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Consultar logs nos Insights do contêiner

  • Artigo

Os insights do contêiner coletam métricas de desempenho, dados de inventário e informações de estado de integridade de contêineres e hosts de contêiner. Os dados são coletados a cada três minutos e encaminhados para o workspace do Log Analytics no Azure Monitor, onde estão disponíveis para consultas de log usando o Log Analytics no Azure Monitor.

Você pode aplicar esses dados a cenários que incluem planejamento de migração, análise de capacidade, descoberta e solução de problemas de desempenho sob demanda. Os Logs do Azure Monitor podem ajudá-lo a procurar por tendências, diagnosticar gargalos, prever ou correlacionar dados que podem ajudar a determinar se a configuração do cluster atual está sendo executada corretamente.

Para obter informações sobre como usar essas consultas, consulte Usar consultas no Log Analytics do Azure Monitor. Para obter um tutorial completo sobre como usar o Log Analytics para executar consultas e trabalhar com seus resultados, consulte Tutorial do Log Analytics.

Importante

As consultas neste artigo dependem dos dados coletados pelos Insights do contêiner e armazenados em um workspace do Log Analytics. Se você modificou as configurações de coleta de dados padrão, as consultas podem não retornar os resultados esperados. Mais notavelmente, se você tiver desabilitado a coleta de dados de desempenho desde que habilitou as métricas do Prometheus para o cluster, todas as consultas que usam a tabela Perf não retornarão resultados.

Consulte Configurar a coleta de dados nos Insights do contêiner usando a regra de coleta de dados para configurações predefinidas, incluindo desabilitar a coleta de dados de desempenho. Consulte Configurar a coleta de dados nos Insights do contêiner usando ConfigMap para obter mais opções de coleta de dados.

Abrir Log Analytics

Existem várias opções para iniciar o Log Analytics. Cada opção começa com um escopo diferente. Para acessar todos os dados no espaço de trabalho, no menu Monitoramento, selecione Logs. Para limitar os dados a um único cluster do Kubernetes, selecione Logs no menu desse cluster.

Captura de tela que mostra o início do Log Analytics.

Consultas de log existentes

Você não precisa necessariamente entender como gravar uma consulta de log para usar o Log Analytics. Você pode selecionar entre várias consultas predefinidas. Você pode executar as consultas sem modificação ou usá-las como um início para uma consulta personalizada. Selecione as Consultas na parte superior da tela do Log Analytics e exiba as consultas com um Tipo de recurso de Serviços do Kubernetes.

Captura de tela que mostra consultas do Log Analytics para os Kubernetes.

Tabelas de contêiner

Para obter uma lista de tabelas e suas descrições detalhadas usadas pelos insights do Contêiner, confira a Referência de tabela do Azure Monitor. Todas essas tabelas estão disponíveis para consultas de log.

Exemplos de consultas de log

Costuma ser útil criar consultas começando com um ou dois exemplos e, em seguida, modificá-las de acordo com seus requisitos. Para ajudar a criar consultas mais avançadas, você pode experimentar as seguintes consultas de amostra.

Listar todas as informações do ciclo de vida do contêiner

ContainerInventory
| project Computer, Name, Image, ImageTag, ContainerState, CreatedTime, StartedTime, FinishedTime
| render table

Eventos de Kubernetes

Observação

Por padrão, os eventos do tipo Normal não são coletados e, portanto, você não os verá quando consultar a tabela KubeEvents, a menos que a configuração ConfigMap collect_all_kube_events ConfigMap esteja habilitada. Se você precisar da coleção de Eventos normais, habilite a configuração collect_all_kube_events no ConfigMap container-azm-ms-agentconfig. Consulte Configuração da coleta de dados do agente para insights de contêiner para obter informações sobre como configurar o ConfigMap.

KubeEvents
| where not(isempty(Namespace))
| sort by TimeGenerated desc
| render table

CPU do contêiner

Perf
| where ObjectName == "K8SContainer" and CounterName == "cpuUsageNanoCores" 
| summarize AvgCPUUsageNanoCores = avg(CounterValue) by bin(TimeGenerated, 30m), InstanceName

Memória de contêiner

Essa consulta usa memoryRssBytes o que só está disponível para os nós do Linux.

Perf
| where ObjectName == "K8SContainer" and CounterName == "memoryRssBytes"
| summarize AvgUsedRssMemoryBytes = avg(CounterValue) by bin(TimeGenerated, 30m), InstanceName

Solicitações por minuto com métricas personalizadas

InsightsMetrics
| where Name == "requests_count"
| summarize Val=any(Val) by TimeGenerated=bin(TimeGenerated, 1m)
| sort by TimeGenerated asc
| project RequestsPerMinute = Val - prev(Val), TimeGenerated
| render barchart

Pods por nome e namespace

let startTimestamp = ago(1h);
KubePodInventory
| where TimeGenerated > startTimestamp
| project ContainerID, PodName=Name, Namespace
| where PodName contains "name" and Namespace startswith "namespace"
| distinct ContainerID, PodName
| join
(
    ContainerLog
    | where TimeGenerated > startTimestamp
)
on ContainerID
// at this point before the next pipe, columns from both tables are available to be "projected". Due to both
// tables having a "Name" column, we assign an alias as PodName to one column which we actually want
| project TimeGenerated, PodName, LogEntry, LogEntrySource
| summarize by TimeGenerated, LogEntry
| order by TimeGenerated desc

HPA (expansão de pod)

Essa consulta retorna o número de réplicas em expansão em cada implantação. Calcula o percentual de expansão com o número máximo de réplicas configuradas no HPA.

let _minthreshold = 70; // minimum threshold goes here if you want to setup as an alert
let _maxthreshold = 90; // maximum threshold goes here if you want to setup as an alert
let startDateTime = ago(60m);
KubePodInventory
| where TimeGenerated >= startDateTime 
| where Namespace !in('default', 'kube-system') // List of non system namespace filter goes here.
| extend labels = todynamic(PodLabel)
| extend deployment_hpa = reverse(substring(reverse(ControllerName), indexof(reverse(ControllerName), "-") + 1))
| distinct tostring(deployment_hpa)
| join kind=inner (InsightsMetrics 
    | where TimeGenerated > startDateTime 
    | where Name == 'kube_hpa_status_current_replicas'
    | extend pTags = todynamic(Tags) //parse the tags for values
    | extend ns = todynamic(pTags.k8sNamespace) //parse namespace value from tags
    | extend deployment_hpa = todynamic(pTags.targetName) //parse HPA target name from tags
    | extend max_reps = todynamic(pTags.spec_max_replicas) // Parse maximum replica settings from HPA deployment
    | extend desired_reps = todynamic(pTags.status_desired_replicas) // Parse desired replica settings from HPA deployment
    | summarize arg_max(TimeGenerated, *) by tostring(ns), tostring(deployment_hpa), Cluster=toupper(tostring(split(_ResourceId, '/')[8])), toint(desired_reps), toint(max_reps), scale_out_percentage=(desired_reps * 100 / max_reps)
    //| where scale_out_percentage > _minthreshold and scale_out_percentage <= _maxthreshold
    )
    on deployment_hpa

Expansões de nodepool

Essa consulta retorna o número de nós ativos em cada pool de nós. Calcula o número de nós ativos disponíveis e a configuração máxima do nó nas configurações do dimensionamento automático para determinar o percentual de expansão. Veja as linhas comentadas na consulta para usá-lo para uma regra de alerta de número de resultados.

let nodepoolMaxnodeCount = 10; // the maximum number of nodes in your auto scale setting goes here.
let _minthreshold = 20;
let _maxthreshold = 90;
let startDateTime = 60m;
KubeNodeInventory
| where TimeGenerated >= ago(startDateTime)
| extend nodepoolType = todynamic(Labels) //Parse the labels to get the list of node pool types
| extend nodepoolName = todynamic(nodepoolType[0].agentpool) // parse the label to get the nodepool name or set the specific nodepool name (like nodepoolName = 'agentpool)'
| summarize nodeCount = count(Computer) by ClusterName, tostring(nodepoolName), TimeGenerated
//(Uncomment the below two lines to set this as a log search alert)
//| extend scaledpercent = iff(((nodeCount * 100 / nodepoolMaxnodeCount) >= _minthreshold and (nodeCount * 100 / nodepoolMaxnodeCount) < _maxthreshold), "warn", "normal")
//| where scaledpercent == 'warn'
| summarize arg_max(TimeGenerated, *) by nodeCount, ClusterName, tostring(nodepoolName)
| project ClusterName, 
    TotalNodeCount= strcat("Total Node Count: ", nodeCount),
    ScaledOutPercentage = (nodeCount * 100 / nodepoolMaxnodeCount),  
    TimeGenerated, 
    nodepoolName

Disponibilidade de contêineres do sistema (conjunto de réplicas)

Essa consulta retorna os contêineres do sistema (conjuntos de réplicas) e relata o percentual indisponível. Veja as linhas comentadas na consulta para usá-lo para uma regra de alerta de número de resultados.

let startDateTime = 5m; // the minimum time interval goes here
let _minalertThreshold = 50; //Threshold for minimum and maximum unavailable or not running containers
let _maxalertThreshold = 70;
KubePodInventory
| where TimeGenerated >= ago(startDateTime)
| distinct ClusterName, TimeGenerated
| summarize Clustersnapshot = count() by ClusterName
| join kind=inner (
    KubePodInventory
    | where TimeGenerated >= ago(startDateTime)
    | where Namespace in('default', 'kube-system') and ControllerKind == 'ReplicaSet' // the system namespace filter goes here
    | distinct ClusterName, Computer, PodUid, TimeGenerated, PodStatus, ServiceName, PodLabel, Namespace, ContainerStatus
    | summarize arg_max(TimeGenerated, *), TotalPODCount = count(), podCount = sumif(1, PodStatus == 'Running' or PodStatus != 'Running'), containerNotrunning = sumif(1, ContainerStatus != 'running')
        by ClusterName, TimeGenerated, ServiceName, PodLabel, Namespace
    )
    on ClusterName
| project ClusterName, ServiceName, podCount, containerNotrunning, containerNotrunningPercent = (containerNotrunning * 100 / podCount), TimeGenerated, PodStatus, PodLabel, Namespace, Environment = tostring(split(ClusterName, '-')[3]), Location = tostring(split(ClusterName, '-')[4]), ContainerStatus
//Uncomment the below line to set for automated alert
//| where PodStatus == "Running" and containerNotrunningPercent > _minalertThreshold and containerNotrunningPercent < _maxalertThreshold
| summarize arg_max(TimeGenerated, *), c_entry=count() by PodLabel, ServiceName, ClusterName
//Below lines are to parse the labels to identify the impacted service/component name
| extend parseLabel = replace(@'k8s-app', @'k8sapp', PodLabel)
| extend parseLabel = replace(@'app.kubernetes.io\\/component', @'appkubernetesiocomponent', parseLabel)
| extend parseLabel = replace(@'app.kubernetes.io\\/instance', @'appkubernetesioinstance', parseLabel)
| extend tags = todynamic(parseLabel)
| extend tag01 = todynamic(tags[0].app)
| extend tag02 = todynamic(tags[0].k8sapp)
| extend tag03 = todynamic(tags[0].appkubernetesiocomponent)
| extend tag04 = todynamic(tags[0].aadpodidbinding)
| extend tag05 = todynamic(tags[0].appkubernetesioinstance)
| extend tag06 = todynamic(tags[0].component)
| project ClusterName, TimeGenerated,
    ServiceName = strcat( ServiceName, tag01, tag02, tag03, tag04, tag05, tag06),
    ContainerUnavailable = strcat("Unavailable Percentage: ", containerNotrunningPercent),
    PodStatus = strcat("PodStatus: ", PodStatus), 
    ContainerStatus = strcat("Container Status: ", ContainerStatus)

Disponibilidade de contêineres do sistema (conjuntos de daemon)

Essa consulta retorna os contêineres do sistema (daemonsets) e relata o percentual indisponível. Veja as linhas comentadas na consulta para usá-lo para uma regra de alerta de número de resultados.

let startDateTime = 5m; // the minimum time interval goes here
let _minalertThreshold = 50; //Threshold for minimum and maximum unavailable or not running containers
let _maxalertThreshold = 70;
KubePodInventory
| where TimeGenerated >= ago(startDateTime)
| distinct ClusterName, TimeGenerated
| summarize Clustersnapshot = count() by ClusterName
| join kind=inner (
    KubePodInventory
    | where TimeGenerated >= ago(startDateTime)
    | where Namespace in('default', 'kube-system') and ControllerKind == 'DaemonSet' // the system namespace filter goes here
    | distinct ClusterName, Computer, PodUid, TimeGenerated, PodStatus, ServiceName, PodLabel, Namespace, ContainerStatus
    | summarize arg_max(TimeGenerated, *), TotalPODCount = count(), podCount = sumif(1, PodStatus == 'Running' or PodStatus != 'Running'), containerNotrunning = sumif(1, ContainerStatus != 'running')
        by ClusterName, TimeGenerated, ServiceName, PodLabel, Namespace
    )
    on ClusterName
| project ClusterName, ServiceName, podCount, containerNotrunning, containerNotrunningPercent = (containerNotrunning * 100 / podCount), TimeGenerated, PodStatus, PodLabel, Namespace, Environment = tostring(split(ClusterName, '-')[3]), Location = tostring(split(ClusterName, '-')[4]), ContainerStatus
//Uncomment the below line to set for automated alert
//| where PodStatus == "Running" and containerNotrunningPercent > _minalertThreshold and containerNotrunningPercent < _maxalertThreshold
| summarize arg_max(TimeGenerated, *), c_entry=count() by PodLabel, ServiceName, ClusterName
//Below lines are to parse the labels to identify the impacted service/component name
| extend parseLabel = replace(@'k8s-app', @'k8sapp', PodLabel)
| extend parseLabel = replace(@'app.kubernetes.io\\/component', @'appkubernetesiocomponent', parseLabel)
| extend parseLabel = replace(@'app.kubernetes.io\\/instance', @'appkubernetesioinstance', parseLabel)
| extend tags = todynamic(parseLabel)
| extend tag01 = todynamic(tags[0].app)
| extend tag02 = todynamic(tags[0].k8sapp)
| extend tag03 = todynamic(tags[0].appkubernetesiocomponent)
| extend tag04 = todynamic(tags[0].aadpodidbinding)
| extend tag05 = todynamic(tags[0].appkubernetesioinstance)
| extend tag06 = todynamic(tags[0].component)
| project ClusterName, TimeGenerated,
    ServiceName = strcat( ServiceName, tag01, tag02, tag03, tag04, tag05, tag06),
    ContainerUnavailable = strcat("Unavailable Percentage: ", containerNotrunningPercent),
    PodStatus = strcat("PodStatus: ", PodStatus), 
    ContainerStatus = strcat("Container Status: ", ContainerStatus)

Logs dos contêineres

Os logs de contêiner do AKS são armazenados na tabela ContainerLogV2. Você pode executar as consultas de amostra a seguir para procurar a saída de log stderr/stdout de pods, implantações ou namespaces de destino.

Logs de contêiner para um pod, namespace e contêiner específicos

ContainerLogV2
| where _ResourceId =~ "clusterResourceID" //update with resource ID
| where PodNamespace == "podNameSpace" //update with target namespace
| where PodName == "podName" //update with target pod
| where ContainerName == "containerName" //update with target container
| project TimeGenerated, Computer, ContainerId, LogMessage, LogSource

Logs de contêiner para uma implantação específica

let KubePodInv = KubePodInventory
| where _ResourceId =~ "clusterResourceID" //update with resource ID
| where Namespace == "deploymentNamespace" //update with target namespace
| where ControllerKind == "ReplicaSet"
| extend deployment = reverse(substring(reverse(ControllerName), indexof(reverse(ControllerName), "-") + 1))
| where deployment == "deploymentName" //update with target deployment
| extend ContainerId = ContainerID
| summarize arg_max(TimeGenerated, *)  by deployment, ContainerId, PodStatus, ContainerStatus
| project deployment, ContainerId, PodStatus, ContainerStatus;

KubePodInv
| join
(
    ContainerLogV2
  | where TimeGenerated >= startTime and TimeGenerated < endTime
  | where PodNamespace == "deploymentNamespace" //update with target namespace
  | where PodName startswith "deploymentName" //update with target deployment
) on ContainerId
| project TimeGenerated, deployment, PodName, PodStatus, ContainerName, ContainerId, ContainerStatus, LogMessage, LogSource

Logs de contêiner para qualquer pod com falha em um namespace específico

    let KubePodInv = KubePodInventory
    | where TimeGenerated >= startTime and TimeGenerated < endTime
    | where _ResourceId =~ "clustereResourceID" //update with resource ID
    | where Namespace == "podNamespace" //update with target namespace
    | where PodStatus == "Failed"
    | extend ContainerId = ContainerID
    | summarize arg_max(TimeGenerated, *)  by  ContainerId, PodStatus, ContainerStatus
    | project ContainerId, PodStatus, ContainerStatus;

    KubePodInv
    | join
    (
        ContainerLogV2
    | where TimeGenerated >= startTime and TimeGenerated < endTime
    | where PodNamespace == "podNamespace" //update with target namespace
    ) on ContainerId
    | project TimeGenerated, PodName, PodStatus, ContainerName, ContainerId, ContainerStatus, LogMessage, LogSource

Consultas de visualização padrão de insights de contêiner

Essas consultas são geradas a partir das visualizações prontas para uso a partir de insights de contêiner. Você pode optar por usá-las se tiver habilitado as configurações de otimização de custo personalizadas, em vez dos gráficos padrão.

Contagem de nós por status

As tabelas necessárias para esse gráfico incluem KubeNodeInventory.

 let trendBinSize = 5m;
 let maxListSize = 1000;
 let clusterId = 'clusterResourceID'; //update with resource ID
 
 let rawData = KubeNodeInventory 
| where ClusterId =~ clusterId 
| distinct ClusterId, TimeGenerated 
| summarize ClusterSnapshotCount = count() by Timestamp = bin(TimeGenerated, trendBinSize), ClusterId 
| join hint.strategy=broadcast ( KubeNodeInventory 
| where ClusterId =~ clusterId 
| summarize TotalCount = count(), ReadyCount = sumif(1, Status contains ('Ready')) by ClusterId, Timestamp = bin(TimeGenerated, trendBinSize) 
| extend NotReadyCount = TotalCount - ReadyCount ) on ClusterId, Timestamp 
| project ClusterId, Timestamp, TotalCount = todouble(TotalCount) / ClusterSnapshotCount, ReadyCount = todouble(ReadyCount) / ClusterSnapshotCount, NotReadyCount = todouble(NotReadyCount) / ClusterSnapshotCount;

 rawData 
| order by Timestamp asc 
| summarize makelist(Timestamp, maxListSize), makelist(TotalCount, maxListSize), makelist(ReadyCount, maxListSize), makelist(NotReadyCount, maxListSize) by ClusterId 
| join ( rawData 
| summarize Avg_TotalCount = avg(TotalCount), Avg_ReadyCount = avg(ReadyCount), Avg_NotReadyCount = avg(NotReadyCount) by ClusterId ) on ClusterId 
| project ClusterId, Avg_TotalCount, Avg_ReadyCount, Avg_NotReadyCount, list_Timestamp, list_TotalCount, list_ReadyCount, list_NotReadyCount

Contagem de pods por status

As tabelas necessárias para esse gráfico incluem KubePodInventory.

 let trendBinSize = 5m;
 let maxListSize = 1000;
 let clusterId = 'clusterResourceID'; //update with resource ID
 
 let rawData = KubePodInventory 
| where ClusterId =~ clusterId 
| distinct ClusterId, TimeGenerated 
| summarize ClusterSnapshotCount = count() by bin(TimeGenerated, trendBinSize), ClusterId 
| join hint.strategy=broadcast ( KubePodInventory 
| where ClusterId =~ clusterId 
| summarize PodStatus=any(PodStatus) by TimeGenerated, PodUid, ClusterId 
| summarize TotalCount = count(), PendingCount = sumif(1, PodStatus =~ 'Pending'), RunningCount = sumif(1, PodStatus =~ 'Running'), SucceededCount = sumif(1, PodStatus =~ 'Succeeded'), FailedCount = sumif(1, PodStatus =~ 'Failed'), TerminatingCount = sumif(1, PodStatus =~ 'Terminating') by ClusterId, bin(TimeGenerated, trendBinSize) ) on ClusterId, TimeGenerated 
| extend UnknownCount = TotalCount - PendingCount - RunningCount - SucceededCount - FailedCount - TerminatingCount 
| project ClusterId, Timestamp = TimeGenerated, TotalCount = todouble(TotalCount) / ClusterSnapshotCount, PendingCount = todouble(PendingCount) / ClusterSnapshotCount, RunningCount = todouble(RunningCount) / ClusterSnapshotCount, SucceededCount = todouble(SucceededCount) / ClusterSnapshotCount, FailedCount = todouble(FailedCount) / ClusterSnapshotCount, TerminatingCount = todouble(TerminatingCount) / ClusterSnapshotCount, UnknownCount = todouble(UnknownCount) / ClusterSnapshotCount;

 let rawDataCached = rawData;
 
 rawDataCached 
| order by Timestamp asc 
| summarize makelist(Timestamp, maxListSize), makelist(TotalCount, maxListSize), makelist(PendingCount, maxListSize), makelist(RunningCount, maxListSize), makelist(SucceededCount, maxListSize), makelist(FailedCount, maxListSize), makelist(TerminatingCount, maxListSize), makelist(UnknownCount, maxListSize) by ClusterId 
| join ( rawDataCached 
| summarize Avg_TotalCount = avg(TotalCount), Avg_PendingCount = avg(PendingCount), Avg_RunningCount = avg(RunningCount), Avg_SucceededCount = avg(SucceededCount), Avg_FailedCount = avg(FailedCount), Avg_TerminatingCount = avg(TerminatingCount), Avg_UnknownCount = avg(UnknownCount) by ClusterId ) on ClusterId 
| project ClusterId, Avg_TotalCount, Avg_PendingCount, Avg_RunningCount, Avg_SucceededCount, Avg_FailedCount, Avg_TerminatingCount, Avg_UnknownCount, list_Timestamp, list_TotalCount, list_PendingCount, list_RunningCount, list_SucceededCount, list_FailedCount, list_TerminatingCount, list_UnknownCount

Lista de contêineres por status

As tabelas necessárias para esse gráfico incluem KubePodInventory e Perf.

 let startDateTime = datetime('start time');
 let endDateTime = datetime('end time');
 let trendBinSize = 15m;
 let maxResultCount = 10000;
 let metricUsageCounterName = 'cpuUsageNanoCores';
 let metricLimitCounterName = 'cpuLimitNanoCores';
 
 let KubePodInventoryTable = KubePodInventory 
| where TimeGenerated >= startDateTime 
| where TimeGenerated < endDateTime 
| where isnotempty(ClusterName) 
| where isnotempty(Namespace) 
| where isnotempty(Computer) 
| project TimeGenerated, ClusterId, ClusterName, Namespace, ServiceName, ControllerName, Node = Computer, Pod = Name, ContainerInstance = ContainerName, ContainerID, ReadySinceNow = format_timespan(endDateTime - ContainerCreationTimeStamp , 'ddd.hh:mm:ss.fff'), Restarts = ContainerRestartCount, Status = ContainerStatus, ContainerStatusReason = columnifexists('ContainerStatusReason', ''), ControllerKind = ControllerKind, PodStatus;

 let startRestart = KubePodInventoryTable 
| summarize arg_min(TimeGenerated, *) by Node, ContainerInstance 
| where ClusterId =~ 'clusterResourceID' //update with resource ID
| project Node, ContainerInstance, InstanceName = strcat(ClusterId, '/', ContainerInstance), StartRestart = Restarts;

 let IdentityTable = KubePodInventoryTable 
| summarize arg_max(TimeGenerated, *) by Node, ContainerInstance 
| where ClusterId =~ 'clusterResourceID' //update with resource ID
| project ClusterName, Namespace, ServiceName, ControllerName, Node, Pod, ContainerInstance, InstanceName = strcat(ClusterId, '/', ContainerInstance), ContainerID, ReadySinceNow, Restarts, Status = iff(Status =~ 'running', 0, iff(Status=~'waiting', 1, iff(Status =~'terminated', 2, 3))), ContainerStatusReason, ControllerKind, Containers = 1, ContainerName = tostring(split(ContainerInstance, '/')[1]), PodStatus, LastPodInventoryTimeGenerated = TimeGenerated, ClusterId;

 let CachedIdentityTable = IdentityTable;
 
 let FilteredPerfTable = Perf 
| where TimeGenerated >= startDateTime 
| where TimeGenerated < endDateTime 
| where ObjectName == 'K8SContainer' 
| where InstanceName startswith 'clusterResourceID' 
| project Node = Computer, TimeGenerated, CounterName, CounterValue, InstanceName ;

 let CachedFilteredPerfTable = FilteredPerfTable;
 
 let LimitsTable = CachedFilteredPerfTable 
| where CounterName =~ metricLimitCounterName 
| summarize arg_max(TimeGenerated, *) by Node, InstanceName 
| project Node, InstanceName, LimitsValue = iff(CounterName =~ 'cpuLimitNanoCores', CounterValue/1000000, CounterValue), TimeGenerated;
 let MetaDataTable = CachedIdentityTable 
| join kind=leftouter ( LimitsTable ) on Node, InstanceName 
| join kind= leftouter ( startRestart ) on Node, InstanceName 
| project ClusterName, Namespace, ServiceName, ControllerName, Node, Pod, InstanceName, ContainerID, ReadySinceNow, Restarts, LimitsValue, Status, ContainerStatusReason = columnifexists('ContainerStatusReason', ''), ControllerKind, Containers, ContainerName, ContainerInstance, StartRestart, PodStatus, LastPodInventoryTimeGenerated, ClusterId;

 let UsagePerfTable = CachedFilteredPerfTable 
| where CounterName =~ metricUsageCounterName 
| project TimeGenerated, Node, InstanceName, CounterValue = iff(CounterName =~ 'cpuUsageNanoCores', CounterValue/1000000, CounterValue);

 let LastRestartPerfTable = CachedFilteredPerfTable 
| where CounterName =~ 'restartTimeEpoch' 
| summarize arg_max(TimeGenerated, *) by Node, InstanceName 
| project Node, InstanceName, UpTime = CounterValue, LastReported = TimeGenerated;

 let AggregationTable = UsagePerfTable 
| summarize Aggregation = max(CounterValue) by Node, InstanceName 
| project Node, InstanceName, Aggregation;

 let TrendTable = UsagePerfTable 
| summarize TrendAggregation = max(CounterValue) by bin(TimeGenerated, trendBinSize), Node, InstanceName 
| project TrendTimeGenerated = TimeGenerated, Node, InstanceName , TrendAggregation 
| summarize TrendList = makelist(pack("timestamp", TrendTimeGenerated, "value", TrendAggregation)) by Node, InstanceName;

 let containerFinalTable = MetaDataTable 
| join kind= leftouter( AggregationTable ) on Node, InstanceName 
| join kind = leftouter (LastRestartPerfTable) on Node, InstanceName 
| order by Aggregation desc, ContainerName 
| join kind = leftouter ( TrendTable) on Node, InstanceName 
| extend ContainerIdentity = strcat(ContainerName, ' ', Pod) 
| project ContainerIdentity, Status, ContainerStatusReason = columnifexists('ContainerStatusReason', ''), Aggregation, Node, Restarts, ReadySinceNow, TrendList = iif(isempty(TrendList), parse_json('[]'), TrendList), LimitsValue, ControllerName, ControllerKind, ContainerID, Containers, UpTimeNow = datetime_diff('Millisecond', endDateTime, datetime_add('second', toint(UpTime), make_datetime(1970,1,1))), ContainerInstance, StartRestart, LastReportedDelta = datetime_diff('Millisecond', endDateTime, LastReported), PodStatus, InstanceName, Namespace, LastPodInventoryTimeGenerated, ClusterId;
containerFinalTable 
| limit 200

Lista de controladores por status

As tabelas necessárias para esse gráfico incluem KubePodInventory e Perf.

 let endDateTime = datetime('start time');
 let startDateTime = datetime('end time');
 let trendBinSize = 15m;
 let metricLimitCounterName = 'cpuLimitNanoCores';
 let metricUsageCounterName = 'cpuUsageNanoCores';
 
 let primaryInventory = KubePodInventory 
| where TimeGenerated >= startDateTime 
| where TimeGenerated < endDateTime 
| where isnotempty(ClusterName) 
| where isnotempty(Namespace) 
| extend Node = Computer 
| where ClusterId =~ 'clusterResourceID' //update with resource ID
| project TimeGenerated, ClusterId, ClusterName, Namespace, ServiceName, Node = Computer, ControllerName, Pod = Name, ContainerInstance = ContainerName, ContainerID, InstanceName, PerfJoinKey = strcat(ClusterId, '/', ContainerName), ReadySinceNow = format_timespan(endDateTime - ContainerCreationTimeStamp, 'ddd.hh:mm:ss.fff'), Restarts = ContainerRestartCount, Status = ContainerStatus, ContainerStatusReason = columnifexists('ContainerStatusReason', ''), ControllerKind = ControllerKind, PodStatus, ControllerId = strcat(ClusterId, '/', Namespace, '/', ControllerName);

let podStatusRollup = primaryInventory 
| summarize arg_max(TimeGenerated, *) by Pod 
| project ControllerId, PodStatus, TimeGenerated 
| summarize count() by ControllerId, PodStatus = iif(TimeGenerated < ago(30m), 'Unknown', PodStatus) 
| summarize PodStatusList = makelist(pack('Status', PodStatus, 'Count', count_)) by ControllerId;

let latestContainersByController = primaryInventory 
| where isnotempty(Node) 
| summarize arg_max(TimeGenerated, *) by PerfJoinKey 
| project ControllerId, PerfJoinKey;

let filteredPerformance = Perf 
| where TimeGenerated >= startDateTime 
| where TimeGenerated < endDateTime 
| where ObjectName == 'K8SContainer' 
| where InstanceName startswith 'clusterResourceID' //update with resource ID
| project TimeGenerated, CounterName, CounterValue, InstanceName, Node = Computer ;

let metricByController = filteredPerformance 
| where CounterName =~ metricUsageCounterName 
| extend PerfJoinKey = InstanceName 
| summarize Value = percentile(CounterValue, 95) by PerfJoinKey, CounterName 
| join (latestContainersByController) on PerfJoinKey 
| summarize Value = sum(Value) by ControllerId, CounterName 
| project ControllerId, CounterName, AggregationValue = iff(CounterName =~ 'cpuUsageNanoCores', Value/1000000, Value);

let containerCountByController = latestContainersByController 
| summarize ContainerCount = count() by ControllerId;

let restartCountsByController = primaryInventory 
| summarize Restarts = max(Restarts) by ControllerId;

let oldestRestart = primaryInventory 
| summarize ReadySinceNow = min(ReadySinceNow) by ControllerId;

let trendLineByController = filteredPerformance 
| where CounterName =~ metricUsageCounterName 
| extend PerfJoinKey = InstanceName 
| summarize Value = percentile(CounterValue, 95) by bin(TimeGenerated, trendBinSize), PerfJoinKey, CounterName 
| order by TimeGenerated asc 
| join kind=leftouter (latestContainersByController) on PerfJoinKey 
| summarize Value=sum(Value) by ControllerId, TimeGenerated, CounterName 
| project TimeGenerated, Value = iff(CounterName =~ 'cpuUsageNanoCores', Value/1000000, Value), ControllerId 
| summarize TrendList = makelist(pack("timestamp", TimeGenerated, "value", Value)) by ControllerId;

let latestLimit = filteredPerformance 
| where CounterName =~ metricLimitCounterName 
| extend PerfJoinKey = InstanceName 
| summarize arg_max(TimeGenerated, *) by PerfJoinKey 
| join kind=leftouter (latestContainersByController) on PerfJoinKey 
| summarize Value = sum(CounterValue) by ControllerId, CounterName 
| project ControllerId, LimitValue = iff(CounterName =~ 'cpuLimitNanoCores', Value/1000000, Value);

let latestTimeGeneratedByController = primaryInventory 
| summarize arg_max(TimeGenerated, *) by ControllerId 
| project ControllerId, LastTimeGenerated = TimeGenerated;

primaryInventory 
| distinct ControllerId, ControllerName, ControllerKind, Namespace 
| join kind=leftouter (podStatusRollup) on ControllerId 
| join kind=leftouter (metricByController) on ControllerId 
| join kind=leftouter (containerCountByController) on ControllerId 
| join kind=leftouter (restartCountsByController) on ControllerId 
| join kind=leftouter (oldestRestart) on ControllerId 
| join kind=leftouter (trendLineByController) on ControllerId 
| join kind=leftouter (latestLimit) on ControllerId 
| join kind=leftouter (latestTimeGeneratedByController) on ControllerId 
| project ControllerId, ControllerName, ControllerKind, PodStatusList, AggregationValue, ContainerCount = iif(isempty(ContainerCount), 0, ContainerCount), Restarts, ReadySinceNow, Node = '-', TrendList, LimitValue, LastTimeGenerated, Namespace 
| limit 250;

Lista de nós por status

As tabelas necessárias para esse gráfico incluem KubeNodeInventory, KubePodInventory e Perf.

 let endDateTime = datetime('start time');
 let startDateTime = datetime('end time');
 let binSize = 15m;
 let limitMetricName = 'cpuCapacityNanoCores';
 let usedMetricName = 'cpuUsageNanoCores'; 
 
 let materializedNodeInventory = KubeNodeInventory 
| where TimeGenerated < endDateTime 
| where TimeGenerated >= startDateTime 
| project ClusterName, ClusterId, Node = Computer, TimeGenerated, Status, NodeName = Computer, NodeId = strcat(ClusterId, '/', Computer), Labels 
| where ClusterId =~ 'clusterResourceID'; //update with resource ID

 let materializedPerf = Perf 
| where TimeGenerated < endDateTime 
| where TimeGenerated >= startDateTime 
| where ObjectName == 'K8SNode' 
| extend NodeId = InstanceName;

 let materializedPodInventory = KubePodInventory 
| where TimeGenerated < endDateTime 
| where TimeGenerated >= startDateTime 
| where isnotempty(ClusterName) 
| where isnotempty(Namespace) 
| where ClusterId =~ 'clusterResourceID'; //update with resource ID

 let inventoryOfCluster = materializedNodeInventory 
| summarize arg_max(TimeGenerated, Status) by ClusterName, ClusterId, NodeName, NodeId;

 let labelsByNode = materializedNodeInventory 
| summarize arg_max(TimeGenerated, Labels) by ClusterName, ClusterId, NodeName, NodeId;

 let countainerCountByNode = materializedPodInventory 
| project ContainerName, NodeId = strcat(ClusterId, '/', Computer) 
| distinct NodeId, ContainerName 
| summarize ContainerCount = count() by NodeId;

 let latestUptime = materializedPerf 
| where CounterName == 'restartTimeEpoch' 
| summarize arg_max(TimeGenerated, CounterValue) by NodeId 
| extend UpTimeMs = datetime_diff('Millisecond', endDateTime, datetime_add('second', toint(CounterValue), make_datetime(1970,1,1))) 
| project NodeId, UpTimeMs;

 let latestLimitOfNodes = materializedPerf 
| where CounterName == limitMetricName 
| summarize CounterValue = max(CounterValue) by NodeId 
| project NodeId, LimitValue = CounterValue;

 let actualUsageAggregated = materializedPerf 
| where CounterName == usedMetricName 
| summarize Aggregation = percentile(CounterValue, 95) by NodeId //This line updates to the desired aggregation
| project NodeId, Aggregation;

 let aggregateTrendsOverTime = materializedPerf 
| where CounterName == usedMetricName 
| summarize TrendAggregation = percentile(CounterValue, 95) by NodeId, bin(TimeGenerated, binSize) //This line updates to the desired aggregation
| project NodeId, TrendAggregation, TrendDateTime = TimeGenerated;

 let unscheduledPods = materializedPodInventory 
| where isempty(Computer) 
| extend Node = Computer 
| where isempty(ContainerStatus) 
| where PodStatus == 'Pending' 
| order by TimeGenerated desc 
| take 1 
| project ClusterName, NodeName = 'unscheduled', LastReceivedDateTime = TimeGenerated, Status = 'unscheduled', ContainerCount = 0, UpTimeMs = '0', Aggregation = '0', LimitValue = '0', ClusterId;

 let scheduledPods = inventoryOfCluster 
| join kind=leftouter (aggregateTrendsOverTime) on NodeId 
| extend TrendPoint = pack("TrendTime", TrendDateTime, "TrendAggregation", TrendAggregation) 
| summarize make_list(TrendPoint) by NodeId, NodeName, Status 
| join kind=leftouter (labelsByNode) on NodeId 
| join kind=leftouter (countainerCountByNode) on NodeId 
| join kind=leftouter (latestUptime) on NodeId 
| join kind=leftouter (latestLimitOfNodes) on NodeId 
| join kind=leftouter (actualUsageAggregated) on NodeId 
| project ClusterName, NodeName, ClusterId, list_TrendPoint, LastReceivedDateTime = TimeGenerated, Status, ContainerCount, UpTimeMs, Aggregation, LimitValue, Labels 
| limit 250;

 union (scheduledPods), (unscheduledPods) 
| project ClusterName, NodeName, LastReceivedDateTime, Status, ContainerCount, UpTimeMs = UpTimeMs_long, Aggregation = Aggregation_real, LimitValue = LimitValue_real, list_TrendPoint, Labels, ClusterId

Métricas do Prometheus

Os exemplos a seguir exigem a configuração descrita em Enviar métricas do Prometheus para o workspace do Log Analytics com Insights de contêiner.

Para exibir as métricas do Prometheus extraídas pelo Azure Monitor e filtradas por namespace, especifique "prometheus". Aqui está um exemplo de consulta para exibir as métricas do Prometheus a partir do namespace do Kubernetes default.

InsightsMetrics 
| where Namespace contains "prometheus"
| extend tags=parse_json(Tags)
| summarize count() by Name

Os dados do Prometheus também podem ser consultados diretamente por nome.

InsightsMetrics 
| where Namespace contains "prometheus"
| where Name contains "some_prometheus_metric"

Para identificar o volume de ingestão de cada tamanho de métrica em GB por dia a fim de entender se ele é alto, a consulta a seguir é fornecida.

InsightsMetrics
| where Namespace contains "prometheus"
| where TimeGenerated > ago(24h)
| summarize VolumeInGB = (sum(_BilledSize) / (1024 * 1024 * 1024)) by Name
| order by VolumeInGB desc
| render barchart

A saída mostrará resultados semelhantes ao exemplo a seguir.

Captura de tela que mostra os resultados da consulta de log do volume de ingestão de dados.

Para estimar qual é o tamanho de cada métrica em GB por um mês a fim de entender se o volume de dados ingeridos recebidos no workspace é alto, a consulta a seguir é fornecida.

InsightsMetrics
| where Namespace contains "prometheus"
| where TimeGenerated > ago(24h)
| summarize EstimatedGBPer30dayMonth = (sum(_BilledSize) / (1024 * 1024 * 1024)) * 30 by Name
| order by EstimatedGBPer30dayMonth desc
| render barchart

A saída mostrará resultados semelhantes ao exemplo a seguir.

Captura de tela que mostra os resultados da consulta de log do volume de ingestão de dados.

Erros de extração ou configuração

Para investigar qualquer erro de configuração ou de extração, a consulta de exemplo a seguir retorna eventos informacionais da tabela KubeMonAgentEvents.

KubeMonAgentEvents | where Level != "Info"

A saída mostra resultados semelhantes ao exemplo a seguir:

Captura de tela que mostra resultados de consulta de log de eventos informativos de um agente.

Perguntas frequentes

Esta seção fornece respostas para perguntas comuns.

Posso exibir as métricas coletadas no Grafana?

Os insights do contêiner dão suporte à exibição de métricas armazenadas no seu workspace do Log Analytics nos painéis do Grafana. Fornecemos um modelo que você pode fazer download do repositório do painel do Grafana. Use-o para começar e faça referência a ele para ajudá-lo a aprender como consultar dados de seus clusters monitorados para visualizar em painéis personalizados do Grafana.

Por que as linhas de log maiores que 16 KB são divididas em vários registros no Log Analytics?

O agente usa o driver de log de arquivo JSON do Docker para capturar o StdOut e o stderr de contêineres. Esse driver de registro em log divide as linhas de log maiores que 16 KB em várias linhas quando são copiadas de stdout ou stderr para um arquivo. Use o registro em log de várias linhas para obter o tamanho do registro de log de até 64 KB.

Próximas etapas

As informações de contêiner não incluem um conjunto predefinido de alertas. Para saber como criar alertas recomendados para alta utilização de CPU e memória, para dar suporte aos seus processos e procedimentos operacionais ou do DevOps, consulte Criar alertas de desempenho com insights de Contêiner.