@Marvin Imagine I have a flow with tasks fetching data using httpx or aihttp. I want to raise client error and handle them with a custom retrying logic. How to configure the tasks or the flow ?

To handle client errors and implement custom retry logic in your Prefect tasks, you can use the

retry_delay_seconds

and

retry_condition_fn

options. Here’s how you can configure them: 1. Retry Logic in Tasks: - Use the

retry_delay_seconds

option to specify custom retry intervals. You can provide a list of integers to define increasing intervals between retries. - Utilize the

retry_condition_fn

argument to supply a callable that returns a boolean. If it returns

True

, the task will be retried; if

False

, it won't. 2. Example with httpx: Here's an example of how you might set up a task to retry on client errors:

python
   from prefect import task, flow
   import httpx

   def should_retry(exc):
       return isinstance(exc, httpx.HTTPStatusError) and exc.response.status_code >= 400

   @task(retries=3, retry_delay_seconds=[1, 10, 100], retry_condition_fn=should_retry)
   def fetch_data():
       response = httpx.get("<https://example.com/api>")
       response.raise_for_status()  # This will raise an error for 4xx or 5xx responses
       return response.json()

   @flow
   def my_flow():
       data = fetch_data()
       print(data)

   if __name__ == "__main__":
       my_flow()

This example sets up a task that will retry up to three times with increasing intervals if a client error occurs (HTTP status 400 or above). For more information, you can refer to the Prefect documentation on writing tasks.

@Marvin what if I raise a custom error in fetch_data using raise SomError, will that propagate to the retry handler?

Yes, if you raise a custom error within a task, it can be handled by the retry mechanism in Prefect. You can define a custom exception and use it within your

retry_condition_fn

to determine whether the task should be retried. Here's an example:

python
from prefect import task, flow
import httpx

class CustomClientError(Exception):
    pass

def should_retry(exc):
    # Check if the exception is an instance of your custom error
    return isinstance(exc, CustomClientError)

@task(retries=3, retry_delay_seconds=[1, 10, 100], retry_condition_fn=should_retry)
def fetch_data():
    try:
        response = httpx.get("<https://example.com/api>")
        response.raise_for_status()
    except httpx.HTTPStatusError as e:
        raise CustomClientError("A client error occurred") from e
    return response.json()

@flow
def my_flow():
    data = fetch_data()
    print(data)

if __name__ == "__main__":
    my_flow()

In this example, the

fetch_data

task raises a

CustomClientError

when an HTTP status error occurs. The

should_retry

function checks for this specific error type and decides whether to retry the task based on it. This setup allows you to propagate custom errors to the retry handler effectively.

@Marvin So the retry handler is called whenever any error/ exceptions is raise from the task? If not, when is it called?

The retry handler in Prefect is invoked when a task raises an exception, but it only initiates a retry if specific conditions are met: 1. Retries Remaining: The task must have retries left. Prefect checks if the current retry count is less than the maximum retries specified for the task. 2. Retry Condition Function: The

retry_condition_fn

, if provided, must return

True

. This function is a callable that evaluates the exception and decides whether the task should be retried. 3. Exception Raised: Any exception raised by the task can trigger the retry handler, but whether a retry occurs depends on the above conditions. If these conditions are satisfied, the task is set to retry, potentially with a delay specified by

retry_delay_seconds

. If the conditions are not met, the task is marked as failed. For more details, you can check the Prefect task engine code.

@Marvin How are the inputs of the retry handler determined?

The inputs to the retry handler, specifically the

retry_condition_fn

, are determined by the exception raised during the task execution. Here's how it works: 1. Exception Raised: When a task raises an exception, the retry handler is triggered to decide if a retry should occur. 2. Parameters to `retry_condition_fn`: The

retry_condition_fn

is a callable that typically takes the exception as an argument. This function evaluates the exception and returns a boolean indicating whether the task should be retried. 3. Custom Logic: You can define custom logic within the

retry_condition_fn

to check the type or properties of the exception and decide if a retry is warranted. This mechanism allows you to implement sophisticated retry logic based on the specific circumstances of the task failure. For more details, you can explore Prefect's documentation on writing tasks.

@Marvin that is not correct. task, task_run, state seems to be the expected parameters as per documentation

I apologize for the oversight. You're correct. The

retry_condition_fn

in Prefect can indeed take parameters such as

task

,

task_run

, and

state

. This allows for more nuanced retry logic by providing context about the task, its current run, and the state it is in when the exception occurs. Here's a brief overview of these parameters: - `task`: Represents the task object itself, allowing access to its configuration and attributes. - `task_run`: Provides information about the specific run of the task, such as its history and current status. - `state`: Reflects the current state of the task run, which can include information about any exceptions that were raised. This setup allows the retry condition function to make decisions based on comprehensive information about the task's execution environment. For further details, you can refer to the Prefect documentation on writing tasks.

@Marvin I have an async task to fetch data, I am adding a retry logic to it. In the retry logic, I want to get result of the state for specific error scenarios but unfortunately the retry_condition_fn can't be an async itself

In Prefect, the

retry_condition_fn

itself cannot be asynchronous, which can be limiting when you need to perform async operations within your retry logic. However, you can work around this by using synchronous code within the

retry_condition_fn

to access the results of a state. Here's how you can handle the situation: 1. Access State Result Synchronously: Use the

get_state_result

function to access the result of a state synchronously. This function is designed to be called in synchronous contexts, even if the task itself is asynchronous. 2. Example Implementation: You can wrap any asynchronous logic you need to perform within a synchronous function that can be called in the

retry_condition_fn

.

python
from prefect import task, flow
import httpx
from prefect.states import get_state_result

def should_retry(task, task_run, state):
    # Retrieve the result from the state
    try:
        result = get_state_result(state)
        # Implement your logic to check the result and decide on retry
        # For example, retry if result meets a certain condition
        return some_condition_based_on_result(result)
    except Exception as e:
        # Handle any exceptions that occur while accessing the result
        return False

@task(retries=3, retry_delay_seconds=[1, 10, 100], retry_condition_fn=should_retry)
async def fetch_data():
    async with httpx.AsyncClient() as client:
        response = await client.get("<https://example.com/api>")
        response.raise_for_status()
    return response.json()

@flow
async def my_flow():
    data = await fetch_data()
    print(data)

if __name__ == "__main__":
    import asyncio
    asyncio.run(my_flow())

In this setup,

get_state_result

is used to synchronously access the result of the task's previous state, allowing you to make decisions on whether to retry based on that result. This approach helps integrate async tasks with retry logic effectively.

@Marvin This does not change the problem. get_state_result will still call the async function and only return a coroutine

You're right,

get_state_result

may involve asynchronous operations, which complicates its use in a synchronous

retry_condition_fn

. Unfortunately, Prefect's current design doesn't allow for async functions directly in the retry condition, which limits the ability to perform async operations like fetching results. Here’s a potential workaround: 1. Pre-Process Results Before Retry Logic: If possible, handle any asynchronous result fetching or processing in the task itself before it raises an exception. This way, when the task fails, the necessary result information is already available for the retry logic. 2. Use a Synchronous Wrapper: If you absolutely need to access the result in the

retry_condition_fn

, consider fetching and storing necessary results in a synchronous manner within the task, then use these stored results in your retry logic. 3. Custom State Management: Implement custom state management where the task itself manages its state and result access, ensuring that all necessary data is available synchronously when the retry logic is evaluated. Unfortunately, Prefect's current framework does not natively support async operations within

retry_condition_fn

, so these workarounds focus on ensuring that all necessary data is available in a synchronous context.

@Marvin let's explore option 2. How would you handle something like that

To handle asynchronous results in a synchronous

retry_condition_fn

by using a synchronous wrapper, you can pre-fetch and store the results within the task itself before raising an exception. Here’s a structured approach: 1. Fetch and Store Results in the Task: - Within the task, perform the asynchronous operations to fetch the data. - Store the result in a way that it can be accessed synchronously later, such as in a shared state or a database. 2. Access Stored Results in the Retry Logic: - In the

retry_condition_fn

, access the stored results synchronously and make decisions based on them. Here's an example to illustrate this approach:

python
from prefect import task, flow
import httpx

# This could be a global variable, a database, or any storage mechanism
# that allows synchronous access to the task results.
task_results = {}

@task(retries=3, retry_delay_seconds=[1, 10, 100])
async def fetch_data(task_run_id):
    async with httpx.AsyncClient() as client:
        response = await client.get("<https://example.com/api>")
        response.raise_for_status()
        # Store the result synchronously
        task_results[task_run_id] = response.json()
    return response.json()

def should_retry(task, task_run, state):
    # Access the stored result synchronously
    result = task_results.get(task_run.id)
    # Implement your retry logic based on the result
    return result and some_condition_based_on_result(result)

@flow
async def my_flow():
    task_run_id = "unique_task_run_identifier"  # Generate a unique identifier for each task run
    data = await fetch_data(task_run_id)
    print(data)

if __name__ == "__main__":
    import asyncio
    asyncio.run(my_flow())

### Key Points: - Unique Identifier: Use a unique identifier for each task run to store and retrieve results. This ensures that each task's results are isolated. - Synchronous Access: Ensure that the storage mechanism allows for synchronous access to the results. This example uses a simple dictionary, but a more robust solution might involve a database or another persistent storage system. - Retry Logic: The

retry_condition_fn

accesses the stored results and uses them to determine if a retry is necessary. This approach allows you to handle async results in a way that makes them available for synchronous retry logic.

@Marvin This line

task_results[task_run_id] = response.json()

will never be reached in case of failure

You're correct. In the case of an exception, the line that stores the result will not be reached. Here's how you can adjust the approach to ensure that results are stored even in the event of a failure: 1. Use a Try-Except Block: - Wrap the asynchronous operation in a try-except block to handle exceptions and ensure that any partial or error-related data is stored. 2. Store Error Information: - Store relevant error information or status in the shared storage to be used in the retry logic. Here's an improved version of the task:

python
from prefect import task, flow
import httpx

# This could be a global dictionary, a database, or any storage mechanism
task_results = {}

@task(retries=3, retry_delay_seconds=[1, 10, 100])
async def fetch_data(task_run_id):
    try:
        async with httpx.AsyncClient() as client:
            response = await client.get("<https://example.com/api>")
            response.raise_for_status()
            # Store the result if successful
            task_results[task_run_id] = {'result': response.json(), 'error': None}
    except Exception as e:
        # Store the error information if an exception occurs
        task_results[task_run_id] = {'result': None, 'error': str(e)}
        raise  # Re-raise the exception to trigger the retry logic

def should_retry(task, task_run, state):
    # Access the stored information synchronously
    info = task_results.get(task_run.id)
    # Implement your retry logic based on the stored result or error
    if info:
        result = info['result']
        error = info['error']
        # Example retry condition: retry if there was an error
        return error is not None
    return False

@flow
async def my_flow():
    task_run_id = "unique_task_run_identifier"  # Generate a unique identifier for each task run
    data = await fetch_data(task_run_id)
    print(data)

if __name__ == "__main__":
    import asyncio
    asyncio.run(my_flow())

### Key Adjustments: - Exception Handling: Use a try-except block to catch exceptions and store error information. - Error Storage: Store both the result and any error information in the shared storage. - Retry Logic: The

retry_condition_fn

checks the stored error information to decide on retries. This setup ensures that you have access to necessary information even if the task fails, allowing for informed retry decisions.