Balanced trees such as AVL or Red‑Black are self‑balancing binary search trees that keep height proportional to log n by performing rotations after inserts and deletes. This guarantees search/insert/delete in O(log n) even in the worst case.
Advanced answer
Deep dive
Expanding on the short answer — what usually matters in practice:
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
A tiny example (an explanation template):
// Example: discuss trade-offs for "what-are-balanced-trees-(e.g.,-avl,-red-black)?"
function explain() {
// Start from the core idea:
// Self-balancing binary search trees that maintain a height of O(log n), ensuring efficient
}
Common pitfalls
Too generic: no concrete trade-offs or examples.
Mixing average-case and worst-case (e.g., complexity).