@Marvin I have a setup in which I am running

process

type workers, which are running as kubernetes pods, I want to understand if a process worker can handle more than 1 flow run simultaneously. The idea is to have pods which can handle multiple flow runs rather than having multiple pods. How can I achieve this ?

To achieve the capability of running multiple flow runs simultaneously on a single Kubernetes pod utilizing

process

type workers, you'd need to consider the following: 1. Concurrency Limits: Prefect allows you to set concurrency limits on the work queues. This can help manage the number of flow runs executed concurrently by a worker. You can set these limits according to your workload requirements. 2. Worker Configuration: The

prefect worker

command is used to start workers, and this same worker can handle multiple flow runs if configured properly. For process type workers, you would typically use subprocesses to handle flow runs. 3. Custom Kubernetes Configuration: You might need to customize your Kubernetes job manifest template to ensure that your pods are capable of handling multiple flow runs. Ensure that resources like memory and CPU are appropriately allocated to handle multiple workloads. 4. Environment Variables: Set environment variables that control Prefect worker behavior. For instance, setting concurrency environment variables. ### Practical Steps 1. Set Concurrency Limits:

yaml
   work_pools:
     - name: kubernetes-pool
       concurrency_limit: 10  # Adjust this number based on your requirements

2. Start a Kubernetes Worker:

sh
   prefect worker start --pool 'my-work-pool' --type kubernetes

3. Custom Kubernetes Job Manifest: Refer to the Prefect documentation for creating a custom Kubernetes job manifest. 4. Environment Variables:

sh
   export PREFECT_KUBERNETES_WORKER_STORE_PREFECT_API_IN_SECRET="true"
   export PREFECT_WORKER_CONCURRENCY_LIMIT=10  # Example limit, adjust as needed

### Links and References: - Prefect Work Pools - Prefect Kubernetes Worker By following these guidelines and making the necessary configurations, a single process worker within a Kubernetes pod should be able to handle multiple flow runs concurrently, thus optimizing resource usage and reducing the need for multiple pods.

@Marvin cool, but I want to use a process type worker which is running on a kubernetes pod, I dont want a kubernetes type worker

It seems running a

process

type worker within a Kubernetes pod to handle multiple flow runs involves setting appropriate concurrency limits and ensuring the pod has sufficient resources. Here’s how you can achieve this: ### Steps to Configure a Process Worker on Kubernetes: 1. Set Concurrency Limits: Within your work pool configuration, set a concurrency limit that determines the maximum number of flow runs the worker can handle simultaneously.

yaml
   work_pools:
     - name: process-worker-pool
       concurrency_limit: 10  # Adjust this as per your needs

2. Configure the Process Worker in a Kubernetes Pod: In your Kubernetes setup, you need to ensure that your pod can handle multiple subprocesses. Here’s an example Kubernetes deployment YAML for a Prefect process worker:

yaml
   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: prefect-process-worker
     labels:
       app: prefect
   spec:
     replicas: 1
     selector:
       matchLabels:
         app: prefect
     template:
       metadata:
         labels:
           app: prefect
       spec:
         containers:
         - name: prefect-worker
           image: prefecthq/prefect:latest  # Use the appropriate image version
           command: ["prefect", "worker", "start", "--pool", "process-worker-pool", "--type", "process"]
           env:
           - name: PREFECT_API_URL
             value: "YOUR_PREFECT_API_URL"
           - name: PREFECT_WORKER_CONCURRENCY_LIMIT
             value: "10"  # This should match the concurrency limit in your work pool configuration
           resources:
             requests:
               memory: "2Gi"
               cpu: "1"
             limits:
               memory: "4Gi"
               cpu: "2"

3. Deploy the Kubernetes Deployment: Apply the Kubernetes deployment configuration to your cluster. This pod will start a Prefect process worker that can handle multiple flows.

sh
   kubectl apply -f prefect-process-worker-deployment.yaml

### Key Points: - Concurrency Limits: Set both in the work pool and as environment variables. This ensures the worker respects the concurrency configuration. - Resource Allocation: Ensure your Kubernetes pod has enough resources to handle multiple subprocesses for flow runs. Adjust memory and CPU limits based on your workload requirements. - Environment Variables: Add any other environment variables required by your worker or flows. ### References: - Prefect Work Pools - Prefect Kubernetes Deployment Guide By following these steps, you can run a

process

type Prefect worker within a Kubernetes pod capable of handling multiple flow runs concurrently. This approach optimizes resource usage and reduces the need for multiple pods.

@Marvin what is the difference between a work pool concurrency and the worker concurrency ?

The concepts of work pool concurrency and worker concurrency in Prefect are essential for managing the execution of flow runs. Here’s a brief explanation of each: ### Work Pool Concurrency - Definition: Concurrency limit applied at the work pool level which restricts the total number of flow runs that can be executed concurrently from that pool. - Scope: It controls the overall flow execution across all workers associated with that particular work pool. - Example Use: If you set a concurrency limit of 10 for a work pool, the combined total of concurrent flow runs across all workers pulling work from that pool will not exceed 10.

yaml
  work_pools:
    - name: process-worker-pool
      concurrency_limit: 10  # Total concurrent flows allowed for the work pool

### Worker Concurrency - Definition: Concurrency limit applied at the individual worker level which restricts the number of flow runs a single worker can handle concurrently. - Scope: It controls the execution capacity of a specific worker. - Example Use: If a worker has a concurrency limit of 5, it will handle up to 5 concurrent flow runs regardless of the overall work pool limit.

sh
  export PREFECT_WORKER_CONCURRENCY_LIMIT=5  # Concurrency for an individual worker

### How They Work Together: - Combined Effect: If a work pool has a concurrency limit of 10 and you have two workers each with a concurrency limit of 5, the overall system respects both limits. This setup means each worker will not handle more than 5 flows concurrently (due to worker limit), and altogether they will not exceed 10 concurrent flows (due to work pool limit). Here's an example setup illustrating this: 1. Work Pool Configuration with Concurrency Limit:

yaml
   work_pools:
     - name: process-worker-pool
       concurrency_limit: 10

2. Worker Configuration with Concurrency Limit: - Worker's environment variables in Kubernetes deployment:

yaml
     env:
     - name: PREFECT_API_URL
       value: "YOUR_PREFECT_API_URL"
     - name: PREFECT_WORKER_CONCURRENCY_LIMIT
       value: "5"

This setup ensures that while the work pool can handle up to 10 concurrent flow runs, each individual worker will only manage up to 5 concurrent runs. ### Example:

yaml
# Kubernetes Deployment for Prefect Process Worker
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prefect-process-worker
  labels:
    app: prefect
spec:
  replicas: 2  # Two workers
  selector:
    matchLabels:
      app: prefect
  template:
    metadata:
      labels:
        app: prefect
    spec:
      containers:
      - name: prefect-worker
        image: prefecthq/prefect:latest
        command: ["prefect", "worker", "start", "--pool", "process-worker-pool", "--type", "process"]
        env:
        - name: PREFECT_API_URL
          value: "YOUR_PREFECT_API_URL"
        - name: PREFECT_WORKER_CONCURRENCY_LIMIT
          value: "5"
        resources:
          requests:
            memory: "2Gi"
            cpu: "1"
          limits:
            memory: "4Gi"
            cpu: "2"

### Summary: - Work Pool Concurrency: Limits concurrency across the entire work pool. - Worker Concurrency: Limits concurrency on a per-worker basis. - Combined Control: Ensures overall system stability and resource optimization by managing concurrent executions both globally (work pool) and locally (individual workers). These configurations allow you to tailor the behavior of your Prefect deployment to fit your resource availability and execution requirements efficiently.

@Marvin, how do I delete all the flow runs that are late

To delete all flow runs that are in a "Late" state, you can use the Prefect Client to filter and delete these flow runs programmatically. Below is a Python script that uses the Prefect Client to achieve this: ### Script to Delete Late Flow Runs

python
from prefect import get_client
from prefect.client.schemas.filters import FlowRunFilter, FlowRunFilterStateName
from prefect.client.schemas.sorting import FlowRunSort
from prefect.client.schemas.states import StateType

async def delete_late_flow_runs(deployment_name: str):
    async with get_client() as client:
        # Find all flow runs that are in a "Late" state
        flow_runs = await client.read_flow_runs(
            flow_run_filter=FlowRunFilter(
                state=dict(name=dict(any_=["Late"])),
                expected_start_time=dict(
                    before_=datetime.utcnow()
                ),
            ),
            deployment_filter=DeploymentFilter(
                name={'like_': deployment_name}
            ),
            sort=FlowRunSort.START_TIME_DESC,
        )

        if not flow_runs:
            print("No late flow runs found.")
            return

        # Delete each flow run
        for flow_run in flow_runs:
            print(f"Deleting flow run '{flow_run.id}' with state: {flow_run.state.name}")
            await client.delete_flow_run(flow_run.id)

# Example usage
import asyncio

deployment_name = "your-deployment-name"  # Replace with your deployment name
asyncio.run(delete_late_flow_runs(deployment_name))

### Steps to Use the Script: 1. Replace `your-deployment-name`: with the actual name of your deployment. 2. Run the Script: Execute the script to delete all flow runs that are in the "Late" state for the specified deployment. ### References: - Prefect Client Guide - Flow Run Filters By using this script, you can manage and clean up flow runs programmatically, reducing clutter and ensuring your Prefect workspace stays organized.

@Marvin, python sdk delete all flow runs

To delete all flow runs using the Python SDK for Prefect, you can use the Prefect Client to accomplish this task programmatically. Here’s an example script for deleting all flow runs: ### Script to Delete All Flow Runs

python
from prefect import get_client
from prefect.client.schemas.filters import FlowRunFilter
import asyncio

async def delete_all_flow_runs():
    async with get_client() as client:
        # Fetch all flow runs
        flow_runs = await client.read_flow_runs(
            flow_run_filter=FlowRunFilter()
        )

        if not flow_runs:
            print("No flow runs found.")
            return

        # Delete each flow run
        for flow_run in flow_runs:
            print(f"Deleting flow run '{flow_run.id}' with state: {flow_run.state.name}")
            await client.delete_flow_run(flow_run.id)

# Example usage
asyncio.run(delete_all_flow_runs())

### Explanation: 1. Fetching Flow Runs: The

read_flow_runs

method fetches all flow runs. The

FlowRunFilter

is used without any conditions to get all flow runs. 2. Deleting Flow Runs: Each flow run is deleted using the

client.delete_flow_run

method. ### Steps to Use the Script: 1. Install Required Packages: Make sure Prefect is installed:

sh
   pip install prefect

2. Run the Script: Save the script in a

.py

file and run it.

sh
   python delete_all_flow_runs.py

### References: - Prefect Client Guide By running this script, you can programmatically delete all flow runs in your Prefect workspace, helping you manage and clean up your environment efficiently.

@Marvin give me example for FlowRunFilter with state late

To give you a proper example of how to use

FlowRunFilter

in Prefect, here is a comprehensive example that demonstrates filtering based on various attributes of the flow runs: ### Example Using

FlowRunFilter

python
from prefect import get_client
from prefect.client.schemas.filters import FlowRunFilter, FlowRunFilterStateName
from prefect.client.schemas.sorting import FlowRunSort
import asyncio
from datetime import datetime, timezone

async def fetch_filtered_flow_runs():
    async with get_client() as client:
        # Define your filter criteria
        filter_criteria = FlowRunFilter(
            state=FlowRunFilterStateName(
                any_=["Failed", "Completed"]  # Filter flow runs that are either "Failed" or "Completed"
            ),
            start_time=dict(
                after_=datetime(2023, 1, 1, tzinfo=timezone.utc),  # Only include flow runs that started after January 1, 2023
                before_=datetime(2023, 12, 31, tzinfo=timezone.utc)  # Only include flow runs that started before December 31, 2023
            ),
        )

        # Fetch the filtered flow runs
        flow_runs = await client.read_flow_runs(
            flow_run_filter=filter_criteria,
            sort=FlowRunSort.START_TIME_DESC,  # Sort the results by start time in descending order
        )

        if not flow_runs:
            print("No flow runs found with the specified criteria.")
            return

        # Process the filtered flow runs
        for flow_run in flow_runs:
            print(f"Flow run ID: {flow_run.id}, State: {flow_run.state.name}, Start Time: {flow_run.start_time}")

# Run the example function
asyncio.run(fetch_filtered_flow_runs())

### Key Points: 1. Filter Criteria: - `state`: Filters flow runs that are either "Failed" or "Completed". - `start_time`: Filters flow runs that started within a specific time range. 2. Sorting: - Flow runs are sorted by start time in descending order. 3. Fetching and Processing Flow Runs: - Fetches the flow runs based on the filter and processes them by printing their ID, state, and start time. ### Steps to Use the Script: 1. Install Required Packages: Ensure Prefect is installed:

sh
   pip install prefect

2. Run the Script: Save the script in a

.py

file and run it.

sh
   python fetch_filtered_flow_runs.py

By using

FlowRunFilter

, you can efficiently query and operate on your flow runs based on various attributes, ensuring you can manage your flows effectively. ### References: - Prefect Client Guide - Prefect API Reference This example should help you understand how to use

FlowRunFilter

to fetch and manage specific flow runs based on given criteria.