Functional reactive programming

Functional Reactive
Programming
“It is NOT the strongest of the species that
survives, nor the most intelligent that survives. It
is the one that is the most adaptable to change.”
- Charles Darwin
Araf Karsh Hamid

ARCHITECTURE STYLES
• Hexagonal Architecture
• Onion Architecture

Services
for UI
Hexagonal Architecture
Ports & Adapters
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Ports
File
system Database
Product
JPARepository
Implementation
Adapters
OrderProcessing
Domain Service
(Business Rules)
Implementation
Domain
Models
Domain Layer
Source : https://skillsmatter.com/skillscasts/5744-decoupling-from-asp-net-hexagonal-architectures-in-net
Ports and Adapters architecture style (Hexagonal
Architecture) is a variation of layered architecture style
which makes clear the separation between the Domain
Model which contains the rules of theApplication and
the adapters, which abstract the inputs to the system
and our outputs.
The advantage is the Application is decoupled from
the nature of input / output device and any
framework used to implement them.
For Eg.,When a client makes a RESTAPI request the
Adapter receives the HTTP Request and transforms
that into a call into the Domain Layer and marshals the
response back out to the client over HTTP. Similarly if
the app needs to retrieve persisted Entity to initialize
the domain it calls out to an Adapter that wraps the
access to the DB.
Order Data
Validation
OrderService
REST Service
Implementation
OrderProcessing
Interface
p
The layer between the Adapter and the Domain is identified as the Ports layer.
The Domain is inside the port, adapters for external entities are on the outside of
the port.
The notion of a “port” invokes the OS idea that any device that adheres to a known
protocol can be plugged into a port. Similarly many adapters may use the Ports.
Product,
ProductRepository
Interface
p
A
A
External
Apps
A
A A
Others
A
AOrderService
Interface
p
Web
Services
Data
Store
Use Case
Boundary
A

Traditional Architecture Vs. Onion Architecture + DDD
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Presentation
Business Logic (BLL)
Data Access (DAL)
Infrastructure
Others
File
system
User Interface
G
Application Services
M
Domain Services
Domain
Model
Services
Database
Order
ViewController
Product
JPARepository
Implementation
Product,
ProductRepository
Interface
OrderProcessing
DomainService
Interface
OrderProcessing
DomainService
Implementation
Source: Onion Architecture
By Jeffrey Palermo, Ex Microsoft
• Interface Driven
• Dependency Injection
• Auto wiring
• Adaptable to future
changes / enhancements
UserName
Available
Order Data
Validation
Order
Discount
Validation

Functional Reactive Programming
Reactive (Principles)
Responsive, Resilient,
Elastic,
Message-Driven
Micro Services (Strategy)
Bounded Context (DDD)
Event Sourcing, CQRS
Eventual Consistency

Functional Reactive Programming
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ResilientElastic
Message – Driven
1. A responsive, maintainable &
Extensible application is the
goal.
2. A responsive application is both
scalable (Elastic) and resilient.
3. Responsiveness is impossible to
achieve without both scalability
and resilience.
4. A Message-Driven architecture
is the foundation of scalable,
resilient, and ultimately
responsive systems.
Value
Means
Form
Principles What it means?
Responsive thus React to users demand
Resilient thus React to errors and failures
Elastic thus React to load
Message-Driven thus React to events and messages
Source: http://reactivex.io/
Responsive Maintainable Extensible

1.Containment of
1.Failures
2.Implementation Details
3.Responsibility
2.Shared Nothing Architecture, Clear
Boundaries
3.Micro Services: Single Responsibility Principle
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Functional Reactive Programming : Result = Decoupling

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Functional Reactive Programming : Design Patterns
Single
Component Pattern
A Component shall do ONLY one thing,
But do it in FULL.
Single Responsibility Principle By DeMarco : Structured
Analysis & System Specification (Yourdon, New York, 1979)
Let-It-Crash
Pattern
Prefer a FULL component restart to
complex internal failure handling.
Candea & Fox: Crash-Only Software (USENIX HotOS IX, 2003)
Popularized by Netflix Chaos Monkey. Erlang Philosophy
Saga
Pattern
Divide long-lived distributed
transactions into quick local ones with
compensating actions for recovery.
Pet Helland: Life Beyond Distributed Transactions CIDR 2007

4 Building Blocks of RxJava
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Observer Listens for emitted values [ Receiver ]
Observable Source of Data Stream [ Sender ]
Client Server
Request
Response
Traditional Synchronous Pull
Communications
1. The Observer subscribes (listens) to the Observable
2. Observer react to what ever item or sequence of items the Observable emits.
3. Many Observers can subscribe to the same Observable
Observer Observableon Next
on Completed
on Error
Time
Subscribes
Non Blocking
Time
1
2

4 Building Blocks of RxJava
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Schedulers
Operators
Content Filtering
Time Filtering
Transformation
Schedulers are used to manage and control
concurrency.
1. observeOn: Thread Observable is executed
2. subscribeOn: Thread subscribe is executed
Operators that let you
Transform, Combine,
Manipulate, and work
with the sequence of
items emitted by
Observables
3
4

Observable Design Pattern (Marble Diagram)
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• An Observer subscribes to an Observable.
• Then that observer reacts to whatever item or sequence of items the
Observable emits.
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Source: http://reactivex.io/RxJava/javadoc/index.html?rx/Observable.html | http://rxmarbles.com

Observable / Observer Design Pattern
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• Allows for Concurrent Operations: the
observer does not need to block while
waiting for the observable to emit values
• Observer waits to receive values when
the observable is ready to emit them
• Based on push rather than pull
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Source: http://reactivex.io/RxJava/javadoc/index.html?rx/Observable.html

What’s missing from GOF Observer Pattern
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Source:http://reactivex.io/intro.html
1.The ability for the producer to signal to the consumer that there
is no more data available (a foreach loop on an Iterable
completes and returns normally in such a case; an Observable
calls its observer’s onCompleted method)
2.The ability for the producer to signal to the consumer that an
error has occurred (an Iterable throws an exception if an error
takes place during iteration; an Observable calls its
observer’s onError method)
3.Multiple Thread Implementations and hiding those details.
4.Dozens of Operators to handle data.

Compare Iterable Vs. Observable
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Observable is the asynchronous / push
dual to the synchronous pull Iterable
• Composable: Easily chained together or
combined
• Flexible: Can be used to emit:
• A scalar value (network result)
• Sequence (items in a list)
• Infinite streams (weather sensor)
• Free from callback hell: Easy to transform
one asynchronous stream into another
Observables are:
Event
Iterable
(Pull)
Observable
(Push)
Retrieve Data T next() onNext(T)
Discover Error
throws
Exception
onError
(Exception)
Complete !hasNext() onComplete()
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Comparison : Iterable / Streams / Observable
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First Class Visitor (Consumer)
Serial Operations
Parallel Streams (10x Speed)
Still On Next, On Complete and
On Error are Serial Operations
Completely Asynchronous
Operations
Java 8 – Blocking CallJava 6 – Blocking Call Rx Java - Freedom

Observer<T> Contract
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Methods:
• onNext(T)
• onError(Throwable T)
• onCompleted()
onError / onCompleted called exactly
once
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X
|
Time Line

RxJava Scheduler Details
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Source: http://reactivex.io/documentation/scheduler.html
• If you want to introduce multithreading into your cascade of
Observable operators, you can do so by instructing those operators
(or particular Observables) to operate on particular Schedulers.
• By default, an Observable and the chain of operators that you apply
to it will do its work, and will notify its observers, on the same thread
on which its Subscribe method is called.
• The SubscribeOn operator changes this behavior by specifying a
different Scheduler on which the Observable should operate.
TheObserveOn operator specifies a different Scheduler that the
Observable will use to send notifications to its observers.
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RxJava Scheduler Threading Details
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Source: http://reactivex.io/documentation/scheduler.html
Scheduler Purpose
1 Schedulers.computation ( ) Meant for computational work such as event-loops and callback
processing; do not use this scheduler for I/O.
(useSchedulers.io( ) instead)
The number of threads, by default, is equal to the number of processors
2 Schedulers.from(executor) Uses the specified Executor as a Scheduler
3 Schedulers.immediate ( ) Schedules work to begin immediately in the current thread
4 Schedulers.io ( ) Meant for I/O-bound work such as asynchronous performance of
blocking I/O, this scheduler is backed by a thread-pool that will grow as
needed; for ordinary computational work, switch to
Schedulers.computation( );
Schedulers.io( ) by default is a CachedThreadScheduler, which is
something like a new thread scheduler with thread caching
5 Schedulers.newThread ( ) Creates a new thread for each unit of work
6 Schedulers.trampoline ( ) Queues work to begin on the current thread after any already-queued
work
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RxJava Operator : Frequently used
Content Filtering
1 Observable<T> filter (Func1<T, Boolean> Predicate)
2 Observable<T> skip (int num)
3 Observable<T> take (int num)
4 Observable<T> takeLast (int num)
5 Observable<T> elementAt (int index)
6 Observable<T> distinct ()
7 Observable<T> distinctUntilChanged ()
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Time Filtering
1 Observable<T> throttleFirst (long duration, TimeUnit unit)
2 Observable<T> throttleLast (long duration, TimeUnit unit)
3 Observable<T> timeout (long duration, TimeUnit unit)
Transformation Use
1 Observable<T> map (Func1<T, R> func) Transforms Objects
2 Observable<T> flatMap (Func1<T, Observable<R>> func) Transforms an Observable to another Observable
3 Observable<T> cast (Class<R> klass)
4 Observable<GroupedObservable<K, T>> groupBy (Func1<T, K> keySelector)
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The Problem? How Rx Helps!
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Apple Basket
Fruit Processor
Scenario
1. A Basket Full of Apples
2. Fruit Processor capacity (limit) is 3 Apples at a time.
The Problem
1. I don’t have time to wait till the Fruit Processor finishes it’s job.
2. I don’t mind having multiple Fruit Processors too. But that still
doesn’t solve the problem
What I need
1. I want to start the Process and leave.
2. Once the processing is done, then the system should inform me.
3. If something goes wrong, then the system should inform me.
Me = the calling program

RxJava : Examples – Handling Fruit Processor
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Completely Asynchronous
Operations
Rx Java - Freedom
1. Abstract Fruit
2. Fruit (Interface)
3. Apple
4. Orange
5. Grapes
6. Mixed Fruit
(Aggregate Root)
Entities
1. Fruit Processor (Domain
Service) - Observer
2. Fruit Basket Repository
3. Fruit Basket Observable
Factory
Business Layer
Next Section focuses more on Operators and how to handle
business logic in Asynchronous world!
1. Rx Java Example : Observable / Observer / Scheduler
Implementation
2. Rx Java Example 2 : Merge / Filters (on Weight) / Sort (on Price)
Source Code GIT: https://meta-magic.github.io/rxjava/

RxJava : Entities & Repositories
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Fruit Interface Abstract Fruit Fruit Repository

RxJava : Entities
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Apple Orange Grapes

RxJava : Apple Fruit Basket : Observable
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1. This function calls the Apple Basket Repository
function to load the data.
2. Observable is created using the collection from
the data returned by the Repository
3. Call method checks if the Observer is
subscribed
4. If Yes for Each Apple it calls the Observer to do
the action = observer.onNext (fruit)
5. Once the process is completed
6. Observable will call the on Completed method
in Observer.
7. If an error occurs then Observable will call the
on Error method in Observer
Fruit Basket Observable

RxJava : Fruit Processor : Observer
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1. Fruit Processor implements Subscriber
(Observer) interface
2. On Next Method Implementation. Main
Business logic is done over here. Observable
will call on Next and pass the Fruit Object for
processing.
3. On Complete Method. Observable will call this
method once the processing is done.
4. If Any error occurred then Observable will call
on Error method and pass on the Exception.
5. call() implements the Func1 for filtering. Weight
is passed as a parameter in Fruit Processor
Constructor.
FruitProcessorObserver
Fruit Processor Handles the Business Logic

26
Bringing all the
pieces together
Apple Basket
Fruit ProcessorObservable / Observer / Schedulers / Operators

RxJava Operator : Merge 3 Streams
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Objective:
Merge Three Data Streams (Apple Fruit Basket, Orange Fruit Basket & Grapes Fruit Basket) into a Single Stream and
do the Processing on that new Stream Asynchronously
A1 is the Apple Series, O1 is the Orange Series & G1 is the Grapes Series
Rx Example 2

RxJava Operator : Merge / Filter Streams
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Objective:
From the previous three Merged streams Filter Fruits which weighs more than 50 grams.
Rx Example 2

RxJava Operator : Merge / Filter / Sort
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Objective:
From the previous three Merged streams Filter Fruits which weighs more than 50 grams and sort on Price (Asc).
Rx Example 2
Don’t Use
toBlocking()
in Production
Code

RxJava Operator : Filter / Sort / FlatMap
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Objective:
toSortedList() returns an Observable with a single List containing Fruits.
Using FlatMap to Transform Observable <List> to Observable <Fruit>
Rx Example 2

The Problem? How Rx Helps!
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Scenario – Three Different Data streams
1. Movie database, Movie Reviews, Movie Trivia
2. Based on the Recommendation Engine combine all the three above and
send the final result to end-user / customer.
The Problem / Rule
1. No Synchronous Calls allowed
What I need
1. Movie Suggestion based on recommendation Engine
2. Filter the suggested movies based on the User filter (Movie Ratings)
3. Once the movies are identified, combine all the other related info and
Zip (Movies, Reviews & Trivia) it into a Single Entity (Movie Titles) and
send the collection back to the caller.

Movie Example
RxJava Operator : Filter / Sort / FlatMap
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Objective:
toSortedList() returns an Observable with a single List containing Movies. Using
FlatMap to Transform Observable <List> to Observable <MovieTitle>

RxJava Operator Details
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Creating Observables
Operators that originate new Observables.
• Create — create an Observable from scratch by calling observer methods programmatically
• Defer — do not create the Observable until the observer subscribes, and create a fresh
Observable for each observer
• Empty/Never/Throw — create Observables that have very precise and limited behavior
• From — convert some other object or data structure into an Observable
• Interval — create an Observable that emits a sequence of integers spaced by a particular
time interval
• Just — convert an object or a set of objects into an Observable that emits that or those
objects
• Range — create an Observable that emits a range of sequential integers
• Repeat — create an Observable that emits a particular item or sequence of items repeatedly
• Start — create an Observable that emits the return value of a function
• Timer — create an Observable that emits a single item after a given delay

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Transforming Observables
Operators that transform items that are emitted by an Observable.
• Buffer — periodically gather items from an Observable into bundles and emit these
bundles rather than emitting the items one at a time
• FlatMap — transform the items emitted by an Observable into Observables, then flatten
the emissions from those into a single Observable
• GroupBy — divide an Observable into a set of Observables that each emit a different group
of items from the original Observable, organized by key
• Map — transform the items emitted by an Observable by applying a function to each item
• Scan — apply a function to each item emitted by an Observable, sequentially, and emit
each successive value
• Window — periodically subdivide items from an Observable into Observable windows and
emit these windows rather than emitting the items one at a time

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Filtering Observables
Operators that selectively emit items from a source Observable.
• Debounce — only emit an item from an Observable if a particular timespan has passed without it
emitting another item
• Distinct — suppress duplicate items emitted by an Observable
• ElementAt — emit only item n emitted by an Observable
• Filter — emit only those items from an Observable that pass a predicate test
• First — emit only the first item, or the first item that meets a condition, from an Observable
• IgnoreElements — do not emit any items from an Observable but mirror its termination notification
• Last — emit only the last item emitted by an Observable
• Sample — emit the most recent item emitted by an Observable within periodic time intervals
• Skip — suppress the first n items emitted by an Observable
• SkipLast — suppress the last n items emitted by an Observable
• Take — emit only the first n items emitted by an Observable
• TakeLast — emit only the last n items emitted by an Observable

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Combining Observables
Operators that work with multiple source Observables to create a single Observable
• And/Then/When — combine sets of items emitted by two or more Observables by means of Pattern
and Plan intermediaries
• CombineLatest — when an item is emitted by either of two Observables, combine the latest item
emitted by each Observable via a specified function and emit items based on the results of this
function
• Join — combine items emitted by two Observables whenever an item from one Observable is
emitted during a time window defined according to an item emitted by the other Observable
• Merge — combine multiple Observables into one by merging their emissions
• StartWith — emit a specified sequence of items before beginning to emit the items from the source
Observable
• Switch — convert an Observable that emits Observables into a single Observable that emits the
items emitted by the most-recently-emitted of those Observables
• Zip — combine the emissions of multiple Observables together via a specified function and emit
single items for each combination based on the results of this function

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Observable Utility Operators
A toolbox of useful Operators for working with Observables
• Delay — shift the emissions from an Observable forward in time by a particular amount
• Do — register an action to take upon a variety of Observable lifecycle events
• Materialize/Dematerialize — represent both the items emitted and the notifications sent as
emitted items, or reverse this process
• ObserveOn — specify the scheduler on which an observer will observe this Observable
• Serialize — force an Observable to make serialized calls and to be well-behaved
• Subscribe — operate upon the emissions and notifications from an Observable
• SubscribeOn — specify the scheduler an Observable should use when it is subscribed to
• TimeInterval — convert an Observable that emits items into one that emits indications of the
amount of time elapsed between those emissions
• Timeout — mirror the source Observable, but issue an error notification if a particular period of
time elapses without any emitted items
• Timestamp — attach a timestamp to each item emitted by an Observable
• Using — create a disposable resource that has the same lifespan as the Observable

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Conditional and Boolean Operators
Operators that evaluate one or more Observables or items emitted by Observables
• All — determine whether all items emitted by an Observable meet some criteria
• Amb — given two or more source Observables, emit all of the items from only the first of these
Observables to emit an item
• Contains — determine whether an Observable emits a particular item or not
• DefaultIfEmpty — emit items from the source Observable, or a default item if the source
Observable emits nothing
• SequenceEqual — determine whether two Observables emit the same sequence of items
• SkipUntil — discard items emitted by an Observable until a second Observable emits an item
• SkipWhile — discard items emitted by an Observable until a specified condition becomes false
• TakeUntil — discard items emitted by an Observable after a second Observable emits an item or
terminates
• TakeWhile — discard items emitted by an Observable after a specified condition becomes false

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Mathematical and Aggregate Operators
Operators that operate on the entire sequence of items emitted by an Observable
• Average — calculates the average of numbers emitted by an Observable and emits this average
• Concat — emit the emissions from two or more Observables without interleaving them
• Count — count the number of items emitted by the source Observable and emit only this value
• Max — determine, and emit, the maximum-valued item emitted by an Observable
• Min — determine, and emit, the minimum-valued item emitted by an Observable
• Reduce — apply a function to each item emitted by an Observable, sequentially, and emit the
final value
• Sum — calculate the sum of numbers emitted by an Observable and emit this sum
Backpressure Operators
• backpressure operators — strategies for coping with Observables that produce items more
rapidly than their observers consume them

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Connectable Observable Operators
Specialty Observables that have more precisely-controlled subscription dynamics
• Connect — instruct a connectable Observable to begin emitting items to its subscribers
• Publish — convert an ordinary Observable into a connectable Observable
• RefCount — make a Connectable Observable behave like an ordinary Observable
• Replay — ensure that all observers see the same sequence of emitted items, even if they subscribe after the
Observable has begun emitting items
Operators to Convert Observables
• To — convert an Observable into another object or data structure
Error Handling Operators
Operators that help to recover from error notifications from an Observable
• Catch — recover from an onError notification by continuing the sequence without error
• Retry — if a source Observable sends an onError notification, re-subscribe to it in the hopes that it will
complete without error

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Thank You
Araf Karsh Hamid
email : araf.karsh@ozazo.com | araf.karsh@metamagic.in
Social : arafkarsh | Skype, Facebook, LinkedIn, Twitter, G+
Phone : +91.999.545.8627 | http://www.metamagic.in/

Functional reactive programming

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