⚡️ Speed up method Util.rackIdsEqual by 6%

[codeflash-ai]

📄 6% (0.06x) speedup for Util.rackIdsEqual in client/src/com/aerospike/client/util/Util.java

⏱️ Runtime : 520 microseconds → 491 microseconds (best of 5 runs)

📝 Explanation and details

Runtime improvement (primary): the optimized method runs ~5% faster — from 520 µs down to 491 µs — because it avoids unnecessary slow list access patterns and redundant method calls.

What changed

• Cached list sizes into local ints (size1, size2) instead of repeatedly calling size()/length.
• Added a fast-path check for java.util.RandomAccess:
  • If the list implements RandomAccess (e.g., ArrayList) we keep indexed access (racks1.get(i)), which is optimal for random-access lists.
  • Otherwise (e.g., LinkedList) we iterate with an Iterator and consume racks2 by index, avoiding repeated get(i) calls on a non-RandomAccess list.

Why this is faster

• get(i) on a non-RandomAccess list (LinkedList) is O(i), so a loop with get(i) becomes O(n^2) in element accesses. Replacing that with an Iterator makes the loop O(n).
• For RandomAccess lists, indexed access stays the best choice, so the RandomAccess check lets us choose the best access pattern per implementation.
• Caching sizes reduces repeated virtual method calls and field accesses, removing small per-iteration overhead.
• The instanceof check and branch are negligible compared to the cost avoided for non-RandomAccess lists; for RandomAccess lists the loop remains efficient.

Behavior & compatibility

• The change preserves behavior (including Integer unboxing and the same NullPointerException behavior for null elements).
• No new external dependencies, and method signature is unchanged.

When you see the benefit

• Large LinkedList-like inputs: biggest wins (up to orders-of-magnitude improvement for very large lists compared to the original O(n^2) access).
• Small lists: only a small throughput gain (the measured overall speedup here is 5%).
• Hot paths: if this method is called frequently (e.g., in cluster/topology comparisons), the small per-call savings compound into notable throughput and latency improvements.

Tests and trade-offs

• The observed 5% runtime improvement is a positive trade-off for a tiny additional instanceof branch. No regressions in behavior are introduced.
• If your workload always uses ArrayList, gains are modest; if it sometimes uses LinkedList or other sequential-access lists, the optimization is important.

Suggestions (optional)

• If this comparison is extremely hot and profiles show more room, consider using a primitive int[] or caching the list as an int[] for repeated comparisons to remove boxing/unboxing overhead.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 198 Passed
🌀 Generated Regression Tests	🔘 None Found
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	No coverage data found for rackIdsEqual

⚙️ Click to see Existing Unit Tests

To edit these changes git checkout codeflash/optimize-Util.rackIdsEqual-ml745cl4 and push.