# In Which Data Structure Searching Is Very Easy?

//

Angela Bailey

Searching for data is a common operation in computer programming. The efficiency of the search algorithm depends on the data structure used to store the data.

Some data structures are more suitable for searching than others. In this article, we will explore a few data structures where searching is very easy.

## Array

An array is a simple and widely used data structure that stores a fixed-size sequence of elements of the same type. Searching in an array is straightforward and can be done in constant time if the index of the element is known. However, if the index is unknown, we need to perform a linear search by iterating through each element until we find the desired value.

### Example:

Let’s say we have an array of integers:

``````<pre>
<code>
int[] numbers = {4, 2, 7, 1, 9};
</code>
</pre>``````
``` ```

To search for a specific number, such as 7, we can iterate over each element and compare it with our Target value:

``````<pre>
<code>
int Target = 7;
for (int i = 0; i < numbers.length; i++) {
if (numbers[i] == Target) {
System.out.println("Element found at index " + i);
break;
}
}
</code>
</pre>``````
``` ```

In this example, the element with value 7 is found at index 2.

## Binary Search Tree

A binary search tree (BST) is a hierarchical data structure that stores elements in a sorted manner. Each node has at most two children: a left child and a right child. The values in the left subtree are less than the value of the current node, while the values in the right subtree are greater.

Searching in a binary search tree is efficient and can be done in logarithmic time complexity. We start at the root node and compare the Target value with the current node's value.

If the Target value is smaller, we move to the left child; if it's larger, we move to the right child. We repeat this process until we find the Target value or reach a null node.

### Example:

Let's create a binary search tree with the following elements: 5, 3, 8, 2, 4, 7, and 9.

``````<pre>
<code>
class Node {
int data;
Node left;
Node right;

public Node(int data) {
this.data = data;
left = null;
right = null;
}
}

Node root = new Node(5);
root.left = new Node(3);
root.right = new Node(8);
root.left.left = new Node(2);
root.right = new Node(4);
root.right.left = new Node(7);
root.right = new Node(9);
</code>
</pre>``````
``` ```

To search for a specific value, such as 7:

``````<pre>
<code>
int Target = 7;

Node current = root;

while (current != null) {
if (Target == current.data) {
System.println("Element found!");
break;
} else if (Target < current.data) {
current = current.left;
} else {
current = current.right;
}
}
</code>
</pre>``````
``` ```

In this example, the Target value 7 is found in the binary search tree.

## Hash Table

A hash table, also known as a hash map, is a data structure that allows for efficient retrieval of values based on keys. It uses a hash function to map keys to indices in an array. Searching in a hash table has an average time complexity of O(1) because direct access to elements is possible using the computed index.

### Example:

Let's create a hash table using Java's HashMap class:

``````<pre>
<code>
import java.util.HashMap;

HashMap<String, Integer> scores = new HashMap<>();
scores.put("John", 90);
scores.put("Emily", 85);
scores.put("Michael", 95);
scores.put("Sarah", 88);
</code>
</pre>``````
``` ```

To search for a specific key and retrieve its corresponding value:

``````<pre>
<code>
String Target = "Michael";

if (scores.containsKey(target)) {
int score = scores.get(target);
System.println(Target + "'s score: " + score);
} else {
}
</code>
</pre>``````
``` ```

In this example, the key "Michael" is found in the hash table, and its associated value of 95 is retrieved.

## Conclusion

Searching for data can be made easy by choosing the right data structure. Arrays are suitable for simple and small-scale searches, binary search trees provide efficient searching in sorted data sets, and hash tables offer constant-time access based on keys. Understanding the strengths and weaknesses of different data structures can greatly improve the efficiency of search operations in your programs.