Arrays are fundamental data structures in computer science, and understanding how to manipulate them efficiently is crucial for any developer. In this blog post, we'll dive deep into array insertion techniques using JavaScript, covering concepts from basic to advanced levels. We'll explore various scenarios, provide 20 examples, discuss time complexities, and even tackle some LeetCode-style problems.
An array is a collection of elements stored at contiguous memory locations. In JavaScript, arrays are dynamic, meaning they can grow or shrink in size. Before we dive into insertion techniques, let's quickly recap the basics of JavaScript arrays.
// Creating an array let fruits = ['apple', 'banana', 'orange']; // Accessing elements console.log(fruits[0]); // Output: 'apple' // Getting array length console.log(fruits.length); // Output: 3
The simplest way to insert an element into an array is to add it at the end using the push() method.
let numbers = [1, 2, 3]; numbers.push(4); console.log(numbers); // Output: [1, 2, 3, 4]
Time Complexity: O(1) - Constant time
To insert an element at the beginning of an array, use the unshift() method.
let colors = ['red', 'green'];
colors.unshift('blue');
console.log(colors); // Output: ['blue', 'red', 'green']
Time Complexity: O(n) - Linear time, where n is the number of elements in the array
The spread operator can be used to create a new array with additional elements.
let animals = ['cat', 'dog']; let newAnimals = ['bird', ...animals, 'fish']; console.log(newAnimals); // Output: ['bird', 'cat', 'dog', 'fish']
Time Complexity: O(n) - Linear time, where n is the total number of elements in the new array
The splice() method can be used to insert elements at a specific position in the array.
let fruits = ['apple', 'banana', 'orange']; fruits.splice(1, 0, 'mango'); console.log(fruits); // Output: ['apple', 'mango', 'banana', 'orange']
Time Complexity: O(n) - Linear time, where n is the number of elements after the insertion point
You can insert multiple elements at once using splice().
let numbers = [1, 2, 5, 6]; numbers.splice(2, 0, 3, 4); console.log(numbers); // Output: [1, 2, 3, 4, 5, 6]
Time Complexity: O(n) - Linear time, where n is the number of elements after the insertion point
You can also use array indexing to overwrite elements at specific positions.
let letters = ['A', 'B', 'D', 'E']; letters[2] = 'C'; console.log(letters); // Output: ['A', 'B', 'C', 'E']
Time Complexity: O(1) - Constant time
The concat() method can be used to combine multiple arrays.
let arr1 = [1, 2]; let arr2 = [3, 4]; let arr3 = [5, 6]; let combined = arr1.concat(arr2, arr3); console.log(combined); // Output: [1, 2, 3, 4, 5, 6]
Time Complexity: O(n) - Linear time, where n is the total number of elements in all arrays
You can use push() with the spread operator to insert multiple elements at the end.
let numbers = [1, 2, 3]; let newNumbers = [4, 5, 6]; numbers.push(...newNumbers); console.log(numbers); // Output: [1, 2, 3, 4, 5, 6]
Time Complexity: O(m) - Linear time, where m is the number of elements being inserted
Here's how you can insert an entire array into another at a specific position.
function insertArray(mainArray, subArray, position) {
return [...mainArray.slice(0, position), ...subArray, ...mainArray.slice(position)];
}
let main = [1, 2, 5, 6];
let sub = [3, 4];
console.log(insertArray(main, sub, 2)); // Output: [1, 2, 3, 4, 5, 6]
Time Complexity: O(n) - Linear time, where n is the total number of elements in the resulting array
When you know the final size of the array, preallocating can improve performance.
function createSequence(n) {
let arr = new Array(n);
for (let i = 0; i < n; i++) {
arr[i] = i + 1;
}
return arr;
}
console.log(createSequence(5)); // Output: [1, 2, 3, 4, 5]
Time Complexity: O(n) - Linear time, but more efficient than growing the array dynamically
For large arrays of numeric data, using typed arrays can be more efficient.
let floatArray = new Float64Array(5);
for (let i = 0; i < 5; i++) {
floatArray[i] = i * 1.1;
}
console.log(floatArray); // Output: Float64Array(5) [0, 1.1, 2.2, 3.3000000000000003, 4.4]
Time Complexity: O(n) - Linear time, but with better memory efficiency for numeric data
When inserting multiple elements, doing it in batches can be more efficient than one at a time.
function batchInsert(arr, elements, batchSize = 1000) {
for (let i = 0; i < elements.length; i += batchSize) {
arr.push(...elements.slice(i, i + batchSize));
}
return arr;
}
let numbers = [1, 2, 3];
let newNumbers = Array.from({ length: 10000 }, (_, i) => i + 4);
console.log(batchInsert(numbers, newNumbers).length); // Output: 10003
Time Complexity: O(n) - Linear time, but with better performance for large insertions
When inserting into a sorted array, we can use binary search to find the correct position.
function insertSorted(arr, element) {
let left = 0;
let right = arr.length;
while (left < right) {
let mid = Math.floor((left + right) / 2);
if (arr[mid] < element) {
left = mid + 1;
} else {
right = mid;
}
}
arr.splice(left, 0, element);
return arr;
}
let sortedArray = [1, 3, 5, 7, 9];
console.log(insertSorted(sortedArray, 4)); // Output: [1, 3, 4, 5, 7, 9]
Time Complexity: O(log n) for finding the position + O(n) for insertion = O(n)
A circular buffer is a fixed-size array that wraps around. Here's an implementation of insertion in a circular buffer.
class CircularBuffer {
constructor(size) {
this.buffer = new Array(size);
this.size = size;
this.head = 0;
this.tail = 0;
this.count = 0;
}
insert(element) {
this.buffer[this.tail] = element;
this.tail = (this.tail + 1) % this.size;
if (this.count < this.size) {
this.count++;
} else {
this.head = (this.head + 1) % this.size;
}
}
getBuffer() {
return this.buffer.slice(this.head, this.head + this.count);
}
}
let cb = new CircularBuffer(3);
cb.insert(1);
cb.insert(2);
cb.insert(3);
cb.insert(4);
console.log(cb.getBuffer()); // Output: [2, 3, 4]
Time Complexity: O(1) for insertion
Sparse arrays have "empty" slots. Here's how to work with them:
let sparseArray = new Array(5);
sparseArray[2] = 'Hello';
sparseArray[4] = 'World';
console.log(sparseArray); // Output: [empty × 2, 'Hello', empty, 'World']
console.log(sparseArray.length); // Output: 5
// Iterating over sparse array
sparseArray.forEach((item, index) => {
if (item !== undefined) {
console.log(`${index}: ${item}`);
}
});
// Output:
// 2: Hello
// 4: World
Time Complexity: O(1) for insertion, O(n) for iteration
When inserting elements, you might want to avoid duplicates:
function insertUnique(arr, element) {
if (!arr.includes(element)) {
arr.push(element);
}
return arr;
}
let uniqueNumbers = [1, 2, 3, 4];
console.log(insertUnique(uniqueNumbers, 3)); // Output: [1, 2, 3, 4]
console.log(insertUnique(uniqueNumbers, 5)); // Output: [1, 2, 3, 4, 5]
Time Complexity: O(n) due to the includes check
Implementing a basic priority queue using an array:
class PriorityQueue {
constructor() {
this.queue = [];
}
insert(element, priority) {
const item = { element, priority };
let added = false;
for (let i = 0; i < this.queue.length; i++) {
if (item.priority < this.queue[i].priority) {
this.queue.splice(i, 0, item);
added = true;
break;
}
}
if (!added) {
this.queue.push(item);
}
}
getQueue() {
return this.queue.map(item => item.element);
}
}
let pq = new PriorityQueue();
pq.insert('Task 1', 2);
pq.insert('Task 2', 1);
pq.insert('Task 3', 3);
console.log(pq.getQueue()); // Output: ['Task 2', 'Task 1', 'Task 3']
Time Complexity: O(n) for insertion
Creating and inserting into a dynamic 2D array:
function create2DArray(rows, cols) {
return Array.from({ length: rows }, () => new Array(cols).fill(0));
}
function insert2D(arr, row, col, value) {
// Expand array if necessary
while (arr.length <= row) {
arr.push([]);
}
while (arr[row].length <= col) {
arr[row].push(0);
}
arr[row][col] = value;
}
let matrix = create2DArray(2, 2);
insert2D(matrix, 1, 1, 5);
insert2D(matrix, 3, 3, 7);
console.log(matrix);
// Output: [
// [0, 0],
// [0, 5],
// [0],
// [0, 0, 0, 7]
// ]
Time Complexity: O(1) average case, O(n) worst case when expanding the array
While not strictly an array, a trie uses arrays (or objects) internally and is useful for string insertions:
class TrieNode {
constructor() {
this.children = {};
this.isEndOfWord = false;
}
}
class Trie {
constructor() {
this.root = new TrieNode();
}
insert(word) {
let current = this.root;
for (let char of word) {
if (!current.children[char]) {
current.children[char] = new TrieNode();
}
current = current.children[char];
}
current.isEndOfWord = true;
}
search(word) {
let current = this.root;
for (let char of word) {
if (!current.children[char]) {
return false;
}
current = current.children[char];
}
return current.isEndOfWord;
}
}
let trie = new Trie();
trie.insert("apple");
trie.insert("app");
console.log(trie.search("apple")); // Output: true
console.log(trie.search("app")); // Output: true
console.log(trie.search("appl")); // Output: false
Time Complexity: O(m) for insertion and search, where m is the length of the word
Inserting elements while maintaining a custom sort order:
function insertSorted(arr, element, compareFn) {
let index = arr.findIndex(item => compareFn(element, item) <= 0);
if (index === -1) {
arr.push(element);
} else {
arr.splice(index, 0, element);
}
return arr;
}
// Example: Sort by string length, then alphabetically
let words = ['cat', 'elephant', 'dog'];
let compareFn = (a, b) => {
if (a.length !== b.length) {
return a.length - b.length;
}
return a.localeCompare(b);
};
console.log(insertSorted(words, 'bear', compareFn));
// Output: ['cat', 'dog', 'bear', 'elephant']
Time Complexity: O(n) for finding the insertion point + O(n) for insertion = O(n)
Now that we've covered various insertion techniques, let's look at some LeetCode-style problems that involve array insertions.
Given a sorted array of non-overlapping intervals and a new interval, insert the new interval and merge if necessary.
function insert(intervals, newInterval) {
let result = [];
let i = 0;
// Add all intervals that come before newInterval
while (i < intervals.length && intervals[i][1] < newInterval[0]) {
result.push(intervals[i]);
i++;
}
// Merge overlapping intervals
while (i < intervals.length && intervals[i][0] <= newInterval[1]) {
newInterval[0] = Math.min(newInterval[0], intervals[i][0]);
newInterval[1] = Math.max(newInterval[1], intervals[i][1]);
i++;
}
// Add the merged interval
result.push(newInterval);
// Add remaining intervals
while (i < intervals.length) {
result.push(intervals[i]);
i++;
}
return result;
}
console.log(insert([[1,3],[6,9]], [2,5]));
// Output: [[1,5],[6,9]]
Time Complexity: O(n), where n is the number of intervals
Given two sorted arrays nums1 and nums2, merge nums2 into nums1 as one sorted array.
function merge(nums1, m, nums2, n) {
let p1 = m - 1;
let p2 = n - 1;
let p = m + n - 1;
while (p2 >= 0) {
if (p1 >= 0 && nums1[p1] > nums2[p2]) {
nums1[p] = nums1[p1];
p1--;
} else {
nums1[p] = nums2[p2];
p2--;
}
p--;
}
}
let nums1 = [1,2,3,0,0,0];
let m = 3;
let nums2 = [2,5,6];
let n = 3;
merge(nums1, m, nums2, n);
console.log(nums1);
// Output: [1,2,2,3,5,6]
Time Complexity: O(m + n), where m and n are the lengths of nums1 and nums2 respectively
To further test your understanding of array insertions, here are 15 LeetCode problems that involve various aspects of array manipulation and insertion:
These problems cover a wide range of difficulty levels and will help you practice various array insertion and manipulation techniques.
Array insertion is a fundamental operation in data structures and algorithms. As we've seen, there are many ways to insert elements into arrays, each with its own use cases and time complexities. From simple push operations to more complex scenarios like maintaining sorted order or implementing data structures like circular buffers and tries, understanding these techniques will greatly enhance your ability to work with arrays efficiently.
Remember that the choice of insertion method can significantly impact the performance of your algorithm, especially when dealing with large datasets. Always consider the specific requirements of your problem and the trade-offs between time and space complexity when choosing an insertion technique.
Practice with the provided examples and LeetCode problems to solidify your understanding and improve your problem-solving skills.
Happy coding!
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