Introduction to Functional Programming and usage of basic constructs in Java 7 using Guava. Further, the session introduces Reactive Systems architecture and design.

1. Beyond null & more
Taming the monsters, the right way!
jayas@cisco.com

2. NullPointerException !!!

3. It must be some kind of race condition!!!

6. Functional and Reactive
Programming 101
for Java 7 Developers

7. What will you get?
● Get started with basic
Functional Programming
principles
● Reactive System Design
● Adopt in your day to day
work from now!

8. Functional Programming Intro
● Function
○ y = f(x)
● Functions as ‘First Class’ Citizens
○ Similar to values
○ Pass to / return from other functions
● Separate Data from Functions
○ Data is passed to functions
○ Data may be returned from functions
○ State Management outside of Functions
Programming with Functions

9. Functional Programming Facets
● Pure Functions
○ No Side Effects
○ Referential Transparency
● Immutable Data Structures
● Expressive Power
○ Ability to Reason - Equational Reasoning
○ Function Composition
■ f : b -> c , g : a -> b
■ f o g : a -> c
● f o g = f(g(x))

10. Pure Functions
● No side effects , including IO
○ For a set of inputs, always produce the same output
● Referential Transparency
○ Can ‘inline’ the function everywhere with no changes to the
behavior
○ Works with substitution model of computation
● Ensures Testability

11. Pure Functions
Is this a pure function?
func increment(int value ) {
return value + 1;
}

12. Pure Functions
How about this?
func increment(int value )
{
if(value < maxVal) {
return value + 1;
} else {
return maxVal;
}
}
func increment(int value, int maxVal )
{
if(value < maxVal) {
return value + 1;
} else {
return maxVal;
}
}

13. Pure Functions
..and this too?
func increment(int value ) {
int newValue = value +1;
localStorage.put(“curValue”,
newValue);
return newValue;
}
func increment(int value ) {
return {
“val” : value +1,
“task” : {
“task” : localStorage,
“key” : “curValue”,
“val” : value+1
}
};
}

14. Pure Functions
Ignore IO and let’s discuss this
func updatePwd(String pwd )
{
Node node = nodeFromExternal();
if(node.id == 2) {
invokeApi(node,pwd);
}
}
func updatePwd(String pwd , int id) {
Node node = nodeFromExternal();
if(id == node.id ) {
invokeApi(node,pwd);
}
}

15. Immutability
Reference to a data structure is *only* for reading from the data structure
Once you have a reference:
● Guarantees that no one will modify its contents
● Pass along to any number of threads without fear of ‘stepping on each
other’
○ Enables safe concurrency and parallelism
● Any updates to the data structure returns a new reference

16. Immutability : an Example
Defects (holds list of open defects)
val defectList = List(Defect(id="CSSid1234",age=48,engineer="jayas"),Defect(id="CSCid5678",age=12,engineer="otherone"))
defectList.foreach { defect =>
if (defect.age > 28) {
sendMsg(defect.engineer)
}
}
defectList.dropWhile { defect =>
defect.engineer == "jayas" }
Thread 1 Thread 2
val myDefectList =

17. Why Functional Programming
Pure Functions
● Guarantees Testability
● Easy Refactoring
● Reduce Defects
Immutable Data Structures
● Safe Concurrency and Parallelism
○ Eliminate concurrency / multi-threading defects
○ Enables lock free operations
● Ensures Testability

18. Why Functional Programming
Expressive Power
● Readability and Maintainability
● Adhering to mathematical laws and
principles
● Programming by whole values
○ Lesser Lines of Code , and thus
lesser the possibility of defects
More Info
● View “Why Functional Programming
Matters”

19. OOP

20. OO - in its Original Form
● State Encapsulation in Objects
● Message Passing for Communication
○ Objects to process messages sent to it
○ Respond by sending messages
● Not intended to use as Data
Containers/Holders

21. OO - as what we do for living
● Primarily a Data Holder
● No Message Passing - arbitrary retrieval and
update of data
● Imperative Programming to the core
○ Manipulating State
○ Instructing Computer on How to Do
● Hard to do Concurrency

22. Imperative Vs Functional
● Programming By Word Vs Whole Values
○ No word at a time processing
○ Work with entire value (data structures)
● How to Do Vs What To Do
○ Focus on business logic implementation
○ E.g: No instructions on how to traverse a list
● Less Code that Does more
○ Reduce Lines of Code
○ Reduce Possibility of Defects
for(int i=0; i< j; i++)
{
....
}

23. Whole Value Programming
map (A ->B) List<A> -> List<B>
● List of agents -> List of agent UserIds
flatMap (A -> List<B>) List<A> -> List<B>
● List of agents -> List of extension numbers
filter (A->Boolean) List<A> -> List<A>
● Get all supervisors from user list
foldl (acc x -> y) x List<x> -> y
● Total talk time from a list of call data records
Do a map, filter and fold it!

24. map a list of agents to list of strings (using Guava in Java 7)
import com.google.common.base.Function;
import com.google.common.collect.Collections2;
Function<Agent, String> agentTransformer= new Function<Agent,String>() {
@Override
public String apply(Agent agent) {
return agent.getId();
}
};
List<String> agentIds = new ArrayList<String>(Collections2.transform(agents,agentTransformer));
Whole Value Programming

25. filter a list of agents to list of supervisors (using Guava in Java 7)
import com.google.common.base.Function;
import com.google.common.base.Predicate;
import com.google.common.collect.Collections2;
Predicate<Agent> supervisorFilter = new Predicate<Agent>() {
@Override
public boolean apply(Agent agent) {
return agent.role == Role.SUPERVISOR;
}
};
Collection<Agent> supervisors = Collections2.filter(agents, supervisorFilter);
List<String> supervisorIds = new ArrayList<String>(Collections2.transform(supervisors,agentTransformer));
Whole Value Programming

26. Strong Static Typing
● Type System and Compiler as First Line of defense
● Reduces dependency on Unit Tests
○ Most of the time, if it compiles, it will run
● Expressive Power By Type
○ e.g: Optional for indicating Value being present or not (vs returning null)

27. Strong Static Typing
Get rid of null, Now. (using Guava in Java 7)
import com.google.common.base.Optional;
class Agent {
Optional<String> agentAlias; // alias may not may not be set
public void setAlias(String alias) {
agentAlias = Optional.fromNullable(alias); // alias could be null, Java !!
}
public Optional<String> getAlias() {
return agentAlias; // returns Optional indicating that alias may or may not be there
}
}

28. Strong Static Typing
Indicate Error by Type, Later. ( in Scala)
def fetchDefectsFromDB() : Try[List[Defect]]= {
Try {
readFromDB// Get the list from DB
}
}
val defectList : Try[List[Defect]] = fetchDefectsFromDB()
defectList.map { defects => defects.foreach {defect => if (defect.age > 28) { sendMsg(defect.engineer)} } }
def fetchDefectsWhenDBDown() : Try[List[Defect]]= {
Failure(new Throwable(new IllegalStateException("DB Service is not in Running State)))
}

30. I am not sure.. What Can I do
Adopt these rules
● No nulls to indicate absence. Use Optional
○ Use it judiciously.
○ Do not use it purely to indicate an un-initialized internal state
○ Use it to indicate to other that data may or may not be present
● No loops . Use Collections2 utils
○ Simple for expression may be still efficient for simple iterations.
○ Use Guava if it avoids code duplication and does not cause performance overhead
● Make all method parameters final
○ Unfortunately there are no immutable collections in Java

31. I am not sure.. What Can I do
Adopt these rules
● No Explicit Multi threading / Concurrency
○ For state, in case concurrency is a need, ensure all messages to the object are processed in
sequence (yes, sequentially)
○ Use a single threaded executor to handle messages received by an object
● Question and Revisit Impure functions / methods
○ Strictly adhere to Single Responsibility Principle of OO
○ Write pure functions unless it’s unavoidable
○ It’s fine to have a class with all static methods, which are pure!
● Isolate State management and IO to one part of the system
○ Let 80 % of the implementation be pure and stateless

32. I am not sure.. What Can I do
Adopt these rules
● As Always, there are exceptions to all the rules , check with Architect.
● Performance , Maintainability and Readability are of most importance
○ Do not compromise on these

33. Learn Functional Programming
Haskell: https://github.com/data61/fp-course
Elm: https://www.elm-tutorial.org/en/
Stay Tuned for Elm training and other FP initiatives!

34. Architecture & Design
Reactive Programming

35. Corner cases are Corner Stones in Customer Experience

36. Design For Failure - Failures are Inevitable

37. Ensure testability at all levels - architecture, design, code

38. Reactive Systems Traits
Responsive
Message Driven
Resilient Elastic
Source: https://www.reactivemanifesto.org

39. Design & Architecture
Asynchronous Communication
● Message Passing across components
○ Asynchronous APIs another choice across systems
● Non Blocking IO, Lock Free Operations
○ Use Async Servlet (in Servlet 3.0) for web applications (Shindig is a perfect use case)
○ Use non blocking version of libraries, say http-client, jersey server and client
● Work on Futures and Future Compositions
○ Composition of Futures - not present by default in Java 7
○ Use ListenableFuture in Guava / CompletableFuture in Java 8
○ Evaluate and Use RxJava

40. Design & Architecture
Resiliency
● Build for failures
○ Failures are Inevitable
○ Architecture and Design should facilitate failure propagation and recovery
● Communicate Error & Recover from Failures
○ Let the user know
○ Provide error recovery option
● Retries for inter-component interactions / critical operations
● Location Transparency for distributed/ clustered systems
○ Distributed Cache usage does not rely on fetching cache entry from a particular node

41. Design & Architecture
Responsive
● Respond even when subsystems are down
○ Let the user know
● Provide degraded /lower set of functionalities than being totally unresponsive
● Have alternate option for dependent critical subsystems / external systems

42. Design & Architecture
Elastic
● Ability to scale up / down / out / in
○ Build services that caters to 400 users (CCX) , at the same time that can scale for 18000
users (CCE)
○ Utilize all the cores of CPU
○ Ability to add more machines to scale out
■ distributed data processing, location transparency for processing engines

44. Additional Learnings
Guava Wiki
RxJava
John Backus’s Turing Award Lecture
Learn Functional Programming in Haskell
Effects As Data - Talk

45. Thank You!