InterviewBank — Data Structures & Algorithms Examples

This repository provides curated, fully-documented Java examples of fundamental data structures and algorithms framed as short real-world stories. The goal is to help interviewers select clear, progressively challenging problems and to guide interviewees to reason like senior engineers.

Why fundamentals matter (Senior SDE perspective):

• Mastering core data structures and algorithms is essential for building scalable systems. Problems like these test ability to reason about time/space tradeoffs, invariants, and edge cases — skills applied daily when designing services and debugging production issues.
• Interviews assess thought process: clarity, trade-offs, test coverage, and ability to iterate on a solution. Practicing classic problems trains those muscles.
• Focus on correctness first, then performance. Explain assumptions, consider pathological inputs, and prefer simple, maintainable solutions unless optimization is necessary.

Repository layout:

• DataStructures/ — per-structure folders with examples and narratives.
• Algorithms/ — algorithm categories with worked examples.

Guidelines for contributors/interviewers:

• When using these problems in interviews, start by asking for clarifying questions and constraints. Let the candidate articulate their approach before coding.
• Encourage whiteboarding trade-offs, then incremental implementation and tests.
• Focus feedback on reasoning clarity, test coverage, and algorithmic choices rather than just achieving optimal asymptotics.

Guidelines to evaluate a candidate during coding interview rounds

This section captures evaluation guidelines used by experienced interviewers for assessing candidates during coding interviews.

Logical and Maintainable Coding

• Clear problem restatement: candidate asks clarifying questions and states assumptions.
• Readability: code uses descriptive names, small helper methods, and consistent style so reviewers can follow logic quickly.
• Correctness & edge cases: candidate identifies and handles boundary conditions, null/empty inputs, and invalid arguments.
• Modularity: decomposition into well-named functions that each have a single responsibility.
• Tests and examples: candidate walks through small examples and proposes or writes simple tests to validate behavior.
• Defensive but pragmatic: safe handling of inputs without over-engineering; prefers simple, maintainable code.
• Communication: explains trade-offs and incremental improvements rather than jumping straight to micro-optimizations.

Data Structures & Algorithms

• Problem identification: chooses the right abstraction (array, map, set, heap, tree, graph) for the task and justifies the choice.
• Complexity reasoning: provides clear time and space complexity for the proposed solution and considers alternatives.
• Correct algorithmic approach: demonstrates core patterns (two pointers, sliding window, divide-and-conquer, dynamic programming, greedy, graph traversal) where appropriate.
• Scalability and trade-offs: discusses memory vs CPU trade-offs and how designs behave with larger inputs or streaming data.
• Incremental improvement: starts with a correct brute-force idea if needed, then iterates to optimize while explaining each step.
• Robustness: considers integer overflow, precision, concurrency implications, and potential real-world constraints if relevant.

Use these guidelines to provide structured feedback: separate evaluation into coding quality, algorithmic correctness, complexity analysis, and communication. Prioritize clarity and correctness; strong candidates combine solid fundamentals with clear reasoning and maintainable code.