LeetCode #1032 — HARD

Stream of Characters

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

Design an algorithm that accepts a stream of characters and checks if a suffix of these characters is a string of a given array of strings words.

For example, if words = ["abc", "xyz"] and the stream added the four characters (one by one) 'a', 'x', 'y', and 'z', your algorithm should detect that the suffix "xyz" of the characters "axyz" matches "xyz" from words.

Implement the StreamChecker class:

  • StreamChecker(String[] words) Initializes the object with the strings array words.
  • boolean query(char letter) Accepts a new character from the stream and returns true if any non-empty suffix from the stream forms a word that is in words.

Example 1:

Input
["StreamChecker", "query", "query", "query", "query", "query", "query", "query", "query", "query", "query", "query", "query"]
[[["cd", "f", "kl"]], ["a"], ["b"], ["c"], ["d"], ["e"], ["f"], ["g"], ["h"], ["i"], ["j"], ["k"], ["l"]]
Output
[null, false, false, false, true, false, true, false, false, false, false, false, true]

Explanation
StreamChecker streamChecker = new StreamChecker(["cd", "f", "kl"]);
streamChecker.query("a"); // return False
streamChecker.query("b"); // return False
streamChecker.query("c"); // return False
streamChecker.query("d"); // return True, because 'cd' is in the wordlist
streamChecker.query("e"); // return False
streamChecker.query("f"); // return True, because 'f' is in the wordlist
streamChecker.query("g"); // return False
streamChecker.query("h"); // return False
streamChecker.query("i"); // return False
streamChecker.query("j"); // return False
streamChecker.query("k"); // return False
streamChecker.query("l"); // return True, because 'kl' is in the wordlist

Constraints:

  • 1 <= words.length <= 2000
  • 1 <= words[i].length <= 200
  • words[i] consists of lowercase English letters.
  • letter is a lowercase English letter.
  • At most 4 * 104 calls will be made to query.
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: Design an algorithm that accepts a stream of characters and checks if a suffix of these characters is a string of a given array of strings words. For example, if words = ["abc", "xyz"] and the stream added the four characters (one by one) 'a', 'x', 'y', and 'z', your algorithm should detect that the suffix "xyz" of the characters "axyz" matches "xyz" from words. Implement the StreamChecker class: StreamChecker(String[] words) Initializes the object with the strings array words. boolean query(char letter) Accepts a new character from the stream and returns true if any non-empty suffix from the stream forms a word that is in words.

Baseline thinking

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

Pattern signal: Array · Design · Trie

Example 1

["StreamChecker","query","query","query","query","query","query","query","query","query","query","query","query"]
[[["cd","f","kl"]],["a"],["b"],["c"],["d"],["e"],["f"],["g"],["h"],["i"],["j"],["k"],["l"]]
Step 02

Core Insight

What unlocks the optimal approach

  • Put the words into a trie, and manage a set of pointers within that trie.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. 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
Largest constraint values
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 #1032: Stream of Characters
class Trie {
    Trie[] children = new Trie[26];
    boolean isEnd = false;

    public void insert(String w) {
        Trie node = this;
        for (int i = w.length() - 1; i >= 0; --i) {
            int idx = w.charAt(i) - 'a';
            if (node.children[idx] == null) {
                node.children[idx] = new Trie();
            }
            node = node.children[idx];
        }
        node.isEnd = true;
    }

    public boolean query(StringBuilder s) {
        Trie node = this;
        for (int i = s.length() - 1; i >= 0; --i) {
            int idx = s.charAt(i) - 'a';
            if (node.children[idx] == null) {
                return false;
            }
            node = node.children[idx];
            if (node.isEnd) {
                return true;
            }
        }
        return false;
    }
}

class StreamChecker {
    private StringBuilder sb = new StringBuilder();
    private Trie trie = new Trie();

    public StreamChecker(String[] words) {
        for (String w : words) {
            trie.insert(w);
        }
    }

    public boolean query(char letter) {
        sb.append(letter);
        return trie.query(sb);
    }
}

/**
 * Your StreamChecker object will be instantiated and called as such:
 * StreamChecker obj = new StreamChecker(words);
 * boolean param_1 = obj.query(letter);
 */
Step 06

Interactive Study Demo

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

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(1) per op
Space
O(n)

Approach Breakdown

NAIVE
O(n) per op time
O(n) space

Use a simple list or array for storage. Each operation (get, put, remove) requires a linear scan to find the target element — O(n) per operation. Space is O(n) to store the data. The linear search makes this impractical for frequent operations.

OPTIMIZED DESIGN
O(1) per op time
O(n) space

Design problems target O(1) amortized per operation by combining data structures (hash map + doubly-linked list for LRU, stack + min-tracking for MinStack). Space is always at least O(n) to store the data. The challenge is achieving constant-time operations through clever structure composition.

Shortcut: Combine two data structures to get O(1) for each operation type. Space is always O(n).
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.