I want to create a resource tracker for my pipelines (the majority are deployed in a Cloud Run Service). so that I can save a log every time they run with the CPU & Memory usage, something like this:

  "Resources Tracker": {
     "Memory Consumed (MB)": 15.01171875,
     "CPU Consumed (%)": 0.0
   }

1. I’ve tried to use psutil but it doesn’t work when the pipeline is deployed (CPU tracks 0.0%).
2. I’m currently trying to use the Google Monitoring API to get this metrics, however, it has been complicated, this is what I have

from google.cloud import monitoring_v3
from google.cloud.monitoring_v3 import types
import datetime


client = monitoring_v3.MetricServiceClient()
project_name = client.common_project_path('tracker')

def cpu_allocation_time():
    metric_type = 'run.googleapis.com/container/cpu/allocated'
    now = datetime.datetime.now()

    interval = types.TimeInterval({
        'end_time': {'seconds': int(now.timestamp())},
        'start_time': {'seconds': int((now - datetime.timedelta(minutes=5)).timestamp())},
    })

    aggregation = types.Aggregation({
        'alignment_period': {'seconds': 60},  
        'per_series_aligner': monitoring_v3.Aggregation.Aligner.ALIGN_MEAN,
    })

    request = types.ListTimeSeriesRequest({
        'name': project_name,
        'filter': f'metric.type="{metric_type}"',
        'interval': interval,
        'view': monitoring_v3.ListTimeSeriesRequest.TimeSeriesView.FULL,
        'aggregation': aggregation,
    })

    response = client.list_time_series(request=request)
    for result in response:
        print(result)

3. I’m trying to guide myself using the following websites:
   Monitoring-Api-Metrics
   Monitoring-resources-cloud-run-revisions
   Monitoring-rest-resources-timeseries