Explain Binary Search.

Deep dive

Expanding on the short answer — what usually matters in practice:

• Context (tags): search, binary-search, algorithm, complexity
• Complexity: compare typical operations (average vs worst-case).
• Invariants: what must always hold for correctness.
• When the choice is wrong: production symptoms (latency, GC, cache misses).
• Explain the "why", not just the "what" (intuition + consequences).
• Trade-offs: what you gain/lose (time, memory, complexity, risk).
• Edge cases: empty inputs, large inputs, invalid inputs, concurrency.

Examples

Here’s an additional example (building on the short answer):

function binarySearch(arr: number[], target: number): number {
    let left = 0;
    let right = arr.length - 1;

    while (left <= right) {
        const mid = Math.floor((left + right) / 2);
        if (arr[mid] === target) return mid;
        if (arr[mid] < target) left = mid + 1;
        else right = mid - 1;
    }
    return -1;
}

Common pitfalls

• Too generic: no concrete trade-offs or examples.
• Mixing average-case and worst-case (e.g., complexity).
• Ignoring constraints: memory, concurrency, network/disk costs.

Interview follow-ups

• When would you choose an alternative and why?
• What production issues show up and how do you diagnose them?
• How would you test edge cases?