Data Structures in JavaScript

12 essential data structures for JavaScript developers. Each one explained with Big O complexity, animated visuals, and real code samples you can copy.

Python

Java

JavaScript TypeScript logo

Array Dynamic Array Stack Queue Hash Map Hash Set Linked List Sorted Set (BST)Sorted Map (BST)Priority Queue (Heap)Concurrent Hash Map Memory View / Slice

Array

Array

Fixed-size, contiguous block of memory. Elements are stored sequentially and accessed by index in constant time. The foundation of most other data structures.

[0]

[1]

[2]

[3]

[4]

arr[0] = 7

Complexity

Access by indexO(1)

SearchO(n)

Insert / DeleteO(n)

When to use

+You know the exact size at creation time
+You need the fastest possible index-based access
+Working with fixed-length data like matrices or buffers

JavaScript

// Fixed-length array (typed - contiguous memory)
const buf = new Int32Array(5);     // O(1) - fixed size
buf[0] = 42;                       // O(1) access
buf[3] = 99;                       // O(1) assign

// Iterate
for (const val of buf)
  console.log(val);

// No resize - typed arrays are fixed
console.log(buf.length);           // 5
const idx = buf.indexOf(99);       // O(n) search -> 3
const sorted = buf.toSorted();     // O(n log n) - ES2023

Dynamic Array

Array

Resizable array that automatically grows when capacity is exceeded. The most commonly used data structure in most languages. Doubles its internal storage when full, giving amortized O(1) appends.

[0]

[1]

[2]

[3]

[4]

arr[0] = 7

Complexity

Access by indexO(1)

SearchO(n)

AppendO(1)*

Insert / Remove (middle)O(n)

When to use

+You need a resizable collection (most common case)
+You frequently access elements by index
+You mostly add or remove at the end

JavaScript

// Array IS the dynamic array in JS
const nums = [10, 20, 30];

nums.push(40);          // O(1) amortized - append
nums.push(50);          // [10, 20, 30, 40, 50]
nums[1] = 25;           // O(1) - index access

nums.splice(2, 0, 28);  // O(n) - insert at index 2
nums.splice(1, 1);      // O(n) - remove at index 1

const has = nums.includes(40);    // O(n) -> true
const idx = nums.indexOf(28);     // O(n) -> 1

nums.sort((a, b) => a - b);       // O(n log n)
console.log(nums);                // [10, 28, 30, 40, 50]

Stack

Array (push/pop)

Last-In-First-Out (LIFO) collection. Only the top element is accessible. Used for tracking state that must be unwound in reverse order.

arrow_downward top

Push 10

Complexity

PushO(1)

PopO(1)

PeekO(1)

SearchO(n)

When to use

+Undo/redo functionality
+Expression parsing and evaluation
+DFS traversal of trees and graphs
+Matching brackets, parentheses validation

JavaScript

// Use Array as a stack (LIFO)
const stack = [];

stack.push(10);    // O(1)
stack.push(20);
stack.push(30);

const top = stack.at(-1);   // O(1) peek -> 30
const val = stack.pop();    // O(1) -> 30

// Interview pattern: valid parentheses
function isValid(s) {
  const st = [];
  const map = { ")": "(", "]": "[", "}": "{" };
  for (const c of s) {
    if (!map[c]) st.push(c);
    else if (st.pop() !== map[c]) return false;
  }
  return st.length === 0;
}

Queue

Array (shift/push)

First-In-First-Out (FIFO) collection. Elements are added at the back and removed from the front. Fundamental for breadth-first processing.

front

back

Enqueue 10

Complexity

EnqueueO(1)

DequeueO(1)

PeekO(1)

SearchO(n)

When to use

+BFS traversal of trees and graphs
+Task scheduling and job queues
+Message passing between components
+Rate limiting, buffering

JavaScript

// Array-based queue (shift is O(n) - fine for interviews)
const queue = [];

queue.push("A");             // O(1) enqueue
queue.push("B");
queue.push("C");

const front = queue[0];     // O(1) peek -> "A"
const next = queue.shift(); // O(n) dequeue -> "A"

// Interview pattern: BFS
function bfs(root) {
  const q = [root];
  while (q.length > 0) {
    const node = q.shift();          // O(n)
    console.log(node.val);
    if (node.left) q.push(node.left);
    if (node.right) q.push(node.right);
  }
}

Hash Map

Map

Maps keys to values using a hash function for near-constant-time lookups. The single most important data structure for coding interviews. Every language has a built-in implementation.

age:30

name:Al

city:NY

hash("age") = 0

Complexity

InsertO(1)*

LookupO(1)

DeleteO(1)

Contains keyO(1)

When to use

+Two Sum and frequency counting patterns
+Caching computed results (memoization)
+Grouping data by a key
+Any problem requiring O(1) lookup by key

JavaScript

// Map - keys can be any type
const map = new Map();

map.set("apple", 3);              // O(1)
map.set("banana", 5);
map.set("apple", 10);             // O(1) update

const val = map.get("apple");     // O(1) -> 10
const has = map.has("banana");    // O(1) -> true
map.delete("banana");             // O(1)

// Interview pattern: Two Sum
function twoSum(nums, target) {
  const seen = new Map();
  for (let i = 0; i < nums.length; i++) {
    const need = target - nums[i];
    if (seen.has(need)) return [seen.get(need), i];
    seen.set(nums[i], i);
  }
}

Hash Set

Set

Unordered collection of unique elements. Uses hashing internally for O(1) membership testing. Supports mathematical set operations like union, intersection, and difference.

age:30

name:Al

city:NY

hash("age") = 0

Complexity

AddO(1)*

ContainsO(1)

RemoveO(1)

Union / IntersectO(n)

When to use

+Checking if an element exists in O(1)
+Removing duplicates from a collection
+Set operations: union, intersection, difference
+Visited tracking in graph traversal

JavaScript

const set = new Set([1, 2, 3, 4, 5]);

set.add(6);             // O(1)
set.add(3);             // O(1) - no duplicate added
set.delete(1);          // O(1)
const has = set.has(4); // O(1) -> true

// Set operations (ES2025 - or polyfill)
const a = new Set([1, 2, 3]);
const b = new Set([2, 3, 4]);
const union = a.union(b);             // {1, 2, 3, 4}
const inter = a.intersection(b);      // {2, 3}
const diff = a.difference(b);         // {1}

// Interview pattern: contains duplicate
const hasDup = (nums) => new Set(nums).size !== nums.length;

Linked List

manual impl

Sequence of nodes where each node points to the next (singly linked) or both next and previous (doubly linked). Efficient insertion and deletion at any known position, but no index-based access.

traversing: 5

Complexity

Insert at head/tailO(1)

Remove (given node)O(1)

SearchO(n)

Access by indexO(n)

When to use

+Frequent insertion/deletion in the middle
+Implementing LRU cache (with a hash map)
+When you need a deque (double-ended queue)
+Problems involving pointer manipulation

JavaScript

// No built-in - manual implementation
class ListNode {
  constructor(val, next = null) {
    this.val = val;
    this.next = next;
  }
}

// Build: 1 -> 2 -> 3
let head = new ListNode(1, new ListNode(2, new ListNode(3)));

// Insert at head - O(1)
head = new ListNode(0, head);   // 0 -> 1 -> 2 -> 3

// Interview pattern: reverse linked list
function reverse(head) {
  let prev = null, curr = head;
  while (curr) {
    const next = curr.next;     // O(1) per node
    curr.next = prev;
    prev = curr;
    curr = next;
  }
  return prev;
}

Sorted Set (BST)

sorted Array

Collection of unique elements maintained in sorted order, typically backed by a balanced binary search tree (red-black tree). Supports range queries and O(log n) min/max.

search(8)

Complexity

AddO(log n)

ContainsO(log n)

RemoveO(log n)

Min / MaxO(log n)

When to use

+Maintaining a sorted collection of unique items
+Range queries (all elements between X and Y)
+Sliding window problems needing sorted order
+Leaderboards, ranking systems

JavaScript

// No built-in sorted set - use a sorted array approach
// For interviews, sort + binary search is typical
const sorted = [1, 3, 5, 8, 9];

// Binary search helper - O(log n)
function bisect(arr, val) {
  let lo = 0, hi = arr.length;
  while (lo < hi) {
    const mid = (lo + hi) >> 1;
    if (arr[mid] < val) lo = mid + 1;
    else hi = mid;
  }
  return lo;
}

// Insert maintaining order - O(n) for shift
const pos = bisect(sorted, 4);
sorted.splice(pos, 0, 4);       // [1, 3, 4, 5, 8, 9]

const min = sorted[0];                  // O(1)
const max = sorted[sorted.length - 1];  // O(1)

Sorted Map (BST)

sorted Map

Key-value pairs maintained in sorted key order, typically backed by a balanced BST. Enables ordered iteration and range lookups that hash maps cannot provide.

search(8)

Complexity

InsertO(log n)

LookupO(log n)

RemoveO(log n)

Iterate (sorted)O(n)

When to use

+You need sorted key-value pairs
+Ordered iteration over entries
+Range lookups by key
+When insertion order or sorted order matters

JavaScript

// No built-in sorted map - use sorted array of entries
// For interviews, sort keys or use a Map + sort when needed
const map = new Map();
map.set("banana", 2);
map.set("apple", 5);
map.set("cherry", 1);

// Iterate in sorted key order
const sortedEntries = [...map.entries()]
  .sort(([a], [b]) => a.localeCompare(b));

for (const [key, val] of sortedEntries)
  console.log(`${key}: ${val}`);
// apple: 5, banana: 2, cherry: 1

// For frequent sorted access, maintain a sorted keys array
// or use a third-party balanced BST library

Priority Queue (Heap)

manual MinHeap

Collection where elements are dequeued by priority rather than insertion order. Typically implemented as a binary heap. Essential for shortest-path algorithms and top-K problems.

min-heap

Complexity

InsertO(log n)

Extract min/maxO(log n)

PeekO(1)

SearchO(n)

When to use

+Dijkstra's shortest path algorithm
+Merge K sorted lists/streams
+Top-K / Kth largest element problems
+Event-driven simulation, scheduling

JavaScript

// No built-in - minimal binary heap for interviews
class MinHeap {
  #data = [];
  push(val) {                      // O(log n)
    this.#data.push(val);
    this.#bubbleUp(this.#data.length - 1);
  }
  pop() {                          // O(log n)
    const top = this.#data[0];
    const last = this.#data.pop();
    if (this.#data.length) { this.#data[0] = last; this.#sinkDown(0); }
    return top;
  }
  peek() { return this.#data[0]; } // O(1)
  get size() { return this.#data.length; }
  #bubbleUp(i) {
    while (i > 0) { const p = (i-1)>>1; if (this.#data[p]<=this.#data[i]) break; [this.#data[p],this.#data[i]]=[this.#data[i],this.#data[p]]; i=p; }
  }
  #sinkDown(i) {
    const n=this.#data.length; while(true) { let s=i,l=2*i+1,r=2*i+2; if(l<n&&this.#data[l]<this.#data[s])s=l; if(r<n&&this.#data[r]<this.#data[s])s=r; if(s===i)break; [this.#data[s],this.#data[i]]=[this.#data[i],this.#data[s]]; i=s; }
  }
}

Concurrent Hash Map

N/A (single-threaded)

Thread-safe hash map designed for concurrent read/write access from multiple threads. Uses fine-grained locking or lock-free techniques instead of a single global lock.

age:30

name:Al

city:NY

hash("age") = 0

Complexity

InsertO(1)*

LookupO(1)

DeleteO(1)

Atomic updateO(1)*

When to use

+Multi-threaded caching
+Shared state across threads or async tasks
+Producer-consumer patterns with keyed data
+When you need concurrent reads and writes

JavaScript

// JS is single-threaded - no ConcurrentDictionary needed
// For SharedArrayBuffer workers, use Atomics:

// Main thread
const sab = new SharedArrayBuffer(1024);
const view = new Int32Array(sab);

// Worker thread - atomic operations
Atomics.store(view, 0, 42);        // O(1) thread-safe write
const val = Atomics.load(view, 0); // O(1) thread-safe read

// Compare-and-swap (lock-free pattern)
Atomics.compareExchange(view, 0, 42, 100);

// For key-value: use a regular Map
// JS event loop guarantees no data races in single-thread

Memory View / Slice

TypedArray

Zero-copy view over a contiguous region of memory. Lets you reference a portion of an array or buffer without allocating new memory. Critical for performance-sensitive parsing and processing.

Span[0..3] = [1, 2, 3]

Complexity

Create sliceO(1)

Access by indexO(1)

SearchO(n)

CopyO(n)

When to use

+Parsing strings or binary data without copies
+Processing sub-arrays without allocation
+High-performance, zero-allocation code paths
+Interop with native or unmanaged memory

JavaScript

// TypedArray = contiguous memory view (like Span)
const buffer = new ArrayBuffer(16);
const view = new Float64Array(buffer);  // O(1) view
view[0] = 3.14;
view[1] = 2.72;

// Subarray - zero-copy slice, shares memory
const slice = view.subarray(0, 1);   // O(1)
slice[0] = 99.9;                     // mutates original

// DataView for mixed types on same buffer
const dv = new DataView(buffer);
dv.setInt8(0, 127);                  // O(1)
const byte = dv.getInt8(0);          // O(1) -> 127

// ArrayBuffer.transfer for resize (ES2024)
const bigger = buffer.transfer(32);  // O(n) copy

Big O Comparison

Average-case time complexity. * = amortized.

Structure	Access	Search	Insert	Delete
Array	O(1)	O(n)	O(n)	O(n)
Dynamic Array	O(1)	O(n)	O(1)*	O(n)
Stack	O(n)	O(n)	O(1)*	O(1)
Queue	O(n)	O(n)	O(1)*	O(1)
Hash Map	O(1)	O(1)	O(1)*	O(1)
Hash Set	N/A	O(1)	O(1)*	O(1)
Linked List	O(n)	O(n)	O(1)	O(1)
Sorted Set	O(n)	O(log n)	O(log n)	O(log n)
Sorted Map	O(log n)	O(log n)	O(log n)	O(log n)
Priority Queue	O(n)	O(n)	O(log n)	O(log n)
Concurrent Map	O(1)	O(1)	O(1)*	O(1)
Memory View	O(1)	O(n)	N/A	N/A

Which collection should I use?

I need to...	Use
Store items by index, resize dynamically	`List / Dynamic Array`
Map keys to values with O(1) lookup	`HashMap / Dictionary`
Track unique items, check existence in O(1)	`HashSet / Set`
Last-in-first-out (undo, DFS, brackets)	`Stack`
First-in-first-out (BFS, task queues)	`Queue`
Keep elements sorted at all times	`SortedSet / TreeSet`
Process items by priority (Dijkstra, top-K)	`PriorityQueue / Heap`
Insert/delete at a known position in O(1)	`LinkedList`
Sorted key-value pairs	`SortedDictionary / TreeMap`
Thread-safe shared cache	`ConcurrentDictionary`
Slice arrays/strings without copying	`Span / Slice / memoryview`

Frequently Asked Questions

What are the most important data structures in JavaScript?add

The most commonly used are dynamic arrays (List/ArrayList/vector), hash maps (Dictionary/HashMap/dict), and hash sets. For interviews, also know stacks, queues, trees, and priority queues. These cover 90%+ of coding interview problems.

Which JavaScript data structure should I learn first?add

Start with the dynamic array and hash map. Together they solve the majority of interview problems. Then learn stacks (for DFS, bracket matching) and queues (for BFS). After that, tackle trees, heaps, and graphs.

Does Big O complexity change between languages?add

No. Big O measures algorithmic complexity, not language-specific performance. A hash map lookup is O(1) whether you use Python dict, Java HashMap, or C# Dictionary. Constant factors differ (C++ is faster than Python in wall-clock time), but Big O is the same.

Is there a built-in priority queue in JavaScript?add

It depends on the language runtime. Check the JavaScript standard library documentation for heap or priority queue support.

Know the structures.
Now use them under pressure.

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Data Structures in JavaScript

Array

Dynamic Array

Stack

Queue

Hash Map

Hash Set

Linked List

Sorted Set (BST)

Sorted Map (BST)

Priority Queue (Heap)

Concurrent Hash Map

Memory View / Slice

Big O Comparison

Which collection should I use?

Frequently Asked Questions

Know the structures.Now use them under pressure.

Know the structures.
Now use them under pressure.