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Apache Spark in 24 Hours, Sams Teach Yourself
- ByJeffrey Aven
- Published Aug 31, 2016 bySams. Part of theSams Teach Yourselfseries.
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Description
- 版权2017
- Dimensions: 7" x 9-1/8"
- Pages: 445
- 版本:1
- eBook (Watermarked)
- ISBN-10: 0-13-444580-5
- ISBN-13: 978-0-13-444580-9
Apache Spark is a fast, scalable, and flexible open source distributed processing engine for big data systems and is one of the most active open source big data projects to date. In just 24 lessons of one hour or less,Sams Teach Yourself Apache Spark in 24 Hourshelps you build practical Big Data solutions that leverage Spark’s amazing speed, scalability, simplicity, and versatility.
This book’s straightforward, step-by-step approach shows you how to deploy, program, optimize, manage, integrate, and extend Spark–now, and for years to come. You’ll discover how to create powerful solutions encompassing cloud computing, real-time stream processing, machine learning, and more. Every lesson builds on what you’ve already learned, giving you a rock-solid foundation for real-world success.
Whether you are a data analyst, data engineer, data scientist, or data steward, learning Spark will help you to advance your career or embark on a new career in the booming area of Big Data.
Learn how to
• Discover what Apache Spark does and how it fits into the Big Data landscape
• Deploy and run Spark locally or in the cloud
• Interact with Spark from the shell
• Make the most of the Spark Cluster Architecture
• Develop Spark applications with Scala and functional Python
• Program with the Spark API, including transformations and actions
• Apply practical data engineering/analysis approaches designed for Spark
• Use Resilient Distributed Datasets (RDDs) for caching, persistence, and output
• Optimize Spark solution performance
• Use Spark with SQL (via Spark SQL) and with NoSQL (via Cassandra)
• Leverage cutting-edge functional programming techniques
• Extend Spark with streaming, R, and Sparkling Water
• Start building Spark-based machine learning and graph-processing applications
• Explore advanced messaging technologies, including Kafka
• Preview and prepare for Spark’s next generation of innovations
Instructions walk you through common questions, issues, and tasks; Q-and-As, Quizzes, and Exercises build and test your knowledge; "Did You Know?" tips offer insider advice and shortcuts; and "Watch Out!" alerts help you avoid pitfalls. By the time you're finished, you'll be comfortable using Apache Spark to solve a wide spectrum of Big Data problems.
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Table of Contents
Preface xii
PART I: GETTING STARTED WITH APACHE SPARK
Hour 1: Introducing Apache Spark 1
What Is Spark? 1
What Sort of Applications Use Spark? 3
Programming Interfaces to Spark 3
Ways to Use Spark 5
Q&A 8
Workshop 8
Hour 2: Understanding Hadoop 11
Hadoop and a Brief History of Big Data 11
Hadoop Explained 12
Introducing HDFS 13
Introducing YARN 19
Anatomy of a Hadoop Cluster 22
How Spark Works with Hadoop 24
Q&A 25
Workshop 25
Hour 3: Installing Spark 27
Spark Deployment Modes 27
Preparing to Install Spark 28
Installing Spark in Standalone Mode 29
Exploring the Spark Install 38
Deploying Spark on Hadoop 39
Q&A 43
Workshop 43
Exercises 44
Hour 4: Understanding the Spark Application Architecture 45
Anatomy of a Spark Application 45
Spark Driver 46
Spark Executors and Workers 48
主人和Clust火花er Manager 49
Spark Applications Running on YARN 51
Local Mode 56
Q&A 59
Workshop 59
Hour 5: Deploying Spark in the Cloud 61
Amazon Web Services Primer 61
Spark on EC2 64
Spark on EMR 73
Hosted Spark with Databricks 81
Q&A 89
Workshop 89
PART II: PROGRAMMING WITH APACHE SPARK
Hour 6: Learning the Basics of Spark Programming with RDDs 91
Introduction to RDDs 91
Loading Data into RDDs 93
Operations on RDDs 106
Types of RDDs 111
Q&A 113
Workshop 113
Hour 7: Understanding MapReduce Concepts 115
MapReduce History and Background 115
Records and Key Value Pairs 117
MapReduce Explained 118
Word Count: The “Hello, World” of MapReduce 126
Q&A 135
Workshop 136
Hour 8: Getting Started with Scala 137
Scala History and Background 137
Scala Basics 138
Object-Oriented Programming in Scala 153
Functional Programming in Scala 157
Spark Programming in Scala 160
Q&A 163
Workshop 163
Hour 9: Functional Programming with Python 165
Python Overview 165
Data Structures and Serialization in Python 170
Python Functional Programming Basics 178
Interactive Programming Using IPython 183
Q&A 194
Workshop 194
Hour 10: Working with the Spark API (Transformations and Actions) 197
RDDs and Data Sampling 197
Spark Transformations 199
Spark Actions 206
Key Value Pair Operations 211
Join Functions 219
Numerical RDD Operations 229
Q&A 232
Workshop 233
Hour 11: Using RDDs: Caching, Persistence, and Output 235
RDD Storage Levels 235
Caching, Persistence, and Checkpointing 239
Saving RDD Output 247
Introduction to Alluxio (Tachyon) 254
Q&A 257
Workshop 258
Hour 12: Advanced Spark Programming 259
Broadcast Variables 259
Accumulators 265
Partitioning and Repartitioning 270
Processing RDDs with External Programs 278
Q&A 280
Workshop 280
PART III: EXTENSIONS TO SPARK
Hour 13: Using SQL with Spark 283
Introduction to Spark SQL 283
Getting Started with Spark SQL DataFrames 294
Using Spark SQL DataFrames 305
Accessing Spark SQL 316
Q&A 321
Workshop 322
Hour 14: Stream Processing with Spark 323
Introduction to Spark Streaming 323
Using DStreams 326
State Operations 335
Sliding Window Operations 337
Q&A 340
车间340
Hour 15: Getting Started with Spark and R 343
Introduction to R 343
Introducing SparkR 350
Using SparkR 355
Using SparkR with RStudio 358
Q&A 361
Workshop 361
Hour 16: Machine Learning with Spark 363
Introduction to Machine Learning and MLlib 363
Classification Using Spark MLlib 367
Collaborative Filtering Using Spark MLlib 373
Clustering Using Spark MLlib 375
Q&A 378
Workshop 379
Hour 17: Introducing Sparkling Water (H20 and Spark) 381
Introduction to H2O 381
Sparkling Water—H2O on Spark 387
Q&A 397
Workshop 397
Hour 18: Graph Processing with Spark 399
Introduction to Graphs 399
Graph Processing in Spark 402
Introduction to GraphFrames 406
Q&A 414
Workshop 414
Hour 19: Using Spark with NoSQL Systems 417
Introduction to NoSQL 417
Using Spark with HBase 419
Using Spark with Cassandra 425
Using Spark with DynamoDB and More 429
Q&A 431
Workshop 432
Hour 20: Using Spark with Messaging Systems 433
Overview of Messaging Systems 433
Using Spark with Apache Kafka 435
Spark, MQTT, and the Internet of Things 443
Using Spark with Amazon Kinesis 446
Q&A 451
Workshop 451
PART IV: MANAGING SPARK
Hour 21: Administering Spark 453
Spark Configuration 453
Administering Spark Standalone 461
Administering Spark on YARN 471
Q&A 477
Workshop 478
Hour 22: Monitoring Spark 479
Exploring the Spark Application UI 479
Spark History Server 488
Spark Metrics 490
Logging in Spark 492
Q&A 499
Workshop 499
Hour 23: Extending and Securing Spark 501
Isolating Spark 501
Securing Spark Communication 504
Securing Spark with Kerberos 512
Q&A 517
Workshop 517
Hour 24: Improving Spark Performance 519
Benchmarking Spark 519
Application Development Best Practices 526
Optimizing Partitions 534
Diagnosing Application Performance Issues 536
Q&A 540
Workshop 541
Index 543
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