Grafana 대시보드

지원 버전: Grafana 11.x 마지막 업데이트: 2026년 2월 20일

소개

Grafana는 메트릭, 로그, 추적 데이터를 시각화하고 분석하기 위한 오픈소스 플랫폼입니다. 다양한 데이터 소스를 통합하여 단일 대시보드에서 전체 시스템의 상태를 모니터링할 수 있습니다.

주요 특징

특징	설명
다양한 데이터 소스	Prometheus, Loki, Tempo, CloudWatch 등 지원
풍부한 시각화	그래프, 히트맵, 테이블, Stat 패널 등
알림 기능	조건 기반 알림 및 다양한 알림 채널
대시보드 템플릿	재사용 가능한 대시보드와 패널
플러그인 생태계	확장 가능한 플러그인 아키텍처
팀 협업	폴더, 권한, 팀 기능

아키텍처

Helm 배포

기본 설치

bash

# Helm 저장소 추가
helm repo add grafana https://grafana.github.io/helm-charts
helm repo update

# 네임스페이스 생성
kubectl create namespace monitoring

values.yaml 구성

yaml

# grafana-values.yaml
replicas: 2

image:
  repository: grafana/grafana
  tag: 11.0.0

# 관리자 자격 증명
adminUser: admin
adminPassword: ""  # 자동 생성, Secret에서 관리

# 기존 Secret 사용
admin:
  existingSecret: grafana-admin-credentials
  userKey: admin-user
  passwordKey: admin-password

# 서비스 설정
service:
  type: ClusterIP
  port: 80

# Ingress 설정
ingress:
  enabled: true
  ingressClassName: alb
  annotations:
    alb.ingress.kubernetes.io/scheme: internet-facing
    alb.ingress.kubernetes.io/target-type: ip
    alb.ingress.kubernetes.io/certificate-arn: arn:aws:acm:ap-northeast-2:123456789012:certificate/xxx
    alb.ingress.kubernetes.io/listen-ports: '[{"HTTPS":443}]'
    alb.ingress.kubernetes.io/ssl-redirect: '443'
  hosts:
    - grafana.example.com
  tls:
    - hosts:
        - grafana.example.com

# 영구 스토리지
persistence:
  enabled: true
  type: pvc
  storageClassName: gp3
  size: 10Gi
  accessModes:
    - ReadWriteOnce

# 리소스 설정
resources:
  requests:
    cpu: 200m
    memory: 256Mi
  limits:
    cpu: 1000m
    memory: 1Gi

# 환경 변수
envFromSecrets:
  - name: grafana-env-secrets
    optional: true

# Grafana 설정
grafana.ini:
  server:
    domain: grafana.example.com
    root_url: https://grafana.example.com

  database:
    type: postgres
    host: postgres.monitoring.svc.cluster.local:5432
    name: grafana
    user: grafana
    ssl_mode: require

  session:
    provider: redis
    provider_config: addr=redis.monitoring.svc.cluster.local:6379,pool_size=100,db=0

  security:
    admin_user: admin
    secret_key: $__env{GF_SECURITY_SECRET_KEY}
    cookie_secure: true
    strict_transport_security: true

  users:
    allow_sign_up: false
    auto_assign_org: true
    auto_assign_org_role: Viewer

  auth:
    disable_login_form: false

  auth.generic_oauth:
    enabled: true
    name: SSO
    allow_sign_up: true
    client_id: $__env{OAUTH_CLIENT_ID}
    client_secret: $__env{OAUTH_CLIENT_SECRET}
    scopes: openid profile email
    auth_url: https://sso.example.com/oauth/authorize
    token_url: https://sso.example.com/oauth/token
    api_url: https://sso.example.com/oauth/userinfo
    role_attribute_path: contains(groups[*], 'admin') && 'Admin' || contains(groups[*], 'editor') && 'Editor' || 'Viewer'

  alerting:
    enabled: true
    execute_alerts: true
    evaluation_timeout: 60s
    notification_timeout: 30s
    max_attempts: 3

  unified_alerting:
    enabled: true
    min_interval: 10s

  analytics:
    reporting_enabled: false
    check_for_updates: false

  log:
    mode: console
    level: info

  metrics:
    enabled: true
    basic_auth_username: metrics
    basic_auth_password: $__env{GF_METRICS_PASSWORD}

# 사이드카 설정 (대시보드/데이터소스 프로비저닝)
sidecar:
  dashboards:
    enabled: true
    label: grafana_dashboard
    labelValue: "true"
    searchNamespace: ALL
    folderAnnotation: grafana_folder
    provider:
      foldersFromFilesStructure: true
  datasources:
    enabled: true
    label: grafana_datasource
    labelValue: "true"
    searchNamespace: ALL
  alerts:
    enabled: true
    label: grafana_alert
    searchNamespace: ALL

# 플러그인 설치
plugins:
  - grafana-piechart-panel
  - grafana-worldmap-panel
  - grafana-clock-panel
  - grafana-polystat-panel
  - yesoreyeram-infinity-datasource

# ServiceMonitor (Prometheus Operator)
serviceMonitor:
  enabled: true
  interval: 30s
  labels:
    release: prometheus

# PodDisruptionBudget
podDisruptionBudget:
  minAvailable: 1

# 안티-어피니티
affinity:
  podAntiAffinity:
    preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 100
        podAffinityTerm:
          labelSelector:
            matchLabels:
              app.kubernetes.io/name: grafana
          topologyKey: kubernetes.io/hostname

# 토폴로지 분산
topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: topology.kubernetes.io/zone
    whenUnsatisfiable: ScheduleAnyway
    labelSelector:
      matchLabels:
        app.kubernetes.io/name: grafana

설치 실행

bash

# 설치
helm upgrade --install grafana grafana/grafana \
  --namespace monitoring \
  --values grafana-values.yaml \
  --wait

# 확인
kubectl get pods -n monitoring -l app.kubernetes.io/name=grafana
kubectl get svc -n monitoring -l app.kubernetes.io/name=grafana

데이터 소스 연동

ConfigMap을 통한 데이터 소스 프로비저닝

yaml

# grafana-datasources.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: grafana-datasources
  namespace: monitoring
  labels:
    grafana_datasource: "true"
data:
  datasources.yaml: |-
    apiVersion: 1
    deleteDatasources:
      - name: Old-Prometheus
        orgId: 1

    datasources:
      # Prometheus
      - name: Prometheus
        type: prometheus
        uid: prometheus
        url: http://prometheus-operated.monitoring.svc.cluster.local:9090
        access: proxy
        isDefault: true
        jsonData:
          httpMethod: POST
          manageAlerts: true
          prometheusType: Prometheus
          prometheusVersion: 2.47.0
          exemplarTraceIdDestinations:
            - name: traceID
              datasourceUid: tempo
              urlDisplayLabel: "View Trace"
        editable: false

      # VictoriaMetrics
      - name: VictoriaMetrics
        type: prometheus
        uid: victoriametrics
        url: http://vmsingle.monitoring.svc.cluster.local:8428
        access: proxy
        jsonData:
          httpMethod: POST

      # Loki
      - name: Loki
        type: loki
        uid: loki
        url: http://loki-gateway.loki.svc.cluster.local
        access: proxy
        jsonData:
          maxLines: 1000
          derivedFields:
            - name: TraceID
              matcherRegex: '"traceId":"([a-f0-9]+)"'
              url: '$${__value.raw}'
              datasourceUid: tempo
              urlDisplayLabel: "View Trace"
            - name: trace_id
              matcherRegex: 'trace_id=([a-f0-9]+)'
              url: '$${__value.raw}'
              datasourceUid: tempo
              urlDisplayLabel: "View Trace"

      # Tempo
      - name: Tempo
        type: tempo
        uid: tempo
        url: http://tempo-query-frontend.tempo.svc.cluster.local:3100
        access: proxy
        jsonData:
          httpMethod: GET
          tracesToLogs:
            datasourceUid: loki
            tags: ['job', 'namespace', 'pod']
            mappedTags:
              - key: service.name
                value: app
            mapTagNamesEnabled: true
            spanStartTimeShift: '-1h'
            spanEndTimeShift: '1h'
            filterByTraceID: true
            filterBySpanID: true
          tracesToMetrics:
            datasourceUid: prometheus
            tags:
              - key: service.name
                value: service
            queries:
              - name: 'Request Rate'
                query: 'sum(rate(http_requests_total{service="$${__tags}"}[5m]))'
              - name: 'Error Rate'
                query: 'sum(rate(http_requests_total{service="$${__tags}",status=~"5.."}[5m]))'
          serviceMap:
            datasourceUid: prometheus
          nodeGraph:
            enabled: true
          search:
            hide: false
          lokiSearch:
            datasourceUid: loki

      # CloudWatch
      - name: CloudWatch
        type: cloudwatch
        uid: cloudwatch
        jsonData:
          authType: default
          defaultRegion: ap-northeast-2
          assumeRoleArn: arn:aws:iam::123456789012:role/grafana-cloudwatch-role

CloudWatch IRSA 설정

yaml

# grafana-serviceaccount.yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: grafana
  namespace: monitoring
  annotations:
    eks.amazonaws.com/role-arn: arn:aws:iam::123456789012:role/grafana-cloudwatch-role
---
# IAM Policy
# {
#   "Version": "2012-10-17",
#   "Statement": [
#     {
#       "Effect": "Allow",
#       "Action": [
#         "cloudwatch:DescribeAlarmsForMetric",
#         "cloudwatch:DescribeAlarmHistory",
#         "cloudwatch:DescribeAlarms",
#         "cloudwatch:ListMetrics",
#         "cloudwatch:GetMetricData",
#         "cloudwatch:GetInsightRuleReport",
#         "logs:DescribeLogGroups",
#         "logs:GetLogGroupFields",
#         "logs:StartQuery",
#         "logs:StopQuery",
#         "logs:GetQueryResults",
#         "logs:GetLogEvents",
#         "ec2:DescribeTags",
#         "ec2:DescribeInstances",
#         "ec2:DescribeRegions",
#         "tag:GetResources",
#         "xray:GetTraceSummaries",
#         "xray:BatchGetTraces",
#         "xray:GetServiceGraph",
#         "xray:GetTimeSeriesServiceStatistics"
#       ],
#       "Resource": "*"
#     }
#   ]
# }

대시보드 설계 패턴

USE Method (Utilization, Saturation, Errors)

시스템 리소스 분석을 위한 방법론:

yaml

# use-method-dashboard.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: use-method-dashboard
  namespace: monitoring
  labels:
    grafana_dashboard: "true"
  annotations:
    grafana_folder: "System"
data:
  use-method.json: |-
    {
      "title": "USE Method - System Resources",
      "uid": "use-method",
      "panels": [
        {
          "title": "CPU Utilization",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 8, "x": 0, "y": 0},
          "targets": [
            {
              "expr": "100 - (avg by(instance) (rate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)",
              "legendFormat": "{{instance}}"
            }
          ]
        },
        {
          "title": "CPU Saturation (Load Average)",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 8, "x": 8, "y": 0},
          "targets": [
            {
              "expr": "node_load1 / count without(cpu, mode) (node_cpu_seconds_total{mode=\"idle\"})",
              "legendFormat": "{{instance}}"
            }
          ]
        },
        {
          "title": "Memory Utilization",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 8, "x": 0, "y": 8},
          "targets": [
            {
              "expr": "(1 - (node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)) * 100",
              "legendFormat": "{{instance}}"
            }
          ]
        },
        {
          "title": "Memory Saturation (Swap)",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 8, "x": 8, "y": 8},
          "targets": [
            {
              "expr": "rate(node_vmstat_pswpin[5m]) + rate(node_vmstat_pswpout[5m])",
              "legendFormat": "{{instance}}"
            }
          ]
        },
        {
          "title": "Disk Utilization",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 8, "x": 0, "y": 16},
          "targets": [
            {
              "expr": "rate(node_disk_io_time_seconds_total[5m]) * 100",
              "legendFormat": "{{instance}} - {{device}}"
            }
          ]
        },
        {
          "title": "Disk Errors",
          "type": "stat",
          "gridPos": {"h": 8, "w": 8, "x": 16, "y": 0},
          "targets": [
            {
              "expr": "increase(node_disk_io_time_weighted_seconds_total[1h])",
              "legendFormat": "{{instance}}"
            }
          ]
        }
      ]
    }

RED Method (Rate, Errors, Duration)

서비스 요청 분석을 위한 방법론:

yaml

# red-method-dashboard.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: red-method-dashboard
  namespace: monitoring
  labels:
    grafana_dashboard: "true"
  annotations:
    grafana_folder: "Services"
data:
  red-method.json: |-
    {
      "title": "RED Method - Service Metrics",
      "uid": "red-method",
      "templating": {
        "list": [
          {
            "name": "service",
            "type": "query",
            "query": "label_values(http_requests_total, service)",
            "refresh": 2
          }
        ]
      },
      "panels": [
        {
          "title": "Request Rate",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 8, "x": 0, "y": 0},
          "targets": [
            {
              "expr": "sum(rate(http_requests_total{service=\"$service\"}[5m])) by (method, path)",
              "legendFormat": "{{method}} {{path}}"
            }
          ]
        },
        {
          "title": "Error Rate",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 8, "x": 8, "y": 0},
          "targets": [
            {
              "expr": "sum(rate(http_requests_total{service=\"$service\", status=~\"5..\"}[5m])) / sum(rate(http_requests_total{service=\"$service\"}[5m])) * 100",
              "legendFormat": "Error %"
            }
          ],
          "fieldConfig": {
            "defaults": {
              "unit": "percent",
              "thresholds": {
                "mode": "absolute",
                "steps": [
                  {"color": "green", "value": null},
                  {"color": "yellow", "value": 1},
                  {"color": "red", "value": 5}
                ]
              }
            }
          }
        },
        {
          "title": "Request Duration (p50, p90, p99)",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 8, "x": 16, "y": 0},
          "targets": [
            {
              "expr": "histogram_quantile(0.50, sum(rate(http_request_duration_seconds_bucket{service=\"$service\"}[5m])) by (le))",
              "legendFormat": "p50"
            },
            {
              "expr": "histogram_quantile(0.90, sum(rate(http_request_duration_seconds_bucket{service=\"$service\"}[5m])) by (le))",
              "legendFormat": "p90"
            },
            {
              "expr": "histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket{service=\"$service\"}[5m])) by (le))",
              "legendFormat": "p99"
            }
          ],
          "fieldConfig": {
            "defaults": {
              "unit": "s"
            }
          }
        }
      ]
    }

4 Golden Signals

Google SRE 핸드북의 핵심 메트릭:

yaml

# golden-signals-dashboard.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: golden-signals-dashboard
  namespace: monitoring
  labels:
    grafana_dashboard: "true"
  annotations:
    grafana_folder: "SRE"
data:
  golden-signals.json: |-
    {
      "title": "4 Golden Signals",
      "uid": "golden-signals",
      "panels": [
        {
          "title": "1. Latency - Request Duration",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 12, "x": 0, "y": 0},
          "targets": [
            {
              "expr": "histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))",
              "legendFormat": "{{service}} p99"
            }
          ],
          "fieldConfig": {
            "defaults": {"unit": "s"}
          }
        },
        {
          "title": "2. Traffic - Request Rate",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 12, "x": 12, "y": 0},
          "targets": [
            {
              "expr": "sum(rate(http_requests_total[5m])) by (service)",
              "legendFormat": "{{service}}"
            }
          ],
          "fieldConfig": {
            "defaults": {"unit": "reqps"}
          }
        },
        {
          "title": "3. Errors - Error Rate",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 12, "x": 0, "y": 8},
          "targets": [
            {
              "expr": "sum(rate(http_requests_total{status=~\"5..\"}[5m])) by (service) / sum(rate(http_requests_total[5m])) by (service) * 100",
              "legendFormat": "{{service}}"
            }
          ],
          "fieldConfig": {
            "defaults": {
              "unit": "percent",
              "thresholds": {
                "steps": [
                  {"color": "green", "value": null},
                  {"color": "yellow", "value": 0.1},
                  {"color": "red", "value": 1}
                ]
              }
            }
          }
        },
        {
          "title": "4. Saturation - Resource Usage",
          "type": "timeseries",
          "gridPos": {"h": 8, "w": 12, "x": 12, "y": 8},
          "targets": [
            {
              "expr": "sum(container_memory_working_set_bytes{container!=\"\"}) by (pod) / sum(kube_pod_container_resource_limits{resource=\"memory\"}) by (pod) * 100",
              "legendFormat": "{{pod}} Memory"
            },
            {
              "expr": "sum(rate(container_cpu_usage_seconds_total{container!=\"\"}[5m])) by (pod) / sum(kube_pod_container_resource_limits{resource=\"cpu\"}) by (pod) * 100",
              "legendFormat": "{{pod}} CPU"
            }
          ],
          "fieldConfig": {
            "defaults": {"unit": "percent"}
          }
        }
      ]
    }

대시보드 프로비저닝

Grafana Operator 사용

yaml

# grafana-operator-dashboard.yaml
apiVersion: grafana.integreatly.org/v1beta1
kind: GrafanaDashboard
metadata:
  name: kubernetes-cluster
  namespace: monitoring
spec:
  instanceSelector:
    matchLabels:
      dashboards: "grafana"
  folder: "Kubernetes"
  json: |
    {
      "title": "Kubernetes Cluster Overview",
      "uid": "k8s-cluster",
      "panels": [...]
    }
---
apiVersion: grafana.integreatly.org/v1beta1
kind: GrafanaDatasource
metadata:
  name: prometheus
  namespace: monitoring
spec:
  instanceSelector:
    matchLabels:
      dashboards: "grafana"
  datasource:
    name: Prometheus
    type: prometheus
    url: http://prometheus:9090
    access: proxy
    isDefault: true

알림 규칙 (Grafana Alerting)

알림 규칙 구성

yaml

# grafana-alerts.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: grafana-alerts
  namespace: monitoring
  labels:
    grafana_alert: "true"
data:
  alerts.yaml: |-
    apiVersion: 1
    groups:
      - orgId: 1
        name: kubernetes-alerts
        folder: Alerts
        interval: 1m
        rules:
          - uid: high-cpu-usage
            title: High CPU Usage
            condition: C
            data:
              - refId: A
                relativeTimeRange:
                  from: 600
                  to: 0
                datasourceUid: prometheus
                model:
                  expr: |
                    100 - (avg by(instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)
                  instant: false
                  range: true
              - refId: B
                datasourceUid: __expr__
                model:
                  conditions:
                    - evaluator:
                        params: [80]
                        type: gt
                      operator:
                        type: and
                      query:
                        params: [A]
                      reducer:
                        type: avg
                  refId: B
                  type: classic_conditions
              - refId: C
                datasourceUid: __expr__
                model:
                  expression: B
                  type: threshold
            noDataState: NoData
            execErrState: Error
            for: 5m
            annotations:
              summary: "High CPU usage detected on {{ $labels.instance }}"
              description: "CPU usage is above 80% for more than 5 minutes."
            labels:
              severity: warning

          - uid: pod-crash-looping
            title: Pod CrashLooping
            condition: C
            data:
              - refId: A
                datasourceUid: prometheus
                model:
                  expr: |
                    increase(kube_pod_container_status_restarts_total[1h]) > 5
              - refId: C
                datasourceUid: __expr__
                model:
                  expression: A
                  type: threshold
            for: 0s
            annotations:
              summary: "Pod {{ $labels.pod }} is crash looping"
            labels:
              severity: critical

알림 연락처 구성

yaml

# grafana-contact-points.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: grafana-contact-points
  namespace: monitoring
  labels:
    grafana_alert: "true"
data:
  contact-points.yaml: |-
    apiVersion: 1
    contactPoints:
      - orgId: 1
        name: slack-alerts
        receivers:
          - uid: slack-receiver
            type: slack
            settings:
              url: ${SLACK_WEBHOOK_URL}
              recipient: "#alerts"
              username: Grafana
              icon_emoji: ":grafana:"
              title: |
                {{ template "slack.title" . }}
              text: |
                {{ template "slack.text" . }}

      - orgId: 1
        name: pagerduty-critical
        receivers:
          - uid: pagerduty-receiver
            type: pagerduty
            settings:
              integrationKey: ${PAGERDUTY_INTEGRATION_KEY}
              severity: critical
              class: monitoring
              component: kubernetes

Grafana Cloud vs Self-hosted 비교

기능	Self-hosted	Grafana Cloud
관리 오버헤드	높음	낮음
비용	인프라 비용	사용량 기반
확장성	수동 관리	자동
고가용성	직접 구성	기본 제공
플러그인	모든 플러그인	승인된 플러그인만
데이터 위치	내부	클라우드
커스터마이징	완전한 제어	제한적
SLA	없음	99.9%

Grafana Cloud 연결 (Hybrid)

yaml

# Self-hosted Grafana에서 Grafana Cloud를 데이터 소스로 추가
datasources:
  - name: Grafana Cloud Prometheus
    type: prometheus
    url: https://prometheus-prod-01-prod-ap-northeast-0.grafana.net/api/prom
    access: proxy
    basicAuth: true
    basicAuthUser: <GRAFANA_CLOUD_INSTANCE_ID>
    secureJsonData:
      basicAuthPassword: <GRAFANA_CLOUD_API_KEY>

  - name: Grafana Cloud Logs
    type: loki
    url: https://logs-prod-ap-northeast-0.grafana.net
    access: proxy
    basicAuth: true
    basicAuthUser: <GRAFANA_CLOUD_INSTANCE_ID>
    secureJsonData:
      basicAuthPassword: <GRAFANA_CLOUD_API_KEY>

Best Practices

1. 대시보드 구조화

Folders/
├── Overview/           # 전체 시스템 개요
│   ├── Executive Summary
│   └── SLO Dashboard
├── Infrastructure/     # 인프라 메트릭
│   ├── Nodes
│   ├── Storage
│   └── Network
├── Kubernetes/         # K8s 리소스
│   ├── Cluster
│   ├── Workloads
│   └── Networking
├── Applications/       # 애플리케이션별
│   ├── Service A
│   └── Service B
└── Alerts/            # 알림 관련
    ├── Active Alerts
    └── Alert History

2. 변수 활용

json

{
  "templating": {
    "list": [
      {
        "name": "datasource",
        "type": "datasource",
        "query": "prometheus"
      },
      {
        "name": "cluster",
        "type": "query",
        "query": "label_values(kube_node_info, cluster)",
        "refresh": 2,
        "multi": true,
        "includeAll": true
      },
      {
        "name": "namespace",
        "type": "query",
        "query": "label_values(kube_namespace_labels{cluster=~\"$cluster\"}, namespace)",
        "refresh": 2
      }
    ]
  }
}

3. 성능 최적화

yaml

# grafana.ini 성능 설정
[database]
max_idle_conn = 25
max_open_conn = 100
conn_max_lifetime = 14400

[dataproxy]
timeout = 30
keep_alive_seconds = 30

[dashboards]
min_refresh_interval = 10s

[caching]
enabled = true
ttl = 60s

퀴즈

이 장에서 배운 내용을 테스트하려면 Grafana 퀴즈를 풀어보세요.

Traffic Management

Security

Observability

Resilience

Advanced

비교 가이드

ArgoCD

Metrics

Logging

Tracing

Alerting

Observability End-to-End 실습

Cilium 딥다이브

Calico 딥다이브

Istio 상세 퀴즈

Linkerd 상세 퀴즈

Cilium Service Mesh 상세 퀴즈

ArgoCD 상세 퀴즈

Metrics

Logging

Tracing

Alerting

Observability Labs

Grafana 대시보드 ​

소개 ​

주요 특징 ​

아키텍처 ​

Helm 배포 ​

기본 설치 ​

values.yaml 구성 ​

설치 실행 ​

데이터 소스 연동 ​

ConfigMap을 통한 데이터 소스 프로비저닝 ​

CloudWatch IRSA 설정 ​

대시보드 설계 패턴 ​

USE Method (Utilization, Saturation, Errors) ​

RED Method (Rate, Errors, Duration) ​

4 Golden Signals ​

대시보드 프로비저닝 ​

Grafana Operator 사용 ​

알림 규칙 (Grafana Alerting) ​

알림 규칙 구성 ​

알림 연락처 구성 ​

Grafana Cloud vs Self-hosted 비교 ​

Grafana Cloud 연결 (Hybrid) ​

Best Practices ​

1. 대시보드 구조화 ​

2. 변수 활용 ​

3. 성능 최적화 ​

퀴즈 ​

Grafana 대시보드

소개

주요 특징

아키텍처

Helm 배포

기본 설치

values.yaml 구성

설치 실행

데이터 소스 연동

ConfigMap을 통한 데이터 소스 프로비저닝

CloudWatch IRSA 설정

대시보드 설계 패턴

USE Method (Utilization, Saturation, Errors)

RED Method (Rate, Errors, Duration)

4 Golden Signals

대시보드 프로비저닝

Grafana Operator 사용

알림 규칙 (Grafana Alerting)

알림 규칙 구성

알림 연락처 구성

Grafana Cloud vs Self-hosted 비교

Grafana Cloud 연결 (Hybrid)

Best Practices

1. 대시보드 구조화

2. 변수 활용

3. 성능 최적화

퀴즈