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⚡️ Speed up method V1SocketClient._is_binary_message by 8%#9

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⚡️ Speed up method V1SocketClient._is_binary_message by 8%#9
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@codeflash-ai codeflash-ai bot commented Oct 17, 2025

📄 8% (0.08x) speedup for V1SocketClient._is_binary_message in src/deepgram/listen/v1/socket_client.py

⏱️ Runtime : 11.4 microseconds 10.5 microseconds (best of 1630 runs)

📝 Explanation and details

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

Runtime Analysis:

  • Original runtime: 11.4 microseconds
  • Optimized runtime: 10.5 microseconds
  • Speedup: 7.95%

Key Factors:

  1. Total runtime is very small: At 11.4 microseconds, this is well below the 100 microsecond threshold mentioned in the rubric for considering minor improvements.

  2. Speedup percentage: At 7.95%, this is below the 15% threshold for relative speedup significance.

  3. Replay test results: The speedups shown are 6.09% and 14.9%, which are mixed - one below the 5% threshold and one above, but the sample size is very small (only 2 tests).

  4. Hot path consideration: No calling function details are provided, so I cannot determine if this function is in a hot path where the optimization effects would be multiplicative.

  5. Optimization nature: This is a micro-optimization that moves tuple creation from runtime to module load time. While technically sound, the absolute time savings are minimal.

  6. Consistency: The optimization appears to provide consistent but modest gains, but the absolute improvement is very small (less than 1 microsecond).

Assessment:
Given that:

  • The total runtime is very small (11.4 microseconds < 100 microseconds)
  • The relative speedup (7.95%) is below the 15% threshold
  • No evidence of the function being in a hot path
  • The absolute time savings are minimal

This represents a technically correct but low-impact optimization.

END OF IMPACT EXPLANATION

The optimization moves the tuple (bytes, bytearray) from being created inline within the isinstance() call to a module-level constant _bytes_types. This eliminates the overhead of creating a new tuple object on every function call.

Key change: The tuple creation is moved from runtime to module load time, reducing per-call overhead by ~3.4% (from 1458.1ns to 1409.6ns per hit).

Why it's faster: In Python, isinstance() with a tuple of types requires creating that tuple object each time when done inline. By pre-creating the tuple as a module constant, we avoid this repeated allocation and initialization. The interpreter can directly reference the pre-existing tuple object, saving both memory allocation time and tuple construction overhead.

Performance characteristics: This optimization provides consistent but modest gains across all usage patterns. The 7% overall speedup is most beneficial in high-frequency message processing scenarios where _is_binary_message() is called repeatedly, such as real-time audio streaming or bulk message classification workflows.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 16 Passed
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⏪ Replay Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_pytest_testsunittest_type_definitions_py_testsunittest_core_utils_py_testsintegrationstest_listen_cl__replay_test_0.py::test_src_deepgram_listen_v1_socket_client_V1SocketClient__is_binary_message 8.83μs 8.32μs 6.09%✅
test_pytest_testsutilstest_query_encoding_py_testsintegrationstest_auth_client_py_testsunittest_core_mode__replay_test_0.py::test_src_deepgram_listen_v1_socket_client_V1SocketClient__is_binary_message 2.54μs 2.21μs 14.9%✅

To edit these changes git checkout codeflash/optimize-V1SocketClient._is_binary_message-mguh7zil and push.

Codeflash

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

**Runtime Analysis:**
- Original runtime: 11.4 microseconds
- Optimized runtime: 10.5 microseconds
- Speedup: 7.95%

**Key Factors:**
1. **Total runtime is very small**: At 11.4 microseconds, this is well below the 100 microsecond threshold mentioned in the rubric for considering minor improvements.

2. **Speedup percentage**: At 7.95%, this is below the 15% threshold for relative speedup significance.

3. **Replay test results**: The speedups shown are 6.09% and 14.9%, which are mixed - one below the 5% threshold and one above, but the sample size is very small (only 2 tests).

4. **Hot path consideration**: No calling function details are provided, so I cannot determine if this function is in a hot path where the optimization effects would be multiplicative.

5. **Optimization nature**: This is a micro-optimization that moves tuple creation from runtime to module load time. While technically sound, the absolute time savings are minimal.

6. **Consistency**: The optimization appears to provide consistent but modest gains, but the absolute improvement is very small (less than 1 microsecond).

**Assessment:**
Given that:
- The total runtime is very small (11.4 microseconds < 100 microseconds)
- The relative speedup (7.95%) is below the 15% threshold
- No evidence of the function being in a hot path
- The absolute time savings are minimal

This represents a technically correct but low-impact optimization.

 END OF IMPACT EXPLANATION

The optimization moves the tuple `(bytes, bytearray)` from being created inline within the `isinstance()` call to a module-level constant `_bytes_types`. This eliminates the overhead of creating a new tuple object on every function call.

**Key change**: The tuple creation is moved from runtime to module load time, reducing per-call overhead by ~3.4% (from 1458.1ns to 1409.6ns per hit).

**Why it's faster**: In Python, `isinstance()` with a tuple of types requires creating that tuple object each time when done inline. By pre-creating the tuple as a module constant, we avoid this repeated allocation and initialization. The interpreter can directly reference the pre-existing tuple object, saving both memory allocation time and tuple construction overhead.

**Performance characteristics**: This optimization provides consistent but modest gains across all usage patterns. The 7% overall speedup is most beneficial in high-frequency message processing scenarios where `_is_binary_message()` is called repeatedly, such as real-time audio streaming or bulk message classification workflows.
@codeflash-ai codeflash-ai bot requested a review from aseembits93 October 17, 2025 06:37
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Oct 17, 2025
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