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Wang
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Wang
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Wang
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Wang
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Wang
Istio playbook
Cloud platforms provide a wealth of benefits for the organizations that use them. However, there’s no denying that adopting the cloud can put strains on DevOps teams. Developers must use microservices to architect for portability, meanwhile operators are managing extremely large hybrid and multi-cloud deployments. Istio lets you connect, secure, control, and observe services.
First, download Istio release version, unzip the package and enter the directory.
Second, verify installation environment
bin/istioctl verify-install
Next, deploy Istio and select the demo profile which enable many features like tracing/kiali/grafana
bin/istioctl manifest apply --set profile=demo
Then, check Istio pods’ status, make sure all the related pods are running
Istio Commands
- authn: Interact with Istio authentication policies
- authz: (authz is experimental. Use
istioctl experimental authz) - convert-ingress: Convert Ingress configuration into Istio VirtualService configuration
- dashboard: Access to Istio web UIs like kiali, grafana, prometheus, jaeger
- deregister: De-registers a service instance
- experimental: Experimental commands that may be modified or deprecated
- help: Help about any command
- kube-inject: Inject Envoy sidecar into Kubernetes pod resources
- manifest: Commands related to Istio manifests
- profile: Commands related to Istio configuration profiles
- proxy-config: Retrieve information about proxy configuration from Envoy [kube only]
- proxy-status: Retrieves the synchronization status of each Envoy in the mesh [kube only]
- register: Registers a service instance (e.g. VM) joining the mesh
- validate: Validate Istio policy and rules
- verify-install: Verifies Istio Installation Status or performs pre-check for the cluster before Istio installation
- version: Prints out build version information
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Wang
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Wang
Guarantee service availability in kubernetes
A good service not only provide good functionalities, but also ensure the availability and uptime.
We reinforce our service from QoS, QPS, Throttling, Scaling, Throughput, Monitoring.
Qos
There’re 3 kinds of QoS in kubernetes: Guaranteed, Burstable, BestEffort. We usually use Guaranteed, Burstable for different services.
#Guaranteed resources: requests: cpu: 1000m memory: 4Gi limits: cpu: 1000m memory: 4Gi #Burstable resources: requests: cpu: 1000m memory: 4Gi limits: cpu: 6000m memory: 8GiQPS
We did lots of stress test on APIs by Gatling before we release them, we mainly care about mean response time, std deviation, mean requests/sec, error rate (API Testing Report), during testing we monitor server metrics by Datadog to find out bottlenecks.
We usually test APIs in two scenarios: internal, external. External testing result is much lower than internal testing because of network latency, network bandwidth and son on.
Internal testing result
================================================================================ ---- Global Information -------------------------------------------------------- > request count 246000 (OK=246000 KO=0 ) > min response time 16 (OK=16 KO=- ) > max response time 5891 (OK=5891 KO=- ) > mean response time 86 (OK=86 KO=- ) > std deviation 345 (OK=345 KO=- ) > response time 50th percentile 30 (OK=30 KO=- ) > response time 75th percentile 40 (OK=40 KO=- ) > response time 95th percentile 88 (OK=88 KO=- ) > response time 99th percentile 1940 (OK=1940 KO=- ) > mean requests/sec 817.276 (OK=817.276 KO=- ) ---- Response Time Distraaibution ------------------------------------------------ > t < 800 ms 240565 ( 98%) > 800 ms < t < 1200 ms 1110 ( 0%) > t > 1200 ms 4325 ( 2%) > failed 0 ( 0%) ================================================================================
External testing result
================================================================================ ---- Global Information -------------------------------------------------------- > request count 33000 (OK=32999 KO=1 ) > min response time 477 (OK=477 KO=60001 ) > max response time 60001 (OK=41751 KO=60001 ) > mean response time 600 (OK=599 KO=60001 ) > std deviation 584 (OK=484 KO=0 ) > response time 50th percentile 497 (OK=497 KO=60001 ) > response time 75th percentile 506 (OK=506 KO=60001 ) > response time 95th percentile 1366 (OK=1366 KO=60001 ) > response time 99th percentile 2125 (OK=2122 KO=60001 ) > mean requests/sec 109.635 (OK=109.631 KO=0.003 ) ---- Response Time Distribution ------------------------------------------------ > t < 800 ms 29826 ( 90%) > 800 ms < t < 1200 ms 1166 ( 4%) > t > 1200 ms 2007 ( 6%) > failed 1 ( 0%) ---- Errors -------------------------------------------------------------------- > i.g.h.c.i.RequestTimeoutException: Request timeout after 60000 1 (100.0%) ms ================================================================================
Throttling
We throttle API by Nginx limit, we configured ingress like this:
annotations: nginx.ingress.kubernetes.io/limit-connections: '30' nginx.ingress.kubernetes.io/limit-rps: '60'
And it will generate Nginx configuration dynamically like this:
limit_conn_zone $limit_ZGVsaXZlcnktY2RuYV9kc2QtYXBpLWNkbmEtZ2F0ZXdheQ zone=xxx_conn:5m; limit_req_zone $limit_ZGVsaXZlcnktY2RuYV9kc2QtYXBpLWNkbmEtZ2F0ZXdheQ zone=xxx_rps:5m rate=60r/s; server { server_name xxx.xxx ; listen 80; location ~* "^/xxx/?(?<baseuri>.*)" { ... ... limit_conn xxx_conn 30; limit_req zone=xxx_rps burst=300 nodelay; ... ... }Scaling
We use HPA in kubernetes to ensure auto (Auto scaling in kubernetes), you could check HPA status in server:
[xxx@xxx ~]$ kubectl get hpa -n test-ns NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE api-demo Deployment/api-demo 39%/30%, 0%/30% 3 10 3 126d [xxx@xxx ~]$ kubectl get pod -n test-ns NAME READY STATUS RESTARTS AGE api-demo-76b9954f57-6hvzx 1/1 Running 0 126d api-demo-76b9954f57-mllsx 1/1 Running 0 126d api-demo-76b9954f57-s22k8 1/1 Running 0 126d
Throughput & Monitoring
We integrated Datadog for monitoring(Monitoring by Datadog), we could check detail API metrics from various dashboards.
Also we could calculate throughout from user, request, request time.
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Wang
Monitoring by Datadog
We have thousands of containers running on hundreds of servers, so we need comprehensive monitoring system to monitor service and server metrics.
We investigated popular cloud monitoring platform: New Relic and Datadog, finally we decided to use datadog.
Dashboard: Datadog could detect services and configure dashboards for you automatically.
Container & Process: You could check all your containers & process in all environments clearly.
Monitors: Datadog will create monitors according to service type automatically, if it doesn’t your requirement, you could create your own. It’s also convenient to send alert message through Slack, Email.
APM: Datadog provide various charts for API analysis, also there’s Service Map which you could check service dependencies.
Synthetics: New feature in Datadog which could test your API around the world to check availability and uptime.
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Wang
Sticky session in Kubernetes
As we know RESTful API is stateless, every request will be forward to backend server by round robin mechanism.
But in some scenario we need sticky session which means request from one client should be forward to one backend server.
After checking kubernetes documentation we added some annotations under ingress configuration, and it works well.
annotations: nginx.ingress.kubernetes.io/affinity: "cookie" nginx.ingress.kubernetes.io/session-cookie-name: "router" nginx.ingress.kubernetes.io/session-cookie-hash: "sha1"
If you open Developer Tools in Chrome, you will find the cookie.
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Wang
Nginx ingress in kubernetes
There are 3 ways to expose your service: NodePort, LoadBalancer, Ingress, next I will introduce about how to use ingress.
1.Deploy ingress controller
You need deploy ingress controller at first which will start nginx pods, then nginx will bind domains and listen to the requests.
I built a common ingress chart for different service, I only need change values-<service>.yaml and deploy script if any changes.
Another key point is that you must be clear about ingress-class, different service use different ingress-class, it will be quite messy if you mistake them.
args: - /nginx-ingress-controller - --default-backend-service=$(POD_NAMESPACE)/default-http-backend - --configmap=$(POD_NAMESPACE)/nginx-configuration - --tcp-services-configmap=$(POD_NAMESPACE)/tcp-services - --udp-services-configmap=$(POD_NAMESPACE)/udp-services - --ingress-class={{ .Values.server.namespace }} - --sort-backends=true2.Configure service ingress
Next we need configure service ingress which will append nginx server configuration dynamically.
I also built a service chart which include environment configurations, Jenkins & Helm will use different values-<env>.yaml when execute pipeline deployment.
Ingress example:
apiVersion: extensions/v1beta1 kind: Ingress metadata: name: {{ .Values.app.name }}{{ .Values.deploy.subfix }} namespace: {{ .Values.app.namespace }} annotations: kubernetes.io/ingress.class: "{{ .Values.ingress.class }}" kubernetes.io/tls-acme: "true" nginx.ingress.kubernetes.io/enable-cors: "false" nginx.ingress.kubernetes.io/rewrite-target: / nginx.ingress.kubernetes.io/proxy-body-size: 10m spec: rules: - host: {{ .Values.ingress.hostname }} http: paths: - path: {{ .Values.ingress.path }} backend: serviceName: {{ .Values.app.name }}{{ .Values.deploy.subfix }} servicePort: {{ .Values.container.port }}
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