Modern Java in Action
Lambda, streams, functional and reactive programming
This is an excellent introduction to the newest features in Java 8 and 9. It provides clear and concise examples to help clarify how to use Java's newest features such as streams, lambda functions, and reactive streams.
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Manning's bestselling Java 8 book has been revised for Java 9! In Modern Java in Action, you'll build on your existing Java language skills with the newest features and techniques. After a practical introduction to lambdas using real-world Java code, you'll dive into the Streams API. Next, you'll discover event-driven reactive programming and see how the Java Module System (aka Jigsaw) will help change how you structure your code. This book also explains functional programming in Java, working with collections, and more.
Table of Contents takes you straight to the bookdetailed table of contents
Part 1: Fundamentals
1 Java 8, 9 and 10: what’s happening?
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 and Java modules
1.5 Other good ideas from functional programming
1.6 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
2.5 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
3.10 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 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 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
6.7 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
7.4 Summary
Part 3: Effective programming with streams and lambdas
8 Collection API Enhancements
8.1 Collection factories
8.1.1 Creating collections
8.2 Working with List and Set
8.3 Working with Map
8.4 Improved ConcurrentHashMap
8.5 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
9.5 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 Streams 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 Nested functions
10.3.3 Function sequencing with lambda expressions
10.3.4 Putting it all together
10.3.5 Using method references in DSL
10.4 Real World Java 8 DSL
10.4.1 jOOQ
10.4.2 Cucumber
10.4.3 Spring Integration
10.5 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 Optional
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 Putting it all together
11.5 Summary
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 Using time zones
12.3.2 Fixed offset from UTC/Greenwich
12.3.3 Using alternative calendar systems
12.4 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
13.5 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
14.10 Summary
Part 5: Enhanced Java Concurrency
15 Enhanced Java Concurrency: 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 we want from threads?
15.2 Synchronous and Asynchronous APIs
15.2.1 Sleeping (and other blocking operations) considered harmful
15.2.2 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
15.6 Reactive Systems versus Reactive Programming
15.7 Summary
16 CompletableFuture: composable asynchronous programming
16.1 Simple use of Futures
16.1.1 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 Make your code non-blocking
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 Effectively using timeouts
16.5 Reacting to a CompletableFuture completion
16.5.1 Refactoring the best-price-finder application
16.5.2 Putting it to work
16.6 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 class Flow
17.2.2 Our 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 a reactive library: RxJava
17.3.1 Creating and using an Observable
17.3.2 Transforming and combining Observables
17.4 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
18.4 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
19.6 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
20.4 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 Review of 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
About the Technology
The release of Java 9 builds on what made Java 8 so exciting. In addition to Java 8's lambdas and streams, Java 9 adds a host of new features of its own. It includes new library features to support reactive programming, which give you new ways of thinking about programming and writing code that is easier to read and maintain. Java 9 also introduces the long-awaited Java Module System. Modules encourage you to write your code in smaller units that are easier to test, manage and release. Java 9 also helps programmers by enriching the functional-programming and streams features of Java 8.What's inside
- All of Java 9's new changes and features
- Lambda expressions
- Data processing with streams
- Testing and debugging with lambdas
- Reactive programming in Java
- The Java Module System
- Practical design with functional programming
About the authors
Raoul-Gabriel Urma is CEO and a co-founder of Cambridge Spark, a leading learning community for data scientists and developers in UK. Mario Fusco is a senior software engineer at Red Hat working at the development of the core of Drools, the JBoss rule engine. Alan Mycroft is a Professor of Computing at Cambridge and cofounder of the Raspberry Pi Foundation.
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