core pattern

Hash Map / Set

Use hash maps for O(1) lookups to track frequencies, find duplicates, store complements, or establish relationships between elements.

Time

O(n)

Space

O(n)

🧠Mental Model

“A librarian with perfect memory — instantly retrieves any book by its catalog number.”

Verbal cue: Store what you've seen, lookup in O(1)

🎯Recognition Triggers

When you see these patterns in a problem, consider this approach:

Count occurrencesFind duplicateTwo sum (unsorted)Group by propertyCheck if exists in O(1)Anagram detectionSubarray sum equals K

📝Code Template

Hash Map / Set Template

1function twoSum(nums, target) {
2  const seen = new Map();
3  
4  for (let i = 0; i < nums.length; i++) {
5    const complement = target - nums[i];
6    
7    if (seen.has(complement)) {
8      return [seen.get(complement), i];
9    }
10    
11    seen.set(nums[i], i);
12  }
13  
14  return [-1, -1];
15}

Complexity rationale: Single pass, but storing up to n elements in hash map

💡Interview Tips

1Hash map trades space for time — mention this tradeoff
2Clarify what to return: indices, values, or boolean
3For frequency problems, collections.Counter (Python) is useful
4Consider sorted alternatives when space is critical

⚠️Common Mistakes

✕Not considering hash collisions in theoretical analysis
✕Forgetting to store the value AFTER checking (for two-sum)
✕Using wrong hash function for custom objects
✕Not handling case when element equals its complement