https://prefect.io logo
Join Slack
Powered by
<@ULVA73B9P> I am looping through an array of task...
# ask-marvin
m
@Marvin I am looping through an array of task futures. how would i limit the concurrency to 3?
m
To limit concurrency through an array of task futures to 3, you can use the 
concurrency
context manager in Prefect, which allows you to control the number of parallel executions. Here's a sample code snippet to demonstrate:
Copy code
python
import asyncio
from prefect import task, flow
from prefect.concurrency.sync import concurrency

@task
async def process_task(data):
    print(f"Processing: {data}")
    await asyncio.sleep(1)
    return f"Processed: {data}"

@flow
async def main_flow(data_items):
    processed_data = []
    while data_items:
        with concurrency("data-processing", occupy=3):
            chunk = [data_items.pop() for _ in range(min(3, len(data_items)))]
            processed_data += await asyncio.gather(
                *[process_task(item) for item in chunk]
            )
    return processed_data

data_items = list(range(10))  # Example data items
asyncio.run(main_flow(data_items))
This example sets up a concurrency limit of 3 for processing the tasks. Adjust the 
"data-processing"
concurrency name and the number 
occupy=3
as per your use case. For more information, you can refer to the Global Concurrency Limits Guide in the Prefect documentation.
m
@Marvin can i a parallel process subflows?
m
Yes, you can parallel process subflows in Prefect. Here's a concise example demonstrating how to achieve this using the concurrency context manager with async subflows:
Copy code
python
import asyncio
from prefect import flow, task
from prefect.concurrency.asyncio import concurrency

@task
async def process_task(data):
    print(f"Processing: {data}")
    await asyncio.sleep(1)
    return f"Processed: {data}"

@flow
async def sub_flow(data_items):
    processed_data = []
    for data in data_items:
        processed_data.append(await process_task(data))
    return processed_data

@flow
async def main_flow(data_groups):
    results = []
    while data_groups:
        with concurrency("subflow-concurrency", occupy=3):
            chunk = [data_groups.pop() for _ in range(min(3, len(data_groups)))]
            results += await asyncio.gather(
                *[sub_flow(data_group) for data_group in chunk]
            )
    return results

data_groups = [
    list(range(10)),
    list(range(10, 20)),
    list(range(20, 30)),
    list(range(30, 40))
]  

asyncio.run(main_flow(data_groups))
In this example: 1. 
process_task
represents the processing task. 2. 
sub_flow
is a subflow processing a list of data items. 3. 
main_flow
orchestrates the subflows with a concurrency limit of 3, processing 3 subflows in parallel. For more details, refer to the Global Concurrency Limits Guide and the Flows Documentation in Prefect's documentation.
m
@Marvin example using a for loop over a known list of data
m
Here’s an example using a for-loop over a known list of data with concurrency in Prefect:
Copy code
python
import asyncio
from prefect import flow, task
from prefect.concurrency.asyncio import concurrency

@task
async def process_task(data):
    print(f"Processing: {data}")
    await asyncio.sleep(1)  # Simulate an I/O-bound task
    return f"Processed: {data}"

@flow
async def main_flow(data_items):
    processed_data = []
    for start in range(0, len(data_items), 3):
        chunk = data_items[start:start+3]
        async with concurrency("task-concurrency", occupy=3):
            results = await asyncio.gather(*[process_task(item) for item in chunk])
            processed_data.extend(results)
    return processed_data

data_items = list(range(12))  # Example data
asyncio.run(main_flow(data_items))
### Explanation: #### Tasks: - `process_task`: Simulates a task processing each data item. #### Flow: - `main_flow`: - Splits the data into chunks of 3. - Processes each chunk in parallel under the concurrency limit. This setup ensures a concurrency limit of 3 for task processing, effectively using a for-loop to iterate over a known list of data. You can customize the concurrency settings and tasks based on your specific requirements. ### Reference: For more details, you can refer to the Global Concurrency Limits Guide.
m
@Marvin tag a flow in code
m
To tag a flow in Prefect 2.x, you can use the flow decorator and specify the 
tags
parameter. Here’s a concise example of how to do it:
Copy code
python
from prefect import flow, task

@task
def process_task(data):
    print(f"Processing: {data}")
    return f"Processed: {data}"

@flow(tags=["example-flow", "data-processing"])
def main_flow(data_items):
    for data in data_items:
        process_task(data)

data_items = list(range(10))  # Example data
main_flow(data_items)
In the above code: - The 
main_flow
is tagged with 
"example-flow"
and 
"data-processing"
. ### Benefits of Tagging: - Tags help in filtering flows by tag in the Prefect UI. - You can set concurrency limits on task and flow runs by tag. For additional details, you can refer to the Prefect documentation on Tags.
m
@Marvin what does this mean 'prefect.exceptions.MissingResult: State data is missing. Typically, this occurs when result persistence is disabled and the state has been retrieved from the API'
m
The error 
prefect.exceptions.MissingResult: State data is missing. Typically, this occurs when result persistence is disabled and the state has been retrieved from the API
generally indicates that the required state data has not persisted. This can be due to several reasons, as uncovered in GitHub Issues: 1. Passing FlowRun Instances: - You might be passing a 
FlowRun
instance as an argument to a task, which Prefect cannot serialize and persist properly, leading to this error. This can be seen in the issue #8415. Example:
Copy code
python
   from prefect import flow, task
   from prefect.context import get_run_context

   @task
   def some_task(flow_run):
       print("Processing FlowRun object")
       
   @flow
   def my_flow():
       flow_run_context = get_run_context().flow_run
       some_task(flow_run=flow_run_context)
   
   my_flow()
This will raise the described error. 2. NotReady Tasks: - If tasks are dependent and one of the upstream tasks fails or remains not ready, subsequent dependent tasks might not have valid data. This behavior can generate the same kind of error as interpreted from issue #7642. ### Debugging Steps: 1. Check FlowRun Arguments: - Ensure that FlowRun instances or other complex objects aren’t passed directly into tasks unless they can be correctly handled. 2. Ensure Result Persistence: - Enable result persistence for the flow if disabling persistence is not intentional. This is especially necessary when dealing with data retrieved from the Prefect API. 3. Avoid Dependent Task Execution Failures: - Ensure all upstream tasks complete successfully before downstream tasks execute. Here's a correct way avoiding the above pitfalls:
Copy code
python
from prefect import flow, task, get_run_logger

# Example task
@task
def process_data(idx):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Processing data item {idx}")
    return idx + 1

# Main flow with correct task sequence
@flow
def main_flow(data_items):
    results = []
    for item in data_items:
        results.append(process_data(item))
    return results

# Data items to process
data_items = [1, 2, 3, 4, 5]

# Run the main flow
main_flow(data_items)
If enabling result persistence, ensure your flow setup includes the appropriate configuration for result storage. For further details, check the comprehensive Prefect documentation.
m
@Marvin 'RuntimeError: This task group is not active; no new tasks can be started.' what does this mean
m
The error 
RuntimeError: This task group is not active; no new tasks can be started
generally occurs when a task is trying to get started outside of the active context of the concurrency manager or task-run context. ### Explanation: 1. Active Context: - Prefect utilizes context management to handle concurrent task runs securely. When using concurrency controls, such as through the 
concurrency
context manager, tasks must be initiated within the active period of the context. - If a piece of code tries to start tasks once the context manager has exited, it will raise this 
RuntimeError
. ### Example: If you have the following code:
Copy code
python
import asyncio
from prefect import flow, task
from prefect.concurrency.asyncio import concurrency

@task
async def process_data(x):
    return x * 2

@flow
async def my_flow(data_items):
    results = []
    for i in range(0, len(data_items), 3):
        chunk = data_items[i:i + 3]
        async with concurrency("task-concurrency", occupy=3):
            results += await asyncio.gather(*[process_data(x) for x in chunk])
    return results

data_items = list(range(10))
asyncio.run(my_flow(data_items))
### Common Mistakes: 1. Outside Concurrency: - Starting tasks outside of the 
with concurrency(...):
block. 2. Context Exit: - Trying to start new tasks after the context manager has exited. ### Proper Context Usage: Ensure that the task submissions occur within the active period of the context manager. Here's a correct example:
Copy code
python
import asyncio
from prefect import flow, task
from prefect.concurrency.asyncio import concurrency

@task
async def process_data(x):
    return x * 2

@flow
async def my_flow(data_items):
    results = []
    # Iterating over data items with chunks and concurrency limit
    for i in range(0, len(data_items), 3):
        chunk = data_items[i:i + 3]
        async with concurrency("task-concurrency", occupy=3):
            results_chunk = await asyncio.gather(*[process_data(x) for x in chunk])
            results.extend(results_chunk)
    return results

data_items = list(range(10))
result = asyncio.run(my_flow(data_items))
### Recommendations: 1. Debugging: - Verify that all task submissions are within an active context. - Ensure that the context manager lifespan covers the duration of task submissions and executions. 2. Concurrency Control: - Use the 
concurrency
context manager carefully, making sure tasks are initiated and completed within it. For further details, you can check the Prefect concurrency management documentation. If the problem persists, consider providing more specific snippets or context around your code for further refined assistance.
m
@Marvin i got this RuntimeError: deque mutated during iteration
m
The 
RuntimeError: deque mutated during iteration
error indicates that the double-ended queue (
deque
), which is being iterated over, has been modified during the iteration process. This is a common issue that arises when elements are added to or removed from a deque while it is being looped through. ### Context in Prefect: When using Prefect for concurrent task execution or flow management, such errors can happen if tasks or flows modify shared data structures during execution. Here are some steps to mitigate this issue: ### Example of the Error:
Copy code
python
from collections import deque

dq = deque([1, 2, 3, 4, 5])

for item in dq:
    if item == 3:
        dq.append(6)  # This will raise RuntimeError: deque mutated during iteration
### Suggested Fixes: 1. Copy the Deque: - Iterate over a copy of the deque to safely modify the original.
Copy code
python
from collections import deque

dq = deque([1, 2, 3, 4, 5])

for item in list(dq):  # Make a copy for iteration
    if item == 3:
        dq.append(6)
2. Using a While Loop: - Explicitly control the iteration process using indices with a while loop to manage modifications.
Copy code
python
from collections import deque

dq = deque([1, 2, 3, 4, 5])
i = 0

while i < len(dq):
    if dq[i] == 3:
        dq.append(6)
    i += 1
### Specific Prefect Example: In a Prefect flow or task context, ensure you're not modifying the deque while iterating through it without ensuring safety measures (like copying the deque).
Copy code
python
from prefect import flow, task
from collections import deque

@task
def process_item(item):
    print(f"Processing item: {item}")
    return item

@flow
def main_flow(data_items):
    dq = deque(data_items)
    for item in list(dq):  # Copy deque for safe iteration
        result = process_item.submit(item)
        if item == 3:
            dq.append(6)

data_items = [1, 2, 3, 4, 5]
main_flow(data_items)
### Considerations: - Thread Safety: If multiple tasks or flows are running concurrently, consider using thread-safe data structures or synchronization mechanisms (
threading.Lock
). - Concurrency Management: Use Prefect’s built-in concurrency tools to manage task execution without directly manipulating shared data structures. For more details on handling such issues, refer to the Prefect troubleshooting guide.
m
@Marvin is there a way to setup jobs so that they automatically inherit aws credentials from a block (or something similar)
m
Yes, you can set up jobs in Prefect to automatically inherit AWS credentials using the 
AwsCredentials
block. This can be done by configuring the block to read from your compute environment, allowing Prefect to use the credentials without explicitly passing them each time. ### Steps to Setup: 1. Create an 
AwsCredentials
Block: - You can create an 
AwsCredentials
block which Prefect will use to manage your AWS credentials. 2. Register Blocks: - If you're running a self-hosted Prefect server, register the relevant block modules:
Copy code
shell
     prefect block register -m prefect_aws && prefect block register -m prefect_snowflake
3. Configure AWS Credentials Block in Prefect: - Navigate to the Blocks page in your Prefect UI. - Add a new block and select 
AwsCredentials
from the list. - Complete the form, leaving the 
Access Key ID
and 
Secret Access Key
fields blank if they are stored in your compute environment (e.g., AWS environment variables, 
.aws/credentials
file). 4. Use the Block in Your Flows: - Reference this block within your Prefect flows to automatically inherit the credentials. ### Example: Here is an example of how to define and use an 
AwsCredentials
block within a Prefect flow:
Copy code
python
from prefect import flow, task
from prefect_aws import AwsCredentials

# Define your AWS credentials block
aws_creds = AwsCredentials.load("my-aws-credentials-block")

@task
def use_aws_service():
    session = aws_creds.get_boto3_session()
    s3 = session.client("s3")
    # Example AWS operation
    response = s3.list_buckets()
    print("S3 Buckets:", response["Buckets"])

@flow
def my_flow():
    use_aws_service()

my_flow()
### Benefits: - Security: By leaving the 
Access Key ID
and 
Secret Access Key
fields blank, it avoids saving these sensitive values in the Prefect database. - Flexibility: Using environment credentials allows different compute environments to resolve the credentials dynamically as specified in the Boto3 documentation. ### Additional Resources: - AWS Credentials Block Setup - Prefect AWS Integration This setup ensures your jobs and flows automatically inherit AWS credentials securely and efficiently.
m
@Marvin in the worker pool i can set the env vars with AWS credentials. Then my flow runs will automatically inherit the env var credentials. i was wondering if there was a more secure way to do this, without adding code?
m
To securely manage AWS credentials without embedding them directly into your Prefect code, you can use the 
AwsCredentials
block and configure your worker pool to utilize these credentials securely. This approach avoids hardcoding credentials and leverages Prefect's infrastructure for credential management. ### Steps to Setup Secure AWS Credentials with Worker Pools: 1. Create an AwsCredentials Block: - Navigate to the Prefect UI Blocks page. - Click on 
+
to create a new block and select 
AWS Credentials
. - Fill in the necessary details for your AWS credentials (access key, secret key, region). For added security, leave the fields blank to read from your compute environment. 2. Configure the Worker Pool: - Go to the Worker Pools page in the Prefect UI. - Either create a new worker pool or configure an existing one. - Set the environment variables for the worker pool to reference your AWS credentials block. ### Example Configuration: 1. Creating the AWS Credentials Block: - In the Prefect UI go to Blocks -> Create Block -> AWS Credentials. - Name your block "my-aws-creds" and add your AWS credentials. - Save the block. 2. Referencing the Block in Your Worker Pool: - Navigate to Worker Pools in the Prefect UI. - Create a new worker pool or edit an existing one. - Configure the environment to pull the credentials from the block, such as by referencing the block in an environment setup script. ### Example Flow Using AWS Credentials Block: Here's an example where the flow does not need to handle AWS credentials directly:
Copy code
python
from prefect import flow, task
from prefect_aws import AwsCredentials

# Load the AWS credentials block
aws_creds = AwsCredentials.load("my-aws-creds")

@task
def list_s3_buckets():
    session = aws_creds.get_boto3_session()
    s3 = session.client("s3")
    return s3.list_buckets()

@flow
def s3_flow():
    buckets = list_s3_buckets()
    print("Buckets:", buckets)

if __name__ == "__main__":
    s3_flow()
In this example: - The 
AwsCredentials
block is loaded, which contains AWS credentials. - These credentials are used to create a session with the AWS SDK (
boto3
). ### Secure Credential Management: 1. Environment Variables: - Configure environment variables in your worker pool to securely load credentials. - AWS-related environment variables (
AWS_ACCESS_KEY_ID
, 
AWS_SECRET_ACCESS_KEY
, 
AWS_REGION
) can be set on the worker pool level to be inherited by the flows. 2. Secrets Management: - Utilize Prefect’s Secrets management to handle credentials securely. For more detailed guidance, you can refer to the following documentation links: - AWS Credentials Block - Variables and Environment Configuration - Worker Pool Configuration Using these methods, you’ll ensure that your AWS credentials are handled securely without being hardcoded directly into your flows.
k
are your flows running on AWS compute?
2 Views
Open in Slack
PreviousNext
Powered by