Based on the research, there isn't a direct built-in integration for exporting flow run metrics to GCP Monitoring in Prefect 3.x. However, I can suggest a few approaches to accomplish this: 1. Custom Metrics Handler

python
from prefect import flow
from google.cloud import monitoring_v3
from datetime import datetime

def send_duration_to_gcp(flow_run):
    client = monitoring_v3.MetricServiceClient()
    project_name = client.project_path('your-project-id')
    
    series = monitoring_v3.TimeSeries()
    series.metric.type = '<http://custom.googleapis.com/prefect/flow_duration|custom.googleapis.com/prefect/flow_duration>'
    series.metric.labels['flow_name'] = flow_run.flow_name
    series.metric.labels['deployment_id'] = flow_run.deployment_id
    
    point = series.points.add()
    point.value.double_value = flow_run.total_run_time.total_seconds()
    now = datetime.utcnow()
    point.interval.end_time.seconds = int(now.timestamp())
    
    client.create_time_series(request={
        "name": project_name,
        "time_series": [series]
    })

@flow
def your_flow():
    # Your flow logic here
    pass

# Add the metrics handler to the flow
your_flow.on_completion(send_duration_to_gcp)

2. Using Prefect Automations You can create an automation that triggers on flow completion and sends the metrics to GCP Monitoring:

python
from prefect.automations import create_automation
from prefect.events.schemas import FlowRunCompleted

async def export_to_gcp(event: FlowRunCompleted):
    # Similar logic to the handler above
    pass

automation = create_automation(
    name="Export Flow Duration to GCP",
    description="Exports flow run duration to GCP Monitoring",
    trigger=FlowRunCompleted,
    action=export_to_gcp
)

3. Logging Integration You can also set up a custom logging handler that forwards logs to GCP Monitoring (formerly Stackdriver):

python
import google.cloud.logging
from prefect.logging import get_run_logger

@flow
def your_flow():
    # Set up GCP logging client
    logging_client = google.cloud.logging.Client()
    logging_client.setup_logging()
    
    logger = get_run_logger()
    # Your flow logic here
    
    # Log metrics at the end
    <http://logger.info|logger.info>("Flow metrics", extra={
        "flow_duration": flow.total_run_time.total_seconds(),
        "resource": {
            "type": "k8s_container",
            "labels": {
                "namespace_name": "your-gke-namespace",
                "container_name": "prefect-worker"
            }
        }
    })

A few important notes: 1. Make sure your GKE workers have the necessary IAM permissions to write to GCP Monitoring. 2. Consider using the prefect-gcp collection for other GCP integrations you might need. 3. Be mindful of the costs associated with writing custom metrics to GCP Monitoring. 4. Consider batching metrics if you have a high volume of flow runs to avoid hitting API quotas. For a more comprehensive monitoring solution, you might want to consider using Prefect's built-in monitoring capabilities through the UI and API, supplemented by GCP Monitoring for specific metrics or when you need to consolidate monitoring across different services.

Good news! You don't actually need the prometheus-prefect-exporter. Prefect 3.x has built-in capabilities for monitoring these metrics. Let me show you how to track both flow run duration and late runs: 1. Flow Run Duration You can use Prefect's built-in metrics API to query flow run durations:

python
from prefect.client import get_client
from datetime import timedelta

async def get_flow_run_durations():
    async with get_client() as client:
        # Get flow runs from the last 24 hours
        flow_runs = await client.read_flow_runs(
            sort=flow_run.start_time.desc(),
            flow_run_filter=flow_run.start_time > datetime.utcnow() - timedelta(days=1)
        )
        
        for run in flow_runs:
            if run.total_run_time:
                print(f"Flow: {run.name}, Duration: {run.total_run_time}")

2. Late Flow Runs Prefect provides direct methods to query late runs:

python
from prefect.client import get_client

async def get_late_runs():
    async with get_client() as client:
        late_runs = await client.read_flow_runs(
            flow_run_filter=flow_run.state_type == "LATE"
        )
        return late_runs

3. Real-time Monitoring with Prefect UI The Prefect UI provides a dashboard where you can see these metrics without writing code: - Navigate to the "Flow Runs" page to see duration statistics - Use the filter "State: Late" to see currently late runs - Create saved filters for quick access to these views 4. Setting Up Notifications for Late Runs You can set up automations to alert you when flows run late:

python
from prefect.automations import create_automation
from prefect.events.schemas import FlowRunStateChange

async def alert_on_late_run(event: FlowRunStateChange):
    if event.flow_run.state.is_late():
        # Add your notification logic here
        # e.g., send to Slack, email, etc.
        print(f"Flow run {event.flow_run.name} is late!")

automation = create_automation(
    name="Late Flow Run Alert",
    description="Alerts when flow runs are late",
    trigger=FlowRunStateChange,
    action=alert_on_late_run
)

5. Custom Metrics Dashboard If you want to create a custom dashboard, you can use the Prefect REST API to pull these metrics:

python
from prefect.client import get_client
from datetime import datetime, timedelta

async def get_metrics_summary():
    async with get_client() as client:
        # Get stats for the last 24 hours
        since = datetime.utcnow() - timedelta(days=1)
        
        # Get all runs
        runs = await client.read_flow_runs(
            flow_run_filter=flow_run.start_time > since
        )
        
        metrics = {
            "total_runs": len(runs),
            "late_runs": len([r for r in runs if r.state.is_late()]),
            "avg_duration": sum((r.total_run_time.total_seconds() for r in runs if r.total_run_time), 0) / len(runs) if runs else 0
        }
        
        return metrics

If you still want to use Prometheus, you can: 1. Create a custom exporter that uses the above methods to expose metrics 2. Use the Prefect REST API to scrape metrics directly into your monitoring system 3. Set up your own metrics endpoint in your infrastructure code The built-in capabilities should cover most use cases, but if you need specific Prometheus integration, you can wrap these methods in a custom exporter that follows the Prometheus data model. For most users, I recommend starting with the built-in monitoring capabilities and Prefect's UI, then extending with custom solutions only if needed. The built-in tools provide a robust way to monitor both duration and late runs without additional infrastructure. Would you like me to elaborate on any of these approaches or show you how to set up specific alerts

Based on the research and recent GitHub activity, here's the current state of Prometheus metrics in Prefect 3.x: 1. Built-in Prometheus Support Prefect 3.x has built-in Prometheus metrics support that can be enabled in two places: a) Server-side metrics at `/api/metrics`:

bash
# Enable via environment variable
export PREFECT_API_ENABLE_METRICS=True

b) Client-side metrics:

bash
# Enable via environment variable
export PREFECT_CLIENT_ENABLE_METRICS=True
export PREFECT_CLIENT_METRICS_PORT=9090  # Default port

2. Available Metrics The metrics endpoint exposes standard Python-oriented metrics that come with

prometheus_client

. However, Prefect-specific metrics are still being developed and expanded. 3. Accessing the Metrics You can configure Prometheus to scrape these endpoints:

yaml
# prometheus.yml
scrape_configs:
  - job_name: 'prefect'
    static_configs:
      - targets: ['localhost:9090']  # For client metrics
  - job_name: 'prefect-server'
    static_configs:
      - targets: ['prefect-server:4200']  # For server metrics at /api/metrics

4. GCP Monitoring Integration To get these metrics into GCP Monitoring: 1. Deploy the Prometheus server in your GKE cluster 2. Configure it to scrape the Prefect metrics endpoints 3. Use GCP's managed collection for Prometheus metrics For flow run total time specifically, you might need to: 1. Either wait for more Prefect-specific metrics to be added to the native Prometheus endpoint 2. Or implement a custom exporter that queries the Prefect API and exposes the metrics you need Given that the native Prometheus support is still evolving, you might want to consider: 1. Using the Prefect API directly to query metrics (as shown in my previous response) 2. Creating a custom exporter that queries the API and exposes the specific metrics you need 3. Using Prefect's built-in monitoring capabilities through the UI and REST API Would you like me to provide an example of how to create a custom exporter that specifically exposes flow run total time to Prometheus? This might be the most reliable approach until more Prefect-specific metrics are added to the native Prometheus endpoint.

Yes, you need to expose metrics for your flow run pods if you want Prometheus to scrape them. Here's how to set this up: 1. Pod Annotations Add these annotations to your Kubernetes work pool configuration or deployment:

yaml
annotations:
  <http://prometheus.io/scrape|prometheus.io/scrape>: "true"
  <http://prometheus.io/path|prometheus.io/path>: "/metrics"
  <http://prometheus.io/port|prometheus.io/port>: "9090"  # Use the port you configured for PREFECT_CLIENT_METRICS_PORT

2. Enable Client Metrics Make sure client metrics are enabled in your flow run environment:

python
# In your flow code or environment variables
import os
os.environ["PREFECT_CLIENT_ENABLE_METRICS"] = "True"
os.environ["PREFECT_CLIENT_METRICS_PORT"] = "9090"  # Match the port in annotations

3. Kubernetes Work Pool Configuration You can set this up in your work pool's base job template:

python
from prefect.infrastructure import KubernetesJob

job = KubernetesJob(
    pod_annotations={
        "<http://prometheus.io/scrape|prometheus.io/scrape>": "true",
        "<http://prometheus.io/path|prometheus.io/path>": "/metrics",
        "<http://prometheus.io/port|prometheus.io/port>": "9090"
    }
)

4. Prometheus Configuration Configure Prometheus to discover and scrape these pods. Here's a typical configuration:

yaml
scrape_configs:
  - job_name: 'prefect-flow-runs'
    kubernetes_sd_configs:
      - role: pod
    relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
        action: replace
        target_label: __metrics_path__
        regex: (.+)
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_port]
        action: replace
        regex: ([^:]+)(?::\d+)?
        replacement: $1:9090
        target_label: __address__
      - action: labelmap
        regex: __meta_kubernetes_pod_label_(.+)

5. ServiceMonitor (if using Prometheus Operator) If you're using the Prometheus Operator, you can create a ServiceMonitor:

yaml
apiVersion: <http://monitoring.coreos.com/v1|monitoring.coreos.com/v1>
kind: ServiceMonitor
metadata:
  name: prefect-flow-runs
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: prefect-flow  # Match your flow run pod labels
  namespaceSelector:
    matchNames:
      - your-flow-namespace
  endpoints:
    - port: metrics
      interval: 15s

Remember: - The metrics port (9090 in examples) should be consistent across all configurations - Flow run pods need network access to expose the metrics endpoint - The Prometheus server needs access to scrape the pods - Consider resource implications of enabling metrics collection Would you like me to provide more details about any of these configuration steps or help with troubleshooting the setup?

Based on the research, there are a few potential reasons why your flow runs might show as completed in Prefect but have missing timestamps in GCP Metrics Explorer. Here's what you should check: 1. Metrics Configuration: Ensure that your Prefect client metrics server is properly configured with all necessary environment variables:

python
PREFECT_CLIENT_METRICS_ENABLED=True

2. PodMonitor Configuration: Verify that your PodMonitor configuration is correctly set up to collect all the necessary metrics. The configuration should include: - Correct port targeting - Appropriate scrape interval - Proper metric relabeling if used 3. Metrics Endpoint: Confirm that the prometheus-prefect-exporter is exposing all required metrics. You can check this by directly accessing the metrics endpoint (typically

/metrics

) to see if the timestamp metrics are being exposed. 4. Time Synchronization: Ensure that the time settings are properly synchronized between your Prefect server, the metrics exporter, and GCP. To troubleshoot this: 1. First, verify the metrics are actually being exposed by checking the raw metrics endpoint:

bash
   curl <http://your-prefect-exporter:9090/metrics> | grep flow_run

2. Check the Prometheus scrape configuration logs for any errors related to metric collection. 3. If possible, use a local Prometheus instance to verify the metrics are being collected correctly before they reach GCP. If you're still experiencing issues after checking these points, it would be helpful to: - Share your PodMonitor configuration - Check if any specific flow run metrics are being collected at all - Verify if this happens for all flow runs or only specific ones The issue might also be related to how GCP Metrics Explorer is interpreting or displaying the timestamp data. You might want to cross-reference the raw Prometheus metrics with what's showing in GCP to identify where exactly the timestamps are being lost.

Based on your description, this sounds like it could be related to how Prefect's event system interacts with the Prometheus metrics exporter. Here's what might be happening: 1. Event Subscription: The Prometheus exporter needs to be properly subscribed to Prefect's event system to receive flow run details. If you're seeing entries but missing details, it's possible that: - The exporter is receiving basic flow run existence events - But it's not receiving or processing the detailed state change events that would include timestamps 2. Metric Types: The Prometheus exporter typically exposes several types of metrics for flow runs: - Basic counters for flow run existence - Gauges for current state - Histograms for duration - Timestamps for start/end times To troubleshoot this: 1. First, check what metrics are actually being exposed. You can do this directly on your prometheus-prefect-exporter pod:

bash
   curl localhost:9090/metrics | grep flow_run

Look specifically for metrics containing

_time

or

_timestamp

in their names. 2. Verify your environment variables are properly set:

bash
   PREFECT_CLIENT_METRICS_ENABLED=True
   PREFECT_API_URL=<your-api-url>

3. Check if the metrics server is receiving events by looking at the logs of your prometheus-prefect-exporter pod:

bash
   kubectl logs <pod-name> -n <namespace>

4. Verify that your Prefect API server is properly emitting events for flow run state changes. To get more detailed information about what's happening, you could: 1. Temporarily increase the log level of your prometheus-prefect-exporter 2. Check if the metrics appear in raw Prometheus before they get to GCP 3. Compare the metrics available in Prometheus with what shows up in GCP Metrics Explorer Could you share: - A sample of the metrics you do see in GCP Metrics Explorer? - The configuration you're using for the prometheus-prefect-exporter? This would help pinpoint whether this is an issue with: - Event emission from Prefect - Event collection by the exporter - Metric transformation in Prometheus - Or how GCP is interpreting the metrics