@Marvin I’ve clicked on retry button of a failed flow run but it remains in AwaitingRetry state. Why?

When a flow run remains in the "AwaitingRetry" state after clicking the retry button, it typically means that the flow run is scheduled to retry at a specific time. This state is a scheduled state, indicating that Prefect is waiting for the appropriate time to attempt the retry. Key points to consider: 1. Scheduled Time: The flow run will remain in "AwaitingRetry" until the scheduled retry time is reached. Ensure that the scheduled time for the retry is correctly set. 2. Concurrency and Resources: If there are constraints like concurrency limits or resource availability, these might delay the retry. 3. Logs and Configuration: Check the logs and flow configuration for any errors or misconfigurations that might prevent the retry from executing. For more details, you can refer to Prefect's state management in the source code here and here. If the issue persists beyond these typical scenarios, it might be worth checking the Prefect Community or raising a question there, as no related issues were found in the GitHub repository.

@Marvin what does it mean “The flow run will remain in “AwaitingRetry” until the scheduled retry time is reached”?

In Prefect, when a flow run is in the "AwaitingRetry" state, it means that the system has scheduled a retry for the flow run at a future time. This is part of Prefect's built-in retry mechanism to handle transient failures gracefully. Here's what happens: - Scheduled Time: When a flow run fails and is set to retry, Prefect schedules it to attempt running again at a specific time in the future. This is done automatically based on the retry configuration you have set for the flow or task. - AwaitingRetry State: While waiting for this scheduled time, the flow run remains in the "AwaitingRetry" state. This state indicates that the flow is prepared to retry but is currently paused until the retry time is reached. This behavior allows Prefect to manage retries in a controlled manner, avoiding immediate re-execution that might not resolve the underlying issue. If you want to adjust the retry timing or behavior, you can configure retry settings in your flow or task definitions.

@Marvin how can I force a flow run stuck in AwaitingRetry to be executed?

To force a flow run that is stuck in the "AwaitingRetry" state to execute immediately, you can manually set its state to "Pending" or "Running." This can be done using the Prefect Python client. Here's a general approach: 1. Use the Prefect Client: You'll need to interact with the Prefect API using the Prefect client to change the state of the flow run. 2. Set the State Manually: You can set the state of the flow run to "Pending" or "Running" to force it to execute. Here's a basic example:

python
   from prefect.client.orchestration import get_client
   from prefect.states import Pending

   async def force_execute_flow_run(flow_run_id):
       async with get_client() as client:
           # Read the current flow run
           flow_run = await client.read_flow_run(flow_run_id)
           
           # Set the state to Pending
           await client.set_flow_run_state(flow_run_id, Pending())

   # Example usage
   import asyncio
   asyncio.run(force_execute_flow_run("your-flow-run-id"))

3. Execution Context: Ensure that you have the necessary permissions and the Prefect client is properly configured to interact with your Prefect server or cloud. Make sure to replace

"your-flow-run-id"

with the actual ID of the flow run you wish to force execute. This approach will bypass the scheduled retry and attempt to run the flow immediately.

@Marvin how can I check if a flow run is in awaitingretry?

To check if a flow run is in the "AwaitingRetry" state, you can use the Prefect Python client to retrieve the current state of the flow run. Here's a concise way to do it: 1. Use the Prefect Client: Interact with the Prefect API to get the flow run's current state. 2. Check the State: You can retrieve the flow run's details and check if its state is "AwaitingRetry". Here's an example of how you might implement this:

python
from prefect.client.orchestration import get_client

async def check_awaiting_retry(flow_run_id):
    async with get_client() as client:
        # Retrieve the flow run details
        flow_run = await client.read_flow_run(flow_run_id)
        
        # Check if the state is AwaitingRetry
        if flow_run.state.name == "AwaitingRetry":
            print("The flow run is in AwaitingRetry state.")
        else:
            print(f"The flow run is in {flow_run.state.name} state.")

# Example usage
import asyncio
asyncio.run(check_awaiting_retry("your-flow-run-id"))

Replace

"your-flow-run-id"

with the actual ID of the flow run you want to check. This script will print out whether the flow run is in the "AwaitingRetry" state or not.

@Marvin the state AwaitingRetry does not exist

It seems there was a misunderstanding. The state you're looking for is likely "Retrying," not "AwaitingRetry." In Prefect, "Retrying" is the state that indicates a flow run is scheduled to retry after a failure. To check if a flow run is in the "Retrying" state, you can use the Prefect Python client as follows:

python
from prefect.client.orchestration import get_client

async def check_retrying_state(flow_run_id):
    async with get_client() as client:
        # Retrieve the flow run details
        flow_run = await client.read_flow_run(flow_run_id)
        
        # Check if the state is Retrying
        if flow_run.state.name == "Retrying":
            print("The flow run is in Retrying state.")
        else:
            print(f"The flow run is in {flow_run.state.name} state.")

# Example usage
import asyncio
asyncio.run(check_retrying_state("your-flow-run-id"))

Replace

"your-flow-run-id"

with the actual ID of the flow run you want to check. This script will print out whether the flow run is in the "Retrying" state or another state.

@Marvin the state Retrying does not exist

I apologize for any confusion earlier. The correct state name is indeed "AwaitingRetry." This state is used to indicate that a flow run is scheduled to retry at a later time. To check if a flow run is in the "AwaitingRetry" state, you can use the following approach with the Prefect Python client:

python
from prefect.client.orchestration import get_client

async def check_awaiting_retry_state(flow_run_id):
    async with get_client() as client:
        # Retrieve the flow run details
        flow_run = await client.read_flow_run(flow_run_id)
        
        # Check if the state is AwaitingRetry
        if flow_run.state.name == "AwaitingRetry":
            print("The flow run is in AwaitingRetry state.")
        else:
            print(f"The flow run is in {flow_run.state.name} state.")

# Example usage
import asyncio
asyncio.run(check_awaiting_retry_state("your-flow-run-id"))

Replace

"your-flow-run-id"

with the actual ID of the flow run you want to check. This script will print out whether the flow run is in the "AwaitingRetry" state.

@Marvin the states AwaitingRetry and Retrying do not exist

It seems there's been some confusion. The state you're looking for is likely "Scheduled," which is a common state indicating that a flow run is set to execute at a future time. The "AwaitingRetry" function is indeed present in the Prefect codebase but is used internally to create a scheduled state for retries. To check if a flow run is scheduled for a retry, you should look for the "Scheduled" state. Here's how you can check if a flow run is in the "Scheduled" state using the Prefect Python client:

python
from prefect.client.orchestration import get_client

async def check_scheduled_state(flow_run_id):
    async with get_client() as client:
        # Retrieve the flow run details
        flow_run = await client.read_flow_run(flow_run_id)
        
        # Check if the state is Scheduled
        if flow_run.state.name == "Scheduled":
            print("The flow run is in Scheduled state.")
        else:
            print(f"The flow run is in {flow_run.state.name} state.")

# Example usage
import asyncio
asyncio.run(check_scheduled_state("your-flow-run-id"))

Replace

"your-flow-run-id"

with the actual ID of the flow run you want to check. This script will print out whether the flow run is in the "Scheduled" state, which is the state used for retries.

@Marvin how do I get all flow runs of a deployment using Prefect 3 python client?

To retrieve all flow runs of a specific deployment using the Prefect 3.x Python client, you can utilize the

FlowRunFilter

to filter flow runs by their deployment ID. Here's how you can do it:

python
from prefect.client.orchestration import get_client
from prefect.client.schemas.filters import FlowRunFilter, FlowRunFilterDeploymentId

async def get_flow_runs_of_deployment(deployment_id):
    async with get_client() as client:
        # Create a filter for the specific deployment ID
        deployment_filter = FlowRunFilter(deployment_id=FlowRunFilterDeploymentId(any_=[deployment_id]))
        
        # Retrieve flow runs using the filter
        flow_runs = await client.read_flow_runs(flow_run_filter=deployment_filter)
        
        return flow_runs

# Example usage
import asyncio
flow_runs = asyncio.run(get_flow_runs_of_deployment("your-deployment-id"))
for flow_run in flow_runs:
    print(flow_run.id, flow_run.state.name)

Replace

"your-deployment-id"

with the actual deployment ID you want to query. This script will fetch all flow runs associated with the specified deployment and print their IDs and current states.

@Marvin how can I use Prefect 3 with sqlalchemy<2.0.0

To use Prefect 3 with SQLAlchemy versions below 2.0.0, you can utilize the

prefect-sqlalchemy

integration package, which facilitates connecting to a database within your Prefect flows. Here's a concise guide to get you started: 1. Installation: Install the

prefect-sqlalchemy

package, which is compatible with your installed version of Prefect. This package will help you manage database connections and operations.

bash
   pip install prefect-sqlalchemy

2. Register Block Types: After installation, register the block types provided by

prefect-sqlalchemy

to make them available for use in your Prefect flows. 3. Using SQLAlchemy in Prefect Flows: - Save Credentials: Use Prefect's block storage to save your database credentials. - Load Credentials: Load the saved block to access your database credentials. - Database Operations: Use methods like

execute

,

execute_many

, and

fetch_many

to interact with your database. For more detailed examples and usage, you can refer to the Prefect SQLAlchemy Integration documentation. This setup should work seamlessly with SQLAlchemy versions below 2.0.0, allowing you to perform database operations within your Prefect flows.

@Marvin how can I make raw http requests to my Prefect instance?

To make raw HTTP requests to your Prefect instance, you can interact with the Prefect REST API. Here's a general approach: 1. API URL and Authentication: - Set the

PREFECT_API_URL

to point to your Prefect server or cloud API endpoint. - For authentication, use your

PREFECT_API_KEY

if you're accessing Prefect Cloud or any secured Prefect server. 2. HTTP Client: - You can use libraries like

requests

or

httpx

in Python to make HTTP requests. 3. Example Request: Here's how you can make a raw HTTP GET request to fetch flow runs:

python
   import requests

   # Set your API URL and API key
   api_url = "<https://api.prefect.cloud>"  # Replace with your Prefect API URL
   api_key = "YOUR_PREFECT_API_KEY"  # Replace with your API key

   headers = {
       "Authorization": f"Bearer {api_key}",
       "Content-Type": "application/json"
   }

   # Example endpoint to list flow runs
   endpoint = f"{api_url}/flow_runs"

   # Make the GET request
   response = requests.get(endpoint, headers=headers)

   # Check the response
   if response.status_code == 200:
       flow_runs = response.json()
       print(flow_runs)
   else:
       print(f"Failed to fetch data: {response.status_code} - {response.text}")

4. Handling Proxies: - If you're behind a proxy, configure your HTTP client to use proxy environment variables like

HTTPS_PROXY

. 5. Security: - Ensure that you whitelist necessary URLs for outbound communication if you're in a secure environment. For more details on interacting with Prefect's API, you can refer to the Prefect documentation on API access and troubleshooting.

@Marvin what does it mean when a task is in cached state?

In Prefect, when a task is in the "Cached" state, it indicates that the task has already completed successfully in a previous run, and its result is being reused without re-executing the task code. This is part of Prefect's task caching mechanism, which enhances efficiency and ensures idempotency in your workflows. Here are the key points about the "Cached" state: - Efficiency: Caching allows you to avoid redundant computations by reusing results from previous task runs that had the same inputs and code definition. - Idempotency: By using cached results, you ensure that retries or re-executions of your flow do not alter the outcome, maintaining consistent results. - Cache Key: Prefect computes a cache key based on the task's inputs, code definition, and the flow or task run ID. This key determines if a cached result can be used. - Result Persistence: For caching to work, result persistence must be enabled, which is not on by default. For more details on configuring and using task caching, you can refer to the Prefect documentation on task caching.

@Marvin how to disable at all caching?

To disable task caching entirely in Prefect 3, you can configure the environment to ignore cached results by setting the

PREFECT_TASKS_REFRESH_CACHE

environment variable to

true

. This forces Prefect to refresh the cache for all tasks and rerun them regardless of any existing cached results. Here's how you can do it: 1. Set Environment Variable: You can set the

PREFECT_TASKS_REFRESH_CACHE

environment variable in your execution environment to

true

. This will ensure that all tasks refresh their cache and do not use cached results.

bash
   export PREFECT_TASKS_REFRESH_CACHE=true

2. Configure in Code (if needed): If you prefer to set this within your Python code, you can use the

os

module to set environment variables:

python
   import os

   os.environ["PREFECT_TASKS_REFRESH_CACHE"] = "true"

By setting this variable, you instruct Prefect to ignore cached data across all tasks, effectively disabling caching behavior for your flows. For more details, you can refer to the Prefect documentation on task caching.

@Marvin I have deployed Prefect in an AWS EC2 instance. The instance has a role attached which has access to the bucket and folder where I store the flows. If I create an s3 block without credentials, would Prefect be able to read from that bucket?

Yes, if your AWS EC2 instance has an IAM role attached with the necessary permissions to access the S3 bucket and folder where your flows are stored, Prefect should be able to read from that bucket without explicitly providing credentials in the S3 block. Here's why: - IAM Role: The IAM role attached to your EC2 instance allows it to assume specific permissions as defined by the role's policy. This means that any application running on the instance, including Prefect, can use those permissions. - Credential Provider Chain: AWS SDKs and tools, including those used by Prefect, automatically use the instance's IAM role credentials when no explicit credentials are provided. This is part of AWS's default credential provider chain. Therefore, as long as the IAM role has the appropriate permissions (e.g.,

s3:GetObject

,

s3:ListBucket

), Prefect should be able to access the S3 bucket and folder without additional credentials in the S3 block.

@Marvin what are blocks in block_document table named like anonymous-<uuid>?

The "anonymous-<uuid>" naming convention for blocks in the

block_document

table likely refers to blocks that have been automatically generated without explicit user-defined names. Blocks in Prefect serve as configurations or settings that can be reused across different flows and tasks. Each block is an instance of a block type, and it contains specific values for each field defined in its schema. You can find more about blocks in the Prefect documentation. There were no relevant issues found in the GitHub repository regarding this naming convention, indicating it might be a standard or expected behavior in certain scenarios.

@Marvin what is the meaning of the day_or toggle?

The

day_or

toggle in Prefect is used to define how scheduling rules are applied when specifying both a day of the month and a day of the week. - When

day_or

is set to

True

, the schedule operates like an OR statement. This means that the flow will run on both the specified day of the month and the specified day of the week. For example, with a cron schedule like

* * 3 1 2

, the flow would run every minute on the third day of the month and on Tuesday in January. - When

day_or

is set to

False

, the schedule operates like an AND statement. This means that the flow will only run when both conditions are met, such as on the third Tuesday in January. This behavior is similar to the distinction between cron and fcron scheduling. More details can be found in the Prefect documentation on adding schedules.

@Marvin how to cancel a flow run?

To cancel a flow run in Prefect 3.x, you can use the Prefect UI or the Prefect API. Here's a concise explanation: 1. Using the Prefect UI: Navigate to the flow run you wish to cancel and use the provided option to cancel it. This is the most straightforward method if you are using the Prefect Cloud interface. 2. Using the Prefect API: You can programmatically cancel a flow run by interacting with the Prefect REST API. This involves sending a request to cancel the flow run, which Prefect will attempt to do by terminating the infrastructure running the flow. It's important to note that cancellation is dependent on the infrastructure's ability to be cancelled. If the infrastructure does not support cancellation, it might not work as expected. More details on cancellation can be found in the Prefect documentation.

@Marvin how to cancel a flow run using prefect 3 api

To cancel a flow run using the Prefect 3.x API, you can utilize the

PrefectClient

class from the

prefect.client.orchestration

module. Here is a concise guide on how to achieve this programmatically: 1. Initialize the Prefect Client: First, you need to create an instance of

PrefectClient

. 2. Cancel the Flow Run: Use the client to send a request to cancel the specific flow run by its ID. Here's an example of how you might implement this:

python
from prefect.client.orchestration import PrefectClient

async def cancel_flow_run(flow_run_id):
    async with PrefectClient() as client:
        await client.set_flow_run_state(flow_run_id=flow_run_id, state="Cancelled")

# Replace 'your-flow-run-id' with the actual flow run ID you want to cancel
await cancel_flow_run('your-flow-run-id')

This code snippet assumes you are working in an asynchronous environment, which is typical for using the Prefect Client. If you're working synchronously, ensure to adapt the code accordingly. For more information, you can refer to the Prefect documentation on cancellation.

@Marvin I use the following code to cancel a flow run

state_updates = {}
state_updates.setdefault("name", "Cancelled")
state_updates.setdefault("type", StateType.CANCELLED)
state = flow_runs[0].state.copy(update=state_updates)
await client.set_flow_run_state(flow_run_id=flow_run_id, state=state, force=True)

the flow run is cancelled but the tasks are still executed. why?

When you cancel a flow run in Prefect using

set_flow_run_state

, setting the flow run's state to "Cancelled" does not inherently terminate the tasks that are already executing. The cancellation process in Prefect is reliant on the infrastructure's ability to respond to cancellation requests. Here are some potential reasons why tasks may continue to execute even after the flow run is cancelled: 1. Infrastructure Support: The infrastructure running the tasks might not support cancellation. For example, some environments or configurations might not properly handle termination signals. 2. Scope and Identifier Mismatch: If the worker cannot match the infrastructure scope or identifier (e.g., process ID, container ID), it might not be able to cancel the tasks effectively. 3. Force Parameter: The

force=True

parameter forces the state change in the Prefect database but does not guarantee the termination of running tasks. It merely updates the flow run's state in Prefect's orchestration layer. To ensure tasks are terminated, you might need to handle this at the infrastructure level or ensure that your environment correctly processes cancellation requests. More information can be found in the Prefect documentation on cancellation.