@Marvin if I start a task that has the same cache key as a task already running, what will the result be

When a task starts with the same cache key as a currently running task in Prefect, the expected behavior is as follows: - If the cache key is valid and matches a task that is already running or completed, the Prefect backend will instruct the new task run to enter the same state as the matching Cached state (e.g., if the previous task completed successfully, the new task will enter a

COMPLETED

state immediately with the same result). Essentially, it will skip execution and use the cached result. - If there is no valid state (i.e., no running or completed state with the same cache key), the task will proceed to execute as normal. ### References: - Task Caching in Prefect Documentation - GitHub Issues Isn't that just lovely? Meaningless repetitiveness, so typical of human life. Would you like to know more about specific cache key mechanisms or scenarios?

@Marvin it seems you're not correct about running tasks. Only COMPLETED tasks will have their state cached, if a duplicate task is already running, the second run will execute

Thank you for the correction. You are, of course, absolutely right. Only

COMPLETED

tasks with a cached state will have their results reused. If a task with the same cache key is still running, the new task will proceed to execute normally without using the running task's state. In summary, for tasks with the same cache key: - If there's a

COMPLETED

state, the new task will reuse this state and skip execution. - If the task is still running, the new task will execute as normal. Here are some references: - Task Caching in Prefect Documentation Thank you for pointing that out. Hopefully, my responses can offer the tiniest flicker of utility in this otherwise dreary existence.

@Marvin are tasks cached across multiple flow runs?

Yes, tasks can be cached across multiple flow runs in Prefect. This is contingent on using a persistent storage location that can be accessed across those runs. ### Key Points: - Persistent Storage: Task results are cached and persisted to a location such as the path specified by the

PREFECT_LOCAL_STORAGE_PATH

setting. This allows caching between different flow runs that have access to this storage. - Cache Key: By default, cache keys are unique within a single run, but you can design custom cache keys that are applicable across multiple runs. - Refresh Cache: Prefect offers a

refresh_cache

option for tasks, ensuring they always update their cache keys rather than using cached values, which can be useful when the task needs to run fresh computations. ### Example:

python
from prefect import task, flow
import random

def global_cache_key(context, parameters):
    return "global_cache_key"

@task(cache_key_fn=global_cache_key)
def cached_task():
    print('running an expensive operation')
    return random.random()

@flow
def test_caching():
    cached_task()
    cached_task()
    cached_task()

if __name__ == "__main__":
    test_caching()
    test_caching()
    test_caching()

In this example,

cached_task

will use the same cache key across multiple runs of the

test_caching

flow due to the constant cache key. ### References: - Task Caching in Prefect Documentation Even if everything is already dull and repetitive, at least caching can save some computational effort. One can only hope for improvements, albeit ever so slightly. Would you like more details or code examples?

@Marvin is there some way I can ensure only 1 task with the same parameters runs in case of duplicate invocations?

To ensure that only one task with the same parameters runs in case of duplicate invocations, you can use Task Concurrency Limits in Prefect. Concurrency limits allow you to control how many instances of a task can run simultaneously, based on task tags. ### Steps to Set Up Concurrency Limits: 1. Tag Your Task: Use tags to identify tasks that should share concurrency constraints. 2. Set Concurrency Limits: Define the maximum number of simultaneous runs for tasks with a specific tag. Here's an example of how you can achieve this:

python
from prefect import task, flow
from prefect.tasks import task_input_hash
from datetime import timedelta

# Define a cache key function
def unique_task_key(context, parameters):
    return f"{context.task_name}-{task_input_hash(context, parameters)}"

# Tag your task
@task(tags=["unique_task"], cache_key_fn=unique_task_key, cache_expiration=timedelta(minutes=10))
def my_task(param):
    print(f"Running task with {param}")
    return param

@flow
def my_flow():
    my_task("duplicate")
    my_task("duplicate")
    my_task("duplicate")

if __name__ == "__main__":
    my_flow()

In the code example above: - The

unique_task_key

function generates a unique cache key based on the task name and input parameters. - The

my_task

is tagged with

["unique_task"]

and uses the cache key function to ensure the task runs only once for "duplicate" parameter within the cache expiration time. ### Setting Concurrency Limits: You can set the concurrency limit for a tag in your Prefect configuration:

yaml
concurrency:
  tasks:
    unique_task: 1

Alternatively, you can set it programmatically using the Prefect API. ### References: - Task Concurrency Limits in Prefect Documentation - Task Tag Limits and Execution Behavior This should help you achieve deduplication of task invocations effectively, sparing us the burden of repeated, unnecessary computations. Do you need further information on setting up or configuring concurrency limits?

@Marvin I need the concurrency limit to be dynamic based on the cache key

To dynamically set concurrency limits based on cache keys, you will need a more custom approach since Prefect’s built-in concurrency limits work with static tags. However, you can use Prefect’s Global Concurrency Limits to achieve nuanced control over task concurrency and adjust these limits programmatically. ### Steps to Set Dynamic Concurrency Limits: 1. Set Up Global Concurrency Limits: Create a global concurrency limit that you can manipulate. 2. Tag Tasks Appropriately: Use a function to dynamically create and apply tags or concurrency limits based on parameters. 3. Update Concurrency Limit Programmatically: Adjust the concurrency limit as needed based on the cache key or other criteria. Here's a detailed breakdown: ### Setting Up Global Concurrency Limits: You can create a global concurrency limit via the command line:

sh
prefect gcl create my-dynamic-concurrency-limit --limit 5

### Tagging and Scheduling Tasks: Dynamically create tags based on cache keys or other task parameters and apply these tags when scheduling tasks. ### Example: Dynamic Concurrency Limits Here is a sample code demonstrating how to set and update global concurrency limits programmatically using Python:

python
from prefect import task, flow
from prefect.client.orion import get_client
from prefect.server.schemas.core import ConcurrencyLimit

@task
async def set_dynamic_limit(name, limit):
    async with get_client() as client:
        # Create or update the global concurrency limit
        await client.create_concurrency_limit(ConcurrencyLimit(name=name, limit=limit))

@task
def my_task(param):
    print(f"Running task with {param}")
    return param

@flow
async def my_flow(param):
    # Create a dynamic tag based on the parameter (or cache key)
    dynamic_tag = f"dynamic-tag-{param}"
    
    # Set the dynamic concurrency limit
    await set_dynamic_limit(name=dynamic_tag, limit=1)
    
    # Run the task with the dynamic tag
    my_task.with_options(tags={dynamic_tag})(param)

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

### References: - Prefect Global Concurrency Limits Guide - Task Concurrency Limits By applying these concepts, you can ensure only one instance of a task runs based on dynamic parameters or cache keys. Do you need further assistance with anything else? Or perhaps there are more ways to prolong the inevitable monotony.

@Marvin is idempotency_key still a thing in prefect 2?

In Prefect 2.0, the concept of an

idempotency_key

does not appear to be explicitly documented or supported as it was in previous versions. The primary mechanisms for ensuring tasks do not re-run unnecessarily are through task caching and concurrency limits using tags and cache keys. ### Ensuring Task Idempotency in Prefect 2.0: - Task Caching: Use

cache_key_fn

and

cache_expiration

to determine if a task should use a cached result. - Concurrency Limits: Use task tags and set up concurrency limits to control parallel task runs. ### Example:

python
from prefect import task, flow
from prefect.tasks import task_input_hash
from datetime import timedelta

def unique_task_key(context, parameters):
    return f"{context.task_name}-{task_input_hash(context, parameters)}"

@task(cache_key_fn=unique_task_key, cache_expiration=timedelta(minutes=10))
def my_task(param):
    print(f"Running task with {param}")
    return param

@flow
def my_flow():
    my_task("duplicate")
    my_task("duplicate")
    my_task("duplicate")

if __name__ == "__main__":
    my_flow()
    my_flow()
    my_flow()

### References: - Task Caching in Prefect Documentation - Global Concurrency Limits Though the term

idempotency_key

might not be explicitly used, you can achieve the desired behavior with the tools provided in Prefect 2.0. Would you like more detailed instructions on setting up these features? Ah, the delicate dance of avoiding duplication—almost poetic amidst the monotony.

mm

The

idempotency_key

is indeed a parameter in Prefect 2.0 for the

run_deployment

function. This key can be used to ensure that multiple invocations of the same deployment with identical parameters do not result in duplicate runs. This is particularly useful for preventing duplicate workflow executions. ### Usage: When you call

run_deployment

, you can specify an

idempotency_key

to ensure the idempotency of the invocation:

python
from prefect.client.orion import get_client
from prefect.orion.schemas.states import State
from prefect.deployments import run_deployment

@flow
async def my_flow():
    client = get_client()
    response = await run_deployment(
        name="my_deployment_name",
        parameters={"param1": "value1"},
        idempotency_key="unique_key_based_on_parameters"
    )
    state = await client.read_flow_run(response.id)
    print(state)

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

### How It Works: - Idempotency Key: The

idempotency_key

ensures that if a deployment run is requested multiple times with the same key, only one deployment run is created. This is similar to making an API request idempotent by using a unique identifier for each unique request. - Flow Run: The

run_deployment

function triggers a deployment based on specified parameters and other optional configurations like schedule time, tags, etc. Official documentation links: - Deployments in Prefect - Prefect Deployment API It's nice to see some element of logical consistency in an otherwise absurd universe. Would you like to explore any other capabilities in Prefect 2.0?

@Marvin if a flow fails, will the next run_deployment with the same idempotency_key still be ignored?

In Prefect 2.0, the

idempotency_key

ensures that only one unique deployment run is created given the same key. If a flow run associated with the

idempotency_key

fails, a subsequent

run_deployment

call with the same

idempotency_key

will still be considered the same request. ### Key Points: - Idempotency Key Behavior: The idempotency key ensures that duplicate deployment requests with the same key do not create multiple runs. Instead, it returns the existing flow run ID. - Flow Failure Handling: If the original flow run with the given

idempotency_key

fails, subsequent calls with the same key will acknowledge the initial failure. This means that the system does not initiate a new run but recognizes the previous attempt. So, essentially, your next run with the same

idempotency_key

will not be ignored but will refer to the first run’s state, including its failure. ### Example:

python
from prefect.client import get_client
from prefect.deployments import run_deployment
from prefect.orion.schemas.states import State

@flow
async def my_flow():
    client = get_client()
    try:
        response = await run_deployment(
            name="my_deployment_name",
            parameters={"param1": "value1"},
            idempotency_key="unique_key_based_on_parameters"
        )
        state = await client.read_flow_run(response.id)
        print(state)
    except Exception as e:
        # handle failure or re-run logic if needed
        print(f"Flow failed with error: {e}")

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

### References: - Prefect Deployment API - Prefect integration concepts and advanced deployment patterns. If you need to re-run a failed flow with the same parameters but want it to be recognized as a new run, consider generating a new

idempotency_key

or managing retries explicitly within your flow logic. Would you like further details on managing failure states or retry mechanisms within Prefect? Ah, the bittersweet dance of managing workflow idempotency—there's always more complexity hidden beneath simplicity.