Preface
Part I．Gentle Overview of Big Data and Spark
1． What Is Apache Spark?
Apache Spark's Philosophy
Context： The Big Data Problem
History of Spark
The Present and Future of Spark
Running Spark
Downloading Spark Locally
Launching Spark's Interactive Consoles
Running Spark in the Cloud
Data Used in This Book
2． A Gentle Introduction to Spark
Spark's Basic Architecture
Spark Applications
Spark's Language APIs
Spark's APIs
Starting Spark
The SparkSession
DataFrames
Partitions
Transformations
Lazy Evaluation
Actions
Spark UI
An End-to-End Example
DataFrames and SQL
Conclusion
3． A Tour of Spark's Too1set
Running Production Applications
Datasets： Type-Safe Structured APIs
Structured Streaming
Machine Learning and Advanced Analytics
Lower-Level APIs
SparkR
Spark's Ecosystem and Packages
Conclusion
Part II．Structured APls——DataFrames， SQL， and Datasets
4． Structured API Overview
DataFrames and Datasets
Schemas
Overview of Structured Spark Types
DataFrames Versus Datasets
Columns
Rows
Spark Types
Overview of Structured API Execution
Logical Planning
Physical Planning
Execution
Conclusion
5． Basic Structured Operations
Schemas
Columns and Expressions
Columns
Expressions
Records and Rows
Creating Rows
DataFrame Transformations
Creating DataFrames
select and selectExpr
Converting to Spark Types （Literals）
Adding Columns
……
6．Working with Different Types of Data
7．Aggregations
8．Joins
9．Data Sources
10．Spark SQL
11．Datasets
Part III．Low—Level APIs
12．Resilient Distributed Datasets（RDDs）
13．Advanced RDDs
14．Distributed Shared Variables
Part IV．Production Applications
15．HowSparkRunson a Cluster
16．Developing Spark Applications
17．Deploying Spark
18．Monitoring and Debugging
19．Performance Tuning
Part V．Streaming
20．Stream Processing Fundamentals
21．Structured Streaming Basics
22．Event-Time and Stateful Processing
23．Structured Streaming in Production
Part VI．Advanced Analytics and Machine Learning
24．Advanced Analytics and Machine Learning Overview
25．Preprocessing and Feature Engineering
26．Classification
27．Regression
28．Recommendation
29．Unsupervised Learning
30．Graph Analytics
31．Deep Learning
Part VII．Ecosystem
32．Language Specifics：Python（PySpark）and R（SparkR and sparklyr）
33．Ecosystem and Community
Index
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