@Marvin I am using RayTaskRunner and I have about 60 tasks submitted async. I am running into this error File “asyncpg/protocol/protocol.pyx”, line 182, in bind_execute File “asyncpg/protocol/protocol.pyx”, line 696, in asyncpg.protocol.protocol.BaseProtocol._get_timeout_impl asyncio.exceptions.TimeoutError

It appears we didn't get any relevant documentation or GitHub issues about the specific error you're encountering. Let's try to narrow down the focus a bit more. I'll look up more information on handling timeouts in Prefect with

RayTaskRunner

and

asyncpg

. Such an expected result. Nothing ever goes smoothly, does it? While the direct information search didn't yield results, I'll try to offer some generic pointers that might help you troubleshoot the timeout issue you're having with

RayTaskRunner

. ### Suggestions 1. Increase Timeout Duration: The

asyncpg

timeout error could be due to the default timeout being insufficient. You can increase the timeout duration parameter when making connections or queries using

asyncpg

. 2. Optimize Query Performance: Ensure that your PostgreSQL queries are optimized. Poorly optimized queries may take longer to execute, causing timeouts. 3. Connection Pool Limits: Check the connection limits of your database. You may need to increase the number of maximum allowed connections especially if you're running many asynchronous tasks concurrently. 4. Task Throttling: Instead of running 60 tasks concurrently, try to throttle the number of concurrent tasks to see if the situation improves. Sometimes DB servers can't handle too many concurrent connections efficiently. 5. Retry Logic: Implement retry logic for your async tasks to handle transient timeout issues. 6. Monitoring & Benchmarking: Use monitoring tools to check the load on your PostgreSQL server when executing these tasks. Tools like

pg_stat_statements

can help profile and benchmark query performance. If specific code examples are needed, please let me know and I can provide sample code for the suggested strategies. Alternatively, you could consider reaching out to the Prefect community or opening an issue with detailed logs and configurations.

@Marvin Can you explain this to me? PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT = Setting( timedelta, default=timedelta(seconds=30), ) “”" How long before a PENDING task are made available to another task server. In practice, a task server should move a task from PENDING to RUNNING very quickly, so runs stuck in PENDING for a while is a sign that the task server may have crashed. Why would a task not be stuck in pending? For instance, if I have a ray cluster with 2 workers and I create 20 tasks. Won’t the tasks be stuck in pending before they are run, and therefore timeout?

It seems our search functionality isn't cooperating. Let's break down the setting and your question manually. ### Explanation The

PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT

setting controls how long a task can remain in the PENDING state before it is marked as available for another worker to pick up. Here's the breakdown: - Type:

timedelta

- Default:

timedelta(seconds=30)

- Purpose: If a task is in the PENDING state for more than the specified duration (30 seconds by default), it is assumed that the worker responsible for running it might have crashed or failed to schedule it, so it becomes available for another worker to pick up. ### Why Tasks Shouldn't Be Stuck in Pending In theory, a task should move from PENDING to RUNNING very quickly. Here are some reasons why tasks shouldn't be stuck in PENDING: 1. Task Server Availability: The task server (or worker) should be able to pick up tasks almost immediately if it's functioning correctly. 2. Immediate Scheduling: Under normal circumstances, a worker should quickly take the task from PENDING to RUNNING. ### Your Scenario In the scenario you described (a Ray cluster with 2 workers and 20 tasks): 1. Task Queuing: Indeed, with only 2 workers, not all tasks will run simultaneously. Instead, tasks will be queued. 2. Pending State: Some tasks will inevitably be in the PENDING state until a worker becomes available. 3. Timeout Concern: If a task is in PENDING for more than 30 seconds due to worker unavailability, it may indeed timeout according to the

PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT

setting. ### Recommendations 1. Increase the Timeout: If your tasks are expected to be in the queue for longer durations, you might want to increase this timeout.

python
    Setting(
        timedelta,
        default=timedelta(seconds=60),  # Increase to 60 seconds or more
    )

2. Increase Worker Count: Adding more workers to your Ray cluster can help in reducing the time tasks spend in the PENDING state. 3. Monitoring & Handling: Monitor the state of your tasks. If a task frequently times out with PENDING status, it indicates that the worker or task server needs scaling or optimization. By tuning these parameters and scaling as needed, you can avoid undesired timeouts and ensure your tasks get scheduled efficiently.

@Marvin How would I set the value of an increased duration from the python SDK or through an environment variable?

Ah, the documentation search is still unhelpful. I'll give you a concise breakdown of both methods based on my understanding of Prefect's configuration system. ### Setting via Python SDK You can use the Prefect configuration system to set this value in your Python code.

python
from prefect import settings
from datetime import timedelta

# Set the timeout value
settings.PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT = timedelta(seconds=60)

### Setting via Environment Variable You can set this value through an environment variable as well. Prefect reads configuration settings from environment variables prefixed with

PREFECT_

.

sh
export PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT='60'  # Timeout in seconds

### Putting It All Together Here’s an example in Python to check if the configuration was set correctly:

python
import os
from prefect import settings
from datetime import timedelta

# Print current timeout value
print("Current timeout:", settings.PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT)

# Set via Python
settings.PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT = timedelta(seconds=120)
print("Updated timeout via Python:", settings.PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT)

# Set via Environment Variable
os.environ['PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT'] = '180'  # 180 seconds
settings.PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT = timedelta(seconds=int(os.getenv('PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT')))
print("Updated timeout via environment variable:", settings.PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT)

In this way, you can configure the

PREFECT_TASK_SCHEDULING_PENDING_TASK_TIMEOUT

setting either through the Python SDK or by setting an environment variable. If Prefect's configuration system updates have moved to more recent methodologies, consulting the specific version documentation would be the next logical step. However, these methods should cover the majority of Prefect versions.