Spark Scala Learning Note
Introduction
1.
Develope Envirinment Setup
2.
Scala Basic
2.1.
For And Yield
2.2.
Functions
2.3.
Class and Case Class
2.3.1.
Basic Class and Constructor
2.3.2.
Abstract Class
2.3.3.
Case Class
2.4.
Object, Compaion Object
2.5.
Traits
2.6.
Pattern Matching
2.6.1.
Matching on Value
2.6.2.
Matching on Type
2.6.3.
Matching on Class Members
2.7.
Exception
2.8.
Data Structure
2.8.1.
Option
2.8.2.
Map
2.8.3.
Tuple
2.9.
Functional Combinator
2.9.1.
map
3.
Design Pattern
3.1.
Factory
3.2.
Singleton
3.3.
Adaptor
3.4.
Decorate
4.
Loading Data from Spark Client to Cluster
4.1.
Data From S3
4.2.
Reading CSV
4.3.
Using Parquet
4.4.
Loading More Than 22 Features into a Class
4.5.
Caching
5.
Algorithm
5.1.
Linear Regression
5.1.1.
Creating LabeledPoint
5.1.2.
Preparing the Training and Test Data
5.1.3.
Scaling the Features
5.1.4.
Training the Model
5.1.5.
Predicting Against Test Data
6.
Appendix
6.1.
Language Questions
6.1.1.
Differences between val, var and def
6.1.2.
Differences between trait and abstract class
6.1.3.
Differences between an ```object``` and a ```class```.
6.1.4.
Companion Object
6.1.5.
Difference between the following terms and types in Scala: Nil, Null, None, Nothing
6.1.6.
Difference between Call by Name and Call by Value
6.1.7.
Option monad
6.2.
Functional Programming Questions
6.3.
Reactive Programming Questions
6.4.
Coding Questions
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Spark Scala Learning Note
Loading More Than 22 Features into a Class
Case classes have an inherent limitation. They can hold only 22 attributes