Reactive Programming with RxPy

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What Does Reactive Programming Really Means?

Why Reactive Programming?

Why Reactive Programming?

Why Reactive Programming?

How Does Reactive Programming Works?

Observable and Observer onNext() onComplete() onError()

Observables source = Observable.from_list([1,2,3,4,5,6])

Observers source.subscribe(lambda value:print("Received {0}".format(value)))

Subjects mySubject = subject.on_next([1,2,3,4,5,6]) mySubject.subscribe(send_response, on_error)

Data Operators in Reactive Programming • Creating Observables • Transforming Observables • Filtering Observables • Combining Observables • Error Handling Operators • Observable Utility Operators • Conditional and Boolean Operators • Mathematical and Aggregate Operators • Connectable Observable Operators

Observable.just(a,b,c,d)

Observable.filter( lambda text: not text or len(text) > 2 )

Subject.flat_map( lambda value: Observable.from_future( self.get_value(value) ) )

Subject.do_action( lambda x: send_response('clear') )

user_input.combine_latest( interval, lambda input_value, i:input_value )

Advantages of Reactive Programming •Asynchronous •Error Handling •Multiple Subscribers •Functional programming

Trying it out self.subject = Subject() user_input = self.subject.throttle_last( 1000 ).start_with("").filter( lambda text: not text or len(text) > 2 )

Trying it out self.combine_latest_sbs = user_input.combine_latest( interval, lambda input_val, i: input_val ).do_action( lambda x: send_response('clear') ).flat_map( self.get_data ).subscribe(send_response, on_error)

Conclusions

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