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[Part-I: Arrays] Data Structures and Algorithms

Full Stack Developer works on #100DaysOfBlockchain, React, JavaScript, #100DaysOfCloud, #LogicCodeDebug, .Net & exploring #abc_vi (AI | VR | IOT | Quantum)
Updated on ・3 min read

Let's discuss arrays theoretically along with the complexities in Part-I and will solve some Array competitive problems in Part-II. Please stay with me for next 5 minutes. Let's start.


  • Array is one of the data structure that stores multiple items of same data type at contiguous(next or together in sequence) memory locations.

#important This means we can access the element using the index of the array. The advantage of accessing using index and contiguous memory location is the complexity to get the ith index is Big O(1). Means they find the ith element in constant time. Big O(1), because you can easily get the address of any item using x + iy, where x is the initial memory state, and y is the memory of each item.

  • Array provides Cache friendliness, Cache is the memory closed to CPU, then RAM or any other hard disk memory.

#important Due to contiguous memory location, It's more likely that the CPU caches all the contiguous items of Array.

Memory Allocation

  • Fixed Sized Arrays In C# or Java, the arrays have Heap allocated memories. while in C/C++, we can have both heap/stack allocated memories.
int[] array1 = new int[5];
int[] array1 = new int[n];
int[] array1 = {10, 20, 30, 40};
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int[] array1 = new int[5];
int[] array1 = new int[] { 1, 3, 5, 7, 9 };
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  • Dynamic Sized Arrays In dynamic sized array, we can insert as many elements as we want. Whenever the capacity is exhausted for an array and we try to insert something the size of array automatically increases.

Example of dynamic sized arrays:

C++ -> vectors
Java and C# -> ArrayList
Python -> List
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Operations on Array

1. Search

  • Searching an element in an array is about returning the index of an array. If the element is not found, we returns -1.

Example of Linear Search

  • We have to search the first index of the given index.
  • Linear search is about searching the element in the given array. If the event is not found then we have to traverse the whole array.
  • The worst case complexity of this is Big O(n). This happens when the element is not found in the array. as the result will be a linear expression always.
int search(int arr[], int length, int elementtosearch) {
    for(int i =0; i < length; i++) { 
        if(arr[i] == elementtosearch) { 
            return i;
    return -1;
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  • Above elements were not sorted. But what happens when the arrays are sorted.
  • We can do a search with Big O(log n) for sorted array.

2. Insert

Insertion in Fixed Size Arrays

  • We cannot insert in fixed fixed array if it is already full. So, we can insert an element at a position only when it has some empty space.
  • When we insert, we return the size of the updated array. If the size and capacity of the array is same, we will not increase the size of array and will directly returns the current size of array.
  • Time complexity of Insert operation: worst case Big O(n)
  • example:
function insertion(int arr[], int currentSizeOfArray, int elementtoinsert, int position, int capacity) {
    if(currentSizeOfArray == capacity)
        return currentSizeOfArray;

    int indexToInsertAt = position - 1; // as position starts from 1 and index starts from 0

    for(int i = currentSizeOfArray - 1; i >= indexToInsertAt; i++) { 
        arr[i+1] = arr[i];
    arr[indexToInsertAt] = elementtoinsert;
    return currentSizeOfArray + 1;
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  • Insertion in Dynamic Size Arrays
  • Dynamic size array doubles it's size when they are full and releases their existing memory.
  • This is the most costly step when you are using dynamic sized error. Need to be avoided in production environments.
  • add operation in arraylist of java and c#.

3. Delete

  • When we delete an array, we want to remove the element from the array, reduce the current size of array, but capacity remains same for array. Also, the elements should move one position ahead. i Time complexity of delete is Big O(n)
int deleteElement(int arr[], int sizeOfArray, int elementToDelete) {
    int i = 0;
    // searching element in array
    for(i = 0; i < sizeOfArray;i++) {
        if(arr[i] == elementToDelete) {
    // element is not present in array
    if(i == sizeOfArray) { 
        return sizeOfArray;
    for(int j = i; j < n - 1; j++) {
        arr[j] = arr[j+1];
    retun (n -1);
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4. Get and Update

  • We can directly get or update the element in an array with Big O (1)

Stay tuned for part-II, where we will solve multiple array problems. and then later in the series, I will be working on multiple Data Structures. Stay Connected!! Stay Safe!!

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