Skip to content

Conversation

@codeflash-ai
Copy link

@codeflash-ai codeflash-ai bot commented Oct 27, 2025

📄 64% (0.64x) speedup for IndexResourceAsyncio.describe in pinecone/db_control/resources/asyncio/index.py

⏱️ Runtime : 64.2 microseconds 39.1 microseconds (best of 38 runs)

📝 Explanation and details

The optimization removes the @require_kwargs decorator overhead by leveraging Python's built-in keyword-only argument enforcement through the *, name: str signature.

What was optimized:

  • Eliminated the @require_kwargs decorator that was adding significant per-call overhead
  • The method signature async def describe(self, *, name: str) already enforces keyword-only arguments at the Python language level, making the decorator redundant

Why this creates a speedup:

  • Decorator overhead elimination: The line profiler shows the decorator's wrapper function consumed 38.4% of time just checking len(args) > 1 and 51% calling the actual function
  • Reduced stack frames: Each decorated call created an extra function call frame, adding memory allocation and deallocation overhead
  • Avoided runtime introspection: The decorator used inspect.signature() for error messages, which is expensive compared to Python's built-in argument validation

Performance impact:

  • 64% runtime improvement (64.2μs → 39.1μs) by removing unnecessary validation layers
  • The optimized profiler shows the function now spends time only on actual work: the API call (9.1%) and IndexModel creation (90.9%)

Test case suitability:
This optimization benefits all usage patterns since it removes overhead from every single method call, particularly effective for:

  • High-frequency index description calls
  • Concurrent operations where the cumulative decorator overhead would be substantial
  • Any scenario where this method is called repeatedly

The throughput remains the same because the async nature means the bottleneck is likely I/O-bound operations, but the reduced CPU overhead per call is measurable in runtime.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 64 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import asyncio  # used to run async functions
# Helper: Patch IndexModel for test isolation
import sys

import pytest  # used for our unit tests
# --- Function to test (EXACT copy, DO NOT MODIFY) ---
from pinecone.db_control.models import IndexModel
from pinecone.db_control.resources.asyncio.index import IndexResourceAsyncio
from pinecone.utils import require_kwargs

# --- Unit tests below ---

# Helper: Dummy IndexModel for testing
class DummyIndexModel:
    def __init__(self, data):
        self.data = data

    def __eq__(self, other):
        # For test equality, compare data attribute
        return isinstance(other, DummyIndexModel) and self.data == other.data

# Helper: Dummy index_api with async describe_index method
class DummyIndexAPI:
    def __init__(self, responses=None, delay=0):
        # responses: dict mapping name -> description
        self.responses = responses or {}
        self.delay = delay

    async def describe_index(self, name):
        # Simulate async delay if needed
        if self.delay:
            await asyncio.sleep(self.delay)
        if name in self.responses:
            return self.responses[name]
        raise KeyError(f"Index '{name}' not found")

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

@pytest.mark.asyncio



async def test_describe_raises_keyerror_for_missing_index():
    """Test that describe raises KeyError when index name is not found."""
    dummy_api = DummyIndexAPI({'exists': {'name': 'exists'}})
    resource = IndexResourceAsyncio(dummy_api, config={})
    with pytest.raises(KeyError):
        await resource.describe(name='missing')

@pytest.mark.asyncio

async def test_describe_concurrent_with_some_missing():
    """Test concurrent execution where some index names are missing."""
    dummy_api = DummyIndexAPI({'x': {'name': 'x'}, 'y': {'name': 'y'}})
    resource = IndexResourceAsyncio(dummy_api, config={})
    # One call will raise KeyError
    coros = [
        resource.describe(name='x'),
        resource.describe(name='y'),
        resource.describe(name='z')  # missing
    ]
    results = await asyncio.gather(*coros, return_exceptions=True)

@pytest.mark.asyncio


async def test_describe_large_scale_some_missing():
    """Test large scale concurrent describes with some missing indexes."""
    num_indexes = 30
    names = [f'idx_{i}' for i in range(num_indexes)]
    # Only half exist
    responses = {name: {'name': name} for name in names[:num_indexes//2]}
    dummy_api = DummyIndexAPI(responses)
    resource = IndexResourceAsyncio(dummy_api, config={})
    coros = [resource.describe(name=name) for name in names]
    results = await asyncio.gather(*coros, return_exceptions=True)
    # First half should succeed, second half should raise KeyError
    for i, result in enumerate(results):
        if i < num_indexes//2:
            pass
        else:
            pass

# --- 4. Throughput Test Cases ---

@pytest.mark.asyncio




#------------------------------------------------
import asyncio  # used to run async functions
# Patch IndexModel for tests
import sys

import pytest  # used for our unit tests
# function to test
# --- Begin function definition (DO NOT MODIFY) ---
from pinecone.db_control.models import IndexModel
from pinecone.db_control.resources.asyncio.index import \
    IndexResourceAsyncio  # --- End function definition ---
from pinecone.utils import require_kwargs


# Mocks for IndexModel and index_api (since we cannot import actual pinecone dependencies)
class DummyIndexModel:
    def __init__(self, description):
        self.description = description

    def __eq__(self, other):
        # Equality based on description only for test purposes
        if isinstance(other, DummyIndexModel):
            return self.description == other.description
        return False

    def __repr__(self):
        return f"DummyIndexModel({self.description!r})"

class DummyIndexAPI:
    def __init__(self, responses=None, raise_on=None):
        # responses: dict mapping name to description
        # raise_on: set of names that should cause an exception
        self.responses = responses or {}
        self.raise_on = raise_on or set()
        self.call_log = []

    async def describe_index(self, name):
        self.call_log.append(name)
        if name in self.raise_on:
            raise ValueError(f"Index '{name}' not found")
        # Simulate async behavior
        await asyncio.sleep(0)
        return self.responses.get(name, f"default-description-for-{name}")

# Helper to create resource
def make_resource(responses=None, raise_on=None):
    index_api = DummyIndexAPI(responses=responses, raise_on=raise_on)
    config = {}  # config is unused in describe
    resource = IndexResourceAsyncio(index_api, config)
    return resource, index_api

# ------------------- UNIT TESTS -------------------

@pytest.mark.asyncio


async def test_describe_edge_missing_index_raises():
    """Edge: Should raise ValueError if index name is set to raise_on."""
    resource, _ = make_resource(raise_on={'missing-index'})
    with pytest.raises(ValueError) as excinfo:
        await resource.describe(name='missing-index')

@pytest.mark.asyncio





async def test_describe_edge_kwargs_required():
    """Edge: Should raise TypeError if called without kwargs."""
    resource, _ = make_resource()
    # Try calling without keyword arguments
    with pytest.raises(TypeError):
        await resource.describe('no-kwargs')

@pytest.mark.asyncio




#------------------------------------------------
from pinecone.db_control.resources.asyncio.index import IndexResourceAsyncio

To edit these changes git checkout codeflash/optimize-IndexResourceAsyncio.describe-mh9qmuwv and push.

Codeflash

The optimization **removes the `@require_kwargs` decorator overhead** by leveraging Python's built-in keyword-only argument enforcement through the `*, name: str` signature.

**What was optimized:**
- Eliminated the `@require_kwargs` decorator that was adding significant per-call overhead
- The method signature `async def describe(self, *, name: str)` already enforces keyword-only arguments at the Python language level, making the decorator redundant

**Why this creates a speedup:**
- **Decorator overhead elimination**: The line profiler shows the decorator's `wrapper` function consumed 38.4% of time just checking `len(args) > 1` and 51% calling the actual function
- **Reduced stack frames**: Each decorated call created an extra function call frame, adding memory allocation and deallocation overhead  
- **Avoided runtime introspection**: The decorator used `inspect.signature()` for error messages, which is expensive compared to Python's built-in argument validation

**Performance impact:**
- **64% runtime improvement** (64.2μs → 39.1μs) by removing unnecessary validation layers
- The optimized profiler shows the function now spends time only on actual work: the API call (9.1%) and IndexModel creation (90.9%)

**Test case suitability:**
This optimization benefits all usage patterns since it removes overhead from every single method call, particularly effective for:
- High-frequency index description calls
- Concurrent operations where the cumulative decorator overhead would be substantial
- Any scenario where this method is called repeatedly

The throughput remains the same because the async nature means the bottleneck is likely I/O-bound operations, but the reduced CPU overhead per call is measurable in runtime.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 27, 2025 22:57
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Oct 27, 2025
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash

Projects

None yet

Development

Successfully merging this pull request may close these issues.

1 participant