Short Excerpts

Kotlin is known for its concise, expressive syntax and developer-friendly features. However, if you’ve worked with certain Kotlin libraries, you may have encountered a Delicate API warning. This warning often leaves developers wondering: Should I ignore it? or Is it something I need to worry about?

Let’s break it down in simple terms so you can make an informed decision.

What is a Delicate API in Kotlin?

A Delicate API in Kotlin is an API that requires extra caution. It’s not necessarily unsafe, but misusing it can lead to unexpected behavior, bugs, or maintenance headaches.

When you use a Delicate API, the compiler warns you with a message like this:

fun riskyFunction() {
    GlobalScope.launch { // warning: Launching coroutine in an unsafe way
        println("Running in GlobalScope")
    }
}

In this snippet, launching a coroutine in the GlobalScope triggers the delicate API warning, reminding you to handle this coroutine with care. Actually, it creates a coroutine that lives for the application’s lifetime, which can lead to memory leaks if not handled correctly.

Why Does Kotlin Mark APIs as Delicate?

Kotlin marks certain APIs as Delicate for the following reasons:

1. Potential for Misuse — Some APIs can cause memory leaks, thread issues, or unexpected behavior if used incorrectly.
2. Encouraging Safer Alternatives — Kotlin wants developers to use best practices, and marking APIs as Delicate nudges them toward better alternatives.
3. API Evolution — Some APIs might change or be deprecated in the future, and this warning helps developers prepare for that.

Should You Ignore the Delicate API Warning?

Ignoring a Delicate API warning isn’t always a bad idea, but it depends on the situation. Let’s look at both sides:

When It’s Safe to Use

• You Fully Understand the API — If you know the risks and how to mitigate them, you can use the API confidently.
• It’s Necessary for Your Use Case — Some APIs are marked delicate but still provide valuable functionality in specific scenarios.
• You Have Proper Handling — If you ensure proper cleanup and error handling, using a Delicate API can be justified.

For example, if you’re working on a quick prototype and don’t mind the risks, using GlobalScope.launch might be acceptable. But in a production environment, you’d typically use structured concurrency instead, like viewModelScope or lifecycleScope.

When You Should Avoid

• You’re Uncertain About Its Behavior — If you’re not sure why an API is delicate, it’s best to look for alternatives.
• There’s a Recommended Alternative — Many delicate APIs have safer, preferred alternatives that avoid potential pitfalls.
• Long-Term Maintenance Matters — If your codebase is meant to be maintained by a team, using a Delicate API might create future headaches.

How to Properly Use a Delicate API

If you decide to use a Delicate API, Kotlin requires you to explicitly opt-in by adding the @OptIn annotation. Here’s how:

@OptIn(DelicateCoroutinesApi::class)
fun safeUsage() {
    GlobalScope.launch {
        println("Running in a GlobalScope coroutine")
    }
}

Alternatively, if you’re working in a file where you need multiple delicate APIs, you can opt in at the file level:

@file:OptIn(DelicateCoroutinesApi::class)

fun anotherFunction() {
    GlobalScope.launch {
        println("Using a Delicate API")
    }
}

This tells Kotlin that you acknowledge the risks and accept responsibility for using the API correctly.

Best Practices for Avoiding Issues

If you want to write safe and maintainable Kotlin code, follow these best practices:

1. Prefer Structured Concurrency — Instead of GlobalScope.launch, use CoroutineScope and viewModelScope in Android apps.
2. Read the Documentation — Understand why an API is marked delicate before using it.
3. Look for Alternatives — Kotlin often provides safer ways to achieve the same goal.
4. Use @OptIn Wisely – Only opt-in when you are confident in the API’s behavior.
5. Keep Code Maintainable — If others will work on your code, document why you chose to use this API.

Conclusion

Kotlin’s Delicate API warning isn’t there to annoy you — it’s a helpful reminder to be cautious. While you can ignore it by opting in, it’s best to understand why an API is marked delicate before doing so. If there’s a safer alternative, use it. If you must use a Delicate API, do so responsibly and document your reasoning.

Java developers have used the finalize() method for years to handle cleanup operations before an object is garbage collected. But in 2025, does the finalize() method in Java still hold any relevance? With advancements in garbage collection and alternative resource management techniques, many developers question its necessity.

In this blog post, we’ll explore the finalize() method in Java, understand its purpose, see why it has been deprecated, and examine better alternatives.

What is the Java finalize() Method?

The java finalize() method is a special method in Java that belongs to the Object class. It is called by the garbage collector before an object is destroyed. This method was originally designed to provide a mechanism for resource cleanup, such as closing file streams or releasing memory.

Syntax of finalize()

protected void finalize() throws Throwable {
    // Cleanup code
}

Since every class in Java implicitly extends Object, it can override finalize() to perform custom cleanup operations before an object is garbage collected.

How Does finalize() Work?

The java finalize() method is invoked by the Garbage Collector (GC) before an object is removed from memory. However, it is not guaranteed when (or if) it will be executed, making it unreliable for critical cleanup tasks.

class Demo {
    @Override
    protected void finalize() throws Throwable {
        System.out.println("finalize() method called");
    }
}

public class FinalizeExample {
    public static void main(String[] args) {
        Demo obj = new Demo();
        obj = null; // Make the object eligible for garbage collection
        System.gc(); // Suggest the JVM to run garbage collection
        System.out.println("End of main method");
    }
}

Here,

1. An instance of Demo is created.
2. The reference is set to null, making it eligible for garbage collection.
3. System.gc() is called to request garbage collection (not guaranteed to run immediately).
4. If the garbage collector runs, finalize() executes before the object is destroyed.

Note: When you run the code, you will see one warning as well: ‘Warning: [removal] finalize() in Object has been deprecated and marked for removal.’

warning: [removal] finalize() in Object has been deprecated and marked for removal  
protected void finalize() throws Throwable {  
^

Why is finalize() Deprecated?

Starting with Java 9, the java finalize() method was deprecated due to several issues:

1. Unpredictability: The garbage collector decides when to execute finalize(), making it unreliable for resource management.
2. Performance Overhead: finalize() adds extra processing time, slowing down garbage collection.
3. Security Risks: Malicious code could exploit finalize() to resurrect objects, leading to potential security vulnerabilities.
4. Better Alternatives Exist: Modern Java provides try-with-resources and PhantomReferences for safer and more efficient resource cleanup.

Note: In Java 18, finalize() wasn’t completely removed, but it was officially marked for removal. Java Enhancement Proposal (JEP) 421 introduced in Java 18 deprecated finalization, signaling that it will be removed in a future version. So while finalize() still exists in Java 18, developers should avoid using it and switch to better alternatives.

Modern Alternatives to finalize()

1. Using try-with-resources (For Auto-Closeable Resources)

Java introduced try-with-resources in Java 7, which automatically closes resources without needing finalize().

import java.io.FileWriter;
import java.io.IOException;

public class TryWithResourcesExample {
    public static void main(String[] args) {
        try (FileWriter writer = new FileWriter("path\test.txt")) {
            writer.write("Hello, Java!");
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

Why is it Better?

• Ensures resources are closed immediately after use.
• No dependency on garbage collection.
• Improves code readability and performance.

2. Using PhantomReference (For Advanced Cleanup)

If you need to execute cleanup tasks after an object is garbage collected, PhantomReference is a better alternative.

import java.lang.ref.*;

public class PhantomReferenceExample {
    public static void main(String[] args) {
        ReferenceQueue<MyObject> queue = new ReferenceQueue<>();
        MyObject obj = new MyObject();
        PhantomReference<MyObject> phantomRef = new PhantomReference<>(obj, queue);
        obj = null;
        System.gc();
        System.out.println("Phantom reference added to queue: " + (queue.poll() != null));
    }
}
class MyObject {
    @Override
    protected void finalize() {
        System.out.println("Finalizing MyObject");
    }
}

Why is PhantomReference Better?

• Provides better control over garbage collection.
• Avoids unpredictability of finalize().
• Allows cleanup logic to execute only when necessary.

Is finalize() Still Relevant in 2025?

The short answer is no. With its deprecation and removal in newer Java versions, the java finalize() method is no longer a recommended practice. Java developers should use try-with-resources for automatic resource management and PhantomReference for advanced memory cleanup.

Conclusion

The java finalize() method was once a useful tool, but its unpredictable behavior and performance issues have made it obsolete. Modern alternatives like try-with-resources and PhantomReference offer safer and more efficient ways to handle resource cleanup. As of 2025, Java developers should completely avoid finalize() and adopt best practices that align with modern Java standards.

Do you still have legacy code using finalize()? It’s time to refactor and future-proof your Java applications!

If you’re stepping into programming or looking for a scripting language that is lightweight, fast, and powerful, Lua is an excellent choice. Used in game development, embedded systems, and even AI, Lua offers simplicity without sacrificing capability. This guide will walk you through the essentials, helping you master Lua with ease.

What Is Lua?

Lua is a high-level, lightweight scripting language designed for speed and efficiency. It was created in 1993 by a team of Brazilian developers and has since gained popularity in game development (Roblox, World of Warcraft mods) and embedded applications.

Why Learn Lua?

• Easy to Learn: Lua has a simple syntax, making it beginner-friendly.
• Lightweight and Fast: Lua is designed for speed, consuming minimal system resources.
• Highly Flexible: It supports procedural, object-oriented, and functional programming.
• Widely Used in Game Development: Many games and engines, like Unity and Love2D, use Lua.

Setting Up Lua

Before diving into Lua programming, you’ll need to install it.

Installing Lua

1. Windows: Download Lua from https://www.lua.org/download.html and install it.
2. Mac: Use Homebrew:

brew install lua

Linux: Use the package manager:

sudo apt-get install lua5.4

Online Lua Interpreter: If you don’t want to install Lua, try an online interpreter like Replit.

Verify installation by running:

lua -v

This will display the installed Lua version.

Lua Basics

1. Hello, Lua!

Let’s start with the classic “Hello, World!” program.

print("Hello, Lua!")

Simply run this script, and you’ll see Hello, Lua! printed on the screen.

2. Variables and Data Types

Lua has dynamic typing, meaning variables do not require explicit type definitions.

name = "Amol"
age = 25
height = 5.9
isLearning = true

Lua supports:

• Strings: "Hello"
• Numbers: 10, 3.14
• Booleans: true, false
• Tables (similar to arrays and dictionaries)
• Nil (represents an absence of value)

3. Conditional Statements

Lua uses if-then statements for decision-making.

score = 85
if score >= 90 then
    print("Excellent!")
elseif score >= 70 then
    print("Good job!")
else
    print("Keep practicing!")
end

4. Loops

For Loop

for i = 1, 5 do
    print("Iteration: ", i)
end

While Loop

count = 1
while count <= 5 do
    print("Count: ", count)
    count = count + 1
end

Functions in Lua

Functions help in structuring code efficiently.

Defining and Calling Functions

function greet(name)
    print("Hello, " .. name .. "!")
end

greet("Amol")

Here, the .. operator is used for string concatenation.

Returning Values

function add(a, b)
    return a + b
end

result = add(5, 3)
print("Sum: ", result)

Working with Tables (Arrays and Dictionaries)

Lua tables function as both arrays and dictionaries.

Array-like Table

fruits = {"Apple", "Banana", "Cherry"}
print(fruits[1])  -- Output: Apple

Dictionary-like Table

person = {name = "Amol", age = 30}
print(person.name)  -- Output: Amol

Object-Oriented Programming in Lua

While Lua doesn’t have built-in OOP, it can be implemented using tables and metatables.

Creating a Simple Class

Person = {}
Person.__index = Person

function Person:new(name, age)
    local obj = {name = name, age = age}
    setmetatable(obj, Person)
    return obj
end

function Person:greet()
    print("Hi, I am " .. self.name .. " and I am " .. self.age .. " years old.")
end

p1 = Person:new("Amol", 25)
p1:greet()

Error Handling

Use pcall (protected call) to catch errors gracefully.

function divide(a, b)
    if b == 0 then
        error("Cannot divide by zero!")
    end
    return a / b
end

status, result = pcall(divide, 10, 0)
if status then
    print("Result: ", result)
else
    print("Error: ", result)
end

Conclusion

Mastering Lua opens doors to game development, scripting, and embedded systems. With its simple syntax, high efficiency, and flexibility, it’s a fantastic choice for beginners and experienced developers alike. Keep practicing, build projects, and explore Lua’s potential further!

When analyzing algorithms, time complexity plays a crucial role in determining their efficiency. Two common complexities encountered in sorting, searching, and data structure operations are O(n) (linear time) and O(n log n) (linearithmic time). But which one is better? In this blog, we will explore both complexities in detail, understand their significance, and compare them with real-world examples.

Understanding O(n) vs. O(n log n) Complexity

Imagine you’re throwing a party, and you need to greet every single guest. If you shake hands with each person individually, that’s a straightforward, one-to-one interaction. That’s essentially what O(n) is — a linear relationship. The time it takes to greet everyone grows directly with the number of guests.

Now, picture a slightly more complex scenario. You’re not just greeting guests; you’re also organizing them by height, and you’re using a clever method that involves repeatedly dividing the group in half and comparing heights. This is more akin to O(n log n). It’s still efficient, But it involves a bit more ‘thinking’ (checks) for each guest.

What is O(n)?

O(n), or linear time complexity, means that the execution time of an algorithm increases directly in proportion to the size of the input. If we double the input size, the time required also doubles.

A simple example of an O(n) algorithm is traversing an array:

fun printElements(arr: IntArray) {
    for (element in arr) {
        println(element)
    }
}

Here, if we have an array of size n, the loop runs n times, making it O(n).

When O(n) is Used?

• Searching in an unsorted array
• Finding the maximum or minimum element in an arra
• Simple computations that process each element once

What is O(n log n)?

O(n log n), or linearithmic time complexity, appears in algorithms where the problem is divided into smaller subproblems (like divide-and-conquer strategies). The additional log(n) factor results from recursive halving or merging operations.

A common example of O(n log n) complexity is Merge Sort:

fun mergeSort(arr: IntArray): IntArray {
    if (arr.size <= 1) return arr
    
    val mid = arr.size / 2
    val left = mergeSort(arr.sliceArray(0 until mid))
    val right = mergeSort(arr.sliceArray(mid until arr.size))
    
    return merge(left, right)
}

fun merge(left: IntArray, right: IntArray): IntArray {
    var i = 0; var j = 0
    val mergedList = mutableListOf<Int>()
    
    while (i < left.size && j < right.size) {
        if (left[i] < right[j]) {
            mergedList.add(left[i++])
        } else {
            mergedList.add(right[j++])
        }
    }
    
    while (i < left.size) mergedList.add(left[i++])
    while (j < right.size) mergedList.add(right[j++])
    
    return mergedList.toIntArray()
}

In Merge Sort, the array is divided into halves (log(n) times), and each element is processed (n times), resulting in O(n log n) complexity.

When O(n log n) is Used?

• Sorting large datasets (Merge Sort, Quick Sort, Heap Sort)
• Efficient searching in balanced trees
• Solving problems using divide and conquer approach

O(n) vs. O(n log n): Which One is Better?

Example: If n = 1,000,000:

• O(n) → 1,000,000 operations
• O(n log n) → ~20,000,000 operations (log base 2)

Clearly, O(n) is preferable whenever possible, but for sorting and recursion-based problems, O(n log n) is necessary.

Conclusion

Understanding time complexity is essential for writing efficient code. O(n) is ideal for simple iterations, while O(n log n) is crucial for sorting and divide-and-conquer approaches. By recognizing these complexities, developers can optimize their code for better performance.

Generics are a powerful feature in Kotlin that allow you to write flexible, reusable code. However, sometimes you need to restrict the types that can be used with generics. This is where type parameter constraints in Kotlin come into play. By defining constraints, you can ensure that your generic types work only with specific kinds of objects, enhancing type safety and reducing errors.

In this blog post, we will dive deep into type parameter constraints in Kotlin, exploring their importance, syntax, and practical usage with examples.

What Are Type Parameter Constraints?

In Kotlin, generics allow you to write code that can work with multiple types. However, not all types are compatible with every operation. Type parameter constraints help enforce certain conditions on the type arguments, ensuring that they adhere to specific requirements.

A type parameter constraint limits the types that can be used with a generic class, function, or interface. The most common constraint in Kotlin is the upper bound constraint, which specifies that a generic type must be a subclass of a particular type.

When you specify a type as an upper bound constraint for a type parameter of a generic type, the corresponding type arguments in specific instantiations of the generic type must be either the specified type or its subtypes(For now, you can think of subtype as a synonym for subclass).

To specify a constraint, you put a colon after the type parameter name, followed by the type that’s the upper bound for the type parameter. In Java, you use the keyword extends to express the same concept: T sum(List list)

Constraints are defined by specifying an upper bound after a type parameter

Let’s start with an example. Suppose we have a function called sum that calculates the sum of elements in a list. We want this function to work with List<Int> or List<Double>, but not with List<String>. To achieve this, we can define a type parameter constraint that specifies the type parameter of sum must be a number.

fun <T : Number> sum(list: List<T>): T {
    var result = 0.0
    for (element in list) {
        result += element.toDouble()
    }
    return result as T
}

In this example, we specify <T : Number> as the type parameter constraint, indicating that T must be a subclass of Number. Now, when we invoke the function with a list of integers, it works correctly:

println(sum(listOf(1, 2, 3))) // Output: 6

The type argument Int extends Number, so it satisfies the type parameter constraint.

You can also use methods defined in the class used as the bound for the type parameter constraint. Here’s an example:

fun <T : Number> oneHalf(value: T): Double {
    return value.toDouble() / 2.0
}

println(oneHalf(3)) // Output: 1.5

In this case, T is constrained to be a subclass of Number, so we can use methods defined in the Number class, such as toDouble().

Now let’s consider another example where we want to find the maximum of two items. Since it’s only possible to compare items that are comparable to each other, we need to specify that requirement in the function signature using the Comparable interface:

fun <T : Comparable<T>> max(first: T, second: T): T {
    return if (first > second) first else second
}

println(max("kotlin", "java")) // Output: kotlin

In this case, we specify <T : Comparable<T>> as the type parameter constraint. It ensures that T can only be a type that implements the Comparable interface. Hence, we can compare first and second using the > operator.

If you try to call max with incomparable items, such as a string and an integer, it won’t compile:

println(max("kotlin", 42)) // ERROR: Type parameter bound for T is not satisfied

The error occurs because the type argument Any inferred for T is not a subtype of Comparable<Any>, which violates the type parameter constraint.

In some cases, you may need to specify multiple constraints on a type parameter. You can use a slightly different syntax for that. Here’s an example where we ensure that the given CharSequence has a period at the end and can be appended:

fun <T> ensureTrailingPeriod(seq: T)
        where T : CharSequence, T : Appendable {
    if (!seq.endsWith('.')) {
        seq.append('.')
    }
}

val helloWorld = StringBuilder("Hello World")
ensureTrailingPeriod(helloWorld)
println(helloWorld) // Output: Hello World.

In this case, we specify the constraints T : CharSequence and T : Appendable using the where clause. This ensures that the type argument must implement both the CharSequence and Appendable interfaces, allowing us to use operations like endsWith and append on values of that type.

Multiple Constraints Using where

Kotlin allows multiple constraints using the where keyword. This is useful when you want a type to satisfy multiple conditions.

// Generic function with multiple constraints
fun <T> processData(item: T) where T : Number, T : Comparable<T> {
    println("Processing: ${item.toDouble()}")
}

fun main() {
    processData(10)      // Valid (Int is both Number and Comparable)
    processData(5.5)     // Valid (Double is both Number and Comparable)
    // processData("Hello") // Error: String does not satisfy Number constraint
}

Here,

• T : Number, T : Comparable<T> ensures that T must be both a Number and implement Comparable<T>.
• Int and Double satisfy both constraints, so they work fine.
• A String would cause a compilation error because it is not a Number.

Type Parameter Constraints in Classes

You can also apply type parameter constraints to classes. This is useful when defining reusable components.

// Class with type parameter constraints
class Calculator<T : Number> {
    fun square(value: T): Double {
        return value.toDouble() * value.toDouble()
    }
}

fun main() {
    val intCalc = Calculator<Int>()
    println(intCalc.square(4))  // Output: 16.0
    val doubleCalc = Calculator<Double>()
    println(doubleCalc.square(3.5))  // Output: 12.25
}

Here,

• class Calculator<T : Number> ensures that T is always a Number.
• The square function works with Int, Double, or any subclass of Number

Benefits of Using Type Parameter Constraints

Using type parameter constraints in Kotlin offers several advantages:

1. Improved Type Safety — Prevents incorrect type usage at compile-time.
2. Better Code Reusability — Enables generic functions and classes that are still specific enough to avoid errors.
3. Enhanced Readability — Clearly communicates the expected types to developers.
4. Less Boilerplate Code — Reduces the need for multiple overloaded methods.

Conclusion

Type parameter constraints in Kotlin are a powerful tool that helps you enforce stricter type rules while keeping your code flexible and reusable. By using constraints, you ensure type safety and make your code more robust and error-free.

Whether you’re working with generic functions, classes, or interfaces, leveraging type constraints can help you write better Kotlin code.

When working with Java, handling exceptions properly is crucial to writing robust and maintainable applications. Two essential keywords in Java’s exception-handling mechanism are throw and throws. While they may look similar, they serve different purposes. In this guide, we will explore how to use throw and throws effectively in Java, ensuring clarity and proper exception handling.

Understanding throw and throws in Java

Both throw and throws relate to Java’s exception-handling framework, but they are used differently:

• throw: Used within a method to explicitly throw an exception.
• throws: Declares exceptions that a method might throw.

Now, let’s explore both in detail with examples.

Understanding throw in Java

The throw keyword in Java is used to manually create and throw an exception object. This allows the programmer to explicitly indicate exceptional conditions in the program flow.

throw new ArithmeticException("/ by zero");

In this example, an ArithmeticException object is created explicitly with the message “/ by zero” and thrown using the throw keyword.

The main objective of the throw keyword is to hand over the created exception object to the JVM manually.

The following two programs yield exactly the same result:

Without throw keyword:

class Test {
    public static void main(String[] args) {
        System.out.println(10/0);
    }
}


//output

Exception in thread "main" java.lang.ArithmeticException: / by zero
    at Test.main(Test.java:3)

In this case, the main method is responsible for causing the ArithmeticException and implicitly hands over the exception object to the JVM.

With throw keyword:

class Test {
    public static void main(String[] args) {
        throw new ArithmeticException("/ by zero");
    }
}


//o/p

Exception in thread "main" java.lang.ArithmeticException: / by zero
    at Test.main(Test.java:3)

In this case, the main method explicitly creates an ArithmeticException object using throw keyword and hands it over to the JVM.

Here is one more example.

public class ThrowExample {
    static void checkAge(int age) {
        if (age < 18) {
            throw new IllegalArgumentException("Age must be 18 or above");
        } else {
            System.out.println("Access granted.");
        }
    }
    
    public static void main(String[] args) {
        checkAge(15);  // This will throw an exception
    }
}

Here,

• The method checkAge(int age) checks whether the given age is 18 or more.
• If the condition is not met, throw is used to raise an IllegalArgumentException.
• This terminates program execution unless the exception is caught and handled. The throw statement stops execution of the current block and transfers control to the nearest exception handler.

Understanding throws in Java

In Java, if there is a possibility of a checked exception being thrown within a method, the method must either handle the exception using a try-catch block or declare that it throws the exception using the throws keyword in its method signature.

import java.io.*;

class Test {
    public static void main(String[] args) {
        PrintWriter pw = new PrintWriter("abc.txt");
        pw.println("Hello");
    }
}

//Compilation Error: unreported exception java.io.FileNotFoundException; must be caught or declared to be thrown

In this example, the PrintWriter constructor can throw a FileNotFoundException, a checked exception. Since it’s not handled within the main method, it results in a compilation error.

To handle this error, you can either use a try-catch block to handle the exception:

import java.io.*;

class Test {
    public static void main(String[] args) {
        try {
            PrintWriter pw = new PrintWriter("abc.txt");
            pw.println("Hello");
        } catch (FileNotFoundException e) {
            // Handle the exception
            e.printStackTrace();
        }
    }
}

Or you can declare that the method throws the exception using the throws keyword:

import java.io.*;

class Test {
    public static void main(String[] args) throws FileNotFoundException {
        PrintWriter pw = new PrintWriter("abc.txt");
        pw.println("Hello");
    }
}

Using the throws keyword delegates the responsibility of handling the exception to the caller of the method.

Here is another example.

import java.io.IOException;

public class ThrowsExample {
    static void readFile() throws IOException {
        throw new IOException("File not found");
    }
    
    public static void main(String[] args) {
        try {
            readFile();
        } catch (IOException e) {
            System.out.println("Exception handled: " + e.getMessage());
        }
    }
}

Here,

• The readFile method declares throws IOException, meaning it might throw an IOException.
• Instead of handling the exception inside readFile, it delegates handling to the caller (main method).
• The main method catches the exception using a try-catch block, preventing the program from crashing.

Note: It’s recommended to handle exceptions using try-catch blocks where possible, as using throws may propagate exceptions up the call stack without handling them properly.

Key Differences Between throw and throws

Best Practices for Using throw and throws

1. Use throw for Specific Error Conditions

When you detect a problem in your logic, use throw to raise an appropriate exception. Always provide meaningful error messages to help debugging.

2. Use throws for Method-Level Exception Handling

If a method performs an operation that may result in an exception (e.g., file handling, database access), use throws to declare it and let the caller handle it.

3. Catch and Handle Exceptions Properly

Declaring exceptions using throws does not replace exception handling. Always use try-catch blocks where necessary.

4. Avoid Overusing throws

Overusing throws can make a method difficult to use, as the caller must handle multiple exceptions. Only declare exceptions when handling them inside the method is impractical or when the caller needs to decide how to respond.

Conclusion

Understanding how to use throw and throws effectively in Java is key to writing robust applications. throw is used to generate exceptions manually, while throws is used to declare potential exceptions in a method. By following best practices, you can ensure better error handling and maintainable code.

Kotlin, the modern programming language developed by JetBrains, has become the preferred choice for Android and backend development. One of its most powerful features is Objects in Kotlin, which simplify code structure, improve efficiency, and reduce boilerplate. In this blog post, we’ll explore why Objects in Kotlin are a game-changer for developers and how they can be leveraged effectively.

Understanding Objects in Kotlin

In Kotlin, objects are special constructs that allow you to create single instances of a class without explicitly instantiating them. This helps in scenarios where you need a single, global instance, similar to the Singleton pattern in Java.

Objects in Kotlin serve different purposes:

• Singleton Objects — Ensuring only one instance of a class exists.
• Companion Objects — Providing static-like behavior inside classes.
• Object Expressions — Defining anonymous objects for quick, one-time use.
• Object Declarations — Creating globally accessible named singleton instances without manual instantiation.

Let’s dive deeper into each type and see how they make Kotlin development more efficient.

1. Singleton Objects in Kotlin

Singletons are used when you need only one instance of a class throughout your application. Kotlin makes this easy with the object keyword.

object DatabaseManager {
    fun connect() {
        println("Connected to the database")
    }
}

fun main() {
    DatabaseManager.connect()
}

Here,

• The object keyword ensures only one instance of DatabaseManager exists.
• There is no need to manually instantiate the class.
• The connect() function can be called directly.

This is a major improvement over Java, where you would need to implement the Singleton pattern manually.

2. Companion Objects: Static-Like Behavior

Kotlin does not have static methods like Java. Instead, it uses companion objects to provide similar functionality.

class MathUtils {
    companion object {
        fun square(n: Int): Int {
            return n * n
        }
    }
}

fun main() {
    println(MathUtils.square(5)) // Output: 25
}

• The companion object acts like a static block inside the class.
• Methods inside a companion object can be called without creating an instance of the class.

This feature makes Kotlin code cleaner and more concise, eliminating the need for unnecessary instantiation.

3. Object Expressions: Anonymous Objects

Sometimes, you need a one-time-use object without creating a full class. Kotlin provides object expressions for this purpose.

interface ClickListener {
    fun onClick()
}

fun main() {
    val buttonClickListener = object : ClickListener {
        override fun onClick() {
            println("Button clicked!")
        }
    }
    
    buttonClickListener.onClick() // Output: Button clicked!
}

• The object keyword is used to create an anonymous object implementing ClickListener.
• There is no need to define a separate class.
• This is particularly useful for event listeners and callbacks.

4. Object Declarations: Global Instances

Object declarations allow you to create a global instance that can be accessed anywhere in the application.

object Logger {
    fun log(message: String) {
        println("Log: $message")
    }
}

fun main() {
    Logger.log("Application started") // Output: Log: Application started
}

• The Logger object is declared once and can be used globally.
• This is useful for utilities like logging, configuration managers, or network helpers.

Why Objects in Kotlin Are a Game-Changer

1. Reduces Boilerplate Code

With Objects in Kotlin, there is no need to manually implement singletons or static utility classes, making your code cleaner.

2. Improves Memory Efficiency

Since objects are created only once, memory usage is optimized compared to multiple class instances.

3. Enhances Code Readability

Using objects makes the intent of the code clear. Instead of defining unnecessary classes and instances, you can directly declare objects.

4. Encourages Best Practices

Kotlin’s object system aligns with modern design principles like the Singleton pattern, Dependency Injection, and Functional Programming, making your applications more maintainable.

Conclusion

Objects in Kotlin simplify development by reducing boilerplate code, improving efficiency, and enhancing readability. Whether you’re using Singleton Objects, Companion Objects, Object Expressions, or Object Declarations, each of these features helps in writing clean and maintainable code.

If you’re a developer working with Kotlin, mastering objects can significantly improve your productivity. Start implementing them in your projects today and experience the benefits firsthand!

Kotlin is a powerful and expressive language that makes coding both enjoyable and efficient. One of the core concepts in Kotlin (and programming in general) is subtype relationships. Understanding how subtypes work in Kotlin can help you write cleaner, more flexible, and reusable code. In this blog post, we’ll break down this concept in an easy-to-understand manner with examples and explanations.

What Are Subtype Relationships in Kotlin?

In Kotlin, the type of a variable specifies the possible values it can hold. The terms “type” and “class” are sometimes used interchangeably, but they have distinct meanings. In the case of a non-generic class, the class name can be used directly as a type. For example, var x: String declares a variable that can hold instances of the String class. However, the same class name can also be used to declare a nullable type, such as var x: String? which indicates that the variable can hold either a String or null. So each Kotlin class can be used to construct at least two types.

When it comes to generic classes, things get more complex. To form a valid type, you need to substitute a specific type as a type argument for the class’s type parameter. For example, List is a class, not a type itself, but the following substitutions are valid types: List<Int>, List<String?>, List<List<String>>, and so on. Each generic class can generate an infinite number of potential types.

Subtyping Concept in Kotlin

To discuss the relationship between types, it’s important to understand the concept of subtyping. Type B is considered a subtype of type A if you can use a value of type B wherever a value of type A is expected. For example, Int is a subtype of Number, but Int is not a subtype of String. Note that a type is considered a subtype of itself. The term “supertype” is the opposite of subtype: if A is a subtype of B, then B is a supertype of A.

B is a subtype of A if you can use it when A is expected

Understanding subtype relationships is crucial because the compiler performs checks whenever you assign a value to a variable or pass an argument to a function. For example:

fun test(i: Int) {
    val n: Number = i
    fun f(s: String) { /*...*/ }
    f(i)
}

Storing a value in a variable is only allowed if the value’s type is a subtype of the variable’s type. In this case, since Int is a subtype of Number, the declaration val n: Number = i is valid. Similarly, passing an expression to a function is only allowed if the expression’s type is a subtype of the function’s parameter type. In the example, the type Int of the argument i is not a subtype of the function parameter type String, so the invocation of the f function does not compile.

In simpler cases, subtype is essentially the same as subclass. For example, Int is a subclass of Number, so the Int type is a subtype of the Number type. If a class implements an interface, its type is a subtype of the interface type. For instance, String is a subtype of CharSequence.

Subtype Relationships in Nullable Types

Nullable types introduce a scenario where subtype and subclass differ. A non-null type is a subtype of its corresponding nullable type, but they both correspond to the same class.

A non-null type A is a subtype of nullable A?, but not vice versa

You can store the value of a non-null type in a variable of a nullable type, but not vice versa. For example:

val s: String = "abc"
val t: String? = s

In this case, the value of the non-null type String can be stored in a variable of the nullable type String?. However, you cannot assign a nullable type to a non-null type because null is not an acceptable value for a non-null type.

The distinction between subclasses and subtypes becomes particularly important when dealing with generic types. This brings us back to the question from the previous section: is it safe to pass a variable of type List<String> to a function expecting List<Any>? We’ve already seen that treating MutableList<String> as a subtype of MutableList<Any> is not safe. Similarly, MutableList<Any> is not a subtype of MutableList<String> either.

A generic class, such as MutableList, is called invariant on the type parameter if, for any two different types A and B, MutableList<A> is neither a subtype nor a supertype of MutableList<B>. In Java, all classes are invariant, although specific uses of those classes can be marked as non-invariant.

In List, where the subtyping rules are different. The List interface in Kotlin represents a read-only collection. If type A is a subtype of type B, then List<A> is a subtype of List<B>. Such classes or interfaces are called covariant.

Why Understanding Subtype Relationships Matters

Understanding subtype relationships in Kotlin allows you to:

• Write more reusable and maintainable code.
• Use polymorphism effectively.
• Work efficiently with interfaces and generics.
• Avoid type-related errors in large applications.

By leveraging inheritance, interfaces, and variance in generics, you can take full advantage of Kotlin’s type system and build flexible applications.

This is just a part..! Get the full insights here: [Main Article URL]

Conclusion

Subtype relationships in Kotlin are a fundamental (which form the backbone of object-oriented and generic programming) yet powerful concept that enables flexible, reusable, and type-safe code. Whether using class inheritance, interfaces, or variance in generics, understanding how subtypes work can help you write cleaner and more efficient Kotlin applications.

By mastering subtype relationships in Kotlin, you’ll unlock a deeper understanding of type hierarchy, improve your code structure, and avoid common pitfalls.

Resource management is a crucial aspect of software development. Whether you’re working with files, database connections, or network resources, handling them efficiently ensures optimal performance and prevents issues like memory leaks. Kotlin provides a powerful feature called inlined lambdas that can greatly simplify resource management.

In this blog, we’ll explore how inlined lambdas in Kotlin can be useful in resource management, breaking it down with simple explanations and practical examples.

Let’s first understand what inlined lambdas are and why they matter.

What Are Inlined Lambdas?

Kotlin provides a feature called inline functions, which can improve performance by eliminating function call overhead and enabling optimizations such as inlining lambda expressions.

When you declare a function as inline, Kotlin replaces the function call with its actual code during compilation. This reduces object allocation, making it ideal for scenarios where lambda expressions are frequently used.

inline fun execute(block: () -> Unit) {
    block()
}

fun main() {
    execute {
        println("Executing inline function block!")
    }
}

In the compiled bytecode, the block() function call is replaced with its actual implementation, reducing unnecessary function calls.

How Inlined Lambdas Help in Resource Management

Resource management often involves opening, using, and properly closing resources like files, network sockets, or database connections. Kotlin’s inlined lambdas can help by ensuring that resources are always released properly, even if an exception occurs.

Let’s look at simple practical example of using inlined lambdas for efficient resource management.

Managing File Resources with Inlined Lambdas

When working with files, it’s important to ensure that the file is closed properly after use. Kotlin provides the use function, which is an inline function that ensures the resource is closed after execution.

import java.io.File

fun readFileContent(filePath: String): String {
    return File(filePath).bufferedReader().use { reader ->
        reader.readText()  // File is automatically closed after this block
    }
}

fun main() {
    val content = readFileContent("filepath\sample.txt")
    println(content)
}

Here,

• The use function takes a lambda as a parameter.
• It automatically closes the file after the lambda executes.
• Since use is an inline function, the lambda code is inlined, reducing unnecessary function calls and improving performance.

Key Benefits of Using Inlined Lambdas in Resource Management

Using inlined lambdas in Kotlin for resource management provides several advantages:

• Automatic Resource Cleanup: No need to manually close resources; use does it for you.
• Safer Code: Ensures resources are always closed, even if exceptions occur.
• Better Performance: Inlining eliminates unnecessary function calls, improving execution speed.
• Simpler Syntax: Reduces boilerplate code, making it easier to read and maintain.

Learn more at: [Main Article URL]

Conclusion

Inlined lambdas in Kotlin are a powerful feature that significantly improve resource management. Whether handling files, database connections, or network requests, using inline functions like use ensures that resources are properly managed, reducing the risk of memory leaks and improving application efficiency.

By leveraging inlined lambdas, you not only write safer and more concise code but also optimize performance by eliminating unnecessary function calls. Start using this approach in your Kotlin projects and experience the benefits firsthand..!

Try-Catch-Finally in Java is a crucial part of Java programming. It helps prevent programs from crashing due to unexpected errors. Java provides a structured way to handle exceptions using try, catch, and finally blocks. In this post, we’ll explore how multiple catch blocks work in Java’s exception handling flow and how they improve code reliability.

Understanding Try-Catch-Finally in Java

Java provides a structured way to handle exceptions using try, catch, and finally blocks. Let’s break down each component:

• try block: This is where you place the code that may throw an exception.
• catch block: Used to handle specific exceptions that may arise in the try block.
• finally block: This block always executes, regardless of whether an exception occurs or not. It’s typically used for cleanup tasks like closing resources.

try {
    // Risky code
} catch (Exception e) {
    // Handling code
} finally {
    // Clean-up code
}

Control flow in try-catch

try {
    stmt1;
    stmt2;
    stmt3;
} catch(Exception e) {
    stmt4;
}
stmt5;

Case 1: If no exception occurs: Output: (1, 2, 3, 5, Normal Termination)

Case 2: If an exception is raised at statement 2 and the corresponding catch block matches: Output: (1, 4, 5, Normal Termination)

Case 3: If an exception is raised at statement 2 and the corresponding catch block does not match: Output: (1, Abnormal Termination)

Case 4: If an exception is raised at statement 4 or statement 5, it always leads to abnormal termination.

Notes:

1. If an exception occurs anywhere within the try block, subsequent statements within the try block will not be executed, even if the exception is handled. Therefore, it’s crucial to include only risky code within the try block, and the try block’s length should be kept as short as possible.
2. Apart from the try block, exceptions might also occur within catch and finally blocks. If any statement outside of the try block raises an exception, it always results in abnormal termination.

try with multiple catch

Using try with multiple catch blocks is a recommended practice in exception handling as it allows for specific handling tailored to each type of exception encountered.

Worst Practice:

try {
    // Risky code
} catch (Exception e) {
    // Use this single catch block for all exceptions
}

Best Practice:

try {
    // Risky code
} catch (ArithmeticException e) {
    // Perform alternative arithmetic operation
} catch (SQLException e) {
    // Use MySQL database instead of Oracle database
} catch (FileNotFoundException e) {
    // Use local file instead of remote file
} catch (Exception e) {
    // Default exception handling
}

In the best programming practice scenario, each catch block is dedicated to handling a specific type of exception. This approach allows for more precise and targeted handling, improving the robustness and reliability of the code. Additionally, it provides flexibility in dealing with different types of exceptions appropriately.

Some Important Loopholes

1. In a try-with-multiple-catch-blocks scenario, it’s crucial to order the catch blocks properly. Child exceptions should be caught before parent exceptions. Failing to do so results in a compile-time error indicating that the exception has already been caught. For instance:

Incorrect:

try {
    // Risky code
} catch (Exception e) {
    // Parent exception catch block
} catch (ArithmeticException e) {
    // Child exception catch block
}

Correct:

try {
    // Risky code
} catch (ArithmeticException e) {
    // Child exception catch block
} catch (Exception e) {
    // Parent exception catch block
}

2. It’s not allowed to declare two catch blocks for the same type of exception within the same try-catch structure. Attempting to do so will result in a compile-time error.

Incorrect:

try {
    // Risky code
} catch (ArithmeticException e) {
    // Catch block for ArithmeticException
} catch (ArithmeticException e) {
    // Another catch block for ArithmeticException (Duplicate)
}

Correct:

try {
    // Risky code
} catch (ArithmeticException e) {
    // Catch block for ArithmeticException
} catch (Exception e) {
    // Catch block for other exceptions
}

Combinations and Rules for Try-Catch-Finally:

1. In try-catch-finally, the order is important.
2. Whenever we write try, it’s compulsory to include either catch or finally; otherwise, we will get a compile-time error (try without catch or finally is invalid).
3. Whenever we write a catch block, a try block must be present; catch without try is invalid.
4. Whenever we write a finally block, a try block should be present; finally without try is invalid.
5. Inside try-catch-finally blocks, we can nest additional try-catch-finally blocks; nesting of try-catch-finally is allowed.
6. Curly braces ({}) are mandatory for try-catch-finally blocks.

Here are some examples to illustrate these rules:

Valid:

try {
    // code
} catch(Exception e) {
    // exception handling code
} finally {
    // cleanup code
}

Invalid (Compile-time Error):

try {
    // code
}
// CE: try without catch or finally

Invalid (Compile-time Error):

catch(Exception e) {
    // exception handling code
}
// CE: catch without try

Invalid (Compile-time Error):

finally {
    // cleanup code
}
// CE: finally without try

Valid:

try {
    try {
        // code
    } catch(Exception e) {
        // inner catch block
    } finally {
        // inner finally block
    }
} catch(Exception e) {
    // outer catch block
} finally {
    // outer finally block
}

Valid:

try {
    // code
} catch(Exception e) {
    // exception handling code
} finally {
    // cleanup code
}

Conclusion

Using Try-Catch-Finally in Java effectively helps make your code more robust. Multiple catch blocks allow you to handle different types of exceptions separately, improving error management and readability. Always follow best practices when structuring your exception handling to ensure your code remains clean and efficient.