📄 103% (1.03x) speedup for AsyncV1SocketClient._handle_json_message in src/deepgram/agent/v1/socket_client.py

⏱️ Runtime : 3.88 milliseconds → 1.92 milliseconds (best of 42 runs)

📝 Explanation and details

Impact: high
Impact_explanation: Looking at this optimization report, I need to assess the impact based on the provided criteria:

Analysis:

1. Runtime Details:

   • Original runtime: 3.88ms, Optimized runtime: 1.92ms
   • Speedup: 102.66% (essentially 2x faster)
   • This is well above the 100 microsecond threshold and significantly above the 15% relative speedup threshold

2. Technical Merit:

   • The optimization addresses a real performance bottleneck where TypeAdapter construction was consuming 60.7% of runtime
   • Uses @functools.lru_cache(maxsize=16) to cache expensive TypeAdapter objects
   • This is a smart optimization that eliminates redundant object creation for repeated type parsing

3. Test Results:

   • Replay test shows consistent 103% speedup, which is very significant
   • Only one test case is shown, but it demonstrates the optimization works as expected
   • Test coverage is 100%

4. Use Case Context:

   • The explanation mentions this is for "high-frequency message processing scenarios" like WebSocket clients
   • Socket message parsing is typically done repeatedly with the same types, making caching highly effective
   • The function name _handle_json_message and context suggest this is likely in a hot path for message processing

5. Consistency:

   • The single replay test shows a strong, consistent improvement
   • The optimization targets a specific bottleneck (TypeAdapter creation) that would benefit any repeated usage

Verdict: This optimization shows a substantial 2x performance improvement (102% speedup) on a function that appears to be in a message processing hot path. The runtime is well above the microsecond threshold, and the speedup is far above the 15% threshold. The technical approach is sound and addresses a real performance bottleneck.

END OF IMPACT EXPLANATION

The optimization introduces TypeAdapter caching to eliminate the expensive repeated construction of Pydantic TypeAdapter objects.

Key Change:

• Added @functools.lru_cache(maxsize=16) decorator to a new _get_type_adapter() function that wraps pydantic.TypeAdapter(type_) creation
• Modified parse_obj_as() to use the cached adapter instead of creating a new one each time

Why This Works:
The line profiler shows that pydantic.TypeAdapter(type_) construction was consuming 60.7% of runtime (18.48ms out of 30.4ms total). Since the same type (V1SocketClientResponse) is used repeatedly for parsing socket messages, caching the TypeAdapter avoids this expensive object creation on subsequent calls.

Performance Impact:

• TypeAdapter creation time dropped from 18.48ms to 6.50ms (65% reduction)
• Overall function runtime improved from 3.88ms to 1.92ms (102% speedup)
• The cache size of 16 is sufficient for typical socket client usage patterns where a small number of message types are processed repeatedly

This optimization is particularly effective for high-frequency message processing scenarios like WebSocket clients, where the same response types are parsed repeatedly during a session.

✅ Correctness verification report:

To edit these changes git checkout codeflash/optimize-AsyncV1SocketClient._handle_json_message-mgumwh0s and push.