⚡️ Speed up method TeamsDataSource.teams_get_group by 10%

[codeflash-ai]

📄 10% (0.10x) speedup for TeamsDataSource.teams_get_group in backend/python/app/sources/external/microsoft/teams/teams.py

⏱️ Runtime : 10.9 milliseconds → 9.89 milliseconds (best of 109 runs)

📝 Explanation and details

The optimized code achieves a 9% performance improvement (9.89ms vs 10.9ms runtime) and 9% throughput increase (34,880 vs 32,000 operations/second) through two key micro-optimizations:

1. Streamlined Error Response Handling (_handle_teams_response)

Original approach: Used sequential variable assignments (success = True, error_msg = None) followed by multiple elif chains, requiring extra operations even for successful responses.

Optimized approach: Restructured control flow to use early returns and eliminated intermediate variables:

• Fast-path for dict errors: Checks isinstance(response, dict) and 'error' in response first and returns immediately
• Early returns: Each error condition returns directly instead of setting variables and continuing
• Eliminated redundant assignments: Removed success = True and error_msg = None setup for the common success case

This reduces the number of operations from ~8 attribute assignments to ~3 for success cases, as seen in the line profiler showing the success path (return TeamsResponse(success=True, data=response, error=None)) taking 47.7% vs the original's scattered variable assignments.

2. Batch Query Parameter Setting (teams_get_group)

Original approach: Seven separate if statements checking each optional parameter individually.

Optimized approach: Uses a parameter tuple and single loop with setattr():

• Reduced conditionals: From 7 individual if blocks to 1 loop with 1 conditional per iteration
• Fewer method calls: Uses setattr() instead of individual attribute assignments
• Local variable caching: Stores self.client.teams and teams.by_team_id(team_id).group in locals to reduce attribute lookups

The line profiler shows the loop approach (for attr, value in params) is more efficient than multiple individual conditionals, especially when most parameters are None.

Performance Impact

These optimizations are particularly effective for:

• High-volume scenarios: The test cases with 50-200 concurrent calls show consistent improvements
• Success-heavy workloads: Most real-world Teams API calls succeed, benefiting from the fast-path optimizations
• Parameter-light calls: Common usage patterns with few query parameters benefit from the batched approach

The 9% improvement in both runtime and throughput indicates these micro-optimizations compound effectively without changing the function's behavior or API contract.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 344 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

--- Function under test (EXACT COPY, DO NOT MODIFY) ---

import logging
from typing import List, Optional

import pytest # used for our unit tests
from app.sources.external.microsoft.teams.teams import TeamsDataSource

class MockGroupEndpoint:
"""Mock endpoint for group.get()."""
def init(self, team_id, response_map):
self.team_id = team_id
self.response_map = response_map

async def get(self, request_configuration=None):
    # Simulate response based on team_id and query params
    params = request_configuration.query_parameters if request_configuration else None
    key = (self.team_id, tuple(sorted(vars(params).items())) if params else None)
    # Return mapped response or a default
    return self.response_map.get(key, self.response_map.get(self.team_id, {"id": self.team_id, "name": "Default Group"}))

class MockTeamsClient:
"""Mock for .teams property with .by_team_id().group.get()."""
def init(self, response_map):
self.response_map = response_map

def by_team_id(self, team_id):
    # Return an object with .group.get()
    obj = MockGroupEndpoint(team_id, self.response_map)
    obj.team_id = team_id
    return obj

class MockMSGraphServiceClient:
"""Mock for MSGraphServiceClient with .teams property."""
def init(self, response_map):
self.teams = MockTeamsClient(response_map)
self.me = True # To pass hasattr check

class MockMSGraphClient:
"""Mock for MSGraphClient."""
def init(self, response_map):
self._service_client = MockMSGraphServiceClient(response_map)

def get_client(self):
    return self

def get_ms_graph_service_client(self):
    return self._service_client

--- Unit Tests ---

@pytest.fixture
def basic_response_map():
# Maps team_id to response dicts for basic tests
return {
"team_basic": {"id": "team_basic", "name": "Basic Team"},
"team_select": {"id": "team_select", "name": "Selected Team", "fields": ["field1", "field2"]},
"team_expand": {"id": "team_expand", "name": "Expanded Team", "details": {"owner": "Alice"}},
"team_error": {"error": {"code": "NotFound", "message": "Team not found"}},
"team_none": None,
"team_exception": Exception("Simulated exception"),
}

@pytest.fixture
def data_source(basic_response_map):
# Provide a TeamsDataSource with a mock MSGraphClient
client = MockMSGraphClient(basic_response_map)
return TeamsDataSource(client)

--- 1. Basic Test Cases ---

@pytest.mark.asyncio
async def test_teams_get_group_basic_success(data_source):
"""Test basic async/await behavior and success response."""
resp = await data_source.teams_get_group("team_basic")

@pytest.mark.asyncio
async def test_teams_get_group_select_fields(data_source):
"""Test select parameter returns correct fields."""
resp = await data_source.teams_get_group("team_select", select=["field1", "field2"])

@pytest.mark.asyncio
async def test_teams_get_group_expand(data_source):
"""Test expand parameter returns expanded fields."""
resp = await data_source.teams_get_group("team_expand", expand=["details"])

@pytest.mark.asyncio
async def test_teams_get_group_none_response(data_source):
"""Test None response handling."""
resp = await data_source.teams_get_group("team_none")

@pytest.mark.asyncio
async def test_teams_get_group_error_response(data_source):
"""Test error response handling."""
resp = await data_source.teams_get_group("team_error")

--- 2. Edge Test Cases ---

@pytest.mark.asyncio
async def test_teams_get_group_invalid_team_id(data_source):
"""Test with an invalid team_id (not in map). Should return default group."""
resp = await data_source.teams_get_group("team_invalid")

@pytest.mark.asyncio
async def test_teams_get_group_concurrent_calls(data_source):
"""Test concurrent async calls for different team_ids."""
team_ids = ["team_basic", "team_select", "team_expand", "team_error"]
results = await asyncio.gather(*(data_source.teams_get_group(tid) for tid in team_ids))

@pytest.mark.asyncio
async def test_teams_get_group_exception_handling(data_source, basic_response_map):
"""Test exception handling in async context."""
# Patch the response map to raise an exception for a specific team_id
basic_response_map["team_exception"] = None # Simulate None, which triggers error
resp = await data_source.teams_get_group("team_exception")

@pytest.mark.asyncio
async def test_teams_get_group_with_all_parameters(data_source):
"""Test passing all optional parameters."""
resp = await data_source.teams_get_group(
"team_select",
select=["field1", "field2"],
expand=["details"],
filter="name eq 'Selected Team'",
orderby=["name"],
search="Selected",
top=10,
skip=0
)

--- 3. Large Scale Test Cases ---

@pytest.mark.asyncio
async def test_teams_get_group_large_scale_concurrent(data_source):
"""Test large number of concurrent async calls (up to 50)."""
team_ids = [f"team_basic_{i}" for i in range(50)]
# Add responses for these team_ids to the response map
for i, tid in enumerate(team_ids):
data_source.client._service_client.teams.response_map[tid] = {"id": tid, "name": f"Team {i}"}
results = await asyncio.gather(*(data_source.teams_get_group(tid) for tid in team_ids))
for i, resp in enumerate(results):
pass

@pytest.mark.asyncio
async def test_teams_get_group_large_scale_error_handling(data_source):
"""Test large number of concurrent async calls with some errors."""
team_ids = [f"team_basic_{i}" for i in range(25)] + [f"team_error_{i}" for i in range(25)]
# Add responses for these team_ids to the response map
for i, tid in enumerate(team_ids[:25]):
data_source.client._service_client.teams.response_map[tid] = {"id": tid, "name": f"Team {i}"}
for i, tid in enumerate(team_ids[25:]):
data_source.client._service_client.teams.response_map[tid] = {"error": {"code": "NotFound", "message": f"Team {tid} not found"}}
results = await asyncio.gather(*(data_source.teams_get_group(tid) for tid in team_ids))
for i, resp in enumerate(results):
if i < 25:
pass
else:
pass

--- 4. Throughput Test Cases ---

@pytest.mark.asyncio
async def test_teams_get_group_throughput_small_load(data_source):
"""Test throughput with small load (10 concurrent calls)."""
team_ids = [f"team_basic_{i}" for i in range(10)]
for i, tid in enumerate(team_ids):
data_source.client._service_client.teams.response_map[tid] = {"id": tid, "name": f"Team {i}"}
results = await asyncio.gather(*(data_source.teams_get_group(tid) for tid in team_ids))

@pytest.mark.asyncio
async def test_teams_get_group_throughput_medium_load(data_source):
"""Test throughput with medium load (50 concurrent calls)."""
team_ids = [f"team_basic_{i}" for i in range(50)]
for i, tid in enumerate(team_ids):
data_source.client._service_client.teams.response_map[tid] = {"id": tid, "name": f"Team {i}"}
results = await asyncio.gather(*(data_source.teams_get_group(tid) for tid in team_ids))

@pytest.mark.asyncio
async def test_teams_get_group_throughput_high_volume(data_source):
"""Test throughput with high volume (200 concurrent calls)."""
team_ids = [f"team_basic_{i}" for i in range(200)]
for i, tid in enumerate(team_ids):
data_source.client._service_client.teams.response_map[tid] = {"id": tid, "name": f"Team {i}"}
results = await asyncio.gather(*(data_source.teams_get_group(tid) for tid in team_ids))

@pytest.mark.asyncio
async def test_teams_get_group_throughput_mixed_success_error(data_source):
"""Test throughput under mixed success/error conditions (100 calls)."""
team_ids = [f"team_basic_{i}" for i in range(50)] + [f"team_error_{i}" for i in range(50)]
for i, tid in enumerate(team_ids[:50]):
data_source.client._service_client.teams.response_map[tid] = {"id": tid, "name": f"Team {i}"}
for i, tid in enumerate(team_ids[50:]):
data_source.client._service_client.teams.response_map[tid] = {"error": {"code": "NotFound", "message": f"Team {tid} not found"}}
results = await asyncio.gather(*(data_source.teams_get_group(tid) for tid in team_ids))
# First 50 should be success, last 50 should be error
for i, resp in enumerate(results):
if i < 50:
pass
else:
pass

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

The function to test (copied exactly as provided)

import logging

Mocks and stubs for dependencies

from typing import List, Optional

import pytest # used for our unit tests
from app.sources.external.microsoft.teams.teams import TeamsDataSource

Minimal TeamsResponse stub

class TeamsResponse:
def init(self, success: bool, data=None, error=None):
self.success = success
self.data = data
self.error = error

Minimal MSGraphClient stub

class DummyTeamsGroupGet:
def init(self, team_id):
self.team_id = team_id
self.calls = []

async def get(self, request_configuration=None):
    # Simulate different responses based on team_id and query params
    self.calls.append((self.team_id, request_configuration))
    # Basic valid response
    if self.team_id == "valid_team":
        # Simulate select, expand, filter, orderby, etc.
        qp = getattr(request_configuration, "query_parameters", None)
        result = {"id": self.team_id}
        if qp:
            if qp.select:
                result["fields"] = qp.select
            if qp.expand:
                result["expand"] = qp.expand
            if qp.filter:
                result["filter"] = qp.filter
            if qp.orderby:
                result["orderby"] = qp.orderby
            if qp.search:
                result["search"] = qp.search
            if qp.top is not None:
                result["top"] = qp.top
            if qp.skip is not None:
                result["skip"] = qp.skip
        return result
    elif self.team_id == "error_team":
        # Simulate error response
        return {"error": {"code": "NotFound", "message": "Team not found"}}
    elif self.team_id == "exception_team":
        # Simulate raising exception
        raise RuntimeError("Simulated exception in get")
    elif self.team_id == "none_team":
        # Simulate None response
        return None
    elif self.team_id == "large_team":
        # Simulate large response
        return {"id": self.team_id, "members": [f"user{i}" for i in range(500)]}
    else:
        # Default: valid but minimal
        return {"id": self.team_id}

class DummyTeamsByTeamId:
def init(self, team_id):
self.group = DummyTeamsGroupGet(team_id)

class DummyTeams:
def by_team_id(self, team_id):
return DummyTeamsByTeamId(team_id)

class DummyClient:
def init(self):
self.teams = DummyTeams()
self.me = True # for hasattr check

class DummyMSGraphClient:
def get_client(self):
return self
def get_ms_graph_service_client(self):
return DummyClient()

------------------ UNIT TESTS ------------------

Basic Test Cases

@pytest.mark.asyncio
async def test_teams_get_group_basic_success():
"""Test basic successful group retrieval."""
datasource = TeamsDataSource(DummyMSGraphClient())
resp = await datasource.teams_get_group("valid_team")

@pytest.mark.asyncio
async def test_teams_get_group_basic_select_expand():
"""Test select and expand query parameters."""
datasource = TeamsDataSource(DummyMSGraphClient())
resp = await datasource.teams_get_group("valid_team", select=["field1", "field2"], expand=["expand1"])

@pytest.mark.asyncio
async def test_teams_get_group_basic_filter_orderby_search():
"""Test filter, orderby, search query parameters."""
datasource = TeamsDataSource(DummyMSGraphClient())
resp = await datasource.teams_get_group("valid_team", filter="status eq 'active'", orderby=["name"], search="project")

@pytest.mark.asyncio
async def test_teams_get_group_basic_top_skip():
"""Test top and skip query parameters."""
datasource = TeamsDataSource(DummyMSGraphClient())
resp = await datasource.teams_get_group("valid_team", top=10, skip=5)

Edge Test Cases

@pytest.mark.asyncio
async def test_teams_get_group_error_response():
"""Test error response handling."""
datasource = TeamsDataSource(DummyMSGraphClient())
resp = await datasource.teams_get_group("error_team")

@pytest.mark.asyncio
async def test_teams_get_group_exception_handling():
"""Test exception handling in async context."""
datasource = TeamsDataSource(DummyMSGraphClient())
resp = await datasource.teams_get_group("exception_team")

@pytest.mark.asyncio
async def test_teams_get_group_none_response():
"""Test None response handling."""
datasource = TeamsDataSource(DummyMSGraphClient())
resp = await datasource.teams_get_group("none_team")

@pytest.mark.asyncio
async def test_teams_get_group_empty_team_id():
"""Test empty team_id input."""
datasource = TeamsDataSource(DummyMSGraphClient())
resp = await datasource.teams_get_group("")

@pytest.mark.asyncio
async def test_teams_get_group_concurrent_execution():
"""Test concurrent execution of multiple calls."""
datasource = TeamsDataSource(DummyMSGraphClient())
tasks = [
datasource.teams_get_group("valid_team", select=["fieldA"]),
datasource.teams_get_group("error_team"),
datasource.teams_get_group("exception_team"),
datasource.teams_get_group("none_team"),
]
results = await asyncio.gather(*tasks)

Large Scale Test Cases

@pytest.mark.asyncio
async def test_teams_get_group_large_response():
"""Test function with large data response."""
datasource = TeamsDataSource(DummyMSGraphClient())
resp = await datasource.teams_get_group("large_team")

@pytest.mark.asyncio
async def test_teams_get_group_many_concurrent():
"""Test many concurrent calls (scalability)."""
datasource = TeamsDataSource(DummyMSGraphClient())
team_ids = [f"valid_team_{i}" for i in range(20)]
tasks = [datasource.teams_get_group(team_id) for team_id in team_ids]
results = await asyncio.gather(*tasks)
for i, resp in enumerate(results):
pass

Throughput Test Cases

@pytest.mark.asyncio
async def test_teams_get_group_throughput_small_load():
"""Throughput test: small load, 5 concurrent calls."""
datasource = TeamsDataSource(DummyMSGraphClient())
tasks = [datasource.teams_get_group("valid_team") for _ in range(5)]
results = await asyncio.gather(*tasks)
for resp in results:
pass

@pytest.mark.asyncio
async def test_teams_get_group_throughput_medium_load():
"""Throughput test: medium load, 50 concurrent calls."""
datasource = TeamsDataSource(DummyMSGraphClient())
tasks = [datasource.teams_get_group(f"valid_team_{i}") for i in range(50)]
results = await asyncio.gather(*tasks)
for i, resp in enumerate(results):
pass

@pytest.mark.asyncio
async def test_teams_get_group_throughput_large_load():
"""Throughput test: large load, 200 concurrent calls."""
datasource = TeamsDataSource(DummyMSGraphClient())
tasks = [datasource.teams_get_group(f"valid_team_{i}") for i in range(200)]
results = await asyncio.gather(*tasks)
for i, resp in enumerate(results):
pass

@pytest.mark.asyncio
async def test_teams_get_group_throughput_mixed_load():
"""Throughput test: mixed load of success and error responses."""
datasource = TeamsDataSource(DummyMSGraphClient())
team_ids = ["valid_team"] * 10 + ["error_team"] * 5 + ["exception_team"] * 5
tasks = [datasource.teams_get_group(team_id) for team_id in team_ids]
results = await asyncio.gather(*tasks)
for i, resp in enumerate(results):
if i < 10:
pass
elif 10 <= i < 15:
pass
else:
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-TeamsDataSource.teams_get_group-mhttqcxw and push.