String Manipulation

The String Manipulation pattern is the bedrock of technical interviews, transforming complex text processing tasks into efficient algorithms. It moves beyond simple library calls, utilizing techniques like Two Pointers, Rolling Hashes, and Sliding Windows to turn nested

searches into linearscans.

Real-World Analogy

Imagine you are looking for a specific quote in a massive library of books.

• Brute Force: You read every word of every book from start to finish. If you don’t find it, you start again from the second word. This is slow and exhausting.
• Two Pointers: Like checking if a word is a palindrome by looking at the first and last letters simultaneously and moving toward the middle.
• Sliding Window: Like using a magnifying glass that can only see five words at a time, sliding it across the page to find a specific phrase.
• Rabin-Karp: Like giving every sentence a “barcode” (hash). Instead of reading the words, you just scan the barcodes. If a barcode matches your quote’s barcode, only then do you stop to read the words.

Visual Explanation

Let’s visualize the Two Pointers technique for detecting a Palindrome.

  flowchart TD
    subgraph S ["String: racecar"]
        direction LR
        C0["r"] --- C1["a"] --- C2["c"] --- C3["e"] --- C4["c"] --- C5["a"] --- C6["r"]
    end

    L["Left Pointer"] --> C0
    R["Right Pointer"] --> C6

    Compare{"s[left] == s[right]?"}
    C0 --- Compare
    C6 --- Compare

    Compare -- "Yes" --> Move["Move Pointers Inward"]
    Compare -- "No" --> Stop["Not a Palindrome"]

    Move --> L2["Left++"]
    Move --> R2["Right--"]

    style C0 fill:#dcfce7,stroke:#166534
    style C6 fill:#dcfce7,stroke:#166534
    style L fill:#e0f2fe,stroke:#0369a1
    style R fill:#e0f2fe,stroke:#0369a1
    style Compare fill:#fef9c3,stroke:#a16207

How it works:

1. Initialize two pointers: left at index 0 and right at index n-1.
2. Compare characters at both pointers.
3. If they match, increment left and decrement right.
4. If they ever don’t match, it is not a palindrome.
5. Stop when left >= right.

When to Use This Pattern

Use string manipulation techniques when you see these characteristics:

• Input is a string or array of characters.
• Problems involve finding substrings, palindromes, or anagrams.
• Need to perform pattern matching (finding a string within another).
• Optimization is required to avoid nested loops (e.g., finding the longest substring).
• Space complexity must be minimized (processing strings in-place).

Complexity Analysis

Algorithm	Time Complexity	Space Complexity	Explanation
Two Pointers	O(N)	O(1)	Each character is visited at most once; no extra storage.
Sliding Window	O(N)	O(K)	Window moves from left to right; space depends on character set size K.
Rabin-Karp	O(N + M)	O(1)	Average case for pattern matching using rolling hashes.
KMP	O(N + M)	O(M)	Preprocessing the pattern takes O(M) space.

Derivation:

• Time: Most optimized string algorithms aim for linear complexity by ensuring each character is either compared or skipped based on previous information (like KMP) or processed as part of a moving window.
• Space: Two pointers usually require no extra space. Hashing or pattern preprocessing requires space proportional to the character set or the pattern length.

Common Mistakes

1. Immutability Awareness: Forgetting that strings are immutable in languages like Java or Python, leading to accidental
   O(N^2)
   complexity due to repeated concatenations.
2. Character Encoding: Assuming all input is ASCII (256 chars) when it might be Unicode, leading to array index out-of-bounds errors.
3. Empty String Handling: Not checking for empty or single-character inputs, which often trigger edge case bugs.
4. Off-by-One Errors: Mischalculating substring boundaries (e.g., s.substring(start, end) behavior varies by language).

Next Steps

Apply these concepts with our Code Templates or test your skills with Practice Problems.