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⚡️ Speed up method Transcript.get_by_id_async by 819% - #3

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⚡️ Speed up method Transcript.get_by_id_async by 819%#3
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@codeflash-ai codeflash-ai Bot commented Oct 17, 2025

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📄 819% (8.19x) speedup for Transcript.get_by_id_async in assemblyai/transcriber.py

⏱️ Runtime : 3.03 milliseconds 330 microseconds (best of 8 runs)

📝 Explanation and details

The key optimization is moving the ThreadPoolExecutor from instance-level to module-level.

In the original code, every Transcript object created its own ThreadPoolExecutor in __init__(), which is extremely expensive. Each executor spawns multiple threads and allocates significant resources. When creating many Transcript instances (as suggested by the test patterns), this leads to:

  • Thread pool creation overhead for each instance
  • Memory bloat from multiple thread pools
  • OS-level thread context switching penalties

The optimized version uses a single shared _executor at module level. All Transcript instances now share the same thread pool, eliminating the per-instance creation cost while maintaining identical async behavior.

Performance impact: The line profiler shows wait_for_completion_async() improved from 568μs per hit to 63μs per hit (~9x faster), and the overall runtime improved from 3.03ms to 330μs (818% speedup).

This optimization is particularly effective for workloads that create multiple Transcript objects or call get_by_id_async() frequently, as each call creates a new instance. The shared executor maintains thread reuse efficiency while dramatically reducing initialization overhead.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 23 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import concurrent.futures

# imports
import pytest
from assemblyai.transcriber import Transcript

# --- Unit tests ---

# Basic Test Cases





def test_edge_none_id_raises_typeerror():
    """Test that passing None as transcript_id raises TypeError."""
    with pytest.raises(TypeError):
        Transcript.get_by_id_async(None)










#------------------------------------------------
import concurrent.futures
import threading
import time

# imports
import pytest
from assemblyai.transcriber import Transcript

# ================================
# Unit tests for get_by_id_async()
# ================================

# ----------- Basic Tests ------------



















#------------------------------------------------
from assemblyai.transcriber import Transcript
import pytest

def test_Transcript_get_by_id_async():
    with pytest.raises(TypeError, match="__dict__\\ must\\ be\\ set\\ to\\ a\\ dictionary,\\ not\\ a\\ 'ShellMutableMap'"):
        Transcript.get_by_id_async(Transcript, '')

To edit these changes git checkout codeflash/optimize-Transcript.get_by_id_async-mgv02utj and push.

Codeflash

The key optimization is **moving the ThreadPoolExecutor from instance-level to module-level**. 

In the original code, every `Transcript` object created its own `ThreadPoolExecutor` in `__init__()`, which is extremely expensive. Each executor spawns multiple threads and allocates significant resources. When creating many `Transcript` instances (as suggested by the test patterns), this leads to:
- Thread pool creation overhead for each instance
- Memory bloat from multiple thread pools
- OS-level thread context switching penalties

The optimized version uses a single shared `_executor` at module level. All `Transcript` instances now share the same thread pool, eliminating the per-instance creation cost while maintaining identical async behavior.

**Performance impact**: The line profiler shows `wait_for_completion_async()` improved from 568μs per hit to 63μs per hit (~9x faster), and the overall runtime improved from 3.03ms to 330μs (818% speedup).

This optimization is particularly effective for workloads that create multiple `Transcript` objects or call `get_by_id_async()` frequently, as each call creates a new instance. The shared executor maintains thread reuse efficiency while dramatically reducing initialization overhead.
@codeflash-ai
codeflash-ai Bot requested a review from mashraf-222 October 17, 2025 15:25
@codeflash-ai codeflash-ai Bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Oct 17, 2025
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