Monitoramento de ambiente entre projetos com o Terraform

Airflow gerenciado (Geração 3) | Airflow gerenciado (Geração 2) | Airflow gerenciado (Geração 1 legada)

Esta página mostra como implementar um painel de monitoramento integrado para vários ambientes do Airflow gerenciado em projetos selecionados na mesma organização.

Visão geral

A solução descrita pode ajudar as equipes de plataforma empresarial central a oferecer suporte a ambientes do Airflow gerenciado usados por outras equipes. Essa implementação pode ser usada para monitorar todos os ambientes do Airflow gerenciado, mesmo aqueles que não foram criados usando o Terraform.

Este guia implementa o painel do Cloud Monitoring no Airflow gerenciado, além de políticas de alertas que informam continuamente as principais métricas dos ambientes do Airflow gerenciado e geram incidentes em caso de problemas. O painel verifica automaticamente todos os ambientes do Airflow gerenciado em projetos selecionados para esse monitoramento. A implementação depende do Terraform.

O modelo usa um Google Cloud projeto que atua como um projeto de monitoramento, usado para monitorar (somente leitura) ambientes do Airflow gerenciado implantados em vários projetos monitorados. O painel central usa métricas do Cloud Monitoring dos projetos monitorados para renderizar o conteúdo.

Diagrama que mostra o projeto de monitoramento, que contém o painel de monitoramento, e três projetos monitorados, cada um com ambientes do Composer. Cada projeto monitorado tem uma seta apontando para o projeto monitorado rotulado como "métricas".

O painel monitora e cria alertas para várias métricas, incluindo a integridade do ambiente:

Captura de tela do painel de monitoramento mostrando a integridade do ambiente, do banco de dados, do servidor da Web e o heartbeat do programador

ou métricas de CPU:

Captura de tela do painel de monitoramento mostrando CPU do banco de dados, CPU do programador, CPU do worker e CPU do servidor da Web

Mantenha o ponteiro sobre uma linha específica para ver qual ambiente ela representa. Em seguida, o painel mostra um nome de projeto e um recurso:

Captura de tela do painel de monitoramento mostrando o pop-up quando você passa o cursor sobre uma linha. O pop-up mostra quatro recursos, um dos quais corresponde à linha.

Caso uma métrica exceda um limite predefinido, um incidente será gerado e um alerta correspondente será mostrado em um gráfico correspondente a essa métrica:

Captura de tela da visualização de incidentes abertos mostrando dois incidentes. Cada incidente listado tem um link para conferir os detalhes.

Lista de métricas monitoradas

Uma lista completa de métricas monitoradas:

  • Integridade do ambiente do Airflow gerenciado (com base no DAG de monitoramento)
  • Integridade do banco de dados
  • Integridade do servidor da Web
  • Sinais de funcionamento do programador
  • Utilização de CPU e memória para todos os workers
  • Utilização de CPU e memória para o banco de dados do Airflow
  • Utilização de CPU e memória para o servidor da Web
  • Utilização de CPU e memória para programadores do Airflow
  • Proporção de tarefas enfileiradas, programadas, enfileiradas ou programadas em um ambiente (útil para identificar problemas de configuração de simultaneidade do Airflow)
  • Tempo de análise do DAG
  • Número atual versus mínimo de workers: útil para entender problemas de estabilidade ou escalonamento de workers
  • Remoção de pod de workers
  • Número de erros gerados nos registros por workers, programadores, servidor da Web ou outros componentes (gráficos individuais)

Antes de começar

Para usar o Airflow gerenciado e o Cloud Monitoring, crie um Google Cloud projeto e ative o faturamento. O projeto precisa conter um ambiente do Airflow gerenciado. Esse projeto é chamado de projeto de monitoramento neste guia.

  1. Faça login na sua Google Cloud conta do. Se você não conhece o Google Cloud, crie uma conta para avaliar o desempenho dos nossos produtos em cenários reais. Clientes novos também recebem US $300 em créditos para executar, testar e implantar cargas de trabalho.
  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

  3. Verify 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.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

  5. Verify that billing is enabled for your Google Cloud project.

  6. Instale o Terraform se ele ainda não estiver instalado.
  7. Configure o escopo de métricas do projeto. Por padrão, um projeto só pode mostrar ou monitorar dados de séries temporais que ele armazena. Se você quiser mostrar ou monitorar dados armazenados em vários projetos, então configure o escopo de métricas do projeto. Para mais informações, consulte Visão geral dos escopos de métricas.

Etapas da implementação

  1. No computador local em que você executa o Terraform, defina a GOOGLE_CLOUD_PROJECT variável de ambiente como o ID do seu projeto de monitoramento:

    export GOOGLE_CLOUD_PROJECT=MONITORING_PROJECT_ID
    
  2. Verifique se o provedor do Google do Terraform está autenticado e tem acesso às seguintes permissões:

    • Permissão roles/monitoring.editor no projeto de monitoramento
    • roles/monitoring.viewer, roles/logging.viewer em todos os projetos monitorados
  3. Copie o arquivo main.tf a seguir para o computador local em que você executa o Terraform.

    Clique 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. Edite o bloco "google_monitoring_monitored_project" resource:

    1. Substitua a lista de projetos no bloco for_each pelos seus projetos monitorados.
    2. Substitua "YOUR_MONITORING_PROJECT" no metrics_scope pelo nome do seu projeto de monitoramento.
  5. Analise a configuração e verifique se os recursos que o Terraform vai criar ou atualizar correspondem às suas expectativas. Faça correções, se necessário.

    terraform plan
    
  6. Para aplicar a configuração do Terraform, execute o comando a seguir e digite yes no prompt:

    terraform apply
    
  7. No Google Cloud console do seu projeto de monitoramento, acesse a página Painel de monitoramento:

    Acessar o painel de monitoramento

  8. Encontre seu painel personalizado chamado Cloud Composer - Plataforma de monitoramento na guia Personalizado.

A seguir