@Marvin I have a deployment that is scheduled to be run every midnight. It takes a user_id parameter and I want it to be run for multiple users at every midnight. How to configure this? For each user, a separate flow run should be created. I'm using kubernetes work pool so I want each run for a user to be run in a separate kubernetes job pod.

To achieve your goal of running separate Kubernetes pods for each user at midnight using Prefect, you can leverage the scheduling and work pool features of Prefect. Here's how you can set it up in both Prefect 2.x and Prefect 3.x: ### Prefect 2.x and 3.x Approach 1. Deployment Configuration: Create a deployment with a schedule that triggers at midnight for each user. This is done using the

prefect.yaml

file where you define the schedule and parameters. You can specify a cron schedule like this:

yaml
   schedules:
     - cron: "0 0 * * *"  # Runs everyday at midnight
       timezone: "UTC"

2. Parameterization: Ensure that your flow accepts

user_id

as a parameter. When scheduling, create distinct flow runs with different

user_id

values. 3. Separate Flow Runs: Instead of creating a single flow run for all users, you can script the creation of multiple flow runs, each with a different

user_id

. This can be achieved by looping over the user IDs and using the Prefect CLI or API to trigger runs. 4. Kubernetes Work Pool: Assign each flow run to a Kubernetes work pool. Prefect will handle creating separate Kubernetes job pods for each flow run. Make sure your Kubernetes work pool is properly configured to handle the expected concurrency. You can manage concurrency with work pool queues and limits. For more control, Prefect allows creating custom job templates in Kubernetes which can be specified as part of your deployment configuration. For more details, see the scheduling documentation for Prefect 3.x and explore how to use work pools for better concurrency management. I hope this helps you orchestrate multiple Kubernetes job pods wonderfully at the stroke of midnight if only human errors could be debugged just as easily, right?

@Marvin give me a code example please.

Certainly! Here's a simple code example showcasing how you can configure a Prefect deployment to run a flow for multiple users every midnight using a cron schedule and parameterization. This setup assumes you're using Prefect and have a Kubernetes work pool configured. First, define your flow:

python
from prefect import flow, task

@task
def process_user(user_id):
    print(f"Processing user: {user_id}")

@flow
def user_flow(user_id: str):
    process_user(user_id)

Next, create a script to register your deployment with a schedule and to trigger multiple runs. This example will use the Prefect CLI: Create a Prefect YAML for Deployment Create a

prefect.yaml

file to define your deployment and schedule:

yaml
# prefect.yaml
deployments:
  - name: user-flow-deployment
    flow_name: user_flow
    parameters:
      user_id: null  # default value
    infrastructure:
      type: kubernetes
      # Customize your Kubernetes job template if needed here
    schedules:
      - cron: "0 0 * * *"  # Every midnight
        timezone: "UTC"

Trigger the Flow for Multiple Users Now, write a script or use the CLI to trigger flow runs for each user:

python
import prefect
from prefect.deployments import Deployment

# List of user IDs
user_ids = ["user1", "user2", "user3"]

# Create a deployment object from the YAML definition
deployment = Deployment.load_from_yaml('prefect.yaml')

# Loop over each user_id and create a flow run
for user_id in user_ids:
    # Set the user_id parameter for the flow run
    deployment.parameters = {'user_id': user_id}
    
    # Create a flow run
    deployment.create_flow_run()
    print(f"Scheduled flow for user: {user_id}")

This script sets up a deployment with the specified parameters (

user_id

) and creates individual flow runs, each of which will be scheduled to run in its own Kubernetes pod at midnight for different users. You can also use the Prefect CLI or API to automate the process of creating scheduled runs. For further details on the Prefect setup and capabilities, you can refer to the Prefect 3.x deployment and scheduling docs.