Body:
I'm working on a project using PostgreSQL as the database and SQLAlchemy as the ORM. Recently, I've been encountering two issues related to database connections, particularly while running an async Celery task.
Issue 1: Connection Slots Exhaustion
Error:
An error occurred while fetching monitored positions: remaining connection slots are reserved for roles with privileges of the "pg_use_reserved_connections" role
Issue 2: Event Loop Binding
Error:
An error occurred while fetching monitored positions: <Queue at maxsize=25> is bound to a different event loop
Context:
- Database: PostgreSQL
- ORM: SQLAlchemy with async support
- Task Queue: Celery
- Command Used:
celery -A src.core.celery_app worker --loglevel=info -Q position_monitoring
- Connection Pooling: Initially, I set up the connection pool with a high
pool_sizeandmax_overflow:
from sqlalchemy.ext.asyncio import create_async_engine
task_engine = create_async_engine(
DATABASE_URL,
echo=True,
pool_size=100, # High pool size
max_overflow=200 # High overflow
)
Issue Details:
1. Connection Slots Exhaustion: The first error suggests that PostgreSQL has exhausted its available connection slots, and only users with specific privileges (those with the pg_use_reserved_connections role) can connect. This issue has been causing problems with tasks that need to query the database.
2. Event Loop Binding:
The second error indicates that a Queue object is being used across different event loops, which is causing issues with async task execution.
Task Code:
Here's the code for the Celery task that is causing these issues:
import gc
import logging
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy.future import select
from sqlalchemy.sql import text
from src.core.celery_app import celery_app
from src.database_tasks import TaskSessionLocal
from src.models.monitored_positions import MonitoredPosition
from src.services.trade_service import calculate_profit_loss, get_open_position
from src.utils.async_utils import run_async_in_sync
from src.utils.websocket_manager import websocket_manager
logger = logging.getLogger(__name__)
@celery_app.task(name='src.tasks.position_monitor.monitor_positions')
def monitor_positions():
gc.disable()
logger.info("Starting monitor_positions task")
run_async_in_sync(monitor_positions_async)
async def get_monitored_positions():
try:
logger.info("Fetching monitored positions from database")
db: AsyncSession = TaskSessionLocal()
result = await db.execute(select(MonitoredPosition))
positions = result.scalars().all()
db.close()
logger.info(f"Retrieved {len(positions)} monitored positions")
return positions
except Exception as e:
logger.error(f"An error occurred while fetching monitored positions: {e}")
return []
async def monitor_positions_async():
try:
logger.info("Starting monitor_positions_async")
positions = await get_monitored_positions()
for position in positions:
logger.info(f"Processing position {position.position_id}")
await monitor_position(position)
logger.info("Finished monitor_positions_async")
except Exception as e:
logger.error(f"An error occurred in monitor_positions_async: {e}")
async def monitor_position(position):
try:
current_price = websocket_manager.current_prices.get(position.trade_pair)
if current_price:
profit_loss = calculate_profit_loss(
position.entry_price,
current_price,
position.cumulative_leverage,
position.cumulative_order_type,
position.asset_type
)
if should_close_position(profit_loss, position):
db: AsyncSession = TaskSessionLocal()
await close_position(position)
db.close()
return True
except Exception as e:
logger.error(f"An error occurred while monitoring position {position.position_id}: {e}")
async def close_position(position):
try:
db: AsyncSession = TaskSessionLocal()
open_position = await get_open_position(db, position.trader_id, position.trade_pair)
if open_position:
close_submitted = await websocket_manager.submit_trade(position.trader_id, position.trade_pair, "FLAT", 1)
if close_submitted:
await db.execute(
text("DELETE FROM monitored_positions WHERE position_id = :position_id"),
{"position_id": position.position_id}
)
await db.commit()
db.close()
except Exception as e:
logger.error(f"An error occurred while closing position {position.position_id}: {e}")
def should_close_position(profit_loss, position):
try:
result = (
(position.cumulative_order_type == "LONG" and profit_loss >= position.cumulative_take_profit) or
(position.cumulative_order_type == "LONG" and profit_loss <= position.cumulative_stop_loss) or
(position.cumulative_order_type == "SHORT" and profit_loss <= position.cumulative_take_profit) or
(position.cumulative_order_type == "SHORT" and profit_loss >= position.cumulative_stop_loss)
)
logger.info(f"Determining whether to close position: {result}")
# Enable the garbage collector
gc.enable()
return result
except Exception as e:
logger.error(f"An error occurred while determining if position should be closed: {e}")
return False
this task runs every second.
from celery import Celery
celery_app = Celery(
'core.celery_app',
broker='redis://localhost:6379/0',
backend='redis://localhost:6379/0'
)
celery_app.conf.update(
task_routes={
'src.tasks.subscription_manager.manage_subscriptions': {'queue': 'subscription_management'},
'src.tasks.subscription_manager.trade_pair_worker': {'queue': 'trade_pair_workers'},
'src.tasks.position_monitor.monitor_positions': {'queue': 'position_monitoring'},
},
beat_schedule={
'manage_subscriptions-every-10-seconds': {
'task': 'src.tasks.subscription_manager.manage_subscriptions',
'schedule': 10.0, # every 10 seconds
},
'monitor_positions-every-1-second': {
'task': 'src.tasks.position_monitor.monitor_positions',
'schedule': 1.0, # every 1 second
},
},
timezone='UTC',
)
celery_app.autodiscover_tasks(['src.tasks'])
# Ensure tasks are loaded
import src.tasks.subscription_manager
import src.tasks.position_monitor
What I've Tried:
1. Lowering the Connection Pool Size:
I reduced the pool_size and max_overflow to more conservative values to limit the number of connections being opened at once:
task_engine = create_async_engine(
DATABASE_URL,
echo=True,
pool_size=5, # Reduced pool size
max_overflow=10 # Reduced overflow
)
2. Ensuring Proper Session Management: I made sure that all database sessions are properly closed after use to prevent connection leaks:
sync def get_monitored_positions():
try:
logger.info("Fetching monitored positions from database")
db: AsyncSession = TaskSessionLocal()
result = await db.execute(select(MonitoredPosition))
positions = result.scalars().all()
db.close()
logger.info(f"Retrieved {len(positions)} monitored positions")
return positions
except Exception as e:
logger.error(f"An error occurred while fetching monitored positions: {e}")
return []
3. Checking PostgreSQL Configuration:
I increased the max_connections setting in PostgreSQL by editing the postgresql.conf file from 100 to 200, but it didn't work too.
4. Event Loop Consistency: I suspect that the event loop binding issue might be related to how tasks are being scheduled or executed in the application, possibly due to mismanagement of async tasks.
Questions:
1. Best Practices for Connection Pooling: Given the async nature of my application, what are the best practices for configuring connection pools in SQLAlchemy with PostgreSQL? How should I balance pool_size and max_overflow?
2. Increasing max_connections: Is it advisable to increase the max_connections in PostgreSQL? What should I consider before doing this, and how do I determine the appropriate value?
3. Handling Event Loop Issues: How can I ensure that async tasks are consistently bound to the correct event loop? What might cause a Queue to be bound to a different event loop, and how can I resolve this?
4. Connection Pooling Middleware: Would using a tool like PgBouncer be beneficial in this scenario? How does it integrate with an async setup in SQLAlchemy?
5. Handling High Concurrency: My application occasionally needs to handle bursts of concurrent tasks. How can I manage database connections efficiently during these spikes without running into these issues?
Any insights or recommendations would be greatly appreciated!
