Modern Java in Action

Lambdas, streams, functional and reactive programming

Raoul-Gabriel Urma, Mario Fusco, Alan Mycroft

September 2018
ISBN 9781617293566
592 pages
printed in black & white

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Modern Java in Action

Lambdas, streams, functional and reactive programming

Raoul-Gabriel Urma, Mario Fusco, Alan Mycroft

September 2018
ISBN 9781617293566
592 pages
printed in black & white

free previous edition eBook included

An eBook copy of the previous edition of this book is included at no additional cost. It will be automatically added to your Manning Bookshelf within 24 hours of purchase.

A comprehensive and practical introduction to the modern features of the latest Java releases with excellent examples!

Oleksandr Mandryk, EPAM Systems

Manning's bestselling Java 8 book has been revised for Java 9 and 10! In Modern Java in Action, you'll build on your existing Java language skills with the newest features and techniques.

Listen to this book in liveAudio! liveAudio integrates a professional voice recording with the book’s text, graphics, code, and exercises in Manning’s exclusive liveBook online reader. Use the text to search and navigate the audio, or download the audio-only recording for portable offline listening. You can purchase or upgrade to liveAudio here or in liveBook.

About the Technology

Modern applications take advantage of innovative designs, including microservices, reactive architectures, and streaming data. Modern Java features like lambdas, streams, and the long-awaited Java Module System make implementing these designs significantly easier. It’s time to upgrade your skills and meet these challenges head on!

About the book

Modern Java in Action connects new features of the Java language with their practical applications. Using crystal-clear examples and careful attention to detail, this book respects your time. It will help you expand your existing knowledge of core Java as you master modern additions like the Streams API and the Java Module System, explore new approaches to concurrency, and learn how functional concepts can help you write code that’s easier to read and maintain.

Part 1: Fundamentals

1 Java 8, 9, 10, and 11: what’s happening?

1.1 So, what’s the big story?

1.2 Why is Java still changing?

1.2.1 Java’s place in the programming language ecosystem

1.2.2 Stream processing

1.2.3 Passing code to methods with behavior parameterization

1.2.4 Parallelism and shared mutable data

1.2.5 Java needs to evolve

1.3 Functions in Java

1.3.1 Methods and lambdas as first-class citizens

1.3.2 Passing code: an example

1.3.3 From passing methods to lambdas

1.4 Streams

1.4.1 Multithreading is difficult

1.5 Default methods and Java modules

1.6 Other good ideas from functional programming

Summary

2 Passing code with behavior parameterization

2.1 Coping with changing requirements

2.1.1 First attempt: filtering green apples

2.1.2 Second attempt: parameterizing the color

2.1.3 Third attempt: filtering with every attribute you can think of

2.2 Behavior parameterization

2.2.1 Fourth attempt: filtering by abstract criteria

2.3 Tackling verbosity

2.3.1 Anonymous classes

2.3.2 Fifth attempt: using an anonymous class

2.3.3 Sixth attempt: using a lambda expression

2.3.4 Seventh attempt: abstracting over List type

2.4 Real-world examples

2.4.1 Sorting with a Comparator

2.4.2 Executing a block of code with Runnable

2.4.3 Returning a result using Callable

2.4.4 GUI event handling

Summary

3 Lambda expressions

3.1 Lambdas in a nutshell

3.2 Where and how to use lambdas

3.2.1 Functional interface

3.2.2 Function descriptor

3.3 Putting lambdas into practice: the execute-around pattern

3.3.1 Step 1: Remember behavior parameterization

3.3.2 Step 2: Use a functional interface to pass behaviors

3.3.3 Step 3: Execute a behavior!

3.3.4 Step 4: Pass lambdas

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

Summary

Part 2: Functional-style data processing with streams

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 Road map

Summary

5 Working with streams

5.1 Filtering

5.1.1 Filtering with a predicate

5.1.2 Filtering unique elements

5.2 Slicing a stream

5.2.1 Slicing using a predicate

5.2.2 Truncating a stream

5.2.3 Skipping elements

5.3 Mapping

5.3.1 Applying a function to each element of a stream

5.3.2 Flattening streams

5.4 Finding and matching

5.4.1 Checking to see if a predicate matches at least one element

5.4.2 Checking to see if a predicate matches all elements

5.4.3 Finding an element

5.4.4 Finding the first element

5.5 Reducing

5.5.1 Summing the elements

5.5.2 Maximum and minimum

5.6 Putting it all into practice

5.6.1 The domain: Traders and Transactions

5.6.2 Solutions

5.7 Numeric streams

5.7.1 Primitive stream specializations

5.7.2 Numeric ranges

5.7.3 Putting numerical streams into practice: Pythagorean triples

5.8 Building streams

5.8.1 Streams from values

5.8.2 Stream from nullable

5.8.3 Streams from arrays

5.8.4 Streams from files

5.8.5 Streams from functions: creating infinite streams!

5.9 Overview

Summary

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 Manipulating grouped elements

6.3.2 Multilevel grouping

6.3.3 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

Summary

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

Summary

Part 3: Effective programming with streams and lambdas

8 Collection API enhancements

8.1 Collection factories

8.1.1 List factory

8.1.2 Set factory

8.1.3 Map factories

8.2 Working with List and Set

8.2.1 removeIf

8.2.2 replaceAll

8.3 Working with Map

8.3.1 forEach

8.3.2 Sorting

8.3.3 getOrDefault

8.3.4 Compute patterns

8.3.5 Remove patterns

8.3.6 Replacement patterns

8.3.7 Merge

8.4 Improved ConcurrentHashMap

8.4.1 Reduce and Search

8.4.2 Counting

8.4.3 Set views

Summary

9 Refactoring, testing, and debugging

9.1 Refactoring for improved readability and flexibility

9.1.1 Improving code readability

9.1.2 From anonymous classes to lambda expressions

9.1.3 From lambda expressions to method references

9.1.4 From imperative data processing to Streams

9.1.5 Improving code flexibility

9.2 Refactoring object-oriented design patterns with lambdas

9.2.1 Strategy

9.2.2 Template method

9.2.3 Observer

9.2.4 Chain of responsibility

9.2.5 Factory

9.3 Testing lambdas

9.3.1 Testing the behavior of a visible lambda

9.3.2 Focusing on the behavior of the method using a lambda

9.3.3 Pulling complex lambdas into separate methods

9.3.4 Testing high-order functions

9.4 Debugging

9.4.1 Examining the stack trace

9.4.2 Logging information

Summary

10 Domain-specific languages using lambdas

10.1 A specific language for your domain

10.1.1 Pros and cons of DSLs

10.1.2 Different DSL solutions available on the JVM

10.2 Small DSLs in modern Java APIs

10.2.1 The Stream API seen as a DSL to manipulate collections

10.2.2 Collectors as a DSL to aggregate data

10.3 Patterns and techniques to create DSLs in Java

10.3.1 Method chaining

10.3.2 Using nested functions

10.3.3 Function sequencing with lambda expressions

10.3.4 Putting it all together

10.3.5 Using method references in a DSL

10.4 Real World Java 8 DSL

10.4.1 jOOQ

10.4.2 Cucumber

10.4.3 Spring Integration

Summary

Part 4: Everyday Java

11 Using Optional as a better alternative to null

11.1 How do you model the absence of a value?

11.1.1 Reducing NullPointerExceptions with defensive checking

11.1.2 Problems with null

11.1.3 What are the alternatives to null in other languages?

11.2 Introducing the Optional class

11.3 Patterns for adopting Optionals

11.3.1 Creating Optional objects

11.3.2 Extracting and transforming values from Optionals with map

11.3.3 Chaining Optional objects with flatMap

11.3.4 Manipulating a stream of optionals

11.3.5 Default actions and unwrapping an Optional

11.3.6 Combining two Optionals

11.3.7 Rejecting certain values with filter

11.4 Practical examples of using Optional

11.4.1 Wrapping a potentially null value in an Optional

11.4.2 Exceptions vs. Optional

11.4.3 Primitive optionals and why you shouldn’t use them

11.4.4 Putting it all together

Summary

12 New Date and Time API

12.1 LocalDate, LocalTime, LocalDateTime, 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 Using time zones

12.3.2 Fixed offset from UTC/Greenwich

12.3.3 Using alternative calendar systems

Summary

13 Default methods

13.1 Evolving APIs

13.1.1 API version 1

13.1.2 API version 2

13.2 Default methods in a nutshell

13.3 Usage patterns for default methods

13.3.1 Optional methods

13.3.2 Multiple inheritance of behavior

13.4 Resolution rules

13.4.1 Three resolution rules to know

13.4.2 Most specific default-providing interface wins

13.4.3 Conflicts and explicit disambiguation

13.4.4 Diamond problem

Summary

14 The Java Module System

14.1 The driving force: reasoning about software

14.1.1 Separation of concerns

14.1.2 Information hiding

14.1.3 Java software

14.2 Why the Java Module System was designed

14.2.1 Modularity limitations

14.2.2 Monolithic JDK

14.2.3 Comparison with OSGi

14.3 Java modules: the big picture

14.4 Developing an application with the Java Module System

14.4.1 Setting up an application

14.4.2 Fine-grained and coarse-grained modularization

14.4.3 Java Module System basics

14.5 Working with several modules

14.5.1 The exports clause

14.5.2 The requires clause

14.5.3 Naming

14.6 Compiling and packaging

14.7 Automatic modules

14.8 Module declaration and clauses

14.8.1 requires

14.8.2 exports

14.8.3 requires transitive

14.8.4 exports to

14.8.5 open and opens

14.8.6 uses and provides

14.9 A bigger example and where to learn more

Summary

Part 5: Enhanced Java concurrency

15 Concepts behind CompletableFuture and reactive programming

15.1 Evolving Java support for expressing concurrency

15.1.1 Threads and higher-level abstractions

15.1.2 Executors and thread pools

15.1.3 Other abstractions of threads: non-nested with method calls

15.1.4 What do you want from threads?

15.2 Synchronous and asynchronous APIs

15.2.1 Future-style API

15.2.2 Reactive-style API

15.2.3 Sleeping (and other blocking operations) considered harmful

15.2.4 Reality check

15.2.5 How do exceptions work with asynchronous APIs?

15.3 The box-and-channel model

15.4 CompletableFuture and combinators for concurrency

15.5 Publish-subscribe and reactive programming

15.5.1 Example use for summing two flows

15.5.2 Backpressure

15.5.3 A simple form of real backpressure

15.6 Reactive systems vs. reactive programming

15.7 Road map

Summary

16 CompletableFuture: composable asynchronous programming

16.1 Simple use of Futures

16.1.1 Understanding Futures and their limitations

16.1.2 Using CompletableFutures to build an asynchronous application

16.2 Implementing an asynchronous API

16.2.1 Converting a synchronous method into an asynchronous one

16.2.2 Dealing with errors

16.3 Making your code nonblocking

16.3.1 Parallelizing requests using a parallel Stream

16.3.2 Making asynchronous requests with CompletableFutures

16.3.3 Looking for the solution that scales better

16.3.4 Using a custom Executor

16.4 Pipelining asynchronous tasks

16.4.1 Implementing a discount service

16.4.2 Using the Discount service

16.4.3 Composing synchronous and asynchronous operations

16.4.4 Combining two CompletableFutures: dependent and independent

16.4.5 Reflecting on Future vs. CompletableFuture

16.4.6 Using timeouts effectively

16.5 Reacting to a CompletableFuture completion

16.5.1 Refactoring the best-price-finder application

16.5.2 Putting it all together

16.6 Road map

Summary

17 Reactive programming

17.1 The Reactive Manifesto

17.1.1 Reactive at application level

17.1.2 Reactive at system level

17.2 Reactive streams and the Flow API

17.2.1 Introducing the Flow class

17.2.2 Creating your first reactive application

17.2.3 Transforming data with a Processor

17.2.4 Why doesn’t Java provide an implementation of the Flow API?

17.3 Using the reactive library RxJava

17.3.1 Creating and using an Observable

17.3.2 Transforming and combining Observables

Summary

Part 6: Functional programming and future Java evolution

18 Thinking functionally

18.1 Implementing and maintaining systems

18.1.1 Shared mutable data

18.1.2 Declarative programming

18.1.3 Why functional programming?

18.2 What’s functional programming?

18.2.1 Functional-style Java

18.2.2 Referential transparency

18.2.3 Object-oriented vs. functional-style programming

18.2.4 Functional style in practice

18.3 Recursion vs. iteration

Summary

19 Functional programming techniques

19.1 Functions everywhere

19.1.1 Higher-order functions

19.1.2 Currying

19.2 Persistent data structures

19.2.1 Destructive updates vs. functional

19.2.2 Another example with Trees

19.2.3 Using a functional approach

19.3 Lazy evaluation with streams

19.3.1 Self-defining stream

19.3.2 Your own lazy list

19.4 Pattern matching

19.4.1 Visitor design pattern

19.4.2 Pattern matching to the rescue

19.5 Miscellany

19.5.1 Caching or memoization

19.5.2 What does "Return the same object" mean?

19.5.3 Combinators

Summary

20 Blending OOP and FP: Comparing Java and Scala

20.1 Introduction to Scala

20.1.1 Hello beer

20.1.2 Basic data structures: List, Set, Map, Tuple, Stream, Option

20.2 Functions

20.2.1 First-class functions in Scala

20.2.2 Anonymous functions and closures

20.2.3 Currying

20.3 Classes and traits

20.3.1 Less verbosity with Scala classes

20.3.2 Scala traits vs. Java interfaces

Summary

21 Conclusions and where next for Java

21.1 Review of Java 8 features

21.1.1 Behavior parameterization (lambdas and method references)

21.1.2 Streams

21.1.3 CompletableFuture

21.1.4 Optional

21.1.5 Flow API

21.1.6 Default methods

21.2 The Java 9 module system

21.3 Java 10 local variable type inference

21.4 What’s ahead for Java?

21.4.1 Declaration-site variance

21.4.2 Pattern matching

21.4.3 Richer forms of generics

21.4.4 Deeper support for immutability

21.4.5 Value types

21.5 Moving Java forward faster

21.6 The final word

Appendixes

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

What's inside

Thoroughly revised edition of Manning’s bestselling Java 8 in Action
New features in Java 8, Java 9, and beyond
Streaming data and reactive programming
The Java Module System

About the reader

Written for developers familiar with core Java features.

About the authors

Raoul-Gabriel Urma is CEO of Cambridge Spark. Mario Fusco is a senior software engineer at Red Hat. Alan Mycroft is a University of Cambridge computer science professor; he cofounded the Raspberry Pi Foundation.

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