Kubernetes高可用性监控：Thanos的部署
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目录
介绍
对Prometheus高可用性的需求
实现
Thanos架构
Thanos包含以下组件：
重复数据删除
Thanos搭建
先决条件
组件部署
部署Prometheus中的ServiceAccount资源对象，分别创建Clusterrole和Clusterrolebinding
部署Prometheus配置文件configmap.yaml
部署prometheus-rules的configmap 这将创建我们的警报规则，该警报规则将中继到alertmanager进行交付
部署Prometheus的StatefulSet资源
部署Prometheus服务
部署Thanos Querier
部署Thanos Store Gateway
部署Thanos Ruler
部署Alertmanager
部署Kubestate指标
部署Node-Exporter Daemonset
部署Grafana
部署Ingress对象
结论
介绍
对Prometheus高可用性的需求
在过去的几个月中，Kubernetes的采用已经增长了很多倍，现在很明显，Kubernetes是容器编排的事实标准。
同时，监视是任何基础架构的重要方面。Prometheus被认为是监视容器应用和非容器应用的绝佳选择。我们应该确保监视系统具有高可用性和高度可扩展性，以适应不断增长的基础架构的需求，尤其是在Kubernetes的情况下。
因此，今天，我们将部署Prometheus集群，它不仅可以抵抗节点故障，而且还可以确保数据归档以备将来参考。我们的集群也具有很大的可扩展性，以至于我们可以在同一监控系统内跨越多个Kubernetes集群。
当前方案
大多数Prometheus部署都使用具备持久性存储的Pod，而Prometheus使用联邦进行扩展。但是，并非所有数据都可以使用联邦进行聚合，在添加其他服务器时，通常需要一种机制来管理Prometheus配置。
解决方案
Thanos旨在解决上述问题。在Thanos的帮助下，我们不仅可以扩展Prometheus实例，并能够消除重复数据，还可以将数据归档在GCS或S3等持久性存储中。
实现
Thanos架构
Thanos包含以下组件：
- Thanos Sidecar：这是在Prometheus运行的主要组件。它读取并存储object store中的数据。此外，它管理Prometheus的配置和生命周期。为了区分每个Prometheus实例，Sidecar组件将外部标签注入Prometheus配置中。Sidecar组件能够在Prometheus服务器的PromQL接口上运行查询。Sidecar组件还侦听Thanos gRPC协议，并在gRPC查询和REST查询之间转换。
- Thanos Store：此组件在object store中的历史数据之上实现Store API。它主要充当API网关，因此不需要大量的本地磁盘空间。它在启动时加入Thanos集群，并公布它可以访问的数据。它会在本地磁盘上保留有关所有远程块的少量信息，并使它与object store保持同步。通常，在重新启动时可以安全地删除此数据，但会增加启动时间。
- Thanos Query：是个查询组件，负责侦听HTTP并将查询转换为Thanos gRPC格式。它汇总了来自不同来源的查询结果，并且可以从Sidecar和Store中读取数据。在高可用性设置中，它甚至可以对重复数据进行删除。
重复数据删除
Prometheus是有状态的，不允许复制其数据库。这意味着通过运行多个Prometheus副本来增强高可用性并不是最佳选择。
简单的负载平衡将不起作用，例如，在发生崩溃后，副本可能已启动，但是查询此类副本将导致其关闭期间的间隙很小。你有第二个副本可能正在运行，但是又可能在另一时间关闭（例如，滚动重启），因此在这些副本上进行负载平衡将无法正常工作。
- 相反，Thanos Querier从两个副本中提取数据，并对这些信号进行重复数据删除，从而帮助Querier使用者填补了空白。
- Thanos Compact：是Thanos的压缩器组件，它采用Prometheus 2.0存储引擎的压缩过程，来阻止数据存储在object store中。通常，它以单例方式部署。 它还负责数据的向下采样(downsampling)-40小时后执行5m的向下采样，而10天后执行1h的向下采样。
- Thanos Ruler：它基本上与Prometheus的rules具有相同的作用。唯一的区别是它可以与Thanos组件进行通信。
Thanos搭建
先决条件
为了完全理解本教程，需要以下内容：
- Kubernetes的工作原理和熟练使用Kubectl
- Kubernetes集群至少有3个节点（在本演示中，使用GKE集群）
- 实现Ingress Controller和Ingress对象（出于演示目的，使用Nginx Ingress Controller）。尽管这不是强制性的，但强烈建议你这样做以减少外部端点创建的数量。
- 创建供Thanos组件访问object store的凭证（在本例中为GCS存储）
- 创建2个GCS存储，并将其命名为prometheus-long-term和thanos-ruler
- 创建一个角色为“ 存储对象管理员”的服务帐户
- 将密钥文件下载保存为JSON格式，并将其命名为thanos-gcs-credentials.json
- 使用secret创建kubernetes密钥 kubectl create secret generic thanos-gcs-credentials --from-file=thanos-gcs-credentials.json -n monitoring
组件部署
部署Prometheus中的ServiceAccount资源对象，分别创建Clusterrole和Clusterrolebinding
apiVersion: v1
kind: Namespace
metadata:
name: monitoring
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: monitoring
namespace: monitoring
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
name: monitoring
namespace: monitoring
rules:
- apiGroups: [""]
resources:
- nodes
- nodes/proxy
- services
- endpoints
- pods
verbs: ["get", "list", "watch"]
- apiGroups: [""]
resources:
- configmaps
verbs: ["get"]
- nonResourceURLs: ["/metrics"]
verbs: ["get"]
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
name: monitoring
subjects:
- kind: ServiceAccount
name: monitoring
namespace: monitoring
roleRef:
kind: ClusterRole
Name: monitoring
apiGroup: rbac.authorization.k8s.io
---上述清单创建监控命名空间和服务ServiceAccount。
部署Prometheus配置文件configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: prometheus-server-conf
labels:
name: prometheus-server-conf
namespace: monitoring
data:
prometheus.yaml.tmpl: |-
global:
scrape_interval: 5s
evaluation_interval: 5s
external_labels:
cluster: prometheus-ha
# Each Prometheus has to have unique labels.
replica: $(POD_NAME)
rule_files:
- /etc/prometheus/rules/*rules.yaml
alerting:
# We want our alerts to be deduplicated
# from different replicas.
alert_relabel_configs:
- regex: replica
action: labeldrop
alertmanagers:
- scheme: http
path_prefix: /
static_configs:
- targets: ['alertmanager:9093']
scrape_configs:
- job_name: kubernetes-nodes-cadvisor
scrape_interval: 10s
scrape_timeout: 10s
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
kubernetes_sd_configs:
- role: node
relabel_configs:
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
# Only for Kubernetes ^1.7.3.
# See: https://github.com/prometheus/prometheus/issues/2916
- target_label: __address__
replacement: kubernetes.default.svc:443
- source_labels: [__meta_kubernetes_node_name]
regex: (.+)
target_label: __metrics_path__
replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
metric_relabel_configs:
- action: replace
source_labels: [id]
regex: '^/machine\.slice/machine-rkt\\x2d([^\\]+)\\.+/([^/]+)\.service$'
target_label: rkt_container_name
replacement: '${2}-${1}'
- action: replace
source_labels: [id]
regex: '^/system\.slice/(.+)\.service$'
target_label: systemd_service_name
replacement: '${1}'
- job_name: 'kubernetes-pods'
kubernetes_sd_configs:
- role: pod
relabel_configs:
- action: labelmap
regex: __meta_kubernetes_pod_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_pod_name]
action: replace
target_label: kubernetes_pod_name
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scheme]
action: replace
target_label: __scheme__
regex: (https?)
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels: [__address__, __meta_kubernetes_pod_prometheus_io_port]
action: replace
target_label: __address__
regex: ([^:]+)(?::\d+)?;(\d+)
replacement: $1:$2
- job_name: 'kubernetes-apiservers'
kubernetes_sd_configs:
- role: endpoints
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
action: keep
regex: default;kubernetes;https
- job_name: 'kubernetes-service-endpoints'
kubernetes_sd_configs:
- role: endpoints
relabel_configs:
- action: labelmap
regex: __meta_kubernetes_service_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_service_name]
action: replace
target_label: kubernetes_name
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
action: replace
target_label: __scheme__
regex: (https?)
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]
action: replace
target_label: __address__
regex: (.+)(?::\d+);(\d+)
replacement: $1:$2上面的Configmap创建Prometheus配置文件模板。Thanos sidecar组件将读取此配置文件模板，并将生成实际的配置文件，而该配置文件又将由在同一容器中运行的Prometheus容器使用。
在配置文件中添加external_labels部分非常重要，以使Querier可以基于该部分对重复数据进行删除。
部署prometheus-rules的configmap 这将创建我们的警报规则，该警报规则将中继到alertmanager进行交付
apiVersion: v1
kind: ConfigMap
metadata:
name: prometheus-rules
labels:
name: prometheus-rules
namespace: monitoring
data:
alert-rules.yaml: |-
groups:
- name: Deployment
rules:
- alert: Deployment at 0 Replicas
annotations:
summary: Deployment {
{$labels.deployment}} in {
{$labels.namespace}} is currently having no pods running
expr: |
sum(kube_deployment_status_replicas{pod_template_hash=""}) by (deployment,namespace) < 1
for: 1m
labels:
team: devops
- alert: HPA Scaling Limited
annotations:
summary: HPA named {
{$labels.hpa}} in {
{$labels.namespace}} namespace has reached scaling limited state
expr: |
(sum(kube_hpa_status_condition{condition="ScalingLimited",status="true"}) by (hpa,namespace)) == 1
for: 1m
labels:
team: devops
- alert: HPA at MaxCapacity
annotations:
summary: HPA named {
{$labels.hpa}} in {
{$labels.namespace}} namespace is running at Max Capacity
expr: |
((sum(kube_hpa_spec_max_replicas) by (hpa,namespace)) - (sum(kube_hpa_status_current_replicas) by (hpa,namespace))) == 0
for: 1m
labels:
team: devops
- name: Pods
rules:
- alert: Container restarted
annotations:
summary: Container named {
{$labels.container}} in {
{$labels.pod}} in {
{$labels.namespace}} was restarted
expr: |
sum(increase(kube_pod_container_status_restarts_total{namespace!="kube-system",pod_template_hash=""}[1m])) by (pod,namespace,container) > 0
for: 0m
labels:
team: dev
- alert: High Memory Usage of Container
annotations:
summary: Container named {
{$labels.container}} in {
{$labels.pod}} in {
{$labels.namespace}} is using more than 75% of Memory Limit
expr: |
((( sum(container_memory_usage_bytes{image!="",container_name!="POD", namespace!="kube-system"}) by (namespace,container_name,pod_name) / sum(container_spec_memory_limit_bytes{image!="",container_name!="POD",namespace!="kube-system"}) by (namespace,container_name,pod_name) ) * 100 ) < +Inf ) > 75
for: 5m
labels:
team: dev
- alert: High CPU Usage of Container
annotations:
summary: Container named {
{$labels.container}} in {
{$labels.pod}} in {
{$labels.namespace}} is using more than 75% of CPU Limit
expr: |
((sum(irate(container_cpu_usage_seconds_total{image!="",container_name!="POD", namespace!="kube-system"}[30s])) by (namespace,container_name,pod_name) / sum(container_spec_cpu_quota{image!="",container_name!="POD", namespace!="kube-system"} / container_spec_cpu_period{image!="",container_name!="POD", namespace!="kube-system"}) by (namespace,container_name,pod_name) ) * 100) > 75
for: 5m
labels:
team: dev
- name: Nodes
rules:
- alert: High Node Memory Usage
annotations:
summary: Node {
{$labels.kubernetes_io_hostname}} has more than 80% memory used. Plan Capcity
expr: |
(sum (container_memory_working_set_bytes{id="/",container_name!="POD"}) by (kubernetes_io_hostname) / sum (machine_memory_bytes{}) by (kubernetes_io_hostname) * 100) > 80
for: 5m
labels:
team: devops
- alert: High Node CPU Usage
annotations:
summary: Node {
{$labels.kubernetes_io_hostname}} has more than 80% allocatable cpu used. Plan Capacity.
expr: |
(sum(rate(container_cpu_usage_seconds_total{id="/", container_name!="POD"}[1m])) by (kubernetes_io_hostname) / sum(machine_cpu_cores) by (kubernetes_io_hostname) * 100) > 80
for: 5m
labels:
team: devops
- alert: High Node Disk Usage
annotations:
summary: Node {
{$labels.kubernetes_io_hostname}} has more than 85% disk used. Plan Capacity.
expr: |
(sum(container_fs_usage_bytes{device=~"^/dev/[sv]d[a-z][1-9]$",id="/",container_name!="POD"}) by (kubernetes_io_hostname) / sum(container_fs_limit_bytes{container_name!="POD",device=~"^/dev/[sv]d[a-z][1-9]$",id="/"}) by (kubernetes_io_hostname)) * 100 > 85
for: 5m
labels:
team: devops部署Prometheus的StatefulSet资源
apiVersion: storage.k8s.io/v1beta1
kind: StorageClass
metadata:
name: fast
namespace: monitoring
provisioner: kubernetes.io/gce-pd
allowVolumeExpansion: true
---
apiVersion: apps/v1beta1
kind: StatefulSet
metadata:
name: prometheus
namespace: monitoring
spec:
replicas: 3
serviceName: prometheus-service
template:
metadata:
labels:
app: prometheus
thanos-store-api: "true"
spec:
serviceAccountName: monitoring
containers:
- name: prometheus
image: prom/prometheus:v2.4.3
args:
- "--config.file=/etc/prometheus-shared/prometheus.yaml"
- "--storage.tsdb.path=/prometheus/"
- "--web.enable-lifecycle"
- "--storage.tsdb.no-lockfile"
- "--storage.tsdb.min-block-duration=2h"
- "--storage.tsdb.max-block-duration=2h"
ports:
- name: prometheus
containerPort: 9090
volumeMounts:
- name: prometheus-storage
mountPath: /prometheus/
- name: prometheus-config-shared
mountPath: /etc/prometheus-shared/
- name: prometheus-rules
mountPath: /etc/prometheus/rules
- name: thanos
image: quay.io/thanos/thanos:v0.8.0
args:
- "sidecar"
- "--log.level=debug"
- "--tsdb.path=/prometheus"
- "--prometheus.url=http://127.0.0.1:9090"
- "--objstore.config={type: GCS, config: {bucket: prometheus-long-term}}"
- "--reloader.config-file=/etc/prometheus/prometheus.yaml.tmpl"
- "--reloader.config-envsubst-file=/etc/prometheus-shared/prometheus.yaml"
- "--reloader.rule-dir=/etc/prometheus/rules/"
env:
- name: POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
- name : GOOGLE_APPLICATION_CREDENTIALS
value: /etc/secret/thanos-gcs-credentials.json
ports:
- name: http-sidecar
containerPort: 10902
- name: grpc
containerPort: 10901
livenessProbe:
httpGet:
port: 10902
path: /-/healthy
readinessProbe:
httpGet:
port: 10902
path: /-/ready
volumeMounts:
- name: prometheus-storage
mountPath: /prometheus
- name: prometheus-config-shared
mountPath: /etc/prometheus-shared/
- name: prometheus-config
mountPath: /etc/prometheus
- name: prometheus-rules
mountPath: /etc/prometheus/rules
- name: thanos-gcs-credentials
mountPath: /etc/secret
readOnly: false
securityContext:
fsGroup: 2000
runAsNonRoot: true
runAsUser: 1000
volumes:
- name: prometheus-config
configMap:
defaultMode: 420
name: prometheus-server-conf
- name: prometheus-config-shared
emptyDir: {}
- name: prometheus-rules
configMap:
name: prometheus-rules
- name: thanos-gcs-credentials
secret:
secretName: thanos-gcs-credentials
volumeClaimTemplates:
- metadata:
name: prometheus-storage
namespace: monitoring
spec:
accessModes: [ "ReadWriteOnce" ]
storageClassName: fast
resources:
requests:
storage: 20Gi上面提供的清单，重要的是要了解以下几个方面：
- Prometheus部署为具有3个副本的StatefulSet，每个副本动态地配置自己的持久存储卷。
- Thanos sidecar容器使用我们在上面创建的模板文件，生成Prometheus配置信息。
- Thanos需要处理数据压缩，因此我们需要设置--storage.tsdb.min-block-duration = 2h和--storage.tsdb.max-block-duration = 2h
- PrometheusStatefulSet被打上thanos-store-api：true的标签， 因此每个headless 服务都会发现每个Pod，我们将在下面的Service资源中创建它。Thanos Querier将使用此headless服务来查询所有Prometheus实例中的数据。我们还将相同的标签(thanos-store-api：true)应用于Thanos Store和Thanos Ruler组件，以便Querier也会发现它们并将其用于查询指标。
- 使用GOOGLE_APPLICATION_CREDENTIALS环境变量提供了GCS存储凭据路径。这个凭据是我们创建secret获得的。
部署Prometheus服务
apiVersion: v1
kind: Service
metadata:
name: prometheus-0-service
annotations:
prometheus.io/scrape: "true"
prometheus.io/port: "9090"
namespace: monitoring
labels:
name: prometheus
spec:
selector:
statefulset.kubernetes.io/pod-name: prometheus-0
ports:
- name: prometheus
port: 8080
targetPort: prometheus
---
apiVersion: v1
kind: Service
metadata:
name: prometheus-1-service
annotations:
prometheus.io/scrape: "true"
prometheus.io/port: "9090"
namespace: monitoring
labels:
name: prometheus
spec:
selector:
statefulset.kubernetes.io/pod-name: prometheus-1
ports:
- name: prometheus
port: 8080
targetPort: prometheus
---
apiVersion: v1
kind: Service
metadata:
name: prometheus-2-service
annotations:
prometheus.io/scrape: "true"
prometheus.io/port: "9090"
namespace: monitoring
labels:
name: prometheus
spec:
selector:
statefulset.kubernetes.io/pod-name: prometheus-2
ports:
- name: prometheus
port: 8080
targetPort: prometheus
---
#This service creates a srv record for querier to find about store-api's
apiVersion: v1
kind: Service
metadata:
name: thanos-store-gateway
namespace: monitoring
spec:
type: ClusterIP
clusterIP: None
ports:
- name: grpc
port: 10901
targetPort: grpc
selector:
thanos-store-api: "true"我们为StatefulSet中的每个Prometheus Pod创建了不同的服务，这不是必需的，这些仅用于调试目的。上面已经解释了headless服务名称为thanos-store-gateway的目的。稍后我们将使用ingress 对象暴露Prometheus服务。
部署Thanos Querier
apiVersion: v1
kind: Namespace
metadata:
name: monitoring
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: thanos-querier
namespace: monitoring
labels:
app: thanos-querier
spec:
replicas: 1
selector:
matchLabels:
app: thanos-querier
template:
metadata:
labels:
app: thanos-querier
spec:
containers:
- name: thanos
image: quay.io/thanos/thanos:v0.8.0
args:
- query
- --log.level=debug
- --query.replica-label=replica
- --store=dnssrv+thanos-store-gateway:10901
ports:
- name: http
containerPort: 10902
- name: grpc
containerPort: 10901
livenessProbe:
httpGet:
port: http
path: /-/healthy
readinessProbe:
httpGet:
port: http
path: /-/ready
---
apiVersion: v1
kind: Service
metadata:
labels:
app: thanos-querier
name: thanos-querier
namespace: monitoring
spec:
ports:
- port: 9090
protocol: TCP
targetPort: http
name: http
selector:
app: thanos-querierThanos Querier是Thanos部署的主要组件之一。请注意以下几点：
- 容器参数--store=dnssrv+thanos-store-gateway:10901有助于从度量标准数据中发现所有组件。
- thanos-querier服务提供了一个Web界面来运行PromQL查询。它还可以选择在多个Prometheus群集之间删除重复数据。
- Thanos Querier也是Grafana等所有仪表板的数据源。
部署Thanos Store Gateway
apiVersion: v1
kind: Namespace
metadata:
name: monitoring
---
apiVersion: apps/v1beta1
kind: StatefulSet
metadata:
name: thanos-store-gateway
namespace: monitoring
labels:
app: thanos-store-gateway
spec:
replicas: 1
selector:
matchLabels:
app: thanos-store-gateway
serviceName: thanos-store-gateway
template:
metadata:
labels:
app: thanos-store-gateway
thanos-store-api: "true"
spec:
containers:
- name: thanos
image: quay.io/thanos/thanos:v0.8.0
args:
- "store"
- "--log.level=debug"
- "--data-dir=/data"
- "--objstore.config={type: GCS, config: {bucket: prometheus-long-term}}"
- "--index-cache-size=500MB"
- "--chunk-pool-size=500MB"
env:
- name : GOOGLE_APPLICATION_CREDENTIALS
value: /etc/secret/thanos-gcs-credentials.json
ports:
- name: http
containerPort: 10902
- name: grpc
containerPort: 10901
livenessProbe:
httpGet:
port: 10902
path: /-/healthy
readinessProbe:
httpGet:
port: 10902
path: /-/ready
volumeMounts:
- name: thanos-gcs-credentials
mountPath: /etc/secret
readOnly: false
volumes:
- name: thanos-gcs-credentials
secret:
secretName: thanos-gcs-credentials
---这将创建存储组件，该组件存储服务从object store到Querier的指标信息。
部署Thanos Ruler
apiVersion: v1
kind: Namespace
metadata:
name: monitoring
---
apiVersion: v1
kind: ConfigMap
metadata:
name: thanos-ruler-rules
namespace: monitoring
data:
alert_down_services.rules.yaml: |
groups:
- name: metamonitoring
rules:
- alert: PrometheusReplicaDown
annotations:
message: Prometheus replica in cluster {
{$labels.cluster}} has disappeared from Prometheus target discovery.
expr: |
sum(up{cluster="prometheus-ha", instance=~".*:9090", job="kubernetes-service-endpoints"}) by (job,cluster) < 3
for: 15s
labels:
severity: critical
---
apiVersion: apps/v1beta1
kind: StatefulSet
metadata:
labels:
app: thanos-ruler
name: thanos-ruler
namespace: monitoring
spec:
replicas: 1
selector:
matchLabels:
app: thanos-ruler
serviceName: thanos-ruler
template:
metadata:
labels:
app: thanos-ruler
thanos-store-api: "true"
spec:
containers:
- name: thanos
image: quay.io/thanos/thanos:v0.8.0
args:
- rule
- --log.level=debug
- --data-dir=/data
- --eval-interval=15s
- --rule-file=/etc/thanos-ruler/*.rules.yaml
- --alertmanagers.url=http://alertmanager:9093
- --query=thanos-querier:9090
- "--objstore.config={type: GCS, config: {bucket: thanos-ruler}}"
- --label=ruler_cluster="prometheus-ha"
- --label=replica="$(POD_NAME)"
env:
- name : GOOGLE_APPLICATION_CREDENTIALS
value: /etc/secret/thanos-gcs-credentials.json
- name: POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
ports:
- name: http
containerPort: 10902
- name: grpc
containerPort: 10901
livenessProbe:
httpGet:
port: http
path: /-/healthy
readinessProbe:
httpGet:
port: http
path: /-/ready
volumeMounts:
- mountPath: /etc/thanos-ruler
name: config
- name: thanos-gcs-credentials
mountPath: /etc/secret
readOnly: false
volumes:
- configMap:
name: thanos-ruler-rules
name: config
- name: thanos-gcs-credentials
secret:
secretName: thanos-gcs-credentials
---
apiVersion: v1
kind: Service
metadata:
labels:
app: thanos-ruler
name: thanos-ruler
namespace: monitoring
spec:
ports:
- port: 9090
protocol: TCP
targetPort: http
name: http
selector:
app: thanos-ruler现在，在与我们的工作负载相同的名称空间中的输入以下命令，能够查看到thanos-store-gateway对应有哪些Pod ：
root@my-shell-95cb5df57-4q6w8:/# nslookup thanos-store-gateway
Server: 10.63.240.10
Address: 10.63.240.10#53
Name: thanos-store-gateway.monitoring.svc.cluster.local
Address: 10.60.25.2
Name: thanos-store-gateway.monitoring.svc.cluster.local
Address: 10.60.25.4
Name: thanos-store-gateway.monitoring.svc.cluster.local
Address: 10.60.30.2
Name: thanos-store-gateway.monitoring.svc.cluster.local
Address: 10.60.30.8
Name: thanos-store-gateway.monitoring.svc.cluster.local
Address: 10.60.31.2
root@my-shell-95cb5df57-4q6w8:/# exit上面返回的IP对应于我们的Prometheus中的Pod（thanos-store和thanos-ruler）。
可以通过以下命令验证
$ kubectl get pods -o wide -l thanos-store-api="true"
NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES
prometheus-0 2/2 Running 0 100m 10.60.31.2 gke-demo-1-pool-1-649cbe02-jdnv
prometheus-1 2/2 Running 0 14h 10.60.30.2 gke-demo-1-pool-1-7533d618-kxkd
prometheus-2 2/2 Running 0 31h 10.60.25.2 gke-demo-1-pool-1-4e9889dd-27gc
thanos-ruler-0 1/1 Running 0 100m 10.60.30.8 gke-demo-1-pool-1-7533d618-kxkd
thanos-store-gateway-0 1/1 Running 0 14h 10.60.25.4 gke-demo-1-pool-1-4e9889dd-27gc 部署Alertmanager
apiVersion: v1
kind: Namespace
metadata:
name: monitoring
---
kind: ConfigMap
apiVersion: v1
metadata:
name: alertmanager
namespace: monitoring
data:
config.yml: |-
global:
resolve_timeout: 5m
slack_api_url: ""
victorops_api_url: ""
templates:
- '/etc/alertmanager-templates/*.tmpl'
route:
group_by: ['alertname', 'cluster', 'service']
group_wait: 10s
group_interval: 1m
repeat_interval: 5m
receiver: default
routes:
- match:
team: devops
receiver: devops
continue: true
- match:
team: dev
receiver: dev
continue: true
receivers:
- name: 'default'
- name: 'devops'
victorops_configs:
- api_key: ''
routing_key: 'devops'
message_type: 'CRITICAL'
entity_display_name: '{
{ .CommonLabels.alertname }}'
state_message: 'Alert: {
{ .CommonLabels.alertname }}. Summary:{
{ .CommonAnnotations.summary }}. RawData: {
{ .CommonLabels }}'
slack_configs:
- channel: '#k8-alerts'
send_resolved: true
- name: 'dev'
victorops_configs:
- api_key: ''
routing_key: 'dev'
message_type: 'CRITICAL'
entity_display_name: '{
{ .CommonLabels.alertname }}'
state_message: 'Alert: {
{ .CommonLabels.alertname }}. Summary:{
{ .CommonAnnotations.summary }}. RawData: {
{ .CommonLabels }}'
slack_configs:
- channel: '#k8-alerts'
send_resolved: true
---
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: alertmanager
namespace: monitoring
spec:
replicas: 1
selector:
matchLabels:
app: alertmanager
template:
metadata:
name: alertmanager
labels:
app: alertmanager
spec:
containers:
- name: alertmanager
image: prom/alertmanager:v0.15.3
args:
- '--config.file=/etc/alertmanager/config.yml'
- '--storage.path=/alertmanager'
ports:
- name: alertmanager
containerPort: 9093
volumeMounts:
- name: config-volume
mountPath: /etc/alertmanager
- name: alertmanager
mountPath: /alertmanager
volumes:
- name: config-volume
configMap:
name: alertmanager
- name: alertmanager
emptyDir: {}
---
apiVersion: v1
kind: Service
metadata:
annotations:
prometheus.io/scrape: 'true'
prometheus.io/path: '/metrics'
labels:
name: alertmanager
name: alertmanager
namespace: monitoring
spec:
selector:
app: alertmanager
ports:
- name: alertmanager
protocol: TCP
port: 9093
targetPort: 9093 alertmanager将根据Prometheus规则生成所有的警报。
部署Kubestate指标
apiVersion: v1
kind: Namespace
metadata:
name: monitoring
---
apiVersion: rbac.authorization.k8s.io/v1
# kubernetes versions before 1.8.0 should use rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
name: kube-state-metrics
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: kube-state-metrics
subjects:
- kind: ServiceAccount
name: kube-state-metrics
namespace: monitoring
---
apiVersion: rbac.authorization.k8s.io/v1
# kubernetes versions before 1.8.0 should use rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
name: kube-state-metrics
rules:
- apiGroups: [""]
resources:
- configmaps
- secrets
- nodes
- pods
- services
- resourcequotas
- replicationcontrollers
- limitranges
- persistentvolumeclaims
- persistentvolumes
- namespaces
- endpoints
verbs: ["list", "watch"]
- apiGroups: ["extensions"]
resources:
- daemonsets
- deployments
- replicasets
verbs: ["list", "watch"]
- apiGroups: ["apps"]
resources:
- statefulsets
verbs: ["list", "watch"]
- apiGroups: ["batch"]
resources:
- cronjobs
- jobs
verbs: ["list", "watch"]
- apiGroups: ["autoscaling"]
resources:
- horizontalpodautoscalers
verbs: ["list", "watch"]
---
apiVersion: rbac.authorization.k8s.io/v1
# kubernetes versions before 1.8.0 should use rbac.authorization.k8s.io/v1beta1
kind: RoleBinding
metadata:
name: kube-state-metrics
namespace: monitoring
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: Role
name: kube-state-metrics-resizer
subjects:
- kind: ServiceAccount
name: kube-state-metrics
namespace: monitoring
---
apiVersion: rbac.authorization.k8s.io/v1
# kubernetes versions before 1.8.0 should use rbac.authorization.k8s.io/v1beta1
kind: Role
metadata:
namespace: monitoring
name: kube-state-metrics-resizer
rules:
- apiGroups: [""]
resources:
- pods
verbs: ["get"]
- apiGroups: ["extensions"]
resources:
- deployments
resourceNames: ["kube-state-metrics"]
verbs: ["get", "update"]
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: kube-state-metrics
namespace: monitoring
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: kube-state-metrics
namespace: monitoring
spec:
selector:
matchLabels:
k8s-app: kube-state-metrics
replicas: 1
template:
metadata:
labels:
k8s-app: kube-state-metrics
spec:
serviceAccountName: kube-state-metrics
containers:
- name: kube-state-metrics
image: quay.io/mxinden/kube-state-metrics:v1.4.0-gzip.3
ports:
- name: http-metrics
containerPort: 8080
- name: telemetry
containerPort: 8081
readinessProbe:
httpGet:
path: /healthz
port: 8080
initialDelaySeconds: 5
timeoutSeconds: 5
- name: addon-resizer
image: k8s.gcr.io/addon-resizer:1.8.3
resources:
limits:
cpu: 150m
memory: 50Mi
requests:
cpu: 150m
memory: 50Mi
env:
- name: MY_POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
- name: MY_POD_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
command:
- /pod_nanny
- --container=kube-state-metrics
- --cpu=100m
- --extra-cpu=1m
- --memory=100Mi
- --extra-memory=2Mi
- --threshold=5
- --deployment=kube-state-metrics
---
apiVersion: v1
kind: Service
metadata:
name: kube-state-metrics
namespace: monitoring
labels:
k8s-app: kube-state-metrics
annotations:
prometheus.io/scrape: 'true'
spec:
ports:
- name: http-metrics
port: 8080
targetPort: http-metrics
protocol: TCP
- name: telemetry
port: 8081
targetPort: telemetry
protocol: TCP
selector:
k8s-app: kube-state-metrics需要使用Kubestate指标来中继一些重要的容器指标，这些指标不是kubelet本身公开的，因此不能直接用于Prometheus。
部署Node-Exporter Daemonset
apiVersion: v1
kind: Namespace
metadata:
name: monitoring
---
apiVersion: extensions/v1beta1
kind: DaemonSet
metadata:
name: node-exporter
namespace: monitoring
labels:
name: node-exporter
spec:
template:
metadata:
labels:
name: node-exporter
annotations:
prometheus.io/scrape: "true"
prometheus.io/port: "9100"
spec:
hostPID: true
hostIPC: true
hostNetwork: true
containers:
- name: node-exporter
image: prom/node-exporter:v0.16.0
securityContext:
privileged: true
args:
- --path.procfs=/host/proc
- --path.sysfs=/host/sys
ports:
- containerPort: 9100
protocol: TCP
resources:
limits:
cpu: 100m
memory: 100Mi
requests:
cpu: 10m
memory: 100Mi
volumeMounts:
- name: dev
mountPath: /host/dev
- name: proc
mountPath: /host/proc
- name: sys
mountPath: /host/sys
- name: rootfs
mountPath: /rootfs
volumes:
- name: proc
hostPath:
path: /proc
- name: dev
hostPath:
path: /dev
- name: sys
hostPath:
path: /sys
- name: rootfs
hostPath:
path: /Node-Exporter是Daemonset资源，它在每个节点上运行一个pod -exporter的容器，并公开非常重要的与节点相关的度量标准，这些度量标准可以由Prometheus实例提取。
部署Grafana
apiVersion: v1
kind: Namespace
metadata:
name: monitoring
---
apiVersion: storage.k8s.io/v1beta1
kind: StorageClass
metadata:
name: fast
namespace: monitoring
provisioner: kubernetes.io/gce-pd
allowVolumeExpansion: true
---
apiVersion: apps/v1beta1
kind: StatefulSet
metadata:
name: grafana
namespace: monitoring
spec:
replicas: 1
serviceName: grafana
template:
metadata:
labels:
task: monitoring
k8s-app: grafana
spec:
containers:
- name: grafana
image: k8s.gcr.io/heapster-grafana-amd64:v5.0.4
ports:
- containerPort: 3000
protocol: TCP
volumeMounts:
- mountPath: /etc/ssl/certs
name: ca-certificates
readOnly: true
- mountPath: /var
name: grafana-storage
env:
- name: GF_SERVER_HTTP_PORT
value: "3000"
# The following env variables are required to make Grafana accessible via
# the kubernetes api-server proxy. On production clusters, we recommend
# removing these env variables, setup auth for grafana, and expose the grafana
# service using a LoadBalancer or a public IP.
- name: GF_AUTH_BASIC_ENABLED
value: "false"
- name: GF_AUTH_ANONYMOUS_ENABLED
value: "true"
- name: GF_AUTH_ANONYMOUS_ORG_ROLE
value: Admin
- name: GF_SERVER_ROOT_URL
# If you're only using the API Server proxy, set this value instead:
# value: /api/v1/namespaces/kube-system/services/monitoring-grafana/proxy
value: /
volumes:
- name: ca-certificates
hostPath:
path: /etc/ssl/certs
volumeClaimTemplates:
- metadata:
name: grafana-storage
namespace: monitoring
spec:
accessModes: [ "ReadWriteOnce" ]
storageClassName: fast
resources:
requests:
storage: 5Gi
---
apiVersion: v1
kind: Service
metadata:
labels:
kubernetes.io/cluster-service: 'true'
kubernetes.io/name: grafana
name: grafana
namespace: monitoring
spec:
ports:
- port: 3000
targetPort: 3000
selector:
k8s-app: grafana这将创建我们的Grafana的Deployment和Service资源对象，该Service将 通过我们的Ingress对象公开。
为了将Thanos-Querier添加为Grafana数据源。我们可以这样做：
- 在Grafana单击Add DataSource
- 名称：DS_PROMETHEUS
- 类型：Prometheus
- 网址：http://thanos-querier:9090
- 点击Save and Test。现在，你可以构建自定义仪表板，也可以直接从grafana.net导入仪表板。仪表盘＃315和＃1471是很好的开始。
部署Ingress对象
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
name: monitoring-ingress
namespace: monitoring
annotations:
kubernetes.io/ingress.class: "nginx"
spec:
rules:
- host: grafana..com
http:
paths:
- path: /
backend:
serviceName: grafana
servicePort: 3000
- host: prometheus-0..com
http:
paths:
- path: /
backend:
serviceName: prometheus-0-service
servicePort: 8080
- host: prometheus-1..com
http:
paths:
- path: /
backend:
serviceName: prometheus-1-service
servicePort: 8080
- host: prometheus-2..com
http:
paths:
- path: /
backend:
serviceName: prometheus-2-service
servicePort: 8080
- host: alertmanager..com
http:
paths:
- path: /
backend:
serviceName: alertmanager
servicePort: 9093
- host: thanos-querier..com
http:
paths:
- path: /
backend:
serviceName: thanos-querier
servicePort: 9090
- host: thanos-ruler..com
http:
paths:
- path: /
backend:
serviceName: thanos-ruler
servicePort: 9090 这将有助于在Kubernetes集群之外公开我们所有的服务。记得将
现在，您应该可以在http://thanos-querier.
它看起来像这样：
可以选择“ deldupication“ 删除重复数据。
如果单击“ Stores ”，则可以看到thanos-store-gateway服务发现的所有活动端点
现在，您将Thanos Querier添加为Grafana中的数据源，并开始创建仪表板
Kubernetes集群监控仪表板
Kubernetes节点监控仪表板
结论
将Thanos与Prometheus集成无疑提供了水平扩展Prometheus的能力，并且由于Thanos-Querier能够从其他查询器实例中提取指标，因此你实际上可以跨集群提取指标，从而在单个仪表板上可视化它们。
我们还能够将度量标准数据存档在object store中，该object store为我们的监视系统提供了无限的存储空间，并提供了来自object store本身的度量。
但是，要实现所有这些，你需要进行大量配置。上面提供的清单已在生产环境中进行了测试。如果你有任何疑问，请随时与我们联系。
译文连接：https://dzone.com/articles/high-availability-kubernetes-monitoring-using-prom