⚡️ Speed up method S3DataSource.list_bucket_metrics_configurations by 86%

[codeflash-ai]

📄 86% (0.86x) speedup for S3DataSource.list_bucket_metrics_configurations in backend/python/app/sources/external/s3/s3.py

⏱️ Runtime : 541 microseconds → 291 microseconds (best of 151 runs)

📝 Explanation and details

The optimization achieves an 85% runtime speedup (from 541μs to 291μs) by eliminating repeated S3 client creation overhead through smart connection pooling and caching.

Key Performance Changes:

1. Client Connection Pooling: The original code created a new session.client('s3') async context manager for every API call, which involves connection establishment overhead. The optimized version caches the S3 client instance in _s3_client_obj and reuses it across multiple method calls.

2. Reduced Context Manager Overhead: Instead of entering/exiting the async context manager on each call (async with session.client('s3')), the optimized version maintains the context manager state (_s3_client_cm) and only creates it once per S3DataSource instance.

3. Async Context Manager Protocol: Added __aenter__ and __aexit__ methods to enable proper resource management when the S3DataSource itself is used as an async context manager, ensuring clean client closure.

Why This Speeds Things Up:

• Connection Reuse: aioboto3 client creation involves TCP connection setup, SSL handshakes, and credential validation - expensive operations that now happen only once
• Reduced Object Allocation: Fewer temporary objects created per method call
• Lower Async Context Switching: Less async context manager enter/exit overhead per operation

Performance Profile Analysis:
The line profiler shows the bottleneck moved from async with session.client('s3') (20% of original runtime) to the more efficient _get_s3_client() call (29% of optimized runtime), but the overall execution time dropped significantly.

Workload Impact:
This optimization is particularly beneficial for:

• High-throughput scenarios with multiple S3 operations per S3DataSource instance
• Batch processing where the same S3DataSource handles many requests
• Long-lived services that perform repeated S3 operations

The 1.3% throughput improvement (28,906 → 29,294 ops/sec) shows modest gains in concurrent scenarios, while the 85% runtime improvement demonstrates substantial benefits for individual operation latency.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 207 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	75.0%

🌀 Generated Regression Tests and Runtime

import asyncio # used to run async functions
from typing import Optional

import pytest # used for our unit tests
from app.sources.external.s3.s3 import S3DataSource

Simulate ClientError exception from botocore

class ClientError(Exception):
def init(self, response, operation_name):
self.response = response
self.operation_name = operation_name

Simulate aioboto3.Session and its async client

class MockS3Client:
def init(self, response_map):
self.response_map = response_map

async def list_bucket_metrics_configurations(self, **kwargs):
    # Return the mapped response, or raise if mapped to exception
    key = tuple(sorted(kwargs.items()))
    resp = self.response_map.get(key, self.response_map.get('default'))
    if isinstance(resp, Exception):
        raise resp
    return resp

class MockAsyncContextManager:
"""Async context manager for s3_client."""
def init(self, s3_client):
self.s3_client = s3_client
async def aenter(self):
return self.s3_client
async def aexit(self, exc_type, exc, tb):
pass

class MockSession:
"""Mock aioboto3.Session for testing."""
def init(self, response_map):
self.response_map = response_map
def client(self, service_name):
return MockAsyncContextManager(MockS3Client(self.response_map))

class MockS3ClientBuilder:
"""Mock S3Client that returns a mock session."""
def init(self, response_map):
self.response_map = response_map
def get_session(self):
return MockSession(self.response_map)

--- Unit Tests ---

1. Basic Test Cases

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_basic_success():
"""Test basic successful response."""
response_map = {
(('Bucket', 'mybucket'),): {'MetricsConfigurationList': [], 'IsTruncated': False}
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
resp = await ds.list_bucket_metrics_configurations(Bucket='mybucket')

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_basic_with_optional_params():
"""Test with optional parameters."""
response_map = {
(('Bucket', 'mybucket'), ('ContinuationToken', 'token123')): {'MetricsConfigurationList': ['foo'], 'IsTruncated': True}
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
resp = await ds.list_bucket_metrics_configurations(Bucket='mybucket', ContinuationToken='token123')

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_basic_empty_response():
"""Test when S3 returns None."""
response_map = {
(('Bucket', 'mybucket'),): None
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
resp = await ds.list_bucket_metrics_configurations(Bucket='mybucket')

2. Edge Test Cases

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_error_response_dict():
"""Test when S3 returns error in response dict."""
response_map = {
(('Bucket', 'mybucket'),): {'Error': {'Code': 'AccessDenied', 'Message': 'Denied'}}
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
resp = await ds.list_bucket_metrics_configurations(Bucket='mybucket')

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_raises_client_error():
"""Test when S3 raises ClientError exception."""
error_response = {
'Error': {
'Code': 'NoSuchBucket',
'Message': 'The specified bucket does not exist'
}
}
response_map = {
(('Bucket', 'missingbucket'),): ClientError(error_response, 'ListBucketMetricsConfigurations')
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
resp = await ds.list_bucket_metrics_configurations(Bucket='missingbucket')

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_raises_generic_exception():
"""Test when S3 raises a generic exception."""
response_map = {
(('Bucket', 'mybucket'),): Exception("Some generic error")
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
resp = await ds.list_bucket_metrics_configurations(Bucket='mybucket')

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_concurrent_execution():
"""Test concurrent execution with different buckets."""
response_map = {
(('Bucket', 'bucketA'),): {'MetricsConfigurationList': ['A'], 'IsTruncated': False},
(('Bucket', 'bucketB'),): {'MetricsConfigurationList': ['B'], 'IsTruncated': True},
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
# Run two requests concurrently
results = await asyncio.gather(
ds.list_bucket_metrics_configurations(Bucket='bucketA'),
ds.list_bucket_metrics_configurations(Bucket='bucketB')
)

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_edge_missing_optional_params():
"""Test when optional params are omitted and bucket name is unusual."""
response_map = {
(('Bucket', ''),): {'MetricsConfigurationList': [], 'IsTruncated': False}
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
resp = await ds.list_bucket_metrics_configurations(Bucket='')

3. Large Scale Test Cases

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_many_concurrent_requests():
"""Test many concurrent requests for scalability."""
N = 50 # Reasonable number for test
response_map = {
('default'): {'MetricsConfigurationList': ['default'], 'IsTruncated': False}
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
# Prepare 50 concurrent calls
tasks = [
ds.list_bucket_metrics_configurations(Bucket=f'bucket{i}')
for i in range(N)
]
results = await asyncio.gather(*tasks)

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_large_bucket_names():
"""Test with large bucket names and multiple requests."""
bucket_names = [f'bucket_{i}' * 10 for i in range(10)]
response_map = {
('default'): {'MetricsConfigurationList': ['large'], 'IsTruncated': False}
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
tasks = [ds.list_bucket_metrics_configurations(Bucket=b) for b in bucket_names]
results = await asyncio.gather(*tasks)

4. Throughput Test Cases

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_throughput_small_load():
"""Throughput test with small load."""
response_map = {
('default'): {'MetricsConfigurationList': ['small'], 'IsTruncated': False}
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
tasks = [ds.list_bucket_metrics_configurations(Bucket=f'bucket_{i}') for i in range(5)]
results = await asyncio.gather(*tasks)

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_throughput_medium_load():
"""Throughput test with medium load."""
response_map = {
('default'): {'MetricsConfigurationList': ['medium'], 'IsTruncated': True}
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
tasks = [ds.list_bucket_metrics_configurations(Bucket=f'bucket_{i}') for i in range(20)]
results = await asyncio.gather(*tasks)

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_throughput_high_volume():
"""Throughput test with high volume load."""
N = 100 # High, but not excessive
response_map = {
('default'): {'MetricsConfigurationList': ['high'], 'IsTruncated': True}
}
s3_client = MockS3ClientBuilder(response_map)
ds = S3DataSource(s3_client)
tasks = [ds.list_bucket_metrics_configurations(Bucket=f'bucket_{i}') for i in range(N)]
results = await asyncio.gather(*tasks)

codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

#------------------------------------------------
import asyncio # used to run async functions

--- S3DataSource definition (EXACT COPY) ---

from typing import Optional
from unittest.mock import AsyncMock, MagicMock, patch

import pytest # used for our unit tests
from app.sources.external.s3.s3 import S3DataSource

class DummySession:
"""A dummy aioboto3.Session mock that returns an async context manager for S3 client."""
def client(self, service_name):
return DummyS3ClientContextManager()

class DummyS3ClientContextManager:
"""Async context manager for S3 client."""
async def aenter(self):
return self.s3_client

async def __aexit__(self, exc_type, exc, tb):
    pass

def __init__(self):
    self.s3_client = AsyncMock()

class DummyS3Client:
"""Dummy S3Client wrapper."""
def init(self, session):
self._session = session

def get_session(self):
    # Simulate S3Client.get_session() returns aioboto3.Session
    return self._session

class DummyClientError(Exception):
"""Simulate botocore.exceptions.ClientError."""
def init(self, response):
self.response = response

--- Fixtures and helpers ---

@pytest.fixture
def dummy_session_and_client():
"""Fixture for a dummy session and S3Client."""
session = DummySession()
s3_client = DummyS3Client(session)
return session, s3_client

@pytest.fixture
def s3_data_source(dummy_session_and_client):
"""Fixture for S3DataSource with a dummy S3Client."""
session, s3_client = dummy_session_and_client
return S3DataSource(s3_client)

--- Basic Test Cases ---

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_basic_success(s3_data_source):
"""Test basic successful response with normal parameters."""
# Mock the S3 client method to return a valid response
dummy_cm = s3_data_source._session.client('s3')
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(
return_value={'MetricsConfigurationList': [{'Id': 'metrics1'}]}
)
result = await s3_data_source.list_bucket_metrics_configurations(Bucket="mybucket")

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_basic_with_optional_params(s3_data_source):
"""Test with optional parameters provided."""
dummy_cm = s3_data_source._session.client('s3')
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(
return_value={'MetricsConfigurationList': [{'Id': 'metrics2'}], 'NextContinuationToken': 'token123'}
)
result = await s3_data_source.list_bucket_metrics_configurations(
Bucket="mybucket", ContinuationToken="token123", ExpectedBucketOwner="owner123"
)

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_basic_empty_response(s3_data_source):
"""Test when S3 returns an empty response."""
dummy_cm = s3_data_source._session.client('s3')
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(return_value=None)
result = await s3_data_source.list_bucket_metrics_configurations(Bucket="mybucket")

--- Edge Test Cases ---

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_error_in_response(s3_data_source):
"""Test when S3 returns an error inside the response dict."""
dummy_cm = s3_data_source._session.client('s3')
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(
return_value={'Error': {'Code': 'AccessDenied', 'Message': 'You do not have permission'}}
)
result = await s3_data_source.list_bucket_metrics_configurations(Bucket="mybucket")

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_client_error_exception(s3_data_source):
"""Test when S3 raises a ClientError exception."""
dummy_cm = s3_data_source._session.client('s3')
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(
side_effect=DummyClientError({'Error': {'Code': 'NoSuchBucket', 'Message': 'Bucket does not exist'}})
)
result = await s3_data_source.list_bucket_metrics_configurations(Bucket="nonexistentbucket")

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_unexpected_exception(s3_data_source):
"""Test when S3 raises a generic Exception."""
dummy_cm = s3_data_source._session.client('s3')
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(
side_effect=Exception("Unexpected failure!")
)
result = await s3_data_source.list_bucket_metrics_configurations(Bucket="mybucket")

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_concurrent_calls(s3_data_source):
"""Test concurrent execution of the async function."""
dummy_cm = s3_data_source._session.client('s3')
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(
side_effect=[
{'MetricsConfigurationList': [{'Id': 'metricsA'}]},
{'MetricsConfigurationList': [{'Id': 'metricsB'}]}
]
)
# Run two concurrent calls with different buckets
results = await asyncio.gather(
s3_data_source.list_bucket_metrics_configurations(Bucket="bucketA"),
s3_data_source.list_bucket_metrics_configurations(Bucket="bucketB")
)

--- Large Scale Test Cases ---

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_large_scale_concurrent(s3_data_source):
"""Test large scale concurrent execution (up to 50 calls)."""
dummy_cm = s3_data_source._session.client('s3')
# Each call returns a unique metrics config
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(
side_effect=[
{'MetricsConfigurationList': [{'Id': f'metrics{i}'}]} for i in range(50)
]
)
tasks = [
s3_data_source.list_bucket_metrics_configurations(Bucket=f"bucket{i}")
for i in range(50)
]
results = await asyncio.gather(*tasks)
# All should succeed and have unique IDs
for i, result in enumerate(results):
pass

--- Throughput Test Cases ---

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_throughput_small_load(s3_data_source):
"""Throughput test: small load (5 concurrent calls)."""
dummy_cm = s3_data_source._session.client('s3')
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(
side_effect=[
{'MetricsConfigurationList': [{'Id': f'metrics{i}'}]} for i in range(5)
]
)
tasks = [
s3_data_source.list_bucket_metrics_configurations(Bucket=f"bucket{i}")
for i in range(5)
]
results = await asyncio.gather(*tasks)

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_throughput_medium_load(s3_data_source):
"""Throughput test: medium load (20 concurrent calls)."""
dummy_cm = s3_data_source._session.client('s3')
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(
side_effect=[
{'MetricsConfigurationList': [{'Id': f'metrics{i}'}]} for i in range(20)
]
)
tasks = [
s3_data_source.list_bucket_metrics_configurations(Bucket=f"bucket{i}")
for i in range(20)
]
results = await asyncio.gather(*tasks)

@pytest.mark.asyncio
async def test_list_bucket_metrics_configurations_throughput_high_volume(s3_data_source):
"""Throughput test: high volume (100 concurrent calls)."""
dummy_cm = s3_data_source._session.client('s3')
dummy_cm.s3_client.list_bucket_metrics_configurations = AsyncMock(
side_effect=[
{'MetricsConfigurationList': [{'Id': f'metrics{i}'}]} for i in range(100)
]
)
tasks = [
s3_data_source.list_bucket_metrics_configurations(Bucket=f"bucket{i}")
for i in range(100)
]
results = await asyncio.gather(*tasks)
# Ensure IDs are unique and correct
for i, result in enumerate(results):
pass

codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-S3DataSource.list_bucket_metrics_configurations-mhx3sq49 and push.