LeetCode Trie Prefix Tree #68
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WalkthroughA new Trie (prefix tree) data structure implementation has been added for LeetCode problem 0208. The main project README has been updated to reflect the addition, incrementing solved problem counts and including the new problem entry. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~12 minutes
Poem
Pre-merge checks and finishing touches✅ Passed checks (3 passed)
✨ Finishing touches
🧪 Generate unit tests (beta)
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Claude finished @pertrai1's task in 1m 16s —— View job Code Review: LeetCode 0208 - Implement Trie (Prefix Tree)
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| Approach | Insert/Search | Space | Prefix Queries |
|---|---|---|---|
| Trie | O(L) | O(N×M) high | Native O(L) |
| Hash Set | O(L) | O(N×M) medium | Need to iterate all words O(N×M) |
| Sorted Array | O(N×M) | O(N×M) medium | Binary search + iteration O(log N + k) |
Trie wins when prefix operations are frequent!
🎯 Alternative Approaches
Compressed Trie (Radix Tree)
- Store character sequences on edges instead of individual characters
- Reduces space for paths with no branching
- Trade-off: More complex implementation for better space efficiency
- Example: "apple" and "apply" → root → "appl" → {"e", "y"}
Great work on this implementation! The code is production-quality with excellent documentation. This is a fundamental data structure that appears frequently in system design (autocomplete, search engines, etc.).
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✅ Test ResultsTests have been executed for this pull request. 📊 Coverage Summary
Check the workflow logs for detailed test output. |
🧮 Solution AnalysisPR: LeetCode Trie Prefix Tree 📊 Code Quality ResultsESLint Analysis
Issues by file:
🔍 Complexity Analysis💡 Analysis Tips
📁 Analysis Files CreatedDetailed analysis files have been automatically generated and committed to 🎯 Next Steps
Analysis generated for PR files only |
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Here's a detailed review of your Trie implementation: 1. Pattern IdentificationThis solution uses the Trie (Prefix Tree) data structure pattern. A Trie is a tree-like data structure used to store a dynamic set or associative array where keys are usually strings. It is particularly efficient for problems involving prefixes, such as autocomplete, spell checkers, and dictionary searches. 2. Complexity VerificationThe complexity analysis provided in the code comments is largely accurate and well-explained.
3. Key InsightThe key insight that makes the Trie data structure efficient for prefix-based operations is:
4. Edge Case AnalysisThe problem constraints (
5. Learning Points
6. Code QualityYour code quality is excellent.
// Example of an optional JSDoc comment for TrieNode
/**
* Represents a single node in the Trie.
* Each node can have children (representing the next characters in a word)
* and a flag indicating if it marks the end of an inserted word.
*/
class TrieNode {
children: { [key: string]: TrieNode } = {};
isEndOfWord: boolean = false;
}7. Alternative ApproachesWhile the Trie is the optimal data structure for this specific problem due to its prefix-matching efficiency, here are a couple of alternative approaches and their trade-offs:
The Trie solution you've provided is indeed the most efficient and standard approach for this problem. |
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Here's a detailed review of the provided Trie implementation: 1. Pattern IdentificationThis solution implements a Trie (Prefix Tree) data structure. A Trie is a tree-like data structure used to store a dynamic set of strings where the keys are usually strings. It is particularly efficient for operations involving prefixes, such as searching for words, checking for prefixes, and autocomplete features. Each node in a Trie typically represents a character, and paths from the root to a node represent a prefix of a word. 2. Complexity VerificationThe stated complexities are largely accurate, but let's refine the space complexity explanation for clarity.
3. Key InsightThe key insight behind the Trie data structure and this solution lies in:
4. Edge Case AnalysisThe problem constraints (
The current solution handles all relevant edge cases within the problem constraints correctly. 5. Learning Points
6. Code QualityThe code quality is good, adhering to best practices:
Minor potential improvement (more stylistic than functional):
This is a minor point and the current implementation is perfectly fine. 7. Alternative Approaches
The Trie implementation provided is the most optimal and standard approach for this problem due to its |
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Pull request overview
This PR adds a TypeScript solution for LeetCode problem #208 (Implement Trie/Prefix Tree), a medium-difficulty problem involving the classic Trie data structure. The solution implements a Trie class with insert, search, and startsWith operations, along with a helper TrieNode class.
- Adds complete Trie implementation with proper TypeScript types and complexity annotations
- Includes problem description README following repository conventions
- Updates main README to reflect the new problem count (162 total, 70 medium)
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| leetcode/medium/0208-implement-trie-prefix-tree/implement-trie-prefix-tree.ts | Complete Trie implementation with TrieNode helper class and three core methods (insert, search, startsWith) |
| leetcode/medium/0208-implement-trie-prefix-tree/README.md | Problem description, examples, and constraints from LeetCode |
| README.md | Updates problem counts and adds entry under "Stack & Design" category in medium problems section |
| let current = this.root; | ||
| // Traverse word | ||
| for (const char of word) { | ||
| // If char not found in current.children then the word does not exists |
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Grammatical error: "exists" should be "exist" to match the singular subject "the word".
| // If char not found in current.children then the word does not exists | |
| // If char not found in current.children then the word does not exist |
| current = current.children[char]; | ||
| } | ||
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| // Full prefix path exits |
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Spelling error: "exits" should be "exists" (the verb form meaning "is present").
| // Full prefix path exits | |
| // Full prefix path exists |
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Actionable comments posted: 0
🧹 Nitpick comments (1)
leetcode/medium/0208-implement-trie-prefix-tree/implement-trie-prefix-tree.ts (1)
1-76: Trie implementation is correct; only minor optional polish
- The
TrieNode+Triedesign and the logic ininsert,search, andstartsWithcorrectly implement the LeetCode 208 interface with O(L) time per operation.- Types are explicit on fields, constructor, and methods, matching the strict TypeScript guidelines for this repo.
If you want to tighten things up further (optional):
- You could extract the common traversal into a small helper (e.g.,
private traverse(path: string): TrieNode | null) used by bothsearchandstartsWithto reduce duplication.- Fix tiny comment typos for clarity, e.g. “the word does not exists” → “does not exist”, “Full prefix path exits” → “exists”.
These are purely cosmetic; the current implementation is solid.
As per coding guidelines, this keeps type safety and readability high while using a standard trie pattern.
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
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📒 Files selected for processing (3)
README.md(3 hunks)leetcode/medium/0208-implement-trie-prefix-tree/README.md(1 hunks)leetcode/medium/0208-implement-trie-prefix-tree/implement-trie-prefix-tree.ts(1 hunks)
🧰 Additional context used
📓 Path-based instructions (5)
**/*.md
📄 CodeRabbit inference engine (AGENTS.md)
**/*.md: Create comprehensive documentation guides for arrays, trees, graphs, heaps, and other data structures
Document reusable code templates for common algorithmic patterns
Maintain Big O complexity cheatsheets and references
Files:
leetcode/medium/0208-implement-trie-prefix-tree/README.mdREADME.md
**/README.md
📄 CodeRabbit inference engine (CLAUDE.md)
Each problem directory should include a README.md containing: problem title and difficulty badge, link to the problem on the respective platform, problem description, examples and constraints
Files:
leetcode/medium/0208-implement-trie-prefix-tree/README.mdREADME.md
**/*.ts
📄 CodeRabbit inference engine (AGENTS.md)
**/*.ts: Use explicit type annotations for function parameters and return types in TypeScript solutions
Follow strict TypeScript configuration with ES2020 target and CommonJS modules in TypeScript solutions
Enforce full type safety in TypeScript solutions
Files:
leetcode/medium/0208-implement-trie-prefix-tree/implement-trie-prefix-tree.ts
**/*.{js,ts}
📄 CodeRabbit inference engine (AGENTS.md)
**/*.{js,ts}: Use clear, descriptive variable names across all solutions
Add comments for non-obvious logic or algorithm steps in code
Keep functions focused and single-purpose across all solutions
Prefer readability over cleverness in code implementation
**/*.{js,ts}: Use descriptive variable names; single letters likei,j,kare acceptable for loop iterators and standard mathematical contexts
Add comments for complex logic, but prefer self-documenting code with clear logic
Minimize external dependencies and keep solutions self-contained
**/*.{js,ts}: Use clear, descriptive variable names (not just i, j, k unless in simple loops) and add comments for non-obvious logic or algorithm steps
Keep functions focused and single-purpose, preferring readability over cleverness
Use const by default, let only when reassignment needed; avoid var except for LeetCode solution function definitions
Use modern ES6+ syntax (arrow functions, destructuring, spread operators) where appropriate
Prefer built-in methods (.map(), .filter(), .reduce()) when appropriate and use strict equality (===) over loose equality (==)
Files:
leetcode/medium/0208-implement-trie-prefix-tree/implement-trie-prefix-tree.ts
**/*.{ts,tsx}
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
**/*.{ts,tsx}: Use explicit type annotations for function parameters and return types in TypeScript
Follow strict TypeScript configuration as defined in tsconfig.json with ES2020 target and CommonJS modules
Files:
leetcode/medium/0208-implement-trie-prefix-tree/implement-trie-prefix-tree.ts
🧠 Learnings (2)
📚 Learning: 2025-11-26T14:46:46.097Z
Learnt from: CR
Repo: pertrai1/coding-challenges PR: 0
File: .github/copilot-instructions.md:0-0
Timestamp: 2025-11-26T14:46:46.097Z
Learning: Applies to [0-9]*-*/*.md : Problem README files should include: problem title with difficulty badge, problem description from LeetCode, examples with inputs/outputs, and constraints
Applied to files:
leetcode/medium/0208-implement-trie-prefix-tree/README.md
📚 Learning: 2025-11-26T14:46:46.097Z
Learnt from: CR
Repo: pertrai1/coding-challenges PR: 0
File: .github/copilot-instructions.md:0-0
Timestamp: 2025-11-26T14:46:46.097Z
Learning: Applies to [0-9]*-*/*.{js,ts} : Use standard algorithmic patterns (two pointers, sliding window, hash maps, DFS, BFS, DP) and leverage appropriate data structures (Map, Set, arrays)
Applied to files:
leetcode/medium/0208-implement-trie-prefix-tree/implement-trie-prefix-tree.ts
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🔇 Additional comments (2)
leetcode/medium/0208-implement-trie-prefix-tree/README.md (1)
1-36: Problem README structure looks complete and aligned with LeetCode statementTitle + difficulty badge, direct LeetCode link, API description, worked example, and constraints are all present and clearly formatted. No changes needed here.
Based on learnings, this matches the expected problem README template for LeetCode problems.
README.md (1)
9-21: Progress counters and new Medium problem entry are consistentThe LeetCode total, Medium count, “Medium Problems (70 solved)” summary, and the new
0208 - Implement Trie (Prefix Tree)link are all in sync and correctly categorized under Stack & Design.Also applies to: 183-188
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