Reactive Programming in Java 8 with Rx-Java
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Reactive programming with Rx-Java allows building responsive systems that can handle varying workloads and failures. It promotes asynchronous and non-blocking code using observable sequences and operators. Rx-Java was created at Netflix to address issues like network chattiness and callback hell in their API. It transforms callback-based code into declarative pipelines. Key concepts are Observables that emit notifications, Operators that transform Observables, and Subscribers that receive emitted items. Rx-Java gained popularity due to its support for concurrency, error handling, and composability.
Reactive Programming in Java 8 with Rx-Java
- 1. Reactive Programming in Java 8 with Rx-Java Kasun Indrasiri Software Architect, WSO2 January 2016
- 2. Why Reactive Programming? • Why another programming paradigm? • Things are different now! • Conventional software applications won’t be able to fulfill the modern enterprise needs. • Its time to rethink about our software architecture patterns and programming techniques.
- 3. What it actually means to be Reactive? • “Readily responsive to a stimulus” - merriam-webster • Reactive Systems : • responds in a timely manner • stays responsive in the face of failure • stays responsive under varying workload • rely on asynchronous message-passing
- 4. Reactive Programming • A programming paradigm that helps you to build ‘Reactive Systems’. E.g.: Google Spreadsheet • In a reactive world, we can't just wait for a function result, a network call, or a database query to return. • Every moment we wait for something, we lose the opportunity to do other things in parallel. • Reactive programming can be done in several ways • Akka Actors • Reactive Extensions (Rx) – reactive + functional
- 5. Why Rx-Java? • The Netflix story..
- 6. The Netflix API • How to reducing network chattiness? • Granular API. • client applications to make multiple calls that need to be assembled in order to render a single user experience
- 7. The Netflix API • Discrete requests from devices, should be collapsed into a single request • Server-side concurrency is needed to effectively reduce network chattiness. • Nested, conditional, parallel execution of backend network calls.
- 8. Embrace Concurrency • Blocking calls?... Not anymore. • Server-side concurrency is a must. • Low level primitive of concurrency. • Concurrent programming is hard!
- 9. Java Futures • Represents the result of an asynchronous computation. • Can be used to retrieve the result asynchronously at a later point in time • Cons: • Retrieving value from future is a blocking call : Future.get() • Messy with Nested Futures -> complex to implement/maintain.
- 10. Callbacks • A callback is a piece of executable code that is passed as an argument to other code, which is expected to call back the argument at some convenient time.
- 11. Callbacks • Use case : • Client send a request to the Application • Application calls service A • Get the response from ServiceA and send it to service B • Get the response from ServiceB and send it to Service C • Get the response from ServiceC and send it to the Client Service A Service B Service C 1 2 3 ApplicationClient
- 12. Callbacks • Callback Hell! • Callbacks based applications get extremely complex when we have nested callbacks. • Leads to messy code, hard to troubleshoot. • Example : https://github.com/kasun04/rnd/tree/master/rx- java/src/main/java/org/kasun/rnd/rxjava/serviceinvoker/callback
- 13. Reactive Extensions - Rx • ReactiveX is a library for composing asynchronous and event-based programs by using observable sequences. • ‘The Observer pattern done right.’ • Initially implemented for C# by Microsoft, later ported to many languages by Netflix Rx. • Rx-Java is the Java implementation.
- 14. Observer Pattern • ‘Subject’ notifies the ‘observers’ when a change has happened in the subject.
- 15. Observable • Rx-Java introduces ‘Observable’ data type by extending the traditional Observer Pattern. Observers Observable
- 16. Observable vs Observer Pattern • The producer signal that there is no more data available: the • onCompleted() event • The producer can signal that an error occurred • onError() event
- 17. Observable and Subscriber • The producer signal that there is no more data available: the ObservableSubscriber subscribe onNext* onCompleted
- 18. Observable and Subscriber – In Action
- 19. Creating Observable • Observable.create() • Observable.just() • Observable.from()
- 20. Observable and Operators • Observable -> Operator -> Subscriber
- 21. Filtering Observables • Selectively emit items from a source Observable
- 22. Filtering Observables • Filtering Operators
- 23. Transforming Observables • Transform items that are emitted by an Observable
- 24. Transforming Observables • Transforming Operators
- 25. Filtering and Transforming – In Action
- 26. Transforming Observables • FlatMap • Observable.flatMap() takes the emissions of one Observable and returns the emissions of another Observable to take its place. Observable<List<String>> query(String text){...} query("Hello, world!") .flatMap(new Func1<List<String>, Observable<String>>() { @Override public Observable<String> call(List<String> urls) { return Observable.from(urls); } }) .subscribe(url -> System.out.println(url));
- 27. FlatMap – In Action
- 28. More Operators • Combining Observables • Error Handling Operators • Observable Utility Operators • Conditional and Boolean Operators http://reactivex.io/documentation/operators.html
- 29. Rx-Java in Action • Use case : Service Orchestration Source Code : https://github.com/kasun04/rnd/tree/master/rx- java/src/main/java/org/kasun/rnd/rxjava/serviceinvoker/rx/nonblocking Service A Service B Service C 1 2 3 ApplicationClient
- 30. Scheduler • Rx is single threaded by default. • Schedulers are the easiest way to bring multi-threading into your apps. • subscribeOn • observerOn
- 31. Scheduler • Schedulers.io(): meant for I/O-bound work • Schedulers.computation(): meant for computational work such as event-loops • Schedulers.newThread(): creates a new thread for each unit of work
- 32. Rx-Java in Action • Use case : Service Orchestration with Schedulers Source Code : https://github.com/kasun04/rnd/tree/master/rx- java/src/main/java/org/kasun/rnd/rxjava/serviceinvoker/rx/nonblocking Service A Service B Service C 1 2 3 ApplicationClient
- 33. Conclusion • Get rid of blocking code! • Think ‘non-blocking’ • Think reactive • Rx-Java – steep learning curve • Develop use cases • Leverage Java 8
- 34. Questions?
- 35. References • http://reactivex.io/documentation • http://blog.danlew.net/2014/09/15/grokking-rxjava- part-1/ • http://techblog.netflix.com/2013/02/rxjava-netflix- api.html • Source Code : https://github.com/kasun04/rnd/tree/master/rx-java
- 36. Thank You