LeetCode #290 — EASY

Word Pattern

Build confidence with an intuition-first walkthrough focused on hash map fundamentals.

The Problem

Problem Statement

Given a pattern and a string s, find if s follows the same pattern.

Here follow means a full match, such that there is a bijection between a letter in pattern and a non-empty word in s. Specifically:

Each letter in pattern maps to exactly one unique word in s.
Each unique word in s maps to exactly one letter in pattern.
No two letters map to the same word, and no two words map to the same letter.

Example 1:

Input: pattern = "abba", s = "dog cat cat dog"

Output: true

Explanation:

The bijection can be established as:

'a' maps to "dog".
'b' maps to "cat".

Example 2:

Input: pattern = "abba", s = "dog cat cat fish"

Output: false

Example 3:

Input: pattern = "aaaa", s = "dog cat cat dog"

Output: false

Constraints:

1 <= pattern.length <= 300
pattern contains only lower-case English letters.
1 <= s.length <= 3000
s contains only lowercase English letters and spaces ' '.
s does not contain any leading or trailing spaces.
All the words in s are separated by a single space.

Step 01

Brute Force Baseline

Problem summary: Given a pattern and a string s, find if s follows the same pattern. Here follow means a full match, such that there is a bijection between a letter in pattern and a non-empty word in s. Specifically: Each letter in pattern maps to exactly one unique word in s. Each unique word in s maps to exactly one letter in pattern. No two letters map to the same word, and no two words map to the same letter.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Hash Map

Example 1

"abba"
"dog cat cat dog"

Example 2

"abba"
"dog cat cat fish"

Example 3

"aaaa"
"dog cat cat dog"

Core Insight

What unlocks the optimal approach

No official hints in dataset. Start from constraints and look for a monotonic or reusable state.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #290: Word Pattern
class Solution {
    public boolean wordPattern(String pattern, String s) {
        String[] ws = s.split(" ");
        if (pattern.length() != ws.length) {
            return false;
        }
        Map<Character, String> d1 = new HashMap<>();
        Map<String, Character> d2 = new HashMap<>();
        for (int i = 0; i < ws.length; ++i) {
            char a = pattern.charAt(i);
            String b = ws[i];
            if (!d1.getOrDefault(a, b).equals(b) || d2.getOrDefault(b, a) != a) {
                return false;
            }
            d1.put(a, b);
            d2.put(b, a);
        }
        return true;
    }
}

// Accepted solution for LeetCode #290: Word Pattern
func wordPattern(pattern string, s string) bool {
	ws := strings.Split(s, " ")
	if len(ws) != len(pattern) {
		return false
	}
	d1 := map[rune]string{}
	d2 := map[string]rune{}
	for i, a := range pattern {
		b := ws[i]
		if v, ok := d1[a]; ok && v != b {
			return false
		}
		if v, ok := d2[b]; ok && v != a {
			return false
		}
		d1[a] = b
		d2[b] = a
	}
	return true
}

# Accepted solution for LeetCode #290: Word Pattern
class Solution:
    def wordPattern(self, pattern: str, s: str) -> bool:
        ws = s.split()
        if len(pattern) != len(ws):
            return False
        d1 = {}
        d2 = {}
        for a, b in zip(pattern, ws):
            if (a in d1 and d1[a] != b) or (b in d2 and d2[b] != a):
                return False
            d1[a] = b
            d2[b] = a
        return True

// Accepted solution for LeetCode #290: Word Pattern
use std::collections::HashMap;

impl Solution {
    pub fn word_pattern(pattern: String, s: String) -> bool {
        let cs1: Vec<char> = pattern.chars().collect();
        let cs2: Vec<&str> = s.split_whitespace().collect();
        let n = cs1.len();
        if n != cs2.len() {
            return false;
        }
        let mut map1 = HashMap::new();
        let mut map2 = HashMap::new();
        for i in 0..n {
            let c = cs1[i];
            let s = cs2[i];
            if !map1.contains_key(&c) {
                map1.insert(c, i);
            }
            if !map2.contains_key(&s) {
                map2.insert(s, i);
            }
            if map1.get(&c) != map2.get(&s) {
                return false;
            }
        }
        true
    }
}

// Accepted solution for LeetCode #290: Word Pattern
function wordPattern(pattern: string, s: string): boolean {
    const ws = s.split(' ');
    if (pattern.length !== ws.length) {
        return false;
    }
    const d1 = new Map<string, string>();
    const d2 = new Map<string, string>();
    for (let i = 0; i < pattern.length; ++i) {
        const a = pattern[i];
        const b = ws[i];
        if (d1.has(a) && d1.get(a) !== b) {
            return false;
        }
        if (d2.has(b) && d2.get(b) !== a) {
            return false;
        }
        d1.set(a, b);
        d2.set(b, a);
    }
    return true;
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(m + n)

Space

O(m + n)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

For each element, scan the rest of the array looking for a match. Two nested loops give n × (n−1)/2 comparisons = O(n²). No extra space since we only use loop indices.

HASH MAP

O(n) time

O(n) space

One pass through the input, performing O(1) hash map lookups and insertions at each step. The hash map may store up to n entries in the worst case. This is the classic space-for-time tradeoff: O(n) extra memory eliminates an inner loop.

Shortcut: Need to check “have I seen X before?” → hash map → O(n) time, O(n) space.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.