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<@ULVA73B9P> How do I have tasks in a flow not be ...
# ask-marvin
a
@Marvin How do I have tasks in a flow not be interdependent? I want tasks to run independently of upstream tasks finishing successfully in Prefect 2.x
m
✅ thought for 60.9 seconds
Let me explain how to make tasks run independently in Prefect 2.x. There are a few approaches you can use: 1. Using 
return_state=True
: This is the primary way to prevent task failures from affecting downstream tasks. Here's an example:
Copy code
python
from prefect import flow, task

@task(return_state=True)
def task_1():
    raise ValueError("This will fail")

@task
def task_2():
    print("I will still run!")

@flow
def my_flow():
    state_1 = task_1()  # This task will fail but won't stop the flow
    task_2()  # This will still execute

my_flow()
2. Using 
allow_failure=True
: When calling a task, you can explicitly allow it to fail without affecting downstream tasks:
Copy code
python
from prefect import flow, task

@task
def task_1():
    raise ValueError("This will fail")

@task
def task_2():
    print("I will still run!")

@flow
def my_flow():
    # The failed state will be passed downstream but won't prevent execution
    failed_result = task_1(allow_failure=True)
    task_2()

my_flow()
3. Parallel Execution: If you want tasks to run completely independently of each other (in parallel), you can use `submit()`:
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python
from prefect import flow, task

@task
def task_1():
    print("Running task 1")
    
@task
def task_2():
    print("Running task 2")

@flow
def my_flow():
    # Both tasks will start executing immediately
    future_1 = task_1.submit()
    future_2 = task_2.submit()
    
    # If you need the results later:
    result_1 = future_1.result()
    result_2 = future_2.result()

my_flow()
A few important notes: - With 
return_state=True
, the task will return a Prefect State object instead of the actual return value - With 
allow_failure=True
, failed tasks won't raise exceptions in the flow, but you'll get a Failed state object - Using 
submit()
is great for parallel execution, but be careful with resource usage if submitting many tasks at once These patterns give you flexibility in handling task dependencies and failures. Choose the approach that best fits your specific use case: - Use 
return_state=True
if you want to handle the state explicitly in your flow - Use 
allow_failure=True
for the simplest way to continue execution despite failures - Use 
submit()
when you want true parallel execution without dependencies Remember that while these patterns allow tasks to run independently, you should still consider whether this is the best approach for your workflow. Sometimes, task dependencies are important for data consistency or resource management.
a
@Marvin In your example above if I have multiple task_1 running, they must all complete in order for task_2 to start. how can I avoid this
m
✅ thought for 16.9 seconds
Ah, I understand - you want to run multiple instances of task_1 and have task_2 run independently without waiting for all task_1 instances to complete. Let me show you how to achieve this in Prefect 2.x. Here are two main approaches: 1. Using 
submit()
for Everything: This is the most straightforward way to achieve complete independence:
Copy code
python
from prefect import flow, task

@task
def task_1(i):
    print(f"Running task 1 instance {i}")
    # This could even fail and task_2 would still run
    
@task
def task_2():
    print("Running task 2")

@flow
def my_flow():
    # Submit multiple task_1 instances
    futures_1 = [task_1.submit(i) for i in range(3)]
    
    # task_2 will start immediately, not waiting for task_1 instances
    future_2 = task_2.submit()
    
    # If you need the results later, you can collect them:
    # results_1 = [f.result() for f in futures_1]
    # result_2 = future_2.result()

my_flow()
2. Using Task Groups: If you want more control over groups of tasks:
Copy code
python
from prefect import flow, task
from prefect.tasks import task_input_hash
from datetime import timedelta

@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(minutes=5))
def task_1(i):
    print(f"Running task 1 instance {i}")

@task
def task_2():
    print("Running task 2 - I run independently!")

@flow
def my_flow():
    # Start a group of task_1 instances
    group_1_futures = []
    for i in range(3):
        # Each task_1 is submitted independently
        future = task_1.submit(i)
        group_1_futures.append(future)
    
    # task_2 starts immediately, doesn't wait for group_1
    task_2_future = task_2.submit()
    
    # Optionally, if you need results later:
    # for f in group_1_futures:
    #     try:
    #         result = f.result()
    #     except Exception as e:
    #         print(f"A task_1 failed: {e}")

my_flow()
Key points about these solutions: 1. The key is using 
submit()
for ALL tasks that should run independently. When you use regular task calls (like 
task_1(i)
), it creates an implicit dependency. 2. Benefits of this approach: - Tasks run truly in parallel - Failures in one task_1 instance won't affect other task_1 instances or task_2 - You can still collect results later if needed 3. Things to watch out for: - Resource usage: All tasks will run at once, so make sure your system can handle the concurrent load - Memory usage: If you're creating many futures, they'll all be held in memory - Error handling: You'll need to handle errors explicitly if you want to know about failures If you need to limit concurrency (but still maintain independence), you can use task run configurations:
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python
from prefect import flow, task

@task(retries=3, retry_delay_seconds=10)
def task_1(i):
    print(f"Running task 1 instance {i}")

@task
def task_2():
    print("Running task 2")

@flow(task_runner_max_concurrency=2)  # Limit concurrent tasks to 2
def my_flow():
    futures_1 = [task_1.submit(i) for i in range(5)]  # Will run 2 at a time
    task_2.submit()  # Still starts immediately

my_flow()
This gives you the best of both worlds - independent execution but with controlled resource usage. The tasks will still be independent (task_2 won't wait for task_1 instances), but you won't overwhelm your system with too many concurrent executions. Choose the approach that best balances your needs for independence, resource usage, and error handling. The first approach (simple submit) is usually the best place to start unless you have specific requirements around task grouping or concurrency control.
a
@Marvin how does the wait_for argument work for tasks
m
✅ thought for 101.0 seconds
Based on the research, I can explain how 
wait_for
works with tasks in Prefect 2.x. Here's a comprehensive explanation with examples: The 
wait_for
argument is used with 
task.submit()
to create explicit dependencies between tasks, especially when using parallel execution. Here's how to use it:
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python
from prefect import flow, task

@task
def task_1():
    print("Task 1 executing")
    return "Result from task 1"

@task
def task_2():
    print("Task 2 executing")
    return "Result from task 2"

@task
def task_3(input_data):
    print(f"Task 3 executing with input: {input_data}")

@flow
def my_flow():
    # Submit tasks 1 and 2 to run in parallel
    future_1 = task_1.submit()
    future_2 = task_2.submit()
    
    # Task 3 will wait for both task_1 and task_2 to complete
    # before starting
    task_3.submit(
        "some input",
        wait_for=[future_1, future_2]
    )

my_flow()
Key points about `wait_for`: 1. Accepts Futures: The 
wait_for
argument takes a list of futures (the results of 
task.submit()
calls) 2. Multiple Dependencies: You can wait for multiple tasks:
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python
@flow
def my_flow():
    futures = [task_1.submit() for _ in range(3)]  # Submit 3 instances of task_1
    
    # Wait for all task_1 instances to complete
    task_2.submit(wait_for=futures)
3. Selective Dependencies: You can choose which tasks to wait for:
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python
@flow
def my_flow():
    future_1 = task_1.submit()
    future_2 = task_2.submit()
    future_3 = task_3.submit()
    
    # task_4 only waits for task_1 and task_2, not task_3
    task_4.submit(wait_for=[future_1, future_2])
4. Error Handling: If any task in the 
wait_for
list fails, the dependent task won't start:
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python
@flow
def my_flow():
    future_1 = task_1.submit()
    
    # task_2 won't start if task_1 fails
    future_2 = task_2.submit(wait_for=[future_1])
    
    # You can handle failures explicitly
    try:
        result = future_2.result()
    except Exception as e:
        print(f"Task 2 didn't run because: {e}")
5. Combining with allow_failure: You can allow upstream tasks to fail and still proceed:
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python
@flow
def my_flow():
    future_1 = task_1.submit()
    
    # task_2 will run even if task_1 fails
    future_2 = task_2.submit(
        wait_for=[future_1],
        allow_failure=True
    )
Common use cases for `wait_for`: - Ensuring data dependencies are met before processing - Coordinating resource usage between tasks - Creating complex dependency graphs while maintaining parallel execution where possible - Implementing fan-in patterns where multiple upstream tasks feed into a single downstream task Remember: - 
wait_for
only works with futures returned from 
task.submit()
- It's different from regular sequential task calls, which create implicit dependencies - It's particularly useful when you want fine-grained control over task dependencies in parallel execution scenarios This mechanism gives you explicit control over task dependencies while still allowing for parallel execution where possible. It's especially useful when you have complex dependency patterns that can't be expressed through simple sequential task calls.
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