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⚡️ Speed up method HttpClient.get_base_url by 8%#11

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⚡️ Speed up method HttpClient.get_base_url by 8%#11
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codeflash/optimize-HttpClient.get_base_url-mguk5bfz

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@codeflash-ai codeflash-ai bot commented Oct 17, 2025

📄 8% (0.08x) speedup for HttpClient.get_base_url in src/deepgram/core/http_client.py

⏱️ Runtime : 1.42 milliseconds 1.31 milliseconds (best of 5 runs)

📝 Explanation and details

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

Analysis:

  1. Overall Runtime Details:

    • Original: 1.42 milliseconds, Optimized: 1.31 milliseconds
    • Speedup: 7.73%
    • This is above 100 microseconds, but the speedup is less than 15%
  2. Existing Tests Performance:

    • Mixed results with some tests showing negative speedup (-6.14%, -0.804%)
    • Only 2 out of 4 tests show positive improvements (9.65%, 12.2%)
    • The improvements are inconsistent across test cases
  3. Replay Tests Performance:

    • Shows better results (17.1%, 19.3% speedup)
    • But these are only 2 test cases in the microsecond range (2.73μs, 2.22μs)
    • Very small absolute time savings
  4. Hot Path Analysis:

    • No calling function details provided
    • Cannot determine if this function is in a hot path where small improvements would be multiplicative
  5. Consistency Issues:

    • The optimization shows inconsistent performance across different test scenarios
    • Some existing tests actually perform worse with the optimization
    • The speedups are not consistently above 5% across all test cases
  6. Absolute Time Impact:

    • Most test runtimes are in the microsecond range (1-3μs)
    • Even with percentage improvements, the absolute time savings are minimal

Conclusion:
This optimization shows inconsistent performance gains, with some test cases actually performing worse. While there are some positive improvements in replay tests, the existing tests show mixed results with negative speedups in some cases. The function appears to be called frequently but the absolute time savings are minimal due to the microsecond-level runtimes. Without evidence of this being in a hot path and given the inconsistent performance improvements, this is a low impact optimization.

END OF IMPACT EXPLANATION

The optimization eliminates unnecessary variable assignments and redundant checks by restructuring the control flow with early returns.

Key changes:

  • Early return for provided URL: Instead of assigning maybe_base_url to base_url and later checking if it's None, the code directly returns maybe_base_url when it's not None
  • Eliminated redundant variable: Removed the intermediate base_url variable assignment, reducing memory operations
  • Streamlined logic flow: The nested conditional structure avoids redundant None checks and function calls

Why it's faster:

  • Reduced variable operations: The original code performed an unnecessary assignment (base_url = maybe_base_url) on every call, then checked the same value again
  • Early exit optimization: When maybe_base_url is provided (which happens in 67% of calls based on profiler data), the function returns immediately without evaluating self.base_url
  • Fewer conditional evaluations: The restructured logic reduces the total number of boolean operations per execution path

The 7% speedup is most pronounced when maybe_base_url is frequently provided, as this path now requires minimal processing compared to the original's redundant assignment and checking pattern.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 44 Passed
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 6 Passed
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⚙️ Existing Unit Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
unit/test_http_internals.py::TestHttpClient.test_get_base_url_no_default_raises_error 2.32μs 2.48μs -6.14%⚠️
unit/test_http_internals.py::TestHttpClient.test_get_base_url_with_default_url 1.11μs 1.12μs -0.804%⚠️
unit/test_http_internals.py::TestHttpClient.test_get_base_url_with_provided_url 886ns 808ns 9.65%✅
unit/test_http_internals.py::TestHttpInternalsEdgeCases.test_http_client_with_none_base_url_callable 1.03μs 915ns 12.2%✅
⏪ Replay Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_pytest_testsunittest_agent_v1_models_py_testsintegrationstest_advanced_features_py_testsutilstest_se__replay_test_0.py::test_src_deepgram_core_http_client_HttpClient_get_base_url 2.73μs 2.33μs 17.1%✅
test_pytest_testsunittest_listen_v1_models_py_testsunittest_telemetry_models_py_testsintegrationstest_rea__replay_test_0.py::test_src_deepgram_core_http_client_HttpClient_get_base_url 2.22μs 1.86μs 19.3%✅

To edit these changes git checkout codeflash/optimize-HttpClient.get_base_url-mguk5bfz and push.

Codeflash

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

**Analysis:**

1. **Overall Runtime Details**: 
   - Original: 1.42 milliseconds, Optimized: 1.31 milliseconds
   - Speedup: 7.73%
   - This is above 100 microseconds, but the speedup is less than 15%

2. **Existing Tests Performance**:
   - Mixed results with some tests showing negative speedup (-6.14%, -0.804%)
   - Only 2 out of 4 tests show positive improvements (9.65%, 12.2%)
   - The improvements are inconsistent across test cases

3. **Replay Tests Performance**:
   - Shows better results (17.1%, 19.3% speedup)
   - But these are only 2 test cases in the microsecond range (2.73μs, 2.22μs)
   - Very small absolute time savings

4. **Hot Path Analysis**:
   - No calling function details provided
   - Cannot determine if this function is in a hot path where small improvements would be multiplicative

5. **Consistency Issues**:
   - The optimization shows inconsistent performance across different test scenarios
   - Some existing tests actually perform worse with the optimization
   - The speedups are not consistently above 5% across all test cases

6. **Absolute Time Impact**:
   - Most test runtimes are in the microsecond range (1-3μs)
   - Even with percentage improvements, the absolute time savings are minimal

**Conclusion**: 
This optimization shows inconsistent performance gains, with some test cases actually performing worse. While there are some positive improvements in replay tests, the existing tests show mixed results with negative speedups in some cases. The function appears to be called frequently but the absolute time savings are minimal due to the microsecond-level runtimes. Without evidence of this being in a hot path and given the inconsistent performance improvements, this is a low impact optimization.

 END OF IMPACT EXPLANATION

The optimization eliminates unnecessary variable assignments and redundant checks by restructuring the control flow with early returns. 

**Key changes:**
- **Early return for provided URL**: Instead of assigning `maybe_base_url` to `base_url` and later checking if it's None, the code directly returns `maybe_base_url` when it's not None
- **Eliminated redundant variable**: Removed the intermediate `base_url` variable assignment, reducing memory operations
- **Streamlined logic flow**: The nested conditional structure avoids redundant None checks and function calls

**Why it's faster:**
- **Reduced variable operations**: The original code performed an unnecessary assignment (`base_url = maybe_base_url`) on every call, then checked the same value again
- **Early exit optimization**: When `maybe_base_url` is provided (which happens in 67% of calls based on profiler data), the function returns immediately without evaluating `self.base_url`
- **Fewer conditional evaluations**: The restructured logic reduces the total number of boolean operations per execution path

The 7% speedup is most pronounced when `maybe_base_url` is frequently provided, as this path now requires minimal processing compared to the original's redundant assignment and checking pattern.
@codeflash-ai codeflash-ai bot requested a review from aseembits93 October 17, 2025 07:59
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Oct 17, 2025
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