Spell Checker

Introduction

Spell Checker is a portfolio project that turns Trees & Graphs interview patterns into a real product-style system.

Understanding the topic

Problem statement: Build dictionary lookup, prefix search, edit-distance suggestions, and ranking.

• Model the domain as a tree, trie, graph, heap, or combination.
• Define APIs, edge cases, and performance constraints before coding.
• Add tests that prove correctness on small, large, cyclic, and empty inputs.

Visual explanation

Architecture:

client
  |
  v
api layer
  |
  v
graph/tree engine
  |
  +--> index / cache
  +--> persistence
  +--> metrics

Step-by-step explanation

1. Create the input model and parser.
2. Implement the core data structure with tests.
3. Add the query/update API.
4. Handle edge cases and malformed input.
5. Add benchmarking, caching, and documentation.

Informative example

Suggested folder structure:

spell-checker/
  src/main/java/
    model/
    graph/
    service/
    api/
  src/test/java/
  docs/
    architecture.md
    edge-cases.md

Complexity analysis

• Time: Depends on query: usually O(log n), O(L), O(V + E), or O(E log V)
• Space: O(n) or O(V + E)

Real-world use

This project is interview-ready because it demonstrates modeling, algorithms, API design, complexity trade-offs, and production thinking in one story.

Best practices

• Document data structures used and why.
• Expose metrics for query latency and index size.
• Write tests for edge cases before optimizing.

Common mistakes

• Building a clever algorithm without a clear API.
• Skipping large-input tests.
• Not explaining why the chosen data structure beats a simpler list or map.

Optimization strategies

• Cache hot queries.
• Precompute indexes where reads dominate writes.
• Use iterative algorithms for very deep structures.

Advanced interview questions

Pattern recognition guide

• Recognize when Spell Checker appears from the wording: hierarchy, nearest, connected, dependency, route, prefix, or ordering.
• Draw a tiny input and label visited state, queue/stack, parent pointers, or distances.
• Choose the simplest correct pattern before optimizing.

Visual walkthrough

Interview tips

• DFS is natural for exhaustively exploring paths, components, recursion, and backtracking.
• BFS is natural for shortest path in unweighted graphs and level-by-level expansion.
• Use a heap when the next best candidate must be selected repeatedly.

Common mistakes

• Marking visited too late in BFS and enqueueing duplicates.
• Forgetting disconnected components.
• Using Dijkstra with negative edges.

Interview Q&A