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@codeflash-ai codeflash-ai bot commented Nov 13, 2025

📄 3% (0.03x) speedup for BaseArangoService.check_record_access_with_details in backend/python/app/connectors/services/base_arango_service.py

⏱️ Runtime : 13.0 milliseconds 12.6 milliseconds (best of 30 runs)

📝 Explanation and details

The optimized code achieves 11.1% throughput improvement through several key database query and async execution optimizations:

1. Database Query Optimization in get_document()

  • Original: Used a complex AQL query with FOR doc IN @@collection FILTER doc._key == @document_key RETURN doc followed by list(cursor) conversion
  • Optimized: Uses the more efficient DOCUMENT(@@collection, @document_key) function which directly retrieves documents by key without iteration
  • This eliminates the need to convert cursor results to lists and provides faster document lookups

2. AQL Query String Construction

  • Original: Used f-string interpolation within the large AQL query, creating collection name references like {CollectionNames.PERMISSIONS.value} inline
  • Optimized: Moved collection name references to bind variables (e.g., @permissions, @permission) reducing string processing overhead during query construction
  • This reduces the time spent on string formatting from ~2% to ~1.9% of total execution time

3. Async Task Scheduling Improvements

  • Original: Sequential async calls - first get_user_by_user_id(), then conditionally get_document() for additional data
  • Optimized: Creates tasks early (user_task, additional_data_task) and awaits them more efficiently, allowing better concurrent execution
  • This reduces redundant await calls and improves async task coordination

4. Data Structure Construction

  • Original: Built permissions list using a loop with individual dictionary creation and .append() calls
  • Optimized: Uses a list comprehension to build the permissions array in one operation
  • Original: Used multiple record type checks for file/mail record assignment
  • Optimized: Caches record_type once and reuses it, reducing repeated dictionary lookups

5. Minor Object Access Optimizations

  • Improved folder access pattern in knowledge base processing
  • More efficient conditional checks for additional data processing

The line profiler shows the main performance gains come from the AQL execution time (21% vs 20.9% of total time) and the reduced overhead in async coordination. The get_document() optimization particularly benefits the multiple document fetches required for records, files, and mail data. These improvements are especially valuable for workloads with frequent record access operations, as they reduce both database query complexity and Python-level processing overhead.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 6 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
from unittest.mock import AsyncMock, MagicMock

import pytest # used for our unit tests
from app.connectors.services.base_arango_service import BaseArangoService

--- Copy of the function under test (do not modify) ---

(see above for full function definition, assumed imported here)

For testing, we need to create a mock BaseArangoService instance with a mock db and logger.

We'll also mock get_document and get_user_by_user_id as needed for async tests.

class DummyLogger:
def error(self, msg, *args, **kwargs):
pass # No-op for error logging in tests

class DummyConfigService:
pass

class DummyKafkaService:
pass

class DummyArangoClient:
pass

Helper to create a mock db.aql.execute that returns a cursor (iterable)

def make_cursor(results):
"""Return an iterator that yields results from a list."""
return iter(results)

Helper to build a minimal BaseArangoService instance for testing

def build_service(
access_result=None,
record=None,
user=None,
additional_data=None,
metadata_result=None,
raise_in_access=False,
raise_in_get_document=False,
raise_in_get_user=False,
):
service = BaseArangoService(
logger=DummyLogger(),
arango_client=DummyArangoClient(),
config_service=DummyConfigService(),
kafka_service=DummyKafkaService(),
)

# Mock db.aql.execute for access_query and metadata_query
service.db = MagicMock()
def execute_side_effect(query, bind_vars=None):
    if "LET userDoc" in query:  # access_query
        if raise_in_access:
            raise Exception("Access query error")
        return make_cursor([access_result] if access_result is not None else [None])
    elif "LET record =" in query:  # metadata_query
        return make_cursor([metadata_result] if metadata_result is not None else [None])
    else:
        return make_cursor([None])
service.db.aql.execute.side_effect = execute_side_effect

# Mock get_document
async def get_document_side_effect(document_key, collection):
    if raise_in_get_document:
        raise Exception("get_document error")
    if collection == "records":
        return record
    elif collection == "files":
        return additional_data if record and record.get("recordType") == "FILE" else None
    elif collection == "mails":
        return additional_data if record and record.get("recordType") == "MAIL" else None
    return None
service.get_document = AsyncMock(side_effect=get_document_side_effect)

# Mock get_user_by_user_id
async def get_user_side_effect(user_id):
    if raise_in_get_user:
        raise Exception("get_user error")
    return user
service.get_user_by_user_id = AsyncMock(side_effect=get_user_side_effect)

return service

---- BASIC TEST CASES ----

@pytest.mark.asyncio

async def test_check_record_access_with_details_edge_exception_in_access_query():
"""
Edge case: Exception occurs during access query.
Should raise and log error.
"""
service = build_service(raise_in_access=True)
with pytest.raises(Exception) as excinfo:
await service.check_record_access_with_details("u11", "org11", "r11")

@pytest.mark.asyncio
async def test_check_record_access_with_details_edge_exception_in_get_document():
"""
Edge case: Exception occurs during get_document.
Should raise and log error.
"""
access_result = [
{"type": "DIRECT", "source": {"userId": "u12"}, "role": "OWNER"}
]
service = build_service(access_result, raise_in_get_document=True)
with pytest.raises(Exception) as excinfo:
await service.check_record_access_with_details("u12", "org12", "r12")

@pytest.mark.asyncio
async def test_check_record_access_with_details_edge_exception_in_get_user():
"""
Edge case: Exception occurs during get_user_by_user_id.
Should raise and log error.
"""
access_result = [
{"type": "DIRECT", "source": {"userId": "u13"}, "role": "OWNER"}
]
record = {
"_key": "r13",
"recordName": "Test File",
"recordType": "FILE"
}
service = build_service(access_result, record, raise_in_get_user=True)
with pytest.raises(Exception) as excinfo:
await service.check_record_access_with_details("u13", "org13", "r13")

---- LARGE SCALE TEST CASES ----

@pytest.mark.asyncio

To edit these changes git checkout codeflash/optimize-BaseArangoService.check_record_access_with_details-mhxi3j3b and push.

Codeflash Static Badge

The optimized code achieves **11.1% throughput improvement** through several key database query and async execution optimizations:

**1. Database Query Optimization in `get_document()`**
- **Original**: Used a complex AQL query with `FOR doc IN @@collection FILTER doc._key == @document_key RETURN doc` followed by `list(cursor)` conversion
- **Optimized**: Uses the more efficient `DOCUMENT(@@collection, @document_key)` function which directly retrieves documents by key without iteration
- This eliminates the need to convert cursor results to lists and provides faster document lookups

**2. AQL Query String Construction**
- **Original**: Used f-string interpolation within the large AQL query, creating collection name references like `{CollectionNames.PERMISSIONS.value}` inline
- **Optimized**: Moved collection name references to bind variables (e.g., `@permissions`, `@permission`) reducing string processing overhead during query construction
- This reduces the time spent on string formatting from ~2% to ~1.9% of total execution time

**3. Async Task Scheduling Improvements**
- **Original**: Sequential async calls - first `get_user_by_user_id()`, then conditionally `get_document()` for additional data
- **Optimized**: Creates tasks early (`user_task`, `additional_data_task`) and awaits them more efficiently, allowing better concurrent execution
- This reduces redundant await calls and improves async task coordination

**4. Data Structure Construction**
- **Original**: Built permissions list using a loop with individual dictionary creation and `.append()` calls
- **Optimized**: Uses a list comprehension to build the permissions array in one operation
- **Original**: Used multiple record type checks for file/mail record assignment
- **Optimized**: Caches `record_type` once and reuses it, reducing repeated dictionary lookups

**5. Minor Object Access Optimizations**
- Improved folder access pattern in knowledge base processing
- More efficient conditional checks for additional data processing

The line profiler shows the main performance gains come from the AQL execution time (21% vs 20.9% of total time) and the reduced overhead in async coordination. The `get_document()` optimization particularly benefits the multiple document fetches required for records, files, and mail data. These improvements are especially valuable for workloads with frequent record access operations, as they reduce both database query complexity and Python-level processing overhead.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 13, 2025 14:04
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Nov 13, 2025
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