ways to create an RDD in PySpark: you can either .parallelize(...) a collection (list or an array of some elements):

data = sc.parallelize(
    [('Amber', 22), ('Alfred', 23), ('Skye',4), ('Albert', 12), 
     ('Amber', 9)])

Or you can reference a file (or files) located either locally or somewhere externally:

data_from_file = sc.\    
    textFile(
        '/Users/drabast/Documents/PySpark_Data/VS14MORT.txt.gz',
        4)

Note

We downloaded the Mortality dataset VS14MORT.txt file from (accessed on July 31, 2016) ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/DVS/mortality/mort2014us.zip; the record schema is explained in this document http://www.cdc.gov/nchs/data/dvs/Record_Layout_2014.pdf. We selected this dataset on purpose: The encoding of the records will help us to explain how to use UDFs to transform your data later in this chapter. For your convenience, we also host the file here: http:/...

Global versus local scope

One of the things that you, as a prospective PySpark user, need to get used to is the inherent parallelism of Spark. Even if you are proficient in Python, executing scripts in PySpark requires shifting your thinking a bit.

Spark can be run in two modes: Local and cluster. When you run Spark locally your code might not differ to what you are currently used to with running Python: Changes would most likely be more syntactic than anything else but with an added twist that data and code can be copied between separate worker processes.

However, taking the same code and deploying it to a cluster might cause a lot of head-scratching if you are not careful. This requires understanding how Spark executes a job on the cluster.

In the cluster mode, when a job is submitted for execution, the job is sent to the driver (or a master) node. The driver node creates a DAG (see Chapter 1, Understanding Spark) for a job and decides which executor (or worker) nodes will run specific tasks...

Transformations

Transformations shape your dataset. These include mapping, filtering, joining, and transcoding the values in your dataset. In this section, we will showcase some of the transformations available on RDDs.

Note

Due to space constraints we include only the most often used transformations and actions here. For a full set of methods available we suggest you check PySpark's documentation on RDDs http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.

Since RDDs are schema-less, in this section we assume you know the schema of the produced dataset. If you cannot remember the positions of information in the parsed dataset we suggest you refer to the definition of the extractInformation(...) method on GitHub, code for Chapter 03.

The .map(...) transformation

It can be argued that you will use the .map(...) transformation most often. The method is applied to each element of the RDD: In the case of the data_from_file_conv dataset, you can think of this as a transformation...

Internal workings of an RDD

RDDs operate in parallel. This is the strongest advantage of working in Spark: Each transformation is executed in parallel for enormous increase in speed.

The transformations to the dataset are lazy. This means that any transformation is only executed when an action on a dataset is called. This helps Spark to optimize the execution. For instance, consider the following very common steps that an analyst would normally do to get familiar with a dataset:

  1. Count the occurrence of distinct values in a certain column.

  2. Select those that start with an A.

  3. Print the results to the screen.

As simple as the previously mentioned steps sound, if only items that start with the letter A are of interest, there is no point in counting distinct values for all the other items. Thus, instead of following the execution as outlined in the preceding points, Spark could only count the items that start with A, and then print the results to the screen.

Let's break this example down in code. First...

Creating RDDs

There are two ways to create an RDD in PySpark: you can either .parallelize(...) a collection (list or an array of some elements):

data = sc.parallelize(
    [('Amber', 22), ('Alfred', 23), ('Skye',4), ('Albert', 12), 
     ('Amber', 9)])

Or you can reference a file (or files) located either locally or somewhere externally:

data_from_file = sc.\    
    textFile(
        '/Users/drabast/Documents/PySpark_Data/VS14MORT.txt.gz',
        4)

Note

We downloaded the Mortality dataset VS14MORT.txt file from (accessed on July 31, 2016) ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/DVS/mortality/mort2014us.zip; the record schema is explained in this document http://www.cdc.gov/nchs/data/dvs/Record_Layout_2014.pdf. We selected this dataset on purpose: The encoding of the records will help us to explain how to use UDFs to transform your data later in this chapter. For your convenience, we also host the file here: http://tomdrabas.com/data/VS14MORT.txt.gz

The last parameter in sc.textFile...

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Key benefits

  • Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0
  • Develop and deploy efficient, scalable real-time Spark solutions
  • Take your understanding of using Spark with Python to the next level with this jump start guide

Description

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.

Who is this book for?

If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory.

What you will learn

  • Learn about Apache Spark and the Spark 2.0 architecture
  • Build and interact with Spark DataFrames using Spark SQL
  • Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively
  • Read, transform, and understand data and use it to train machine learning models
  • Build machine learning models with MLlib and ML
  • Learn how to submit your applications programmatically using spark-submit
  • Deploy locally built applications to a cluster

Product Details

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Publication date : Feb 27, 2017
Length: 274 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466259
Category :
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Languages :
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Concepts :
Data Processing
Tools :
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Publication date : Feb 27, 2017
Length: 274 pages
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Language : English
ISBN-13 : 9781786466259
Category :
Data
Languages :
Python
Concepts :
Data Processing
Tools :
Apache Spark

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Table of Contents

12 Chapters
1. Understanding Spark Chevron down icon Chevron up icon
1. Understanding Spark
What is Apache Spark?
Spark Jobs and APIs
Spark 2.0 architecture
Summary
2. Resilient Distributed Datasets Chevron down icon Chevron up icon
2. Resilient Distributed Datasets
Internal workings of an RDD
Creating RDDs
Global versus local scope
Transformations
Actions
Summary
3. DataFrames Chevron down icon Chevron up icon
3. DataFrames
Python to RDD communications
Catalyst Optimizer refresh
Speeding up PySpark with DataFrames
Creating DataFrames
Simple DataFrame queries
Interoperating with RDDs
Querying with the DataFrame API
Querying with SQL
DataFrame scenario – on-time flight performance
Spark Dataset API
Summary
4. Prepare Data for Modeling Chevron down icon Chevron up icon
4. Prepare Data for Modeling
Checking for duplicates, missing observations, and outliers
Getting familiar with your data
Visualization
Summary
5. Introducing MLlib Chevron down icon Chevron up icon
5. Introducing MLlib
Overview of the package
Loading and transforming the data
Getting to know your data
Creating the final dataset
Predicting infant survival
Summary
6. Introducing the ML Package Chevron down icon Chevron up icon
6. Introducing the ML Package
Overview of the package
Predicting the chances of infant survival with ML
Parameter hyper-tuning
Other features of PySpark ML in action
Summary
7. GraphFrames Chevron down icon Chevron up icon
7. GraphFrames
Introducing GraphFrames
Installing GraphFrames
Preparing your flights dataset
Building the graph
Executing simple queries
Understanding vertex degrees
Determining the top transfer airports
Understanding motifs
Determining airport ranking using PageRank
Determining the most popular non-stop flights
Using Breadth-First Search
Visualizing flights using D3
Summary
8. TensorFrames Chevron down icon Chevron up icon
8. TensorFrames
What is Deep Learning?
What is TensorFlow?
Introducing TensorFrames
TensorFrames – quick start
Summary
9. Polyglot Persistence with Blaze Chevron down icon Chevron up icon
9. Polyglot Persistence with Blaze
Installing Blaze
Polyglot persistence
Abstracting data
Data operations
Summary
10. Structured Streaming Chevron down icon Chevron up icon
10. Structured Streaming
What is Spark Streaming?
Why do we need Spark Streaming?
What is the Spark Streaming application data flow?
Simple streaming application using DStreams
A quick primer on global aggregations
Introducing Structured Streaming
Summary
11. Packaging Spark Applications Chevron down icon Chevron up icon
11. Packaging Spark Applications
The spark-submit command
Deploying the app programmatically
Databricks Jobs
Summary
Index Chevron down icon Chevron up icon
Index

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Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 12 years' international experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting. Tomasz started his career in 2003 with LOT Polish Airlines in Warsaw, Poland while finishing his Master's degree in strategy management. In 2007, he moved to Sydney to pursue a doctoral degree in operations research at the University of New South Wales, School of Aviation; his research crossed boundaries between discrete choice modeling and airline operations research. During his time in Sydney, he worked as a Data Analyst for Beyond Analysis Australia and as a Senior Data Analyst/Data Scientist for Vodafone Hutchison Australia among others. He has also published scientific papers, attended international conferences, and served as a reviewer for scientific journals. In 2015 he relocated to Seattle to begin his work for Microsoft. While there, he has worked on numerous projects involving solving problems in high-dimensional feature space.
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Joanna Lee has more than 8 years of experience in game development. She has designed and programmed various video games. She first started working with Unreals game engine in 2005 and is very excited to be able to author a book about the newest Unreal Engine 4. She has also worked with many other engines as well as reviewed books and videos on Cry Engine 4.
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