Monitoramento de ambientes entre projetos com o Terraform

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Observação :esta página ainda não foi revisada sobre o Cloud Composer 3 e mostra conteúdo do Cloud Composer 2.

Nesta página, mostramos como implementar um painel de monitoramento integrado para vários ambientes do Cloud Composer em projetos selecionados na mesma organização.

Visão geral

A solução descrita pode ajudar as equipes centrais de plataforma empresarial a oferecer suporte aos ambientes do Cloud Composer usados por outras equipes. Essa implementação pode ser usada para monitorar todos os ambientes do Cloud Composer, mesmo aqueles que não são criados com o Terraform.

Este guia implementa o painel do Cloud Monitoring no Cloud Composer com políticas de alerta que relatam continuamente as principais métricas dos ambientes do Cloud Composer e levantam incidentes em caso de problemas. O painel verifica automaticamente todos os ambientes do Cloud Composer nos projetos selecionados para esse monitoramento. A implementação depende do Terraform.

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

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

ou métricas de CPU:

Mantenha o ponteiro do mouse sobre uma linha específica para ver qual ambiente ela representa. O painel exibe um nome de projeto e um recurso:

Quando uma métrica excede um limite predefinido, um incidente é gerado e um respectivo alerta é mostrado em um gráfico correspondente a essa métrica:

Lista de métricas monitoradas

Uma lista completa de métricas monitoradas:

• Integridade do ambiente do Cloud Composer (com base no DAG do Monitoring)
• Integridade do banco de dados
• Integridade do servidor da Web
• Batimentos do programador
• Uso de CPU e memória para todos os workers
• Uso de CPU e memória do banco de dados do Airflow
• Uso de CPU e memória do servidor da Web
• Uso de CPU e memória para programadores do Airflow
• Proporção de tarefas em fila, programadas, em fila ou programadas em um ambiente (útil para detectar problemas de configuração de simultaneidade do Airflow)
• Tempo de análise do DAG
• Número atual de workers em comparação com o mínimo: útil para entender problemas de estabilidade ou escalonamento.
• Remoção de pod de workers
• Número de erros gerados em registros por workers, programadores, servidor da Web ou outros componentes (gráficos individuais).

Observação: você não precisará modificar o painel quando os ambientes do Cloud Composer forem adicionados ou removidos, desde que a lista de projetos monitorados permaneça a mesma.

Antes de começar

Para usar o Cloud Composer e o Cloud Monitoring, você precisa criar um projeto do Google Cloud e ativar o faturamento. O projeto precisa conter um ambiente do Cloud Composer. Neste guia, esse projeto é chamado de Projeto de monitoramento.

1. Faça login na sua conta do Google Cloud. Se você começou a usar o Google Cloud agora, crie uma conta para avaliar o desempenho de nossos produtos em situações reais. Clientes novos também recebem US$ 300 em créditos para executar, testar e implantar cargas de trabalho.

2. No console do Google Cloud, na página do seletor de projetos, selecione ou crie um projeto do Google Cloud.

   Observação: se você não pretende manter os recursos criados neste procedimento, crie um projeto novo em vez de selecionar um que já existe. Depois de concluir essas etapas, é possível excluir o projeto. Para fazer isso, basta remover todos os recursos associados a ele.

   Acessar o seletor de projetos

3. Verifique se a cobrança está ativada para o seu projeto do Google Cloud.

4. No console do Google Cloud, na página do seletor de projetos, selecione ou crie um projeto do Google Cloud.

   Observação: se você não pretende manter os recursos criados neste procedimento, crie um projeto novo em vez de selecionar um que já existe. Depois de concluir essas etapas, é possível excluir o projeto. Para fazer isso, basta remover todos os recursos associados a ele.

   Acessar o seletor de projetos

5. Verifique se a cobrança está ativada para o seu projeto do Google Cloud.

6. Instale o Terraform se ele ainda não estiver instalado.
7. Configure o escopo de métricas do seu projeto. Por padrão, um projeto só pode exibir ou monitorar dados de série temporal que armazena. Se você quiser exibir dados ou monitorar dados armazenados em vários projetos, 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 variável de ambiente GOOGLE_CLOUD_PROJECT como o ID do seu projeto de monitoramento:

   export GOOGLE_CLOUD_PROJECT=MONITORING_PROJECT_ID
   

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

   • Permissão roles/monitoring.editor no Monitoring Project
   • 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 resource "google_monitoring_monitored_project":

   1. Substitua a lista de projetos no bloco for_each pelos Projetos monitorados.
   2. Substitua "YOUR_MONITORING_PROJECT" em metrics_scope pelo nome do seu projeto de monitoramento.

5. Revise a configuração e verifique se os recursos que o Terraform vai criar ou atualizar atendem às suas expectativas. Faça correções, se necessário.

   terraform plan
   

6. Aplique a configuração do Terraform executando o seguinte comando e inserindo "yes" no prompt:

   terraform apply
   

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

   Acesse o painel de monitoramento.

8. Encontre o painel personalizado Cloud Composer - Monitoring Platform na guia Personalizado.

A seguir

• Como monitorar ambientes