RxJava反应式编程

ForewordIntroduction1. Reactive Programming with RxJava Reactive Programming and RxJava When You Need Reactive Programming How RxJava Works Push versus Pull Async versus Sync Concurrency and Parallelism Lazy versus Eager Duality Cardinality Mechanical Sympathy: Blocking versus Nonblocking I/O Reactive Abstraction2. Reactive Extensions Anatomy of rx.Observable Subscribing to Notifications from Observable Capturing All Notifications by Using Observer Controlling Listeners by Using Subscription and Subscriber Creating Observables Mastering Observable.create0 Infinite Streams Timing: timer() and interval() Hot and Cold Observables Use Case: From Callback API to Observable Stream Manually Managing Subscribers rx.subjects.Subject ConnectableObservable Single Subscription with publishO.refCountO ConnectableObservable Lifecycle Summary3. Operators and Transformations Core Operators: Mapping and Filtering 1-to-1 Transformations Using map() Wrapping Up Using flatMap0 Postponing Events Using the delay() Operator Order of Events After flatMap0 Preserving Order Using concatMap0 More Than One Observable Treating Several Observables as One Using merge() Pairwise Composing Using zip() and zipWith() When Streams Are Not Synchronized with One Another: combineLatest(), withLatestFrom(), and amb() Advanced Operators: collect(), reduce(), scan(), distinct(), and groupBy() Scanning Through the Sequence with Scan and Reduce Reduction with Mutable Accumulator: collect() Asserting Observable Has Exactly One Item Using single() Dropping Duplicates Using distinct() and distinctUntilChanged() Slicing and Dicing Using skip(), takeWhile(), and Others Ways of Combining Streams: concat(), merge(), and switchOnNext() Criteria-Based Splitting of Stream Using groupBy0 Where to Go from Here
Writing Customer Operators Reusing Operators Using compose() Implementing Advanced Operators Using lift() Summary4. Applying Reactive Programming to Existing Applications From Collections to Observables BlockingObservable: Exiting the Reactive World Embracing Laziness Composing Observables Lazy paging and concatenation Imperative Concurrency flatMap0 as Asynchronous Chaining Operator Replacing Callbacks with Streams Polling Periodically for Changes Multithreading in RxJava What Is a Scheduler
Declarative Subscription with subscribeOn() subscribeOn0 Concurrency and Behavior Batching Requests Using groupBy() Declarative Concurrency with observeOn() Other Uses for Schedulers Summary5. Reactive from Top to Bottom Beating the C1Ok Problem Traditional Thread-Based HTTP Servers Nonblocking HTTP Server with Netty and RxNetty Benchmarking Blocking versus Reactive Server Reactive HTTP Servers Tour HTTP Client Code Nonblocking HTTP Client with RxNetty Relational Database Access NOTIFY AND LISTEN on PostgreSQL Case Study CompletableFuture and Streams A Short Introduction to CompletableFuture Interoperability with CompletableFuture Observable versus Single Creating and Consuming Single Combining Responses Using zip, merge, and concat Interoperability with Observable and CompletableFuture When to Use Single
Summary6. Flow Control and Backpressure Flow Control Taking Periodic Samples and Throttling Buffering Events to a List Moving window Skipping Stale Events by Using debounce() Backpressure Backpressure in RxJava Built-in Backpressure Producers and Missing Backpressure Honoring the Requested Amount of Data Summary7. Testing and Troubleshooting Error Handling Where Are My Exceptions
Declarative try-catch Replacement Timing Out When Events Do Not Occur Retrying After Failures Testing and Debugging Virtual Time Schedulers in Unit Testing Unit Testing Monitoring and Debugging doOn...() Callbacks Measuring and Monitoring Summary8. Case Studies Android Development with RxJava Avoiding Memory Leaks in Activities Retrofit with Native RxJava Support Schedulers in Android UI Events as Streams Managing Failures with Hystrix The First Steps with Hystrix Nonblocking Commands with HystrixObservableCommand Bulkhead Pattern and Fail-Fast Batching and Collapsing Commands Monitoring and Dashboards Querying NoSQL Databases Couchbase Client API MongoDB Client API Camel Integration Consuming Files with Camel Receiving Messages from Kafka Java 8 Streams and CompletableFuture Usefulness of Parallel Streams Choosing the Appropriate Concurrency Abstraction When to Choose Observable
Memory Consumption and Leaks Operators Consuming Uncontrolled Amounts of Memory Summary9. Future Directions Reactive Streams Observable and Flowable Performance MigrationA. More HTTP Server ExamplesB. A Decision Tree of Observable OperatorsIndex