My Java 8 in Action

《Java 8 in Action》有500+页，相当地啰嗦。啰嗦的另一面是详细，可以作为我学习Java 8的一个索引。

在“编程导论_codes_补充\Java8Demo”创建了一个Netbeans项目。

第0章 简介

第1章 λ表达式

技术上，λ表达式并不能够让程序员做Java 8之前不能够做的事情，λ表达式只是实现回调函数的更紧凑的方式。观念上，Java引入λ表达式，希望程序员能够以函数式编程的高阶函数考虑问题，而非以多态考虑问题。

1.1λ表达式的本质 通过一个接口DoubleOP，说明应用程序App提供回调函数的3种方式，由此可知，λ表达式最基本的目的：替换匿名类以编写更简洁/concise的代码。

Java 8并没有把函数设计为一种数据类型而是依靠现有的常规类型，Java的lambda表达式，不是匿名函数，而是省略了名字的函数。所以有了目标类型（target typing）、函数接口的概念。

1.1.2λ表达式的语法

1.2高阶函数

1.2.1行为参数化 函数接口作为形参

1.2.2函数作为返回值

1.2.3默认方法

1.2.4 java.util.function.*

第2章 流

2.1什么是流 比较Stream与常用的数组或集合类如List，流的元素可以是无限的。从MapReduce模型，说明需要数据序列的供给过程和使用过程交替进行的结构——流。

2.2管道/Pipelinin
惰性/ laziness、部分地构造、流的一次性

1. λ表达式

Java8： 《3.1. Lambdas in a nutshell》

λ表达式的基本用途 完成了回调的原意——代码的参数化。λ表达式可以赋值给变量，作为实参。

2.流

Stream概念 java.util.stream.Stream 管道/Pipelinin、

创建Stream 《5.7. Building streams》在练习各种操作之前，有流对象在手。

1. Fundamentals

Chapter 1. Java 8: why should you care?

1.1. Why is Java still changing?

1.1.1. Java’s place in the programming language

ecosystem

1.1.2. Stream processing

1.1.3. Passing code to methods with behavior

parameterization

1.1.4. Parallelism and shared mutable data

1.1.5. Java needs to evolve

1.2. Functions in Java

1.2.1. Methods and lambdas as first-class citizens

1.2.2. Passing code: an example

1.2.3. From passing methods to lambdas

1.3. Streams

1.3.1. Multithreading is difficult

1.4. Default methods

1.5. Other good ideas from functional programming

1.6. Summary

Chapter 2 Passing code with behavior parameterization

Chapter 3. Lambda expressions

3.2.1. Functional interface

3.2.2. Function descriptor

3.4. Using functional interfaces

3.4.1. Predicate

3.4.2. Consumer

3.4.3. Function

3.5. Type checking, type inference, and restrictions

3.5.1. Type checking

3.5.2. Same lambda, different functional interfaces

3.5.3. Type inference

3.5.4. Using local variables

3.6. Method references

3.6.1. In a nutshell

3.6.2. Constructor references

3.7. Putting lambdas and method references into

practice!

3.7.1. Step 1: Pass code

3.7.2. Step 2: Use an anonymous class

3.7.3. Step 3: Use lambda expressions

3.7.4. Step 4: Use method references

3.8. Useful methods to compose lambda expressions

3.8.1. Composing Comparators

3.8.2. Composing Predicates

3.8.3. Composing Functions

3.9. Similar ideas from mathematics

3.9.1. Integration

3.9.2. Connecting to Java 8 lambdas

3.10. Summary

2. Functional-style data processing

Chapter 4. Introducing streams

4.1. What are streams?

4.2. Getting started with streams

4.3. Streams vs. collections

4.3.1. Traversable only once

4.3.2. External vs. internal iteration

4.4. Stream operations

4.4.1. Intermediate operations

4.4.2. Terminal operations

4.4.3. Working with streams

4.5. Summary

Chapter 5. Working with streams

5.1. Filtering and slicing

5.1.1. Filtering with a predicate

5.1.2. Filtering unique elements

5.1.3. Truncating a stream

5.1.4. Skipping elements

5.2. Mapping

5.2.1. Applying a function to each element of a stream

5.2.2. Flattening streams

5.3. Finding and matching

5.3.1. Checking to see if a predicate matches at least

one element

5.3.2. Checking to see if a predicate matches all

elements

5.3.3. Finding an element

5.3.4. Finding the first element

5.4. Reducing

5.4.1. Summing the elements

5.4.2. Maximum and minimum

5.5. Putting it all into practice

5.5.1. The domain: Traders and Transactions

5.5.2. Solutions

5.6.3. Putting numerical streams into practice:Pythagorean triples
Java8：创建Stream （5.6.
Numeric streams 

5.7.3. Streams from files

5.8. Summary

Chapter 6. Collecting data with streams

6.1. Collectors in a nutshell

6.1.1. Collectors as advanced reductions

6.1.2. Predefined collectors

6.2. Reducing and summarizing

6.2.1. Finding maximum and minimum in a stream of

values

6.2.2. Summarization

6.2.3. Joining Strings

6.2.4. Generalized summarization with reduction

6.3. Grouping

6.3.1. Multilevel grouping

6.3.2. Collecting data in subgroups

6.4. Partitioning

6.4.1. Advantages of partitioning

6.4.2. Partitioning numbers into prime and nonprime

6.5. The Collector interface

6.5.1. Making sense of the methods declared by

Collector interface

6.5.2. Putting them all together

6.6. Developing your own collector for better

performance

6.6.1. Divide only by prime numbers

6.6.2. Comparing collectors’ performances

6.7. Summary

Chapter 7. Parallel data processing and performance

7.1. Parallel streams

7.1.1. Turning a sequential stream into a parallel one

7.1.2. Measuring stream performance

7.1.3. Using parallel streams correctly

7.1.4. Using parallel streams effectively

7.2. The fork/join framework

7.2.1. Working with RecursiveTask

7.2.2. Best practices for using the fork/join framework

7.2.3. Work stealing

7.3. Spliterator

7.3.1. The splitting process

7.3.2. Implementing your own Spliterator

7.4. Summary

3. Effective Java 8 programming

Chapter 8. Refactoring, testing, and debugging

8.1. Refactoring for improved readability and flexibility

8.1.1. Improving code readability

8.1.2. From anonymous classes to lambda expressions

8.1.3. From lambda expressions to method references

8.1.4. From imperative data processing to Streams

8.1.5. Improving code flexibility

8.2. Refactoring object-oriented design patterns with

lambdas

8.2.1. Strategy

8.2.2. Template method

8.2.3. Observer

8.2.4. Chain of responsibility

8.2.5. Factory

8.3. Testing lambdas

8.3.1. Testing the behavior of a visible lambda

8.3.2. Focusing on the behavior of the method using

a lambda

8.3.3. Pulling complex lambdas into separate methods

8.3.4. Testing high-order functions

8.4. Debugging

8.4.1. Examining the stack trace

8.4.2. Logging information

8.5. Summary

Chapter 9. Default methods

9.1. Evolving APIs

9.1.1. API version 1

9.1.2. API version 2

9.2. Default methods in a nutshell

9.3. Usage patterns for default methods

9.3.1. Optional methods

9.3.2. Multiple inheritance of behavior

9.4. Resolution rules

9.4.1. Three resolution rules to know

9.4.2. Most specific default-providing interface wins

9.4.3. Conflicts and explicit disambiguation

9.4.4. Diamond problem

9.5. Summary

Chapter 10. Using Optional as a better alternative to null

10.1. How do you model the absence of a value?

10.1.1. Reducing NullPointerExceptions with defensive

checking

10.1.2. Problems with null

10.1.3. What are the alternatives to null in other

languages?

10.2. Introducing the Optional class

10.3. Patterns for adopting Optional

10.3.1. Creating Optional objects

10.3.2. Extracting and transforming values from

optionals with map

10.3.3. Chaining Optional objects with flatMap

10.3.4. Default actions and unwrapping an optional

10.3.5. Combining two optionals

10.3.6. Rejecting certain values with filter

10.4. Practical examples of using Optional

10.4.1. Wrapping a potentially null value in an optional

10.4.2. Exceptions vs. Optional

10.4.3. Putting it all together

10.5. Summary

Chapter 11. CompletableFuture: composable asynchronous

programming

11.1. Futures

11.1.1. Futures limitations

11.1.2. Using CompletableFutures to build an

asynchronous application

11.2. Implementing an asynchronous API

11.2.1. Converting a synchronous method into an

asynchronous one

11.2.2. Dealing with errors

11.3. Make your code non-blocking

11.3.1. Parallelizing requests using a parallel Stream

11.3.2. Making asynchronous requests with

CompletableFutures

11.3.3. Looking for the solution that scales better

11.3.4. Using a custom Executor

11.4. Pipelining asynchronous tasks

11.4.1. Implementing a discount service

11.4.2. Using the Discount service

11.4.3. Composing synchronous and asynchronous

operations

11.4.4. Combining two CompletableFutures—dependent

and independent

11.4.5. Reflecting on Future vs. CompletableFuture

11.5. Reacting to a CompletableFuture completion

11.5.1. Refactoring the best-price-finder application

11.5.2. Putting it to work

11.6. Summary

Chapter 12. New Date and Time API

12.1. LocalDate, LocalTime, Instant, Duration, and Period

12.1.1. Working with LocalDate and LocalTime

12.1.2. Combining a date and a time

12.1.3. Instant: a date and time for machines

12.1.4. Defining a Duration or a Period

12.2. Manipulating, parsing, and formatting dates

12.2.1. Working with TemporalAdjusters

12.2.2. Printing and parsing date-time objects

12.3. Working with different time zones and calendars

12.3.1. Fixed offset from UTC/Greenwich

12.3.2. Using alternative calendar systems

12.4. Summary

4. Beyond Java 8

Chapter 13. Thinking functionally

13.1. Implementing and maintaining systems

13.1.1. Shared mutable data

13.1.2. Declarative programming

13.1.3. Why functional programming?

13.2. What’s functional programming?

13.2.1. Functional-style Java

13.2.2. Referential transparency

13.2.3. Object-oriented vs. functional-style programming

13.2.4. Functional style in practice

13.3. Recursion vs. iteration

13.4. Summary

Chapter 14. Functional programming techniques

14.1. Functions everywhere

14.1.1. Higher-order functions

14.1.2. Currying

14.2. Persistent data structures

14.2.1. Destructive updates vs. functional

14.2.2. Another example with Trees

14.2.3. Using a functional approach

14.3. Lazy evaluation with streams

14.3.1. Self-defining stream

14.3.2. Your own lazy list

14.4. Pattern matching

14.4.1. Visitor design pattern

14.4.2. Pattern matching to the rescue

14.5. Miscellany

14.5.1. Caching or memoization

14.5.2. What does “return the same object” mean?

14.5.3. Combinators

14.6. Summary

Chapter 15. Blending OOP and FP: comparing Java 8 and

Scala

15.1. Introduction to Scala

15.1.1. Hello beer

15.1.2. Basic data structures: List, Set, Map, Tuple,

Stream, Option

15.2. Functions

15.2.1. First-class functions in Scala

15.2.2. Anonymous functions and closures

15.2.3. Currying

15.3. Classes and traits

15.3.1. Less verbosity with Scala classes

15.3.2. Scala traits vs. Java 8 interfaces

15.4. Summary

Chapter 16. Conclusions and where next for Java

16.1. Review of Java 8 features

16.1.1. Behavior parameterization (lambdas and method

references)

16.1.2. Streams

16.1.3. CompletableFuture

16.1.4. Optional

16.1.5. Default methods

16.2. What’s ahead for Java?

16.2.1. Collections

16.2.2. Type system enhancements

16.2.3. Pattern matching

16.2.4. Richer forms of generics

16.2.5. Deeper support for immutability

16.2.6. Value types

16.3. The final word

Appendix A. Miscellaneous language updates

A.1. Annotations

A.1.1. Repeated annotations

A.1.2. Type annotations

A.2. Generalized target-type inference

Appendix B. Miscellaneous library updates

B.1. Collections

B.1.1. Additional methods

B.1.2. The Collections class

B.1.3. Comparator

B.2. Concurrency

B.2.1. Atomic

B.2.2. ConcurrentHashMap

B.3. Arrays

B.3.1. Using parallelSort

B.3.2. Using setAll and parallelSetAll

B.3.3. Using parallelPrefix

B.4. Number and Math

B.4.1. Number

B.4.2. Math

B.5. Files

B.6. Reflection

B.7. String

Appendix C. Performing multiple operations in parallel on a

stream

C.1. Forking a stream

C.1.1. Implementing the Results interface with the

ForkingStreamConsumer

C.1.2. Developing the ForkingStreamConsumer and the

BlockingQueueSpliterator

C.1.3. Putting the StreamForker to work

C.2. Performance considerations

Appendix D. Lambdas and JVM bytecode

D.1. Anonymous classes

D.2. Bytecode generation

D.3. InvokeDynamic to the rescue

D.4. Code-generation strategies