Supervisión del entorno entre proyectos con Terraform

Cloud Composer 1 | Cloud Composer 2 | Cloud Composer 3

En esta página, se muestra cómo implementar un panel de supervisión integrado para varios entornos de Cloud Composer en proyectos seleccionados en dentro de la misma organización.

Descripción general

La solución descrita puede ayudar a los equipos de plataformas empresariales centrales a admitir los entornos de Cloud Composer que usan otros equipos. Esta implementación se puede usar para supervisar todos los entornos de Cloud Composer, incluso aquellos que no se crean con Terraform.

En esta guía, se implementa el panel de Cloud Monitoring en Cloud Composer junto con políticas de alertas que informan de forma continua métricas clave de los entornos de Cloud Composer y generan incidentes en caso de problemas. El panel analiza automáticamente todos los entornos de Cloud Composer en los proyectos seleccionados para esta supervisión. La implementación se basa en Terraform.

Nota: Los entornos de Cloud Composer 1 no proporcionan varias métricas y alertas correspondientes (como se indica más adelante) debido a diferencias de arquitectura entre Cloud Composer 2 y Cloud Composer 1.

El modelo usa un proyecto de Google Cloud que actúa como Monitoring Project, que se usa para supervisar (solo lectura) Entornos de Cloud Composer implementados en varias Proyectos El panel central usa las métricas de Cloud Monitoring de los proyectos supervisados para renderizar su contenido.

El panel supervisa y crea alertas para varias métricas, incluido el estado del entorno:

o métricas de CPU:

Mantén el puntero sobre una línea específica para ver qué entorno representa. Luego, en el panel, se muestra un nombre de proyecto y un recurso:

En caso de que una métrica exceda un umbral predefinido, se genera un incidente y alerta correspondiente se muestra en el gráfico correspondiente a esta métrica:

Lista de métricas supervisadas

Lista completa de métricas supervisadas:

• Estado del entorno de Cloud Composer (basado en el DAG de Monitoring)
• Estado de la base de datos
• Estado del servidor web
• Latidos del programador
• Uso de CPU y memoria para todos los trabajadores
• Uso de CPU y memoria para la base de datos de Airflow
• Uso de memoria y CPU para el servidor web (solo disponible en Cloud Composer 2)
• Uso de memoria y CPU para los programadores de Airflow
• Proporción de tareas en cola, programadas, en cola o programadas de un entorno (útil para detectar problemas de configuración de simultaneidad de Airflow)
• Tiempo de análisis del DAG
• Cantidad actual frente a cantidad mínima de trabajadores: información útil para comprender qué son los trabajadores problemas de estabilidad o escalamiento
• Expulsiones de pods de trabajadores
• Cantidad de errores que arrojan los registros de los trabajadores, programadores, el servidor web y otros componentes (gráficos individuales)

Nota: No es necesario modificar el panel cuando se agregan o quitan entornos de Cloud Composer, siempre y cuando la lista de proyectos supervisados permanezca igual.

Antes de comenzar

Para usar Cloud Composer y Cloud Monitoring, debes crear un proyecto de Google Cloud y habilitar la facturación. El proyecto debe contener un entorno de Cloud Composer. En esta guía, se hace referencia a este proyecto como el Proyecto de supervisión.

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

5. Make sure that billing is enabled for your Google Cloud project.

6. Instala Terraform si todavía no está instalada.
7. Configura el permiso de las métricas de tu proyecto. De forma predeterminada, un proyecto puede mostrar o supervisar solo los datos de series temporales que almacena. Si quieres mostrar datos o supervisar datos almacenados en varios proyectos y, luego, configurar los permisos de métricas del proyecto. Para obtener más información, consulta la Descripción general de los permisos de las métricas.

Pasos de implementación

1. En la computadora local donde ejecutas Terraform, configura la Variable de entorno GOOGLE_CLOUD_PROJECT al ID de tu Proyecto de supervisión:

   export GOOGLE_CLOUD_PROJECT=MONITORING_PROJECT_ID
   

2. Asegúrate de que tu proveedor de Google de Terraform esté autenticado y tenga acceso a los siguientes permisos:

   • Permiso roles/monitoring.editor en Monitoring Project
   • roles/monitoring.viewer, roles/logging.viewer en total Proyectos supervisados

3. Copia el siguiente archivo main.tf en la computadora local en la que ejecutas Terraform.

   Haz clic para expandir

   #   Monitoring for multiple Cloud Composer environments
   #
   #   Usage:
   #       1. Create a new project that you will use for monitoring of Cloud Composer environments in other projects
   #       2. Replace YOUR_MONITORING_PROJECT with the name of this project in the "metrics_scope" parameter that is part of the "Add Monitored Projects to the Monitoring project" section
   #       3. Replace the list of projects to monitor with your list of projects with Cloud Composer environments to be monitored in the "for_each" parameter of the "Add Monitored Projects to the Monitoring project" section
   #       4. Set up your environment and apply the configuration following these steps: https://cloud.google.com/docs/terraform/basic-commands. Your GOOGLE_CLOUD_PROJECT environment variable should be the new monitoring project you just created.
   #
   #   The script creates the following resources in the monitoring project:
   #           1. Adds monitored projects to Cloud Monitoring
   #           2. Creates Alert Policies
   #           3. Creates Monitoring Dashboard
   #
   
   
   
   #######################################################
   #
   # Add Monitored Projects to the Monitoring project
   #
   ########################################################
   
   resource "google_monitoring_monitored_project" "projects_monitored" {
     for_each      = toset(["YOUR_PROJECT_TO_MONITOR_1", "YOUR_PROJECT_TO_MONITOR_2", "YOUR_PROJECT_TO_MONITOR_3"])
     metrics_scope = join("", ["locations/global/metricsScopes/", "YOUR_MONITORING_PROJECT"])
     name          = each.value
   }
   
   
   #######################################################
   #
   # Create alert policies in Monitoring project
   #
   ########################################################
   
   resource "google_monitoring_alert_policy" "environment_health" {
     display_name = "Environment Health"
     combiner     = "OR"
     conditions {
       display_name = "Environment Health"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| {metric 'composer.googleapis.com/environment/dagbag_size'",
           "| group_by 5m, [value_dagbag_size_mean: if(mean(value.dagbag_size) > 0, 1, 0)]",
           "| align mean_aligner(5m)",
           "| group_by [resource.project_id, resource.environment_name],    [value_dagbag_size_mean_aggregate: aggregate(value_dagbag_size_mean)];  ",
           "metric 'composer.googleapis.com/environment/healthy'",
           "| group_by 5m,    [value_sum_signals: aggregate(if(value.healthy,1,0))]",
           "| align mean_aligner(5m)| absent_for 5m }",
           "| outer_join 0",
           "| group_by [resource.project_id, resource.environment_name]",
           "| value val(2)",
           "| align mean_aligner(5m)",
           "| window(5m)",
           "| condition val(0) < 0.9"
         ])
         duration = "120s"
         trigger {
           count = "1"
         }
       }
     }
   
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "database_health" {
     display_name = "Database Health"
     combiner     = "OR"
     conditions {
       display_name = "Database Health"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'composer.googleapis.com/environment/database_health'",
           "| group_by 5m,",
           "    [value_database_health_fraction_true: fraction_true(value.database_health)]",
           "| every 5m",
           "| group_by 5m,",
           "    [value_database_health_fraction_true_aggregate:",
           "       aggregate(value_database_health_fraction_true)]",
           "| every 5m",
           "| group_by [resource.project_id, resource.environment_name],",
           "    [value_database_health_fraction_true_aggregate_aggregate:",
           "       aggregate(value_database_health_fraction_true_aggregate)]",
         "| condition val() < 0.95"])
         duration = "120s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "webserver_health" {
     display_name = "Web Server Health"
     combiner     = "OR"
     conditions {
       display_name = "Web Server Health"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'composer.googleapis.com/environment/web_server/health'",
           "| group_by 5m, [value_health_fraction_true: fraction_true(value.health)]",
           "| every 5m",
           "| group_by 5m,",
           "    [value_health_fraction_true_aggregate:",
           "       aggregate(value_health_fraction_true)]",
           "| every 5m",
           "| group_by [resource.project_id, resource.environment_name],",
           "    [value_health_fraction_true_aggregate_aggregate:",
           "       aggregate(value_health_fraction_true_aggregate)]",
         "| condition val() < 0.95"])
         duration = "120s"
         trigger {
           count = "1"
         }
       }
     }
   
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "scheduler_heartbeat" {
     display_name = "Scheduler Heartbeat"
     combiner     = "OR"
     conditions {
       display_name = "Scheduler Heartbeat"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'composer.googleapis.com/environment/scheduler_heartbeat_count'",
           "| group_by 10m,",
           "    [value_scheduler_heartbeat_count_aggregate:",
           "      aggregate(value.scheduler_heartbeat_count)]",
           "| every 10m",
           "| group_by 10m,",
           "    [value_scheduler_heartbeat_count_aggregate_mean:",
           "       mean(value_scheduler_heartbeat_count_aggregate)]",
           "| every 10m",
           "| group_by [resource.project_id, resource.environment_name],",
           "    [value_scheduler_heartbeat_count_aggregate_mean_aggregate:",
           "       aggregate(value_scheduler_heartbeat_count_aggregate_mean)]",
         "| condition val() < 80"])
         duration = "120s"
         trigger {
           count = "1"
         }
       }
     }
   
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "database_cpu" {
     display_name = "Database CPU"
     combiner     = "OR"
     conditions {
       display_name = "Database CPU"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'composer.googleapis.com/environment/database/cpu/utilization'",
           "| group_by 10m, [value_utilization_mean: mean(value.utilization)]",
           "| every 10m",
           "| group_by [resource.project_id, resource.environment_name]",
         "| condition val() > 0.8"])
         duration = "120s"
         trigger {
           count = "1"
         }
       }
     }
   
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "scheduler_cpu" {
     display_name = "Scheduler CPU"
     combiner     = "OR"
     conditions {
       display_name = "Scheduler CPU"
       condition_monitoring_query_language {
         query = join("", [
           "fetch k8s_container",
           "| metric 'kubernetes.io/container/cpu/limit_utilization'",
           "| filter (resource.pod_name =~ 'airflow-scheduler-.*')",
           "| group_by 10m, [value_limit_utilization_mean: mean(value.limit_utilization)]",
           "| every 10m",
           "| group_by [resource.cluster_name],",
           "    [value_limit_utilization_mean_mean: mean(value_limit_utilization_mean)]",
         "| condition val() > 0.8"])
         duration = "120s"
         trigger {
           count = "1"
         }
       }
     }
   
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "worker_cpu" {
     display_name = "Worker CPU"
     combiner     = "OR"
     conditions {
       display_name = "Worker CPU"
       condition_monitoring_query_language {
         query = join("", [
           "fetch k8s_container",
           "| metric 'kubernetes.io/container/cpu/limit_utilization'",
           "| filter (resource.pod_name =~ 'airflow-worker.*')",
           "| group_by 10m, [value_limit_utilization_mean: mean(value.limit_utilization)]",
           "| every 10m",
           "| group_by [resource.cluster_name],",
           "    [value_limit_utilization_mean_mean: mean(value_limit_utilization_mean)]",
         "| condition val() > 0.8"])
         duration = "120s"
         trigger {
           count = "1"
         }
       }
     }
   
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "webserver_cpu" {
     display_name = "Web Server CPU"
     combiner     = "OR"
     conditions {
       display_name = "Web Server CPU"
       condition_monitoring_query_language {
         query = join("", [
           "fetch k8s_container",
           "| metric 'kubernetes.io/container/cpu/limit_utilization'",
           "| filter (resource.pod_name =~ 'airflow-webserver.*')",
           "| group_by 10m, [value_limit_utilization_mean: mean(value.limit_utilization)]",
           "| every 10m",
           "| group_by [resource.cluster_name],",
           "    [value_limit_utilization_mean_mean: mean(value_limit_utilization_mean)]",
         "| condition val() > 0.8"])
         duration = "120s"
         trigger {
           count = "1"
         }
       }
     }
   
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "parsing_time" {
     display_name = "DAG Parsing Time"
     combiner     = "OR"
     conditions {
       display_name = "DAG Parsing Time"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'composer.googleapis.com/environment/dag_processing/total_parse_time'",
           "| group_by 5m, [value_total_parse_time_mean: mean(value.total_parse_time)]",
           "| every 5m",
           "| group_by [resource.project_id, resource.environment_name]",
         "| condition val(0) > cast_units(30,\"s\")"])
         duration = "120s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "database_memory" {
     display_name = "Database Memory"
     combiner     = "OR"
     conditions {
       display_name = "Database Memory"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'composer.googleapis.com/environment/database/memory/utilization'",
           "| group_by 10m, [value_utilization_mean: mean(value.utilization)]",
           "| every 10m",
           "| group_by [resource.project_id, resource.environment_name]",
         "| condition val() > 0.8"])
         duration = "0s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "scheduler_memory" {
     display_name = "Scheduler Memory"
     combiner     = "OR"
     conditions {
       display_name = "Scheduler Memory"
       condition_monitoring_query_language {
         query = join("", [
           "fetch k8s_container",
           "| metric 'kubernetes.io/container/memory/limit_utilization'",
           "| filter (resource.pod_name =~ 'airflow-scheduler-.*')",
           "| group_by 10m, [value_limit_utilization_mean: mean(value.limit_utilization)]",
           "| every 10m",
           "| group_by [resource.cluster_name],",
           "    [value_limit_utilization_mean_mean: mean(value_limit_utilization_mean)]",
         "| condition val() > 0.8"])
         duration = "0s"
         trigger {
           count = "1"
         }
       }
     }
     documentation {
       content = join("", [
         "Scheduler Memory exceeds a threshold, summed across all schedulers in the environment. ",
       "Add more schedulers OR increase scheduler's memory OR reduce scheduling load (e.g. through lower parsing frequency or lower number of DAGs/tasks running"])
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "worker_memory" {
     display_name = "Worker Memory"
     combiner     = "OR"
     conditions {
       display_name = "Worker Memory"
       condition_monitoring_query_language {
         query = join("", [
           "fetch k8s_container",
           "| metric 'kubernetes.io/container/memory/limit_utilization'",
           "| filter (resource.pod_name =~ 'airflow-worker.*')",
           "| group_by 10m, [value_limit_utilization_mean: mean(value.limit_utilization)]",
           "| every 10m",
           "| group_by [resource.cluster_name],",
           "    [value_limit_utilization_mean_mean: mean(value_limit_utilization_mean)]",
         "| condition val() > 0.8"])
         duration = "0s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "webserver_memory" {
     display_name = "Web Server Memory"
     combiner     = "OR"
     conditions {
       display_name = "Web Server Memory"
       condition_monitoring_query_language {
         query = join("", [
           "fetch k8s_container",
           "| metric 'kubernetes.io/container/memory/limit_utilization'",
           "| filter (resource.pod_name =~ 'airflow-webserver.*')",
           "| group_by 10m, [value_limit_utilization_mean: mean(value.limit_utilization)]",
           "| every 10m",
           "| group_by [resource.cluster_name],",
           "    [value_limit_utilization_mean_mean: mean(value_limit_utilization_mean)]",
         "| condition val() > 0.8"])
         duration = "0s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "scheduled_tasks_percentage" {
     display_name = "Scheduled Tasks Percentage"
     combiner     = "OR"
     conditions {
       display_name = "Scheduled Tasks Percentage"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'composer.googleapis.com/environment/unfinished_task_instances'",
           "| align mean_aligner(10m)",
           "| every(10m)",
           "| window(10m)",
           "| filter_ratio_by [resource.project_id, resource.environment_name], metric.state = 'scheduled'",
         "| condition val() > 0.80"])
         duration = "300s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "queued_tasks_percentage" {
     display_name = "Queued Tasks Percentage"
     combiner     = "OR"
     conditions {
       display_name = "Queued Tasks Percentage"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'composer.googleapis.com/environment/unfinished_task_instances'",
           "| align mean_aligner(10m)",
           "| every(10m)",
           "| window(10m)",
           "| filter_ratio_by [resource.project_id, resource.environment_name], metric.state = 'queued'",
           "| group_by [resource.project_id, resource.environment_name]",
         "| condition val() > 0.95"])
         duration = "300s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "queued_or_scheduled_tasks_percentage" {
     display_name = "Queued or Scheduled Tasks Percentage"
     combiner     = "OR"
     conditions {
       display_name = "Queued or Scheduled Tasks Percentage"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'composer.googleapis.com/environment/unfinished_task_instances'",
           "| align mean_aligner(10m)",
           "| every(10m)",
           "| window(10m)",
           "| filter_ratio_by [resource.project_id, resource.environment_name], or(metric.state = 'queued', metric.state = 'scheduled' )",
           "| group_by [resource.project_id, resource.environment_name]",
         "| condition val() > 0.80"])
         duration = "120s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   
   resource "google_monitoring_alert_policy" "workers_above_minimum" {
     display_name = "Workers above minimum (negative = missing workers)"
     combiner     = "OR"
     conditions {
       display_name = "Workers above minimum"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| { metric 'composer.googleapis.com/environment/num_celery_workers'",
           "| group_by 5m, [value_num_celery_workers_mean: mean(value.num_celery_workers)]",
           "| every 5m",
           "; metric 'composer.googleapis.com/environment/worker/min_workers'",
           "| group_by 5m, [value_min_workers_mean: mean(value.min_workers)]",
           "| every 5m }",
           "| outer_join 0",
           "| sub",
           "| group_by [resource.project_id, resource.environment_name]",
         "| condition val() < 0"])
         duration = "0s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "pod_evictions" {
     display_name = "Worker pod evictions"
     combiner     = "OR"
     conditions {
       display_name = "Worker pod evictions"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'composer.googleapis.com/environment/worker/pod_eviction_count'",
           "| align delta(1m)",
           "| every 1m",
           "| group_by [resource.project_id, resource.environment_name]",
         "| condition val() > 0"])
         duration = "60s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "scheduler_errors" {
     display_name = "Scheduler Errors"
     combiner     = "OR"
     conditions {
       display_name = "Scheduler Errors"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'logging.googleapis.com/log_entry_count'",
           "| filter (metric.log == 'airflow-scheduler' && metric.severity == 'ERROR')",
           "| group_by 5m,",
           "    [value_log_entry_count_aggregate: aggregate(value.log_entry_count)]",
           "| every 5m",
           "| group_by [resource.project_id, resource.environment_name],",
           "    [value_log_entry_count_aggregate_max: max(value_log_entry_count_aggregate)]",
         "| condition val() > 50"])
         duration = "300s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "worker_errors" {
     display_name = "Worker Errors"
     combiner     = "OR"
     conditions {
       display_name = "Worker Errors"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'logging.googleapis.com/log_entry_count'",
           "| filter (metric.log == 'airflow-worker' && metric.severity == 'ERROR')",
           "| group_by 5m,",
           "    [value_log_entry_count_aggregate: aggregate(value.log_entry_count)]",
           "| every 5m",
           "| group_by [resource.project_id, resource.environment_name],",
           "    [value_log_entry_count_aggregate_max: max(value_log_entry_count_aggregate)]",
         "| condition val() > 50"])
         duration = "300s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "webserver_errors" {
     display_name = "Web Server Errors"
     combiner     = "OR"
     conditions {
       display_name = "Web Server Errors"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'logging.googleapis.com/log_entry_count'",
           "| filter (metric.log == 'airflow-webserver' && metric.severity == 'ERROR')",
           "| group_by 5m,",
           "    [value_log_entry_count_aggregate: aggregate(value.log_entry_count)]",
           "| every 5m",
           "| group_by [resource.project_id, resource.environment_name],",
           "    [value_log_entry_count_aggregate_max: max(value_log_entry_count_aggregate)]",
         "| condition val() > 50"])
         duration = "300s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   resource "google_monitoring_alert_policy" "other_errors" {
     display_name = "Other Errors"
     combiner     = "OR"
     conditions {
       display_name = "Other Errors"
       condition_monitoring_query_language {
         query = join("", [
           "fetch cloud_composer_environment",
           "| metric 'logging.googleapis.com/log_entry_count'",
           "| filter",
           "    (metric.log !~ 'airflow-scheduler|airflow-worker|airflow-webserver'",
           "     && metric.severity == 'ERROR')",
           "| group_by 5m, [value_log_entry_count_max: max(value.log_entry_count)]",
           "| every 5m",
           "| group_by [resource.project_id, resource.environment_name],",
           "    [value_log_entry_count_max_aggregate: aggregate(value_log_entry_count_max)]",
         "| condition val() > 10"])
         duration = "300s"
         trigger {
           count = "1"
         }
       }
     }
     # uncomment to set an auto close strategy for the alert
     #alert_strategy {
     #    auto_close = "30m"
     #}
   }
   
   
   #######################################################
   #
   # Create Monitoring Dashboard
   #
   ########################################################
   
   
   resource "google_monitoring_dashboard" "Composer_Dashboard" {
     dashboard_json = <<EOF
   {
     "category": "CUSTOM",
     "displayName": "Cloud Composer - Monitoring Platform",
     "mosaicLayout": {
       "columns": 12,
       "tiles": [
         {
           "height": 1,
           "widget": {
             "text": {
               "content": "",
               "format": "MARKDOWN"
             },
             "title": "Health"
           },
           "width": 12,
           "xPos": 0,
           "yPos": 0
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.environment_health.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 1
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.database_health.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 1
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.webserver_health.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 5
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.scheduler_heartbeat.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 5
         },
         {
           "height": 1,
           "widget": {
             "text": {
               "content": "",
               "format": "RAW"
             },
             "title": "Airflow Task Execution and DAG Parsing"
           },
           "width": 12,
           "xPos": 0,
           "yPos": 9
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.scheduled_tasks_percentage.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 10
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.queued_tasks_percentage.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 10
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.queued_or_scheduled_tasks_percentage.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 14
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.parsing_time.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 14
         },
         {
           "height": 1,
           "widget": {
             "text": {
               "content": "",
               "format": "RAW"
             },
             "title": "Workers presence"
           },
           "width": 12,
           "xPos": 0,
           "yPos": 18
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.workers_above_minimum.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 19
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.pod_evictions.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 19
         },
         {
           "height": 1,
           "widget": {
             "text": {
               "content": "",
               "format": "RAW"
             },
             "title": "CPU Utilization"
           },
           "width": 12,
           "xPos": 0,
           "yPos": 23
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.database_cpu.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 24
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.scheduler_cpu.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 24
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.worker_cpu.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 28
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.webserver_cpu.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 28
         },
   
         {
           "height": 1,
           "widget": {
             "text": {
               "content": "",
               "format": "RAW"
             },
             "title": "Memory Utilization"
           },
           "width": 12,
           "xPos": 0,
           "yPos": 32
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.database_memory.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 33
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.scheduler_memory.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 33
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.worker_memory.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 37
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.webserver_memory.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 37
         },
         {
           "height": 1,
           "widget": {
             "text": {
               "content": "",
               "format": "RAW"
             },
             "title": "Airflow component errors"
           },
           "width": 12,
           "xPos": 0,
           "yPos": 41
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.scheduler_errors.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 42
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.worker_errors.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 42
         },
               {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.webserver_errors.name}"
             }
           },
           "width": 6,
           "xPos": 0,
           "yPos": 48
         },
         {
           "height": 4,
           "widget": {
             "alertChart": {
               "name": "${google_monitoring_alert_policy.other_errors.name}"
             }
           },
           "width": 6,
           "xPos": 6,
           "yPos": 48
         },
         {
           "height": 1,
           "widget": {
             "text": {
               "content": "",
               "format": "RAW"
             },
             "title": "Task errors"
           },
           "width": 12,
           "xPos": 0,
           "yPos": 52
         }
       ]
     }
   }
   EOF
   }

4. Edita el bloque resource "google_monitoring_monitored_project":

   1. Reemplaza la lista de proyectos en el bloque for_each con tu Proyectos supervisados.
   2. Reemplaza "YOUR_MONITORING_PROJECT" en metrics_scope por el nombre. de tu Proyecto de supervisión.

5. Revisa la configuración y verifica que los recursos de Terraform que vas a crear o actualizar coinciden con tus expectativas. Haz correcciones si es necesario.

   terraform plan
   

6. Para aplicar la configuración de Terraform, ejecuta el siguiente comando: ingresando “yes” en el prompt:

   terraform apply
   

7. En la consola de Google Cloud de tu proyecto de supervisión, ve a la página Monitoring Dashboard:

   Ve al Panel de Monitoring.

8. Busca el panel personalizado llamado Cloud Composer: Plataforma de supervisión en la pestaña Personalizado.

¿Qué sigue?

• Supervisa entornos