⚡️ Speed up method AsyncV1SocketClient._handle_json_message by 315%
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The optimization introduces **TypeAdapter caching** using `functools.lru_cache` to eliminate the expensive repeated creation of `pydantic.TypeAdapter` objects. **Key change:** - Added `_get_type_adapter()` function with `@functools.lru_cache(maxsize=32)` that caches TypeAdapter instances by type - Replaced direct `pydantic.TypeAdapter(type_)` calls with cached `_get_type_adapter(type_)` calls **Why this speeds up the code:** The line profiler shows that `pydantic.TypeAdapter(type_)` construction was taking **76.3% of execution time** (10.06ms out of 13.19ms total). TypeAdapter creation involves significant internal setup including type analysis, validation schema generation, and metadata processing. By caching these expensive objects, subsequent calls with the same type reuse the pre-built adapter instead of recreating it. **Performance impact:** - Original: 10.06ms spent on TypeAdapter creation (76.3% of total time) - Optimized: 3.39ms spent on cached adapter retrieval (53.1% of total time) - **67% reduction** in TypeAdapter overhead, leading to overall **315% speedup** This optimization is particularly effective for WebSocket message handling scenarios where the same message types (like `V1SocketClientResponse`) are parsed repeatedly, as shown in the test cases. The LRU cache with maxsize=32 provides excellent hit rates for typical usage patterns while preventing unbounded memory growth.
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📄 315% (3.15x) speedup for
AsyncV1SocketClient._handle_json_messageinsrc/deepgram/agent/v1/socket_client.py⏱️ Runtime :
3.79 milliseconds→913 microseconds(best of65runs)📝 Explanation and details
The optimization introduces TypeAdapter caching using
functools.lru_cacheto eliminate the expensive repeated creation ofpydantic.TypeAdapterobjects.Key change:
_get_type_adapter()function with@functools.lru_cache(maxsize=32)that caches TypeAdapter instances by typepydantic.TypeAdapter(type_)calls with cached_get_type_adapter(type_)callsWhy this speeds up the code:
The line profiler shows that
pydantic.TypeAdapter(type_)construction was taking 76.3% of execution time (10.06ms out of 13.19ms total). TypeAdapter creation involves significant internal setup including type analysis, validation schema generation, and metadata processing. By caching these expensive objects, subsequent calls with the same type reuse the pre-built adapter instead of recreating it.Performance impact:
This optimization is particularly effective for WebSocket message handling scenarios where the same message types (like
V1SocketClientResponse) are parsed repeatedly, as shown in the test cases. The LRU cache with maxsize=32 provides excellent hit rates for typical usage patterns while preventing unbounded memory growth.✅ Correctness verification report:
⏪ Replay Tests and Runtime
test_pytest_testsintegrationstest_integration_scenarios_py_testsunittest_core_utils_py_testsutilstest_htt__replay_test_0.py::test_deepgram_agent_v1_socket_client_AsyncV1SocketClient__handle_json_messageTo edit these changes
git checkout codeflash/optimize-AsyncV1SocketClient._handle_json_message-mh4gblr2and push.