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Mastering HashMap in Java: How It Works Internally, Handling Hash Collisions, and Java 8 Improvements

HashMap is one of the most commonly used data structures in Java. It is a part of the Java Collections Framework and is used to store key-value pairs. In this blog post, we will dive into how HashMap works internally, what hash collisions are, and explore some enhancements introduced in Java 8. How HashMap Internally Works At its core, a HashMap uses a data structure called an array to store key-value pairs. Each key is associated with a specific index in the array through a process called hashing. Here's a simplified overview of how it works: Hashing : When you put a key-value pair into a HashMap, the HashMap first calculates a hash code for the key. This hash code is an integer value that represents the key and is used to determine the index where the key-value pair will be stored in the array. Index Calculation : The hash code is then processed to calculate an index within the array. This is typically done by taking the hash code modulo the size of the array. The result is the ...
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Using Java 8 Streams to Find the Second-Highest Salary in an Employee List

To find the second-highest salary from a list of employees using Java 8 streams, you can follow these steps: Create a list of employees with their salaries. Use Java 8 streams to sort the employees by salary in descending order. Skip the first element (which is the employee with the highest salary). Get the first element of the remaining stream (which is the employee with the second-highest salary). Example code: java import java.util.ArrayList; import java.util.List; class Employee { private String name; private double salary; public Employee (String name, double salary) { this .name = name; this .salary = salary; } public double getSalary () { return salary; } } public class SecondHighestSalary { public static void main (String[] args) { List<Employee> employees = new ArrayList <>(); employees.add( new Employee ( "John" , 60000.0 )); employees.add( new Employe...
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Java Data Structures and Algorithms: A Practical Guide with Examples and Top Interview Questions"

Data Structures and Algorithms in Java Understanding Data Structures ArrayList When to Use: Use ArrayList when you need a dynamic array that can grow or shrink in size. It's efficient for random access but less efficient for frequent insertions and deletions. Example Code: java List<String> arrayList = new ArrayList <>(); arrayList.add( "Java" ); arrayList.add( "Data Structures" ); arrayList.add( "Algorithms" ); LinkedList When to Use: LinkedList is suitable for frequent insertions and deletions. It provides better performance than ArrayList in scenarios where elements are frequently added or removed from the middle of the list. Example Code: java LinkedList<String> linkedList = new LinkedList <>(); linkedList.add( "Java" ); linkedList.add( "Data Structures" ); linkedList.add( "Algorithms" ); HashMap When to Use: Use HashMap for fast retrieval of data based on a key. It is efficient for loo...
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Mastering Java Streams: A Complete Guide with Examples and Interview Questions

Java Streams have revolutionized the way data processing tasks are handled in Java programming. Introduced in Java 8, Streams offer a fluent and functional approach to processing collections of objects. In this guide, we'll delve into what Streams are, how they work, and provide practical examples along the way. Understanding Java Streams: Java Streams represent a sequence of elements that can be processed sequentially or in parallel. They provide a pipeline through which data can be manipulated using various operations such as filtering, mapping, sorting, and aggregating. Benefits of Java Streams: Concise and Readable Code : Streams promote a functional programming style, leading to more concise and readable code compared to traditional imperative approaches. Lazy Evaluation : Stream operations are lazily evaluated, meaning elements are processed only when necessary, improving efficiency. Parallelism : Streams can leverage parallel processing for improved performance on multicore ...
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