Java 11 Stream Examples: Solving Real-Life Complex Problems

This repository contains a collection of real-time examples that demonstrate how Java 11 Stream API can be leveraged to solve complex problems efficiently and elegantly. The Stream API introduced in Java 8 provides a functional programming paradigm for processing data in a declarative manner, making it a powerful tool for handling various scenarios.

Why Java 11 Stream?

Java 11 Stream API allows developers to perform advanced operations on collections, such as filtering, mapping, reducing, and aggregating elements. By using streams, you can write concise and expressive code that is both readable and maintainable. Streams also provide parallel processing capabilities, enabling you to take advantage of multi-core processors and greatly improve performance for computationally intensive tasks.

Points to consider before we start

Functional programming has gained popularity and adoption in recent years due to several technical reasons. Here are some key points that highlight the advantages and motivations for embracing functional programming:

1. Growth of Computer Applications: As computer applications become more complex, managing and reasoning about their behavior becomes challenging. Functional programming provides a set of principles and techniques that help address this complexity.

2. Accidental vs. Inherent Complexity: Accidental complexity arises from the way solutions are implemented, while inherent complexity is a result of the problem domain. Functional programming focuses on minimizing accidental complexity by emphasizing simplicity and expressiveness.

3. Power and Perils of Object-Oriented and Mutable State: Object-oriented programming and mutable state can lead to issues such as race conditions, bugs due to shared state, and difficulties in reasoning about code behavior. Functional programming promotes immutability and pure functions, which mitigate these problems.

4. Emergence of Multiple Cores: With the proliferation of multi-core processors, parallel and concurrent programming has become crucial. Functional programming, with its emphasis on immutability and lack of side effects, provides a natural fit for concurrent and parallel execution.

5. What's Wrong with Mutable State: Mutable state can introduce bugs, make code harder to understand and maintain, and hinder composability. By embracing immutability, functional programming reduces these issues and promotes more robust and reliable code.

Functional Programming Concepts:

1. Immutable State: Functional programming encourages immutability, where objects and data structures cannot be modified once created. This promotes safer and easier-to-understand code.

2. Functions as First-Class Citizens: In functional programming, functions are treated as values that can be passed as parameters, returned from other functions, and stored in variables. This enables higher-order functions and greater flexibility in composing code.

3. Pure Functions: Pure functions have no side effects and produce the same output for the same input. They are easier to reason about, test, and optimize. Pure functions do not rely on external state and have referential transparency.

Benefits of No Side Effects:

1. Easier Testing: Pure functions make testing straightforward since their behavior is deterministic and isolated.

2. Idempotent: Pure functions produce the same output for the same input, regardless of the number of times they are called. This property simplifies reasoning about code.

3. Easier Understanding: Pure functions are self-contained and do not have hidden dependencies, making code easier to comprehend.

4. Referential Transparency: In functional programming, expressions can be replaced with their evaluated values without affecting the program's behavior, improving readability and optimization opportunities.

Functional Style in Coding:

1. State Transformation Instead of State Mutation: Functional programming promotes transforming state rather than mutating it. This leads to code that is more maintainable and less error-prone.

2. Imperative vs. Declarative: Imperative style focuses on how to achieve a result, while declarative style focuses on describing what should be achieved. Functional programming leans towards declarative style, making code more expressive and concise.

3. Functional Composition: Functional programming encourages composing smaller functions to build more complex ones. This enables code reuse, modularity, and the creation of higher-level abstractions.

4. Lazy Evaluation: Functional programming supports lazy evaluation, where expressions are not evaluated until their values are explicitly needed. This allows for more efficient use of resources, especially with infinite data structures.

In summary, functional programming offers technical advantages such as better handling of complexity, immutability, purity, composability, and suitability for parallel and concurrent programming. By embracing functional programming principles, developers can write more reliable, maintainable, and scalable code.