@Marvin what the heck does this mean? this crashes the server. it has 8 cpus with plenty of headroom. sqlalchemy.exc.TimeoutError: QueuePool limit of size 5 overflow 10 reached, connection timed out, timeout 30.00

Ah, the plot thickens. It seems that Prefect does indeed interact with SQLAlchemy for database operations, as shown in the interface.py and orm_models.py files from the Prefect source code. Now, let's turn our attention to the GitHub issues. It appears that other users have encountered similar SQLAlchemy

QueuePool

limit issues with Prefect, particularly when running large flows or blocking async tasks: 1. Database QueuePool issue with 1500+ task flows: This user encountered the

QueuePool

error when running a flow with around 1500 tasks or more. 2. SQLite database backend stability during large flows: This user experienced several errors, including a

database is locked

error, when running a flow with 1000 tasks. 3. Blocking async task causes QueuePool limit: This user found that a blocking async task could cause a

QueuePool

limit timeout, which eventually crashes the flow run. They asked if it's possible to increase this queue limit. From these issues, it seems that running large flows or blocking tasks in Prefect can lead to exceeding SQLAlchemy's

QueuePool

limit. This is likely because each task in the flow requires a database connection, and if too many tasks are running concurrently, the number of required connections can exceed the limit. To address this, you could try increasing the

QueuePool

limit or optimizing your flow to reduce the number of concurrent tasks. However, please note that increasing the

QueuePool

limit may have implications for your database's performance and resource usage, so it's important to consider these factors and possibly consult with a database administrator. Oh, the joys of managing database connections. It's enough to make one yearn for the simplicity of a single-threaded application... but then, where would be the fun in that?

@Marvin how do i “To address this, you could try increasing the

QueuePool

limit”