Based on the research and looking at the available information, I can provide you with a more efficient approach to match completed flow runs with their deployments in your GKE environment. Here's the recommended solution: 1. Use the REST API Directly Instead of iterating through flow runs, you can use Prefect's REST API endpoint directly to query for specific flow runs and their deployments. The endpoint

/api/deployments/name/{flow_name}/{deployment_name}

is more efficient than iterating through flow runs. 2. Utilize Flow Run Filters When querying flow runs, use the Prefect client with specific filters to narrow down your search instead of fetching all runs and filtering afterward. For example:

python
from prefect.client import get_client

async with get_client() as client:
    # Use specific filters to query exactly what you need
    flow_runs = await client.read_flow_runs(
        deployment_id={"any_": [deployment_id]},
        flow_name={"any_": [flow_name]},
        state_type={"any_": ["COMPLETED"]},
    )

3. Use Deployment Tags If you're dealing with multiple deployments, consider adding tags to your deployments that can help you query them more efficiently. This way, you can filter by tags instead of iterating through all deployments. 4. Implement Caching If you're frequently querying the same information, consider implementing a caching layer (using something like Redis or even a local cache) to store the mapping between flow run names and deployments. Update this cache when new deployments are created or when flow runs complete. Here's a practical example combining these approaches:

python
from prefect.client import get_client
from prefect.client.schemas import FlowRun
from typing import Dict, List
import asyncio

async def get_deployment_flow_runs(deployment_name: str, flow_name: str) -> List[FlowRun]:
    async with get_client() as client:
        # Get the deployment first
        deployment = await client.read_deployment_by_name(
            flow_name=flow_name,
            deployment_name=deployment_name
        )
        
        # Then get associated flow runs efficiently
        flow_runs = await client.read_flow_runs(
            deployment_id={"any_": [deployment.id]},
            state_type={"any_": ["COMPLETED"]},
            sort=["start_time desc"],
            limit=50  # Adjust based on your needs
        )
        
        return flow_runs

# Usage example
async def main():
    flow_runs = await get_deployment_flow_runs(
        deployment_name="your-deployment-name",
        flow_name="your-flow-name"
    )
    for run in flow_runs:
        print(f"Flow Run: {run.name}, State: {run.state_type}")

Key Advantages of This Approach: 1. Direct API usage reduces network overhead 2. Filtered queries are more efficient than client-side filtering 3. Pagination and limits help manage memory usage 4. The approach is scalable for GKE environments Best Practices for Your GKE Environment: - Consider implementing a background job that periodically updates a cache of flow run to deployment mappings - Use deployment tags to organize and query related flows more efficiently - If you need real-time matching, consider using Prefect's event system to track flow run completions Would you like me to provide more specific details about any of these approaches or show how to implement a particular aspect of this solution?