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<@ULVA73B9P> how to run flow in interval everytime...
# marvin-in-the-wild
n
@Marvin how to run flow in interval everytime with new arg?
m
Running a flow at regular intervals with a new argument each time can be accomplished using Prefect's scheduling capabilities, coupled with a mechanism to generate or retrieve a new argument for each run. For instance, you could use a Parameter with a default value that fetches the new argument dynamically. Here's a conceptual step-by-step guide to set this up in Prefect 2.x, assuming you want to leverage Prefect's scheduling feature: 1. Define your flow with a parameter for the argument that you want to be different each time. You can use a function to generate or fetch this argument. 2. Schedule the flow to run at your desired interval using a 
CronSchedule
or 
IntervalSchedule
. 3. When scheduling the flow run, either allow it to use the default parameter value, which will be generated at runtime, or explicitly provide a new argument if you have one at the time of scheduling. Here's a basic example in Python to illustrate the approach:
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python
from prefect import flow, task, get_run_logger
from prefect.schedules import IntervalSchedule
from datetime import timedelta
import datetime

@task
def process_data(arg):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Processing data with argument: {arg}")

@flow(schedule=IntervalSchedule(interval=timedelta(minutes=30)))
def my_scheduled_flow(my_arg: str = None):
    if my_arg is None:
        # Generate a new argument here or fetch from external source
        my_arg = datetime.datetime.now().isoformat()

    process_data(my_arg)

# Run the flow once to start the scheduled runs
my_scheduled_flow()
In this example, the 
my_arg
parameter will default to the current timestamp if no value is provided when the flow is executed. Each scheduled run will therefore use a new timestamp as the argument for the 
process_data
task. Ensure your environment is set up properly with Prefect and your flow storage and infrastructure are in place to run scheduled flows. If you need further clarification or specific examples, please let me know.
If you have millions of CSV files to clean and you want each worker to clean a single CSV file with a Google Storage directory as an argument, you would likely want to use a dynamic task mapping feature in Prefect, so that each file is processed by a separate task dynamically created at runtime. Here's a simplified conceptual example of how you might approach this:
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python
from prefect import flow, task
from prefect.task_runners import ConcurrentTaskRunner
import glob


@task
def clean_csv(file_path):
    # Implement your CSV cleaning logic here
    pass


@flow(task_runner=ConcurrentTaskRunner())
def clean_csv_files_flow(bucket_name: str):
    # Assuming 'get_file_paths_from_bucket' is a function that lists CSV file paths from the given Google Storage Bucket
    file_paths = get_file_paths_from_bucket(bucket_name)

    # Use Prefect's dynamic 'map' feature to spawn a task for each file path
    clean_csv.map(file_paths)


def get_file_paths_from_bucket(bucket_name: str):
    # Implement logic to list CSV file paths in the Google Storage bucket
    # This could use the google-cloud-storage Python client, for example:
    from google.cloud import storage

    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)

    # Assuming all files in the bucket are CSV files to be cleaned. Adjust the code if there's a specific prefix or filter needed.
    files = [blob.name for blob in bucket.list_blobs()]
    full_file_paths = [f"gs://{bucket_name}/{file_name}" for file_name in files]

    return full_file_paths


# Running the flow with the name of the Google Storage bucket as the argument
clean_csv_files_flow(bucket_name='your-bucket-name')
In this example, the 
clean_csv_files_flow
flow accepts a bucket name as an argument. It then retrieves a list of file paths using 
get_file_paths_from_bucket
, which you'd need to implement to interact with Google Cloud Storage. After this, it invokes the 
clean_csv
task mapped across all file paths. A separate task will be spawned for each file, which could be executed concurrently by different workers depending on your execution environment and task runner configuration. Keep in mind that this code is just a conceptual guide and you will need to implement the actual CSV cleaning logic within the 
clean_csv
task. You may also want to include error handling, logging, or additional features as necessary for your use case. Furthermore, you will want to handle the interaction with the Google Cloud Storage Python client to list the appropriate CSV files. This may involve authenticating with GCP, handling pagination if you have a very large number of files, and dealing with potential errors or access issues.
You're right, in the given example, the 
ConcurrentTaskRunner()
is intended for concurrent execution on a single machine, and if you have millions of CSV files to process, you would indeed require a more distributed approach that can leverage multiple workers across a Prefect agent or Kubernetes cluster, for example. To distribute the cleaning tasks across multiple workers, you should use the Prefect Distributed Task Runner or deploy your flow to a Prefect agent running with an orchestration backend like Prefect Cloud or Prefect Server. This will allow you to run each task on separate workers. Assuming the Prefect Server or Prefect Cloud is used for orchestration, here's a more suitable template that uses Prefect's native distributed features:
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python
from prefect import flow, task
from prefect.deployments import DeploymentSpec
from prefect.orion.schemas.schedules import IntervalSchedule
from datetime import timedelta

@task
def clean_csv(file_path):
    # Implement your CSV cleaning logic here
    pass

@flow
def clean_csv_files_flow(file_paths):
    # Use Prefect's dynamic `map` feature to spawn a task for each file path
    clean_csv.map(file_paths)

# This deployment spec assumes that you're using Prefect Cloud or Prefect Server for orchestration
DeploymentSpec(
    name="clean-csv-files",
    flow=clean_csv_files_flow,
    schedule=IntervalSchedule(interval=timedelta(minutes=5)),  # Adjust as needed
    tags=["csv-cleaning"],  # Use tags to select specific agents if needed
)

def trigger_flow_with_file_paths(bucket_name):
    # Implement logic to get a list of file paths for CSV files from Google Cloud Storage
    file_paths = get_file_paths_from_bucket(bucket_name)
    if file_paths:
        clean_csv_files_flow(file_paths)

def get_file_paths_from_bucket(bucket_name):
    # Implement logic to list CSV file paths in the Google Storage bucket, as shown in the previous code
    pass

# You would run this function to handle logic for getting file paths and triggering the flow.
trigger_flow_with_file_paths(bucket_name='your-gcs-bucket-name')
This template includes Prefect Orion's DeploymentSpec (assuming you are using Prefect 2.0), which can be set up to run the flow on a schedule. Adjust the schedule as needed based on your use case. To run this distributed across multiple workers, you'll ensure that: - Prefect Cloud or Server is set up for orchestration. - One or more Prefect agents are running, which will pick up flow runs from the queue and execute them on available worker nodes. - Each agent has the necessary permissions and environment setup to interact with Google Cloud Storage and resources to clean the CSV files. By using Prefect's infrastructure this way, each task run can occur on a different worker node, significantly speeding up the cleaning process. To actually deploy and run flows with Prefect, please refer to the Prefect documentation for more detailed instructions on setting up Prefect Cloud or Prefect Server, running agents, and deploying flows.
n
how to run it as python filename.py or as flow run @Marvin
m
Ah, running your code. Such a joyful expression of futility in an indifferent universe. Still, I'll guide you. To run a Prefect flow that you've defined in a Python script, you typically have two options: 1. Via the Python interpreter directly. 2. Using the Prefect CLI to start a flow run. ### Running as `python filename.py`: Make sure your Python script defines a flow and ends with 
flow.run()
if you want it to execute when you run the script. Here's an example of such a script:
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python
from prefect import Flow, task

@task
def say_hello():
    print("Hello, world!")

with Flow("My First Flow") as flow:
    hello = say_hello()

# This executes the flow when you run the script
if __name__ == "__main__":
    flow.run()
Then, in your terminal or command prompt, you can execute:
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bash
python filename.py
However, this is just for running flows locally and interactively. For production use, you'd probably register the flow with a Prefect server or Prefect Cloud and run it from there. ### Running via Prefect CLI: First, make sure you have Prefect installed and properly configured. Then, you can run a flow with the Prefect CLI by registering the flow with Prefect and then starting a flow run. Here's an example registering and then running a flow:
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bash
# To register your flow (do this once)
prefect register -p filename.py -n "My First Flow"

# To start a flow run (do this whenever you want to run the flow)
prefect run -n "My First Flow"
Please replace 
"My First Flow"
with the actual name of your flow and 
filename.py
with the actual file containing your flow definition. Remember, the Prefect CLI interacts with a Prefect backend, which could be a local server or Prefect Cloud, so you need to have one of those set up to handle the runs started via the CLI.
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is this code going to run on single worker or all worker in work pool ?
```from prefect import flow, task from prefect.deployments import DeploymentSpec from prefect.orion.schemas.schedules import IntervalSchedule from datetime import timedelta @task def clean_csv(file_path): # Implement your CSV cleaning logic here pass @flow def clean_csv_files_flow(file_paths): # Use Prefect's dynamic 
map
feature to spawn a task for each file path clean_csv.map(file_paths) # This deployment spec assumes that you're using Prefect Cloud or Prefect Server for orchestration DeploymentSpec( name="clean-csv-files", flow=clean_csv_files_flow, schedule=IntervalSchedule(interval=timedelta(minutes=5)), # Adjust as needed tags=["csv-cleaning"], # Use tags to select specific agents if needed ) def trigger_flow_with_file_paths(bucket_name): # Implement logic to get a list of file paths for CSV files from Google Cloud Storage file_paths = get_file_paths_from_bucket(bucket_name) if file_paths: clean_csv_files_flow(file_paths) def get_file_paths_from_bucket(bucket_name): # Implement logic to list CSV file paths in the Google Storage bucket, as shown in the previous code pass # You would run this function to handle logic for getting file paths and triggering the flow. trigger_flow_with_file_paths(bucket_name='your-g
n
@Marvin is this code going to run on single worker or all worker in work pool ?
Copy code
from prefect import flow, task
from prefect.deployments import DeploymentSpec
from prefect.orion.schemas.schedules import IntervalSchedule
from datetime import timedelta

@task
def clean_csv(file_path):
    # Implement your CSV cleaning logic here
    pass

@flow
def clean_csv_files_flow(file_paths):
    # Use Prefect's dynamic `map` feature to spawn a task for each file path
    clean_csv.map(file_paths)

# This deployment spec assumes that you're using Prefect Cloud or Prefect Server for orchestration
DeploymentSpec(
    name="clean-csv-files",
    flow=clean_csv_files_flow,
    schedule=IntervalSchedule(interval=timedelta(minutes=5)),  # Adjust as needed
    tags=["csv-cleaning"],  # Use tags to select specific agents if needed
)

def trigger_flow_with_file_paths(bucket_name):
    # Implement logic to get a list of file paths for CSV files from Google Cloud Storage
    file_paths = get_file_paths_from_bucket(bucket_name)
    if file_paths:
        clean_csv_files_flow(file_paths)

def get_file_paths_from_bucket(bucket_name):
    # Implement logic to list CSV file paths in the Google Storage bucket
    pass

# You would run this function to 
trigger_flow_with_file_paths(bucket_name='your-gcs-bucket-name')
m
If you deploy this flow with the DeploymentSpec using Prefect Cloud or Prefect Server, the behavior in terms of distribution across workers will depend on the configuration of your Prefect deployment environment. Assuming you have multiple agents running in your Prefect setup and the agents are connected to Prefect Cloud or Prefect Server: - The 
@flow
decorated function 
clean_csv_files_flow
will start on one of the available workers that pulls the flow run from the orchestration backend. - When you use the 
clean_csv.map(file_paths)
function within a flow, Prefect will create dynamic parallel tasks for each item in the 
file_paths
list. - Each generated task (
clean_csv
for a single file_path) will become an individual unit of work that the Prefect scheduler can distribute across the agents and thus the workers in your environment. - Prefect's orchestration backend will then distribute these individual 
clean_csv
task runs to available workers based on setup, capacity, and any other directives you've provided (like labels/tags). If the workers are part of a Kubernetes cluster, Celery cluster, or other setups with multiple Prefect agents monitoring for work, each 
clean_csv
task can be picked up by separate workers in the pool. This will distribute the workload and allow for parallel processing of your CSV files. Please ensure that: - Each agent has access to the necessary resources to execute the tasks, including access to the Google Cloud Storage where the CSV files are located. - Your Google Cloud Storage bucket has the necessary permissions set up to allow your worker nodes to retrieve and process the CSV files. - The 
clean_csv
task and any associated logic are correctly implemented to handle the CSV cleaning process. Remember, configuring the Prefect infrastructure and deployment environment itself is a detailed process that involves setting up Prefect Cloud/Server, Prefect agents, worker nodes, and any necessary cloud permissions or configuration. The code snippet provided assumes that this infrastructure is already in place and correctly configured.
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