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LetsGit.IT/Categories/Data Structures
Data Structureshard

AVL vs Red-Black tree — what’s the trade-off?

Tags
#avl#red-black#balancing#tree
Back to categoryPractice quiz

Answer

AVL trees keep stricter balance, so lookups are often faster, but inserts/deletes may require more rebalancing. Red-Black trees relax the balance rules, making updates cheaper while still keeping height O(log n).

Advanced answer

Deep dive

Both AVL and Red-Black trees are self-balancing BSTs that keep height O(log n), so search/insert/delete are O(log n). The difference is how strictly they balance the tree.

AVL:

  • Stronger balance invariant (balance factor -1/0/+1).
  • Typically smaller height, so lookups can be slightly faster.
  • Inserts/deletes may trigger more rotations/rebalancing.

Red-Black:

  • Looser invariant via coloring rules.
  • Height is still O(log n), but usually a bit larger than AVL.
  • Updates tend to require fewer rotations on average, so it’s often better for write-heavy workloads.

Related questions

Data Structures
What are Balanced Trees (e.g., AVL, Red-Black)?
#tree#binary-search-tree#balancing

Rule of thumb

  • Read-heavy, latency-sensitive lookups: AVL can win.
  • Mixed workloads or write-heavy maps/sets: Red-Black is commonly chosen (e.g., many standard libraries use it).

Common pitfalls

  • Thinking AVL is always faster: it depends on the workload and constant factors.
  • Forgetting that both are O(log n); the trade-off is mostly about rotations and height constants.

Interview follow-ups

  • Which tree would you pick for an in-memory index with frequent inserts?
  • What operations trigger rotations and why?