📄 101% (1.01x) speedup for V1SocketClient._handle_json_message in src/deepgram/agent/v1/socket_client.py

⏱️ Runtime : 7.74 milliseconds → 3.85 milliseconds (best of 35 runs)

📝 Explanation and details

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

Key Performance Metrics Analysis:

1. Overall Runtime Details:

   • Original: 7.74ms, Optimized: 3.85ms
   • Speedup: 101.14% (essentially doubling performance)
   • This is well above the 100 microsecond threshold and significantly exceeds the 15% minimum speedup requirement

2. Replay Tests Performance:

   • Test 1: 5.08ms → 2.47ms (105% speedup)
   • Test 2: 2.67ms → 1.37ms (94% speedup)
   • Both tests show consistent, substantial improvements well above 5%

3. Technical Merit:

   • The optimization addresses a real bottleneck: TypeAdapter creation was consuming 60% of execution time
   • Uses intelligent caching with @functools.lru_cache(maxsize=32) to avoid repeated expensive object creation
   • Line profiler data shows TypeAdapter creation time dropped from ~35ms to ~6ms (83% reduction)

4. Consistency:

   • The speedups are consistent across different test scenarios
   • No cases of marginal improvements or regressions
   • All measured improvements are substantial (90%+ speedups)

5. Real-World Impact:

   • The optimization targets a parsing function that would likely be called frequently in WebSocket/message processing scenarios
   • The 32-entry LRU cache is well-sized for typical use cases with repeated message types
   • The improvement is multiplicative for applications that parse the same message types repeatedly

Assessment:
This optimization clearly meets all criteria for high impact:

• Runtime improvements are substantial (>100% speedup)
• Improvements are consistent across test cases
• Addresses a real performance bottleneck
• Has clear real-world applicability for message parsing scenarios

END OF IMPACT EXPLANATION

The key optimization is caching TypeAdapter instances in the Pydantic utilities. The original code created a new TypeAdapter object on every call to parse_obj_as, which is expensive. The optimized version introduces _get_type_adapter() with @functools.lru_cache(maxsize=32) to cache these adapters by type.

Core Performance Impact:

• Line profiler shows TypeAdapter creation time dropped from ~35ms to ~6ms (83% reduction)
• This is the bottleneck that was consuming 60% of parse_obj_as execution time
• Overall speedup of 101% for the complete message handling pipeline

Additional Optimizations:

• Moved IS_PYDANTIC_V2 check to import time rather than runtime string comparison
• Conditional import of TypeAdapter only when needed (Pydantic V2)
• Moved V1SocketClientResponse import to module scope to avoid repeated import overhead

Best For: Applications with repeated parsing of the same message types - like WebSocket clients processing similar JSON structures repeatedly. The LRU cache (32 entries) efficiently handles the common case where a socket client processes the same few message types over and over, avoiding the expensive TypeAdapter instantiation cost.

✅ Correctness verification report:

To edit these changes git checkout codeflash/optimize-V1SocketClient._handle_json_message-mgunbvmu and push.