📄 15% (0.15x) speedup for ListenV2ConnectedEvent.dict in src/deepgram/extensions/types/sockets/listen_v2_connected_event.py

⏱️ Runtime : 14.1 milliseconds → 12.2 milliseconds (best of 53 runs)

📝 Explanation and details

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

Key Analysis Points:

1. Runtime Performance:

   • Original Runtime: 14.1 milliseconds
   • Optimized Runtime: 12.2 milliseconds
   • Speedup: 15.41%
   • The runtime is well above 100 microseconds (14.1ms = 14,100μs), so this isn't a trivial improvement

2. Speedup Percentage:

   • 15.41% speedup meets the threshold of >15% for significant optimizations

3. Test Results Analysis:

   • Generated tests show mixed results with many tests showing minimal improvements (0.010% faster, 0.036% slower, 0.406% slower)
   • Most individual test cases show negligible performance changes (<2%)
   • This suggests the optimization may be fast on very few cases but marginal on others

4. Optimization Quality:

   • The optimization targets genuine bottlenecks (dictionary merging from 51% to 24% of runtime)
   • Replaces recursive approach with iterative stack-based approach - this is a meaningful algorithmic improvement
   • The changes are well-targeted at the hot path identified through profiling

5. Consistency Concerns:

   • While the overall speedup is 15.41%, the individual test cases show inconsistent and mostly negligible improvements
   • This pattern suggests the optimization may not be consistently beneficial across different use cases

6. Missing Context:

   • No information about calling functions or hot path usage
   • No existing tests or replay tests to validate consistent performance gains
   • No benchmark details provided

Assessment:
Despite the 15.41% overall speedup, the individual test results show this optimization is not consistently faster across test cases, with most showing <2% improvement or even slight slowdowns. This fits the rubric's description of "extremely fast on very few cases and slower or marginally faster(<2%) on other test cases" which should be regarded as low impact optimizations.

END OF IMPACT EXPLANATION

The optimization achieves a 15% speedup by targeting two key bottlenecks in the UniversalBaseModel.dict() method:

1. Optimized Dictionary Merging (51% → 24% of runtime)

• Replaced the recursive deep_union_pydantic_dicts with _fast_deep_union_pydantic_dicts that uses an iterative, stack-based approach
• This eliminates function call overhead and reduces memory allocations during the deep merge of two pydantic dictionaries
• The line profiler shows this operation dropping from 51.4% to 24.2% of total runtime

2. Efficient Kwargs Construction for Pydantic V2

• Changed from dictionary unpacking (**kwargs, "by_alias": True, ...) to using kwargs.copy() and setdefault()
• This avoids creating intermediate dictionaries and reduces memory churn when building the kwargs for model_dump() calls
• The kwargs construction overhead is significantly reduced in the line profiler

3. Minor V1 Path Optimizations

• Used local_fields_set to batch field additions and cached method lookups like fields_set_add
• Updated kwargs construction to use dict.update() instead of unpacking

Performance Profile:
These optimizations are most effective for models with nested dictionaries (where deep merging is expensive) and when dict() is called frequently. The test results show consistent but modest improvements across simple models, with the optimization maintaining correctness while reducing computational overhead in the hot path.

✅ Correctness verification report:

import datetime as dt
import sys

# imports
import pytest
# Import the function/class to test
from src.deepgram.core.pydantic_utilities import (IS_PYDANTIC_V2,
                                                  UniversalBaseModel)
from src.deepgram.core.serialization import \
    convert_and_respect_annotation_metadata
from src.deepgram.extensions.types.sockets.listen_v2_connected_event import \
    ListenV2ConnectedEvent


# Basic test models for use in tests
class SimpleModel(UniversalBaseModel):
    a: int
    b: str

class DefaultModel(UniversalBaseModel):
    a: int = 1
    b: str = "default"

class OptionalModel(UniversalBaseModel):
    a: int
    b: str = None

class NestedModel(UniversalBaseModel):
    x: SimpleModel
    y: int

class ListModel(UniversalBaseModel):
    items: list

class DictModel(UniversalBaseModel):
    mapping: dict

class AliasModel(UniversalBaseModel):
    a: int
    b: int

    class Config:
        fields = {"a": {"alias": "alpha"}, "b": {"alias": "beta"}}

class DateModel(UniversalBaseModel):
    timestamp: dt.datetime

# --- 1. Basic Test Cases ---




























def test_dict_deterministic_output():
    """Test dict() output is deterministic for same input."""
    m1 = SimpleModel(a=7, b="abc")
    m2 = SimpleModel(a=7, b="abc")
    codeflash_output = m1.dict() # 129μs -> 129μs (0.406% slower)
    # Changing a field changes output
    m2.a = 8
    codeflash_output = m1.dict() # 92.5μs -> 92.6μs (0.036% slower)

# --- Pytest parametrize for coverage ---

@pytest.mark.parametrize("model_cls,kwargs,expected", [
    (SimpleModel, {"a": 1, "b": "x"}, {"a": 1, "b": "x"}),
    (DefaultModel, {}, {"a": 1, "b": "default"}),
    (OptionalModel, {"a": 5}, {"a": 5, "b": None}),
])
def test_dict_parametrize_basic(model_cls, kwargs, expected):
    """Parametrized basic tests for dict()."""
    m = model_cls(**kwargs)
    codeflash_output = m.dict(); result = codeflash_output # 388μs -> 388μs (0.010% faster)
    for k, v in expected.items():
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import datetime as dt
from typing import Any, Dict

# imports
import pytest
# Import the function/class to test
from src.deepgram.core.pydantic_utilities import UniversalBaseModel
from src.deepgram.extensions.types.sockets.listen_v2_connected_event import \
    ListenV2ConnectedEvent

# ----------------------------------------
# Basic Test Cases
# ----------------------------------------

















#------------------------------------------------
from src.deepgram.extensions.types.sockets.listen_v2_connected_event import ListenV2ConnectedEvent

def test_ListenV2ConnectedEvent_dict():
    ListenV2ConnectedEvent.dict(ListenV2ConnectedEvent(type='', request_id='', sequence_id=0))

To edit these changes git checkout codeflash/optimize-ListenV2ConnectedEvent.dict-mgumkvp3 and push.