Union Find Disjoint Set Data Structure in DSA Javascript - Time & Space Complexity

Time Complexity: Union Find Disjoint Set Data Structure

O(α(n))

Understanding Time Complexity

We want to understand how fast the Union Find data structure works when connecting groups and checking if items belong together.

How does the time needed change as we add more items or connections?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

class UnionFind {
  constructor(size) {
    this.parent = Array(size).fill(0).map((_, i) => i);
    this.rank = Array(size).fill(0);
  }

  find(x) {
    if (this.parent[x] !== x) {
      this.parent[x] = this.find(this.parent[x]);
    }
    return this.parent[x];
  }

  union(x, y) {
    let rootX = this.find(x);
    let rootY = this.find(y);
    if (rootX !== rootY) {
      if (this.rank[rootX] < this.rank[rootY]) {
        this.parent[rootX] = rootY;
      } else if (this.rank[rootX] > this.rank[rootY]) {
        this.parent[rootY] = rootX;
      } else {
        this.parent[rootY] = rootX;
        this.rank[rootX]++;
      }
    }
  }
}
    

This code creates a Union Find structure that groups items and finds their leaders efficiently using path compression and rank.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The find function uses recursion to find the leader of a group and compresses the path.
• How many times: Each union calls find twice, and find may recurse up the tree, but path compression keeps trees shallow.

How Execution Grows With Input

As we add more items, the time to find leaders grows very slowly because the trees stay flat.

Input Size (n)	Approx. Operations per find/union
10	About 4-5 steps
100	About 6-7 steps
1000	About 8-9 steps

Pattern observation: The steps grow very slowly, almost like a flat line, even as input grows large.

Final Time Complexity

Time Complexity: O(α(n)) per operation

This means each find or union operation takes almost constant time, growing so slowly it's practically flat for all real input sizes.

Common Mistake

[X] Wrong: "Each find or union takes linear time because it might check many items."

[OK] Correct: Path compression and rank keep the trees very shallow, so operations rarely check many items, making them almost constant time.

Interview Connect

Understanding Union Find's time complexity shows you can handle efficient grouping problems, a useful skill in many coding challenges and real-world tasks.

Self-Check

"What if we removed path compression from the find method? How would the time complexity change?"

Practice

1. What is the main purpose of the Union Find Disjoint Set data structure?

easy

A. To efficiently track and merge groups of connected elements

B. To sort elements in ascending order

C. To store key-value pairs for fast lookup

D. To implement a queue with fast enqueue and dequeue

Solution

1. Step 1: Understand the role of Union Find

   The Union Find data structure helps keep track of which elements belong to the same group or set.

2. Step 2: Identify its main operation

   It supports merging two groups and checking if two elements are in the same group efficiently.

3. Final Answer:

   To efficiently track and merge groups of connected elements -> Option A

4. Quick Check:

   Union Find = Track and merge groups [OK]

Hint: Union Find connects groups, not sorting or key-value storage [OK]

Common Mistakes:

• Confusing Union Find with sorting algorithms
• Thinking it stores key-value pairs like a dictionary
• Assuming it works like a queue or stack

2. Which of the following is the correct way to initialize a Union Find parent array for 5 elements in JavaScript?

easy

A. const parent = new Array(5).fill(-1);

B. const parent = [0, 1, 2, 3, 4];

C. const parent = new Array(5).fill(0);

D. const parent = [1, 2, 3, 4, 5];

Solution

1. Step 1: Understand parent array initialization

   Each element is initially its own parent, so parent[i] = i for all i.

2. Step 2: Check each option

   const parent = [0, 1, 2, 3, 4]; correctly sets parent to [0,1,2,3,4]. const parent = new Array(5).fill(0); fills all with 0, wrong. const parent = new Array(5).fill(-1); fills with -1, wrong. const parent = [1, 2, 3, 4, 5]; starts from 1, wrong index.

3. Final Answer:

   const parent = [0, 1, 2, 3, 4]; -> Option B

4. Quick Check:

   Parent array = self indices [OK]

Hint: Parent array starts with each element as its own parent [OK]

Common Mistakes:

• Filling parent array with zeros or -1
• Starting indices from 1 instead of 0
• Not initializing parent array at all

3. Consider the following JavaScript code using Union Find:

const parent = [0, 1, 2, 3, 4];
function find(x) {
  if (parent[x] !== x) {
    parent[x] = find(parent[x]);
  }
  return parent[x];
}
function union(a, b) {
  const rootA = find(a);
  const rootB = find(b);
  if (rootA !== rootB) {
    parent[rootB] = rootA;
  }
}

union(0, 1);
union(1, 2);
console.log(parent);

What will be printed?

medium

A. [0, 0, 0, 3, 4]

B. [0, 1, 2, 3, 4]

C. [1, 1, 1, 3, 4]

D. [0, 0, 1, 3, 4]

Solution

1. Step 1: Perform union(0, 1)

   Find roots: root of 0 is 0, root of 1 is 1. Set parent[1] = 0. Parent becomes [0,0,2,3,4].

2. Step 2: Perform union(1, 2)

   Find roots: root of 1 is find(0) = 0, root of 2 is 2. Set parent[2] = 0. Parent becomes [0,0,0,3,4].

3. Final Answer:

   [0, 0, 0, 3, 4] -> Option A

4. Quick Check:

   Union updates parents to root 0 [OK]

Hint: Union sets parent of one root to the other root [OK]

Common Mistakes:

• Not applying path compression in find
• Confusing which root becomes parent
• Assuming parent array stays unchanged

4. The following code attempts to implement Union Find but has a bug:

const parent = [0, 1, 2, 3];
function find(x) {
  if (parent[x] === x) return x;
  return find(parent[x]);
}
function union(a, b) {
  const rootA = find(a);
  const rootB = find(b);
  if (rootA !== rootB) {
    parent[rootA] = rootB;
  }
}

union(0, 1);
union(1, 2);
console.log(parent);

What is the bug in this code?

medium

A. Union assigns parent[rootA] = rootB instead of parent[rootB] = rootA

B. The find function does not return a value

C. Parent array is not initialized correctly

D. Missing path compression in find function

Solution

1. Step 1: Examine the find function

   The find recurses to the root but lacks path compression: it should set parent[x] = find(parent[x]) before returning when parent[x] !== x.

2. Step 2: Verify other options

   Find returns correctly. Parent is initialized properly as self-references. Union direction (parent[rootA] = rootB) is valid.

3. Final Answer:

   Missing path compression in find function -> Option D

4. Quick Check:

   Path compression: parent[x] = find(parent[x]) [OK]

Hint: Path compression flattens tree: parent[x] = find(parent[x]) [OK]

Common Mistakes:

• Ignoring missing path compression
• Thinking find lacks return statement
• Assuming parent array is wrong when it's correct

5. You have 6 elements (0 to 5) initially in separate sets. After these operations:

union(0, 1);
union(2, 3);
union(4, 5);
union(1, 2);
union(0, 4);

How many distinct sets remain?

hard

A. 3

B. 2

C. 1

D. 4

Solution

1. Step 1: Perform unions step-by-step

   union(0,1) merges sets {0},{1} -> {0,1} union(2,3) merges {2},{3} -> {2,3} union(4,5) merges {4},{5} -> {4,5}

2. Step 2: Continue unions

   union(1,2) merges {0,1} and {2,3} -> {0,1,2,3} union(0,4) merges {0,1,2,3} and {4,5} -> {0,1,2,3,4,5}

3. Step 3: Count distinct sets

   All elements are connected in one set.

4. Final Answer:

   1 -> Option C

5. Quick Check:

   All unions connect all elements [OK]

Hint: Count merges; all connected means one set [OK]

Common Mistakes:

• Counting each union as a new set
• Not merging transitive connections
• Forgetting that union merges sets fully