Handling Database Connection Timeout Errors in Python 3.8 with SQLAlchemy
I'm confused about I'm prototyping a solution and I'm currently working on a project using Python 3.8 with SQLAlchemy to interact with a PostgreSQL database. I've set up a connection pool for efficiency, but I've been working with intermittent timeout errors that throw me off during peak loads. The behavior message I receive is: ``` sqlalchemy.exc.OperationalError: (psycopg2.OperationalError) timeout expired ``` I've tried increasing the `pool_timeout` parameter in my SQLAlchemy engine configuration, but the scenario continues during high concurrency periods. Here’s the relevant portion of my code: ```python from sqlalchemy import create_engine, text DATABASE_URL = 'postgresql://user:password@localhost/dbname' engine = create_engine(DATABASE_URL, pool_size=10, max_overflow=20, pool_timeout=30) with engine.connect() as connection: result = connection.execute(text("SELECT * FROM my_table")) for row in result: print(row) ``` I also considered increasing the connection pool size, but I'm hesitant as I’ve read that too many connections might lead to resource exhaustion on the database server. When I profile the database during these moments, I see a important number of active connections but not exceeding the limits I set. Has anyone faced a similar scenario? What best practices can I implement to handle these timeout errors gracefully? Should I be implementing retries, or is there a better way to handle peak loads with SQLAlchemy? Any help would be greatly appreciated! The stack includes Python and several other technologies. I'd really appreciate any guidance on this.