Why should a Java programmer shifts towards Functional Programming Paradigm

Nov 29, 20190 likes256 views

The document discusses the reasons for Java programmers to adopt the functional programming paradigm, highlighting its definition, features, and differences from imperative programming. It emphasizes concepts like immutability, pure functions, and higher-order functions, while advocating for functional programming's benefits such as improved readability, maintainability, and testability. The presentation also introduces the Optional class in Java and its advantages in avoiding null pointer exceptions.

Why should a Java programmer
shift towards
Functional Programming Paradigm
Presented By :
Rishi Khandelwal
Lead Software Consultant
Knoldus Inc

● My Journey
● Functional Programming definition
● Functional Programming Features
● Imperative vs Declarative
● Optional
● Code examples
Agenda

As per wiki -
“In computer science, functional programming is a
programming paradigm—a style of building the
structure and elements of computer programs—that
treats computation as the evaluation of
mathematical functions and avoids changing-state
and mutable data.”
Functional Programming Deﬁnition

An immutable object is one whose state cannot be modified after it is
created.
An immutable variable is one that can’t change its value.
Shared state is fine if it is immutable. Mutable state is fine if it is not
shared.
Immutability

● Returns the same output for same input values
● Has no side effects
Pure Functions

Pure lambda expression ? NO
Pure Functions

● Function as first class citizens
● Pass function as argument
● Return function from a function.
High Order Functions

Closures are nothing but stateful functions!
A few definitions from the web include:
● A closure is a combination of a function bundled together
(enclosed) with references to its surrounding state
● A closure gives you access to an outer function’s scope from an
inner function
Closures

Don’t evaluate immediately, evaluate when it is
needed.
Example:
● && , ||
● ? :
● Lambdas
Lazy evaluation

Should we use Functional
Programming in Java ?

● Easy to read and understand
● Easy to maintain
● Easy to reason
● Less error prone
● Improve testability
● Easy to parallelize
Yes, ofcourse

Imperative vs Declarative
● How | What
● Mutate | Transform
● Side effect | Pure
● Pass Objects | Pass functions
● Hard to compose | Function composition
● Eager Evaluation | Lazy evaluation
Imperative vs Declarative

● A new class introduced in java.util package
● Makes code more readable
● Null checks are not required.
● No more NullPointerException at run-time.
● No more Boilerplate code
Optional

Some useful methods:
● of()
● ofNullable()
● empty();
● ifPresent()
● isPresent()
● map()
● filter()
● flatMap()
● orElse()
Optional

rishi@knoldus.com
@rishckt
https://www.linkedin.com/in/rishckt/
Stay in touch

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This document provides an overview of functional programming concepts like functions, function composition, and monads. It discusses how functions in mathematics and Scala work, how to compose functions, and the properties of pure functions. It defines monads as classes with map, flatMap, and pure methods, and explains how monads can help handle side effects. The document also discusses monad transformers as a way to work with nested monads by "transforming" an inner monad with an outer monad.

Micro Frontends Architecture - Jitendra kumawat (Guavus)Tech Triveni

The document discusses the evolution of web applications, focusing on the transition from server-side rendering (SSR) to client-side rendering (CSR) and the emergence of micro frontends architecture. It explores the advantages and challenges of micro frontends, highlighting organizational benefits such as technology agnosticism and improved scalability. Key takeaways include the importance of consistent design, communication between micro frontend applications, and potential complexities in implementation.

Apache CarbonData+Spark to realize data convergence and Unified high performa...Tech Triveni

The document discusses Apache CarbonData, a technology for unified, high-performance data analytics using Spark. It highlights the challenges of big data, such as managing large datasets from diverse sources and the need for real-time and historical data processing. The presentation covers CarbonData's features, including its scalable data storage format, indexing capabilities for efficient querying, and its roadmap for future development and community involvement.