@Marvin I have pgbouncer in front of my postgres instance and I got the following error when starting the server.

asyncpg/connection.py", line 2027, in _do_execute
    result = await executor(stmt, timeout)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "asyncpg/protocol/protocol.pyx", line 206, in bind_execute
asyncpg.exceptions.InvalidSQLStatementNameError: prepared statement "__asyncpg_stmt_2__" does not exist
HINT:
NOTE: pgbouncer with pool_mode set to "transaction" or
"statement" does not support prepared statements properly.
You have two options:

* if you are using pgbouncer for connection pooling to a
  single server, switch to the connection pool functionality
  provided by asyncpg, it is a much better option for this
  purpose;

* if you have no option of avoiding the use of pgbouncer,
  then you can set statement_cache_size to 0 when creating
  the asyncpg connection object.

But I cannot disaplbe this and I need to use statement_cache_size in my URL BUT if I try to append this to URL connection ?statemente_cache_size=0 I got the following error:

asyncpg/connect_utils.py", line 731, in _parse_connect_arguments
  if var_val is None or isinstance(var_val, bool) or var_val < 0:
                                                     ^^^^^^^^^^^

How can I start Prefect server to work with a postgres server that has pgbouncer on front of it without using session connection?

Hi all, just getting started with Prefect and I'm stuck on error: ValueError: No Prefect API URL provided. Please set PREFECT_API_URL to the address of a running Prefect server. prefect version = 3.1.1 I've confirmed the server is running at http://127.0.0.1:4200 I've updated pyproject.toml with

#  pyproject.tom

[tool.prefect]
logging.level = "DEBUG"
api.url = "<http://127.0.0.1:4200/api>"

Any guidance is appreciated

@Marvin i want to trigger prefect job when my s3 export completes. I want event based trigger

Can you run another independent deployment when a flow run finishes? I have 2 flows that use different pools of workers, one is cpu heavy and another is gpu heavy. I want to run the first one on my cpu nodes, and if they were successful, run the second flow on a different worker.

Hi all! Shouldn't the

config.toml

file be in the path where prefect is installed? There is only

backend.toml

file in the path where my prefect is installed, are those two the same? Also i can't read

backend.toml

. I got the error message like below:

TomlDecodeError: Found invalid character in key name: ':'. Try quoting the key name. (line 1 column 2 char 1)

What's the backend.toml file?

Hi all, is there a preferred way to pass along secrets / env vars to the helm deployment of prefect-server during a CI/CD pipeline? For more context, I'm trying to give the deployment a

AZURE_STORAGE_CONNECTION_STRING

for access to blob storage. I'm not seeing anything baked into the helm values.yaml (though maybe something in

prefectConfig

could work?). I was thinking of attaching a k8s secret after the helm deployment? But I think it needs to be set on the job that the agent kicks off. And I should be able to set the env var as-is, or in secret form like

PREFECT__CONTEXT__SECRETS__AZURE_STORAGE_CONNECTION_STRING

?

Hi everyone, I'am using Prefect version: 3.1.1 and I’ve been encountering an intermittent issue with my Prefect deployments, and I’m hoping someone can help clarify why this might be happening and how to best address it. Here's the context: When I execute a deployment, the flow sometimes enters a

Crashed

state. The logs show the following error:

ERROR   | prefect.engine - Engine execution of flow run '<run-id>' exited with unexpected exception
FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Lenovo\\AppData\\Local\\Temp\\<temp-folder>\\orchestration\\subflows_definition\\coordinador_downloads.py'

This suggests that the deployment is unable to locate the required file in the temporary directory. Interestingly, if I restart Prefect (both the Server and Workers) and rerun the same deployment, it executes successfully without issues. Logs for the successful run show:

INFO    | Task run 'run_script-f9b' - Successfully ran script ...
INFO    | Flow run '<run-id>' - Finished in state Completed()

Why might the flow enter a

Crashed

state with a

FileNotFoundError

for code stored in a temporary directory? Could this be related to my use of

LocalFileSystem

storage on Windows? What happens during a Prefect restart that resolves the issue? Is it forcing a re-synchronization of the code between the Server and Worker? Are there best practices or configurations I can apply to prevent this kind of issue in a Windows environment?

I'm encountering a persistent issue with Dask serialization when updating my flows from Prefect

2.19.1

to Prefect

3.1.2

. Every run incurs the following error, which I've never seen before in 18 months of using Prefect 2:

Task run failed with exception: TypeError('Could not serialize object of type HighLevelGraph', '<ToPickle: HighLevelGraph with 1 layers.\n<dask.highlevelgraph.HighLevelGraph object at 0x7f4e3b547400>\n 0. 139974151916736\n>') - Retries are exhausted
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/distributed/protocol/pickle.py", line 63, in dumps
    result = pickle.dumps(x, **dump_kwargs)
  File "/usr/local/lib/python3.10/dist-packages/prefect/docker/__init__.py", line 20, in __getattr__
    raise ImportError(f"module {__name__!r} has no attribute {name!r}")
ImportError: module 'prefect.docker' has no attribute 'isnan'

<---Long series of stack traces--->

The above exception was the direct cause of the following exception:

<<---Long stack trace--->>

  File "/usr/local/lib/python3.10/dist-packages/distributed/protocol/serialize.py", line 392, in serialize
    raise TypeError(msg, str_x) from exc
TypeError: ('Could not serialize object of type HighLevelGraph', '<ToPickle: HighLevelGraph with 1 layers.\n<dask.highlevelgraph.HighLevelGraph object at 0x7f4e3b547400>\n 0. 139974151916736\n>')

All of my flows write to Zarr N-Dimensional data stores using Xarray's

to_zarr

method,.

to_zarr

leverages Dask under the hood to vastly speed up this operation. I don't use Dask Task Runners or any specialized Prefect Dask objects. Digging around I've found this conversation, which doesn't seem directly related https://linen.prefect.io/t/23198687/hello-everyone-i-m-facing-an-issue-using-prefect-with-a-dask. Any ideas Prefect team? I'll post a full stack trace in follow up

@Marvin is it possible to have multiple instances of the prefect server running with a shared db instance?

Hello! All my flows are stuck in "late" state 😢