The error you're encountering is a PostgreSQL limitation where you've reached the maximum number of allowed concurrent database connections. Here are several approaches to fix this issue: 1. Use Connection Pooling

python
from prefect import flow
from sqlalchemy import create_engine
from sqlalchemy.pool import QueuePool

@flow
def your_flow():
    engine = create_engine(
        "<postgresql://user:password@localhost:5432/dbname>",
        poolclass=QueuePool,
        pool_size=5,  # Adjust based on your needs
        max_overflow=10,  # Additional connections allowed
        pool_timeout=30,  # Seconds to wait for a connection
        pool_pre_ping=True  # Verify connection is still valid
    )
    # Use the engine for your database operations
    with engine.connect() as connection:
        # Your database operations here
        pass
    
    # Engine disposal happens automatically when it goes out of scope

2. Explicitly Close Connections Make sure you're properly closing connections after use:

python
from prefect import flow, task
import psycopg

@task
def database_operation():
    try:
        with psycopg.connect("your_connection_string") as conn:
            with conn.cursor() as cur:
                # Your database operations here
                pass
        # Connection automatically closes after the with block
    except Exception as e:
        raise e

@flow
def your_flow():
    database_operation()

3. Use Task-level Connection Management

python
from prefect import flow, task
from contextlib import contextmanager
import psycopg

@contextmanager
def get_db_connection():
    conn = None
    try:
        conn = psycopg.connect("your_connection_string")
        yield conn
    finally:
        if conn is not None:
            conn.close()

@task
def database_task():
    with get_db_connection() as conn:
        with conn.cursor() as cur:
            # Your database operations here
            pass

@flow
def your_flow():
    database_task()

Additional recommendations: 1. Review Current Connections: You can check current connections in PostgreSQL:

sql
SELECT * FROM pg_stat_activity;

2. Configure PostgreSQL Settings: You might need to adjust your PostgreSQL configuration: - Check and possibly increase

max_connections

in

postgresql.conf

- Review

superuser_reserved_connections

setting 3. Use Prefect Concurrency Controls: If you're running multiple flow runs simultaneously, consider using Prefect's concurrency controls:

python
from prefect import flow
from prefect.tasks import task_input_hash

@flow(flow_run_name="db-flow")
def your_flow():
    # Your flow code here
    pass

4. Connection Pooling with PgBouncer: For production environments, consider using PgBouncer as an external connection pooler. The most immediate solution is typically implementing proper connection pooling using SQLAlchemy or ensuring connections are properly closed. Choose the approach that best fits your specific use case, but the first solution (using SQLAlchemy with connection pooling) is generally the most robust approach for Prefect flows. Remember to: - Always close connections explicitly or use context managers - Monitor the number of concurrent connections - Consider using connection pooling for better resource management - Adjust pool sizes based on your application's needs and server capacity Would you like me to provide more specific details about any of these approaches or help you implement one of these solutions in your specific flow?

bash
# Create a work queue with concurrency limit
prefect work-queue create "database-queue" --limit 3

# Update an existing work queue's concurrency limit
prefect work-queue update "database-queue" --limit 5

Then in your deployment:

python
from prefect.deployments import Deployment

deployment = Deployment.build_from_flow(
    flow=your_flow,
    name="database-flow",
    work_queue_name="database-queue"
)
deployment.apply()

5. Combining Multiple Concurrency Controls:

python
from prefect import flow, task
from prefect.concurrency import global_concurrency_limit

@task(retries=3)
@global_concurrency_limit("database-operations")
def database_task():
    # Your database operations here
    pass

@flow(task_runner=SequentialTaskRunner())
def your_flow():
    database_task()

# Deploy to a work queue with its own concurrency limits
deployment = Deployment.build_from_flow(
    flow=your_flow,
    name="database-flow",
    work_queue_name="database-queue"
)
deployment.apply()

Best Practices: 1. Start with higher-level concurrency controls (work queues) before implementing task-level controls 2. Monitor your concurrency limits through the Prefect UI 3. Use meaningful names for your concurrency limits and work queues 4. Consider setting appropriate slot decay rates for your use case 5. Remember that concurrent flow runs are also limited by your worker count Would you like me to explain any of these approaches in more detail or help you implement a specific concurrency control for your use case?