If you are in a hurry and want to get hands-on with Istio insanely fast, just go to http://learn.openshift.com/servicemesh and start instantly
There are three different and super simple microservices in this system and they are chained together in the following sequence:
customer → preference → recommendationFor now, they have a simple exception handling solution for dealing with a missing dependent service: it just returns the error message to the end-user.
There are two more simple apps that illustrate how Istio handles egress routes: egressgithub and egresshttpbin
Table of Contents
You will need in this tutorial:
-
minishift -
docker
-
Fedora:
dnf install docker
-
kubectl
-
Fedora:
dnf install kubernetes-client
-
oc (eval $(minishift oc-env)) -
Apache Maven
-
Fedora:
dnf install maven
-
-
Mac OS:
brew install stern -
Fedora:
sudo curl --output /usr/local/bin/stern -L https://github.com/wercker/stern/releases/download/1.6.0/stern_linux_amd64 && sudo chmod +x /usr/local/bin/stern
-
-
istioctl (will be installed via the steps below)
-
curl,gunzip,tar-
Mac OS: built-in or part of your bash shell
-
Fedora: should also be installed already, but just in case…
dnf install curl gzip tar
-
-
git
-
dnf install git
-
Assumes minishift, tested with minishift v1.15.1+a5c47dd
#!/bin/bash
# add the location of minishift execuatable to PATH
# I also keep other handy tools like kubectl and kubetail.sh
# in that directory
minishift profile set istio-tutorial
minishift config set memory 8GB
minishift config set cpus 3
minishift config set vm-driver virtualbox ## or kvm, for Fedora
minishift config set image-caching true
minishift addon enable admin-user
minishift starteval $(minishift oc-env)
eval $(minishift docker-env)
oc login $(minishift ip):8443 -u admin -p admin|
Note
|
In this tutorial, you will often be polling the customer endpoint with curl, while simultaneously viewing logs via stern or kubetail.sh and issuing commands via oc and istioctl. Consider using three terminal windows.
|
#!/bin/bash
# Mac OS:
curl -L https://github.com/istio/istio/releases/download/0.6.0/istio-0.6.0-osx.tar.gz | tar xz
# Fedora:
curl -L https://github.com/istio/istio/releases/download/0.6.0/istio-0.6.0-linux.tar.gz | tar xz
# Both:
cd istio-0.6.0
export ISTIO_HOME=`pwd`
export PATH=$ISTIO_HOME/bin:$PATHoc login $(minishift ip):8443 -u admin -p admin
oc adm policy add-scc-to-user anyuid -z istio-ingress-service-account -n istio-system
oc adm policy add-scc-to-user anyuid -z default -n istio-system
oc adm policy add-scc-to-user anyuid -z grafana -n istio-system
oc adm policy add-scc-to-user anyuid -z prometheus -n istio-system
oc create -f install/kubernetes/istio.yaml
oc project istio-system
oc expose svc istio-ingress
oc apply -f install/kubernetes/addons/prometheus.yaml
oc apply -f install/kubernetes/addons/grafana.yaml
oc apply -f install/kubernetes/addons/servicegraph.yaml
oc expose svc servicegraph
oc expose svc grafana
oc expose svc prometheus
oc process -f https://raw.githubusercontent.com/jaegertracing/jaeger-openshift/master/all-in-one/jaeger-all-in-one-template.yml | oc create -f -Wait for Istio’s components to be ready
$ oc get pods -w
NAME READY STATUS RESTARTS AGE
grafana-3617079618-4qs2b 1/1 Running 0 4m
istio-ca-1363003450-tfnjp 1/1 Running 0 4m
istio-ingress-1005666339-vrjln 1/1 Running 0 4m
istio-mixer-465004155-zn78n 3/3 Running 0 5m
istio-pilot-1861292947-25hnm 2/2 Running 0 4m
jaeger-210917857-2w24f 1/1 Running 0 4m
prometheus-168775884-dr5dm 1/1 Running 0 4m
servicegraph-1100735962-tdh78 1/1 Running 0 4mAnd if you need quick access to the OpenShift console
minishift console|
Note
|
On your first launch of the OpenShift console via minishift, you will receive a warning like "Your connection is not private". For our demo, simply select "Proceed to 192.168.xx.xx (unsafe)" to bypass the warning. Both the username and the password are set to admin, thanks to the admin-user add-on.
|
Make sure you are logged in
oc whoamiand you have setup the project/namespace
oc new-project tutorial
oc adm policy add-scc-to-user privileged -z default -n tutorialThen clone the git repository
git clone https://github.com/redhat-developer-demos/istio-tutorial
cd istio-tutorialStart deploying the microservice projects, starting with customer
Make sure istioctl is in your PATH:
$ istioctl version
Version: 0.6.0
GitRevision: 2cb09cdf040a8573330a127947b11e5082619895
User: root@a28f609ab931
Hub: docker.io/istio
GolangVersion: go1.9
BuildStatus: Clean|
Note
|
Your very first Docker build will take a bit of time as it downloads all the layers. Subsequent rebuilds of the Docker image, updating only the microservice layer will be very fast. |
cd customer/java/springboot
mvn clean package
docker build -t example/customer .
docker images | grep customerNow let’s deploy the customer pod with its sidecar
oc apply -f <(istioctl kube-inject -f ../../kubernetes/Deployment.yml) -n tutorial
oc create -f ../../kubernetes/Service.yml -n tutorialoc new-app --name=customer --context-dir=customer/java/springboot -e JAEGER_SERVICE_NAME=customer JAEGER_ENDPOINT=http://jaeger-collector.istio-system.svc:14268/api/traces JAEGER_PROPAGATION=b3 JAEGER_SAMPLER_TYPE=const JAEGER_SAMPLER_PARAM=1 JAVA_OPTIONS='-Xms128m -Xmx256m -Djava.net.preferIPv4Stack=true' fabric8/s2i-java~https://github.com/redhat-developer-demos/istio-tutorial -o yaml > customer.yml
oc apply -f <(istioctl kube-inject -f customer.yml) -n tutorial
oc delete svc/customer ; oc expose dc/customer --port=8080
oc logs bc/customer -fSince the customer service is the one our users will interact with, let’s add an OpenShift Route that exposes that endpoint.
oc expose service customer
oc get route
oc get pods -w|
Important
|
If your pod fails with ImagePullBackOff, it’s possible that your current terminal isn’t using the proper Docker Environment. See Setup environment.
|
Wait until the status is Running and there are 2/2 pods in the Ready column. To exit, press Ctrl+C
Then test the customer endpoint
curl customer-tutorial.$(minishift ip).nip.ioYou should see the following error because the services preference and recommendation are not yet deployed.
customer => I/O error on GET request for "http://preference:8080": preference; nested exception is java.net.UnknownHostException: preference
Also review the logs
stern customer -c customerYou should see a stacktrace containing this cause:
org.springframework.web.client.ResourceAccessException: I/O error on GET request for "http://preference:8080": preference; nested exception is java.net.UnknownHostException: preferenceBack to the main istio-tutorial directory
cd ../../..cd preference/java/springboot
mvn clean package
docker build -t example/preference .
docker images | grep preference
oc apply -f <(istioctl kube-inject -f ../../kubernetes/Deployment.yml) -n tutorial
oc create -f ../../kubernetes/Service.ymloc new-app --name=preference --context-dir=preference/java/springboot -e JAEGER_SERVICE_NAME=preference JAEGER_ENDPOINT=http://jaeger-collector.istio-system.svc:14268/api/traces JAEGER_PROPAGATION=b3 JAEGER_SAMPLER_TYPE=const JAEGER_SAMPLER_PARAM=1 JAVA_OPTIONS='-Xms128m -Xmx256m -Djava.net.preferIPv4Stack=true' fabric8/s2i-java~https://github.com/redhat-developer-demos/istio-tutorial -o yaml > preference.yml
oc apply -f <(istioctl kube-inject -f preference.yml) -n tutorial
oc delete svc/preference ; oc expose dc/preference --port=8080
oc logs bc/preference -foc get pods -wWait until the status is Running and there are 2/2 pods in the Ready column. To exit, press Ctrl+C
curl customer-tutorial.$(minishift ip).nip.ioIt will respond with an error since the service recommendation is not yet deployed.
|
Note
|
We could make this a bit more resilent in a future iteration of this tutorial |
customer => 503 preference => I/O error on GET request for "http://recommendation:8080": recommendation; nested exception is java.net.UnknownHostException: recommendationand check out the logs
stern preference -c preferenceYou should see a stacktrace containing this cause:
org.springframework.web.client.ResourceAccessException: I/O error on GET request for "http://recommendation:8080": recommendation; nested exception is java.net.UnknownHostException: recommendationBack to the main istio-tutorial directory
cd ../../..|
Important
|
The tag v1 at the end of the image name matters. We will be creating a v2 version of recommendation later in this tutorial. Having both a v1 and v2 version of the recommendation code will allow us to exercise some interesting aspects of Istio’s capabilities.
|
cd recommendation/java/vertx
mvn clean package
docker build -t example/recommendation:v1 .
docker images | grep recommendation
oc apply -f <(istioctl kube-inject -f ../../kubernetes/Deployment.yml) -n tutorial
oc create -f ../../kubernetes/Service.yml
oc get pods -woc new-app -l app=recommendation,version=v1 --name=recommendation-v1 --context-dir=recommendation/java/vertx -e JAEGER_SERVICE_NAME=recommendation JAEGER_ENDPOINT=http://jaeger-collector.istio-system.svc:14268/api/traces JAEGER_PROPAGATION=b3 JAEGER_SAMPLER_TYPE=const JAEGER_SAMPLER_PARAM=1 JAVA_OPTIONS='-Xms128m -Xmx256m -Djava.net.preferIPv4Stack=true' fabric8/s2i-java~https://github.com/redhat-developer-demos/istio-tutorial -o yaml > recommendation.yml
oc apply -f <(istioctl kube-inject -f recommendation.yml) -n tutorial
oc delete svc/recommendation-v1 ; oc create -f recommendation/kubernetes/Service.yml
oc logs bc/recommendation-v1 -fWait until the status is Running and there are 2/2 pods in the Ready column. To exit, press Ctrl+C
curl customer-tutorial.$(minishift ip).nip.ioit should now return
customer => preference => recommendation v1 from '99634814-sf4cl': 1and you can monitor the recommendation logs with
stern recommendation -c recommendationBack to the main istio-tutorial directory
cd ../../..When you wish to change code (e.g. editing the .java files) and wish to "redeploy", simply:
cd {servicename}/java/{springboot|vertx}
vi src/main/java/com/redhat/developer/demos/{servicename}/{Servicename}{Controller|Verticle}.javaMake your changes, save it and then:
mvn clean package
docker build -t example/{servicename} .
oc get pods -o jsonpath='{.items[*].metadata.name}' -l app={servicename}
oc get pods -o jsonpath='{.items[*].metadata.name}' -l app={servicename},version=v1
oc delete pod -l app={servicename},version=v1Why the delete pod?
Based on the Deployment configuration, Kubernetes/OpenShift will recreate the pod, based on the new docker image as it attempts to keep the desired replicas available
oc describe deployment {servicename} | grep ReplicasOut of the box, you get monitoring via Prometheus and Grafana.
open "$(minishift openshift service grafana -u)/d/1/istio-dashboard?refresh=5s&orgId=1"
Scroll-down to see the stats for customer, preference and recommendation
Istio also allows you to specify custom metrics which can be seen inside of the Prometheus dashboard
open "$(minishift openshift service prometheus -u)/graph?g0.range_input=5m&g0.expr=&g0.tab=0"Add the custom metric and rule. First make sure you are in the "istio-tutorial" directory and then
istioctl create -f istiofiles/recommendation_requestcount.yml -n istio-systemIn the Prometheus dashboard, add the following
round(increase(istio_request_count{destination_service="recommendation.tutorial.svc.cluster.local" }[60m]))and select Execute
Then run several requests through the system
# Mac OS: run the following command a few times
curl customer-tutorial.$(minishift ip).nip.io
# Fedora:
watch -n 0.5 curl customer-tutorial.$(minishift ip).nip.io|
Note
|
You may have to refresh the browser for the Prometheus graph to update. And you may wish to make the interval 5m (5 minutes) as seen in the screenshot above. |
Distributed Tracing involves propagating the tracing context from service to service, usually done by sending certain incoming HTTP headers downstream to outbound requests. For services embedding a OpenTracing framework instrumentations such as opentracing-spring-cloud, this might be transparent. For services that are not embedding OpenTracing libraries, this context propagation needs to be done manually.
As OpenTracing is "just" an instrumentation library, a concrete tracer is required in order to actually capture the tracing data and report it to a remote server. Our customer and preference services ship with Jaeger as the concrete tracer. the Istio platform automatically sends collected tracing data to Jaeger, so that we are able to see a trace involving all three services, even if our recommendation service is not aware of OpenTracing or Jaeger at all.
Our customer and preference services are using the TracerResolver facility from OpenTracing, so that the concrete tracer can be loaded automatically without our code having a hard dependency on Jaeger. Given that the Jaeger tracer can be configured via environment variables, we don’t need to do anything in order to get a properly configured Jaeger tracer ready and registered with OpenTracing. That said, there are cases where it’s appropriate to manually configure a tracer. Refer to the Jaeger documentation for more information on how to do that.
Let’s open the Jaeger console, select customer from the list of services and click Find Traces
minishift openshift service jaeger-query --in-browser
We can experiment with Istio routing rules by making a change to RecommendationVerticle.java like the following and creating a "v2" docker image.
private static final String RESPONSE_STRING_FORMAT = "recommendation v2 from '%s': %d\n";The "v2" tag during the Docker build is significant.
There is also a second deployment.yml file to label things correctly
cd recommendation/java/vertx
mvn clean package
docker build -t example/recommendation:v2 .
docker images | grep recommendation
example/recommendation v2 c31e399a9628 5 seconds ago 438MB
example/recommendation v1 f072978d9cf6 8 minutes ago 438MBImportant: We have a 2nd Deployment to manage the v2 version of recommendation.
oc apply -f <(istioctl kube-inject -f ../../kubernetes/Deployment-v2.yml) -n tutorial
oc get pods -wmvn clean package -f recommendation/java/vertx
oc new-app -l app=recommendation,version=v2 --name=recommendation-v2 --context-dir=recommendation/java/vertx -e JAEGER_SERVICE_NAME=recommendation JAEGER_ENDPOINT=http://jaeger-collector.istio-system.svc:14268/api/traces JAEGER_PROPAGATION=b3 JAEGER_SAMPLER_TYPE=const JAEGER_SAMPLER_PARAM=1 JAVA_OPTIONS='-Xms128m -Xmx256m -Djava.net.preferIPv4Stack=true' fabric8/s2i-java~https://github.com/redhat-developer-demos/istio-tutorial -o yaml > recommendation-v2.yml
oc apply -f <(istioctl kube-inject -f recommendation-v2.yml) -n tutorial
oc cancel-build bc/recommendation-v2
oc delete svc/recommendation-v2
oc start-build recommendation-v2 --from-dir=. --followWait for those pods to show "2/2", the istio-proxy/envoy sidecar is part of that pod
NAME READY STATUS RESTARTS AGE
customer-3600192384-fpljb 2/2 Running 0 17m
preference-243057078-8c5hz 2/2 Running 0 15m
recommendation-v1-60483540-9snd9 2/2 Running 0 12m
recommendation-v2-2815683430-vpx4p 2/2 Running 0 15sand test the customer endpoint
curl customer-tutorial.$(minishift ip).nip.ioyou likely see "customer => preference => recommendation v1 from '99634814-d2z2t': 3", where '99634814-d2z2t' is the pod running v1 and the 3 is basically the number of times you hit the endpoint.
curl customer-tutorial.$(minishift ip).nip.ioyou likely see "customer => preference => recommendation v2 from '2819441432-5v22s': 1" as by default you get round-robin load-balancing when there is more than one Pod behind a Service
Send several requests to see their responses
#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
doneThe default Kubernetes/OpenShift behavior is to round-robin load-balance across all available pods behind a single Service. Add another replica of recommendation-v2 Deployment.
oc scale --replicas=2 deployment/recommendation-v2Now, you will see two requests into the v2 and one for v1.
customer => preference => recommendation v1 from '2819441432-qsp25': 29
customer => preference => recommendation v2 from '99634814-sf4cl': 37
customer => preference => recommendation v2 from '99634814-sf4cl': 38Scale back to a single replica of the recommendation-v2 Deployment
oc scale --replicas=1 deployment/recommendation-v2and back to the main directory
cd ../../..From the main istio-tutorial directory,
istioctl create -f istiofiles/route-rule-recommendation-v2.yml -n tutorial
curl customer-tutorial.$(minishift ip).nip.ioyou should only see v2 being returned
Note: "replace" instead of "create" since we are overlaying the previous rule
istioctl replace -f istiofiles/route-rule-recommendation-v1.yml -n tutorial
istioctl get routerules -n tutorial
istioctl get routerule recommendation-default -o yaml -n tutorialBy simply removing the rule
istioctl delete routerule recommendation-default -n tutorialand you should see the default behavior of load-balancing between v1 and v2
curl customer-tutorial.$(minishift ip).nip.ioCanary Deployment scenario: push v2 into the cluster but slowly send end-user traffic to it, if you continue to see success, continue shifting more traffic over time
oc get pods -l app=recommendation -n tutorial
NAME READY STATUS RESTARTS AGE
recommendation-v1-3719512284-7mlzw 2/2 Running 6 2h
recommendation-v2-2815683430-vn77w 2/2 Running 0 1hCreate the routerule that will send 90% of requests to v1 and 10% to v2
istioctl create -f istiofiles/route-rule-recommendation-v1_and_v2.yml -n tutorialand send in several requests
#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
doneIn another terminal, change the mixture to be 75/25
istioctl replace -f istiofiles/route-rule-recommendation-v1_and_v2_75_25.yml -n tutorialClean up
istioctl delete routerule recommendation-v1-v2 -n tutorialApply some chaos engineering by throwing in some HTTP errors or network delays. Understanding failure scenarios is a critical aspect of microservices architecture (aka distributed computing)
By default, recommendation v1 and v2 are being randomly load-balanced as that is the default behavior in Kubernetes/OpenShift
oc get pods -l app=recommendation -n tutorial
NAME READY STATUS RESTARTS AGE
recommendation-v1-3719512284-7mlzw 2/2 Running 6 18h
recommendation-v2-2815683430-vn77w 2/2 Running 0 3hYou can inject 503’s, for approximately 50% of the requests
istioctl create -f istiofiles/route-rule-recommendation-503.yml -n tutorial
curl customer-tutorial.$(minishift ip).nip.io
customer => preference => recommendation v1 from '99634814-sf4cl': 88
curl customer-tutorial.$(minishift ip).nip.io
customer => 503 preference => 503 fault filter abort
curl customer-tutorial.$(minishift ip).nip.io
customer => preference => recommendation v2 from '2819441432-qsp25': 51Clean up
istioctl delete routerule recommendation-503 -n tutorialThe most insidious of possible distributed computing faults is not a "down" service but a service that is responding slowly, potentially causing a cascading failure in your network of services.
istioctl create -f istiofiles/route-rule-recommendation-delay.yml -n tutorialAnd hit the customer endpoint
#!/bin/bash
while true
do
time curl customer-tutorial.$(minishift ip).nip.io
sleep .1
doneYou will notice many requets to the customer endpoint now have a delay. If you are monitoring the logs for recommendation v1 and v2, you will also see the delay happens BEFORE the recommendation service is actually called
stern recommendation -n tutorialor `bash ./kubetail.sh recommendation -n tutorial `
Clean up
istioctl delete routerule recommendation-delay -n tutorialInstead of failing immediately, retry the Service N more times
We will use Istio and return 503’s about 50% of the time. Send all users to v2 which will throw out some 503’s
istioctl create -f istiofiles/route-rule-recommendation-v2_503.yml -n tutorialNow, if you hit the customer endpoint several times, you should see some 503’s
#!/bin/bash
while true
do
curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done
customer => preference => recommendation v2 from '2036617847-m9glz': 190
customer => preference => recommendation v2 from '2036617847-m9glz': 191
customer => preference => recommendation v2 from '2036617847-m9glz': 192
customer => 503 preference => 503 fault filter abort
customer => preference => recommendation v2 from '2036617847-m9glz': 193
customer => 503 preference => 503 fault filter abort
customer => preference => recommendation v2 from '2036617847-m9glz': 194
customer => 503 preference => 503 fault filter abort
customer => preference => recommendation v2 from '2036617847-m9glz': 195
customer => 503 preference => 503 fault filter abortNow add the retry rule
istioctl create -f istiofiles/route-rule-recommendation-v2_retry.yml -n tutorialand after a few seconds, things will settle down and you will see it work every time
#!/bin/bash
while true
do
curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done
customer => preference => recommendation v2 from '2036617847-m9glz': 196
customer => preference => recommendation v2 from '2036617847-m9glz': 197
customer => preference => recommendation v2 from '2036617847-m9glz': 198You can see the active RouteRules via
istioctl get routerules -n tutorialNow, delete the retry rule and see the old behavior, some random 503s
istioctl delete routerule recommendation-v2-retry -n tutorial
while true
do
curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done
customer => preference => recommendation v2 from '2036617847-m9glz': 190
customer => preference => recommendation v2 from '2036617847-m9glz': 191
customer => preference => recommendation v2 from '2036617847-m9glz': 192
customer => 503 preference => 503 fault filter abort
customer => preference => recommendation v2 from '2036617847-m9glz': 193
customer => 503 preference => 503 fault filter abort
customer => preference => recommendation v2 from '2036617847-m9glz': 194
customer => 503 preference => 503 fault filter abort
customer => preference => recommendation v2 from '2036617847-m9glz': 195
customer => 503 preference => 503 fault filter abortNow, delete the 503 rule and back to random load-balancing between v1 and v2
istioctl delete routerule recommendation-v2-503 -n tutorial
while true
do
curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done
customer => preference => recommendation v1 from '2039379827-h58vw': 129
customer => preference => recommendation v2 from '2036617847-m9glz': 207
customer => preference => recommendation v1 from '2039379827-h58vw': 130Wait only N seconds before giving up and failing. At this point, no other route rules should be in effect. oc get routerules and oc delete routerule <rulename> if there are some.
First, introduce some wait time in recommendation v2 by uncommenting the line that calls the timeout() method. Update RecommendationVerticle.java making it a slow performer with a 3 second delay.
@Override
public void start() throws Exception {
Router router = Router.router(vertx);
router.get("/").handler(this::logging);
router.get("/").handler(this::timeout);
router.get("/").handler(this::getRecommendations);
router.get("/misbehave").handler(this::misbehave);
router.get("/behave").handler(this::behave);
HealthCheckHandler hc = HealthCheckHandler.create(vertx);
hc.register("dummy-health-check", future -> future.complete(Status.OK()));
router.get("/health").handler(hc);
vertx.createHttpServer().requestHandler(router::accept).listen(8080);
}Rebuild and redeploy
cd recommendation/java/vertx
mvn clean package
docker build -t example/recommendation:v2 .
docker images | grep recommendation
oc delete pod -l app=recommendation,version=v2 -n tutorial
cd ../../..Hit the customer endpoint a few times, to see the load-balancing between v1 and v2 but with v2 taking a bit of time to respond
#!/bin/bash
while true
do
time curl customer-tutorial.$(minishift ip).nip.io
sleep .1
doneThen add the timeout rule
istioctl create -f istiofiles/route-rule-recommendation-timeout.yml -n tutorialYou will see it return v1 OR "upstream request timeout" after waiting about 1 second
#!/bin/bash
while true
do
time curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done
customer => 503 preference => 504 upstream request timeout
curl customer-tutorial.$(minishift ip).nip.io 0.01s user 0.00s system 0% cpu 1.035 total
customer => preference => recommendation v1 from '2039379827-h58vw': 210
curl customer-tutorial.$(minishift ip).nip.io 0.01s user 0.00s system 36% cpu 0.025 total
customer => 503 preference => 504 upstream request timeout
curl customer-tutorial.$(minishift ip).nip.io 0.01s user 0.00s system 0% cpu 1.034 totalClean up, delete the timeout rule
istioctl delete routerule recommendation-timeout -n tutorialWhat is your user-agent?
Note: the "user-agent" header being forwarded in the Customer and Preferences controllers in order for route rule modications around recommendation
istioctl create -f istiofiles/route-rule-recommendation-v1.yml -n tutorialistioctl create -f istiofiles/route-rule-safari-recommendation-v2.yml -n tutorial
istioctl get routerules -n tutorialand test with a Safari (or even Chrome on Mac since it includes Safari in the string). Safari only sees v2 responses from recommendation
and test with a Firefox browser, it should only see v1 responses from recommendation.
There are two ways to get the URL for your browser:
minishift openshift service customer --in-browserThat will open the openshift service customer in browser
Or
if you need just the url alone:
minishift openshift service customer --url
http://customer-tutorial.192.168.99.102.nip.ioYou can also attempt to use the curl -A command to test with different user-agent strings.
curl -A Safari customer-tutorial.$(minishift ip).nip.io
curl -A Firefox customer-tutorial.$(minishift ip).nip.ioYou can describe the routerule to see its configuration
istioctl get routerule recommendation-safari -o yaml -n tutorialRemove the Safari rule
istioctl delete routerule recommendation-safari -n tutorialistioctl create -f istiofiles/route-rule-mobile-recommendation-v2.yml -n tutorial
curl -A "Mozilla/5.0 (iPhone; U; CPU iPhone OS 4(KHTML, like Gecko) Version/5.0.2 Mobile/8J2 Safari/6533.18.5" curl -A Safari customer-tutorial.$(minishift ip).nip.iooc get pods -l app=recommendation -n tutorialYou should have 2 pods for recommendation based on the steps above
istioctl get routerules -n tutorialYou should have NO routerules if so "istioctl delete routerule rulename -n tutorial"
Make sure you are in the main directory of "istio-tutorial"
istioctl create -f istiofiles/route-rule-recommendation-v1-mirror-v2.yml -n tutorial
curl customer-tutorial.$(minishift ip).nip.ioCheck the logs of recommendation-v2
oc logs -f `oc get pods|grep recommendation-v2|awk '{ print $1 }'` -c recommendationWe’ll create a whitelist on the preference service to only allow requests from the recommendation service, which will make the preference service invisible to the customer service. Requests from the customer service to the preference service will return a 404 Not Found HTTP error code.
istioctl create -f istiofiles/acl-whitelist.yml -n tutorialcurl customer-tutorial.$(minishift ip).nip.io
customer => 404 NOT_FOUND:preferencewhitelist.listchecker.tutorial:customer is not whitelistedWe’ll create a blacklist making the customer service blacklist to the preference service. Requests from the customer service to the preference service will return a 403 Forbidden HTTP error code.
istioctl create -f istiofiles/acl-blacklist.yml -n tutorialcurl customer-tutorial.$(minishift ip).nip.io
customer => 403 PERMISSION_DENIED:denycustomerhandler.denier.tutorial:Not allowedBy default, you will see "round-robin" style load-balancing, but you can change it up, with the RANDOM option being fairly visible to the naked eye.
Add another v2 pod to the mix
oc scale deployment recommendation-v2 --replicas=2 -n tutorialWait a bit (oc get pods -w to watch) and curl the customer endpoint many times
curl customer-tutorial.$(minishift ip).nip.ioAdd a 3rd v2 pod to the mix
oc scale deployment recommendation-v2 --replicas=3 -n tutorial
oc get pods -n tutorial
NAME READY STATUS RESTARTS AGE
customer-1755156816-cjd2z 2/2 Running 0 1h
preference-3336288630-2cc6f 2/2 Running 0 1h
recommendation-v1-3719512284-bn42p 2/2 Running 0 59m
recommendation-v2-2815683430-97nnf 2/2 Running 0 43m
recommendation-v2-2815683430-d49n6 2/2 Running 0 51m
recommendation-v2-2815683430-tptf2 2/2 Running 0 33mWait for those 2/2 (two containers in each pod) and then poll the customer endpoint
#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
doneThe results should follow a fairly normal round-robin distribution pattern
customer => preference => recommendation v1 from '99634814-d2z2t': 1145
customer => preference => recommendation v2 from '2819441432-525lh': 1
customer => preference => recommendation v2 from '2819441432-rg45q': 2
customer => preference => recommendation v2 from '2819441432-bs5ck': 181
customer => preference => recommendation v1 from '99634814-d2z2t': 1146
customer => preference => recommendation v2 from '2819441432-rg45q': 3
customer => preference => recommendation v2 from '2819441432-rg45q': 4
customer => preference => recommendation v2 from '2819441432-bs5ck': 182Now, add the Random LB DestinationPolicy
istioctl create -f istiofiles/recommendation_lb_policy_app.yml -n tutorialAnd you should see a different pattern of which pod is being selected
customer => preference => recommendation v2 from '2819441432-rg45q': 10
customer => preference => recommendation v2 from '2819441432-525lh': 3
customer => preference => recommendation v2 from '2819441432-rg45q': 11
customer => preference => recommendation v1 from '99634814-d2z2t': 1153
customer => preference => recommendation v1 from '99634814-d2z2t': 1154
customer => preference => recommendation v1 from '99634814-d2z2t': 1155
customer => preference => recommendation v2 from '2819441432-rg45q': 12
customer => preference => recommendation v2 from '2819441432-525lh': 4
customer => preference => recommendation v2 from '2819441432-525lh': 5
customer => preference => recommendation v2 from '2819441432-rg45q': 13
customer => preference => recommendation v2 from '2819441432-rg45q': 14Clean up
istioctl delete -f istiofiles/recommendation_lb_policy_app.yml -n tutorial
oc scale deployment recommendation-v2 --replicas=1 -n tutorialNote: currently not working
Here we will limit the number of concurrent requests into recommendation v2
Now apply the rate limit handler
istioctl create -f istiofiles/recommendation_rate_limit_handler.ymlNow setup the requestcount quota
istioctl create -f istiofiles/rate_limit_rule.ymlThrow some requests at customer
#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
doneYou should see some 429 errors:
customer => preference => recommendation v2 from '2819441432-f4ls5': 108
customer => preference => recommendation v1 from '99634814-d2z2t': 1932
customer => preference => recommendation v2 from '2819441432-f4ls5': 109
customer => preference => recommendation v1 from '99634814-d2z2t': 1933
customer => 503 preference => 429 Too Many Requests
customer => preference => recommendation v1 from '99634814-d2z2t': 1934
customer => preference => recommendation v2 from '2819441432-f4ls5': 110
customer => preference => recommendation v1 from '99634814-d2z2t': 1935
customer => 503 preference => 429 Too Many Requests
customer => preference => recommendation v1 from '99634814-d2z2t': 1936
customer => preference => recommendation v2 from '2819441432-f4ls5': 111
customer => preference => recommendation v1 from '99634814-d2z2t': 1937
customer => 503 preference => 429 Too Many Requests
customer => preference => recommendation v1 from '99634814-d2z2t': 1938
customer => preference => recommendation v2 from '2819441432-f4ls5': 112Clean up
istioctl delete -f istiofiles/rate_limit_rule.yml
istioctl delete -f istiofiles/recommendation_rate_limit_handler.ymlFirst, make sure to uncomment router.get("/").handler(this::timeout); in the RecommendationVerticle.java:
Router router = Router.router(vertx);
router.get("/").handler(this::logging);
router.get("/").handler(this::timeout);
router.get("/").handler(this::getRecommendations);
router.get("/misbehave").handler(this::misbehave);
router.get("/behave").handler(this::behave);And follow the Updating & redeploying code steps to get this slower v2 deployed.
Second, you need to insure you have a routerule in place. Let’s use a 50/50 split of traffic:
istioctl create -f istiofiles/route-rule-recommendation-v1_and_v2_50_50.yml -n tutorialLet’s perform a load test in our system with siege. We’ll have 20 clients sending 2 concurrent requests each:
siege -r 2 -c 20 -v customer-tutorial.$(minishift ip).nip.ioYou should see an output similar to this:
All of the requests to our system were successful, but it took some time to run the test, as the v2 instance/pod was a slow performer.
But suppose that in a production system this 3s delay was caused by too many concurrent requests to the same instance/pod. We don’t want multiple requests getting queued or making the instance/pod even slower. So we’ll add a circuit breaker that will open whenever we have more than 1 request being handled by any instance/pod.
istioctl create -f istiofiles/recommendation_cb_policy_version_v2.yml -n tutorial
istioctl get destinationpolicies -n tutorialMore information on the fields for the simple circuit-breaker https://istio.io/docs/reference/config/istio.routing.v1alpha1.html#CircuitBreaker.SimpleCircuitBreakerPolicy
Now let’s see what is the behavior of the system running siege again:
siege -r 2 -c 20 -v customer-tutorial.$(minishift ip).nip.io
You can run siege multiple times, but in all of the executions you should see some 503 errors being displayed in the results. That’s the circuit breaker being opened whenever Istio detects more than 1 pending request being handled by the instance/pod.
Pool ejection or outlier detection is a resilience strategy that takes place whenever we have a pool of instances/pods to serve a client request. If the request is forwarded to a certain instance and it fails (e.g. returns a 50x error code), then Istio will eject this instance from the pool for a certain sleep window. In our example the sleep window is configured to be 15s. This increases the overall availability by making sure that only healthy pods participate in the pool of instances.
First, you need to insure you have a routerule in place. Let’s use a 50/50 split of traffic:
istioctl create -f istiofiles/route-rule-recommendation-v1_and_v2_50_50.yml -n tutorialoc scale deployment recommendation-v2 --replicas=2 -n tutorial
oc get pods -wWait for all the pods to be in the ready state.
Throw some requests at the customer endpoint:
#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
doneYou will see the load balancing 50/50 between the two different versions of the recommendation service. And within version v2, you will also see that some requests are handled by one pod and some requests are handled by the other pod.
customer => preference => recommendation v1 from '2039379827-jmm6x': 447
customer => preference => recommendation v2 from '2036617847-spdrb': 26
customer => preference => recommendation v1 from '2039379827-jmm6x': 448
customer => preference => recommendation v2 from '2036617847-spdrb': 27
customer => preference => recommendation v1 from '2039379827-jmm6x': 449
customer => preference => recommendation v1 from '2039379827-jmm6x': 450
customer => preference => recommendation v2 from '2036617847-spdrb': 28
customer => preference => recommendation v1 from '2039379827-jmm6x': 451
customer => preference => recommendation v1 from '2039379827-jmm6x': 452
customer => preference => recommendation v2 from '2036617847-spdrb': 29
customer => preference => recommendation v2 from '2036617847-spdrb': 30
customer => preference => recommendation v2 from '2036617847-hdjv2': 216
customer => preference => recommendation v1 from '2039379827-jmm6x': 453
customer => preference => recommendation v2 from '2036617847-spdrb': 31
customer => preference => recommendation v2 from '2036617847-hdjv2': 217
customer => preference => recommendation v2 from '2036617847-hdjv2': 218
customer => preference => recommendation v1 from '2039379827-jmm6x': 454
customer => preference => recommendation v1 from '2039379827-jmm6x': 455
customer => preference => recommendation v2 from '2036617847-hdjv2': 219
customer => preference => recommendation v2 from '2036617847-hdjv2': 220Let’s get the name of the pods from recommendation v2:
oc get pods -l app=recommendation,version=v2You should see something like this:
recommendation-v2-2036617847-hdjv2 2/2 Running 0 1h
recommendation-v2-2036617847-spdrb 2/2 Running 0 7mNow we’ll get into one the pods and add some erratic behavior on it. Get one of the pod names from your system and replace on the following command accordingly:
oc exec -it recommendation-v2-2036617847-spdrb -c recommendation /bin/bashYou will be inside the application container of your pod recommendation-v2-2036617847-spdrb. Now execute:
curl localhost:8080/misbehave
exitThis is a special endpoint that will make our application return only `503`s.
Throw some requests at the customer endpoint:
#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
doneYou’ll see that whenever the pod recommendation-v2-2036617847-spdrb receives a request, you get a 503 error:
customer => preference => recommendation v1 from '2039379827-jmm6x': 494
customer => preference => recommendation v1 from '2039379827-jmm6x': 495
customer => preference => recommendation v2 from '2036617847-hdjv2': 248
customer => preference => recommendation v1 from '2039379827-jmm6x': 496
customer => preference => recommendation v1 from '2039379827-jmm6x': 497
customer => 503 preference => 503 recommendation misbehavior from '2036617847-spdrb'
customer => preference => recommendation v2 from '2036617847-hdjv2': 249
customer => preference => recommendation v1 from '2039379827-jmm6x': 498
customer => 503 preference => 503 recommendation misbehavior from '2036617847-spdrb'
customer => preference => recommendation v2 from '2036617847-hdjv2': 250
customer => preference => recommendation v1 from '2039379827-jmm6x': 499
customer => preference => recommendation v1 from '2039379827-jmm6x': 500
customer => 503 preference => 503 recommendation misbehavior from '2036617847-spdrb'
customer => preference => recommendation v1 from '2039379827-jmm6x': 501
customer => preference => recommendation v2 from '2036617847-hdjv2': 251
customer => 503 preference => 503 recommendation misbehavior from '2036617847-spdrb'Now let’s add the pool ejection behavior:
istioctl create -f istiofiles/recommendation_cb_policy_pool_ejection.yml -n tutorialThrow some requests at the customer endpoint:
#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
doneYou will see that whenever you get a failing request with 503 from the pod recommendation-v2-2036617847-spdrb, it gets ejected from the pool, and it doesn’t receive any more requests until the sleep window expires - which takes at least 15s.
customer => preference => recommendation v1 from '2039379827-jmm6x': 509
customer => 503 preference => 503 recommendation misbehavior from '2036617847-spdrb'
customer => preference => recommendation v1 from '2039379827-jmm6x': 510
customer => preference => recommendation v1 from '2039379827-jmm6x': 511
customer => preference => recommendation v1 from '2039379827-jmm6x': 512
customer => preference => recommendation v1 from '2039379827-jmm6x': 513
customer => preference => recommendation v1 from '2039379827-jmm6x': 514
customer => preference => recommendation v2 from '2036617847-hdjv2': 256
customer => preference => recommendation v2 from '2036617847-hdjv2': 257
customer => preference => recommendation v1 from '2039379827-jmm6x': 515
customer => preference => recommendation v2 from '2036617847-hdjv2': 258
customer => preference => recommendation v2 from '2036617847-hdjv2': 259
customer => preference => recommendation v2 from '2036617847-hdjv2': 260
customer => preference => recommendation v1 from '2039379827-jmm6x': 516
customer => preference => recommendation v1 from '2039379827-jmm6x': 517
customer => preference => recommendation v1 from '2039379827-jmm6x': 518
customer => 503 preference => 503 recommendation misbehavior from '2036617847-spdrb'
customer => preference => recommendation v1 from '2039379827-jmm6x': 519
customer => preference => recommendation v1 from '2039379827-jmm6x': 520
customer => preference => recommendation v1 from '2039379827-jmm6x': 521
customer => preference => recommendation v2 from '2036617847-hdjv2': 261
customer => preference => recommendation v2 from '2036617847-hdjv2': 262
customer => preference => recommendation v2 from '2036617847-hdjv2': 263
customer => preference => recommendation v1 from '2039379827-jmm6x': 522
customer => preference => recommendation v1 from '2039379827-jmm6x': 523
customer => preference => recommendation v2 from '2036617847-hdjv2': 264
customer => preference => recommendation v1 from '2039379827-jmm6x': 524
customer => preference => recommendation v1 from '2039379827-jmm6x': 525
customer => preference => recommendation v1 from '2039379827-jmm6x': 526
customer => preference => recommendation v1 from '2039379827-jmm6x': 527
customer => preference => recommendation v2 from '2036617847-hdjv2': 265
customer => preference => recommendation v2 from '2036617847-hdjv2': 266
customer => preference => recommendation v1 from '2039379827-jmm6x': 528
customer => preference => recommendation v2 from '2036617847-hdjv2': 267
customer => preference => recommendation v2 from '2036617847-hdjv2': 268
customer => preference => recommendation v2 from '2036617847-hdjv2': 269
customer => 503 preference => 503 recommendation misbehavior from '2036617847-spdrb'
customer => preference => recommendation v1 from '2039379827-jmm6x': 529
customer => preference => recommendation v2 from '2036617847-hdjv2': 270Even with pool ejection your application doesn’t look that resilient. That’s probably because we’re still letting some errors to be propagated to our clients. But we can improve this. If we have enough instances and/or versions of a specific service running into our system, we can combine multiple Istio capabilities to achieve the ultimate backend resilience: - Circuit Breaker to avoid multiple concurrent requests to an instance; - Pool Ejection to remove failing instances from the pool of responding instances; - Retries to forward the request to another instance just in case we get an open circuit breaker and/or pool ejection;
By simply adding a retry configuration to our current routerule, we’ll be able to get rid completely of our `503`s requests. This means that whenever we receive a failed request from an ejected instance, Istio will forward the request to another supposably healthy instance.
istioctl replace -f istiofiles/route-rule-recommendation-v1_and_v2_retry.yml -n tutorialThrow some requests at the customer endpoint:
#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
doneYou won’t receive 503`s anymore. But the requests from recommendation `v2 are still taking more time to get a response:
customer => preference => recommendation v1 from '2039379827-jmm6x': 538
customer => preference => recommendation v1 from '2039379827-jmm6x': 539
customer => preference => recommendation v1 from '2039379827-jmm6x': 540
customer => preference => recommendation v2 from '2036617847-hdjv2': 281
customer => preference => recommendation v1 from '2039379827-jmm6x': 541
customer => preference => recommendation v2 from '2036617847-hdjv2': 282
customer => preference => recommendation v1 from '2039379827-jmm6x': 542
customer => preference => recommendation v1 from '2039379827-jmm6x': 543
customer => preference => recommendation v1 from '2039379827-jmm6x': 544
customer => preference => recommendation v2 from '2036617847-hdjv2': 283
customer => preference => recommendation v2 from '2036617847-hdjv2': 284
customer => preference => recommendation v1 from '2039379827-jmm6x': 545
customer => preference => recommendation v1 from '2039379827-jmm6x': 546
customer => preference => recommendation v1 from '2039379827-jmm6x': 547
customer => preference => recommendation v2 from '2036617847-hdjv2': 285
customer => preference => recommendation v2 from '2036617847-hdjv2': 286
customer => preference => recommendation v1 from '2039379827-jmm6x': 548
customer => preference => recommendation v2 from '2036617847-hdjv2': 287
customer => preference => recommendation v2 from '2036617847-hdjv2': 288
customer => preference => recommendation v1 from '2039379827-jmm6x': 549
customer => preference => recommendation v2 from '2036617847-hdjv2': 289
customer => preference => recommendation v2 from '2036617847-hdjv2': 290
customer => preference => recommendation v2 from '2036617847-hdjv2': 291
customer => preference => recommendation v2 from '2036617847-hdjv2': 292
customer => preference => recommendation v1 from '2039379827-jmm6x': 550
customer => preference => recommendation v1 from '2039379827-jmm6x': 551
customer => preference => recommendation v1 from '2039379827-jmm6x': 552
customer => preference => recommendation v1 from '2039379827-jmm6x': 553
customer => preference => recommendation v2 from '2036617847-hdjv2': 293
customer => preference => recommendation v2 from '2036617847-hdjv2': 294
customer => preference => recommendation v1 from '2039379827-jmm6x': 554Our misbehaving pod recommendation-v2-2036617847-spdrb never shows up in the console, thanks to pool ejection and retry.
There are two examples of egress routing, one for httpbin.org and one for github. Egress routes allow you to apply rules to how internal services interact with external APIs/services.
Create a namespace/project to hold these egress examples
oc new-project istioegress
oc adm policy add-scc-to-user privileged -z default -n istioegresscd egress/egresshttpbin/
mvn spring-boot:run
curl localhost:8080
ctrl-c
mvn clean package
docker build -t example/egresshttpbin:v1 .
docker images | grep egress
docker run -it -p 8080:8080 --rm example/egresshttpbin:v1
curl $(minishift ip):8080
ctrl-c
docker ps | grep egress
docker ps -a | grep egress
oc apply -f <(istioctl kube-inject -f src/main/kubernetes/Deployment.yml) -n istioegress
oc create -f src/main/kubernetes/Service.yml
oc expose service egresshttpbin
curl egresshttpbin-istioegress.$(minishift ip).nip.ioNote: It does not work…yet, more to come.
Back to the main istio-tutorial directory
cd ../..cd egress/egressgithub/
mvn clean package
docker build -t example/egressgithub:v1 .
docker images | grep egress
docker run -it -p 8080:8080 --rm example/egressgithub:v1
curl $(minishift ip):8080Note: it will not work now but it will once Istio-ized
ctrl-c
docker ps | grep egress
oc apply -f <(istioctl kube-inject -f src/main/kubernetes/Deployment.yml) -n istioegress
oc create -f src/main/kubernetes/Service.yml
oc expose service egressgithub
curl egressgithub-istioegress.$(minishift ip).nip.io
cd ../..istioctl create -f istiofiles/egress_httpbin.yml -n istioegress
istioctl get egressrules
curl egresshttpbin-istioegress.$(minishift ip).nip.ioor shell into the pod by getting its name and then using that name with oc exec
oc exec -it $(oc get pods -o jsonpath="{.items[*].metadata.name}" -l app=egresshttpbin,version=v1) -c egresshttpbin /bin/bash
curl localhost:8080
curl httpbin.org/user-agent
curl httpbin.org/headers
exitadd a egressrule for google
cat <<EOF | istioctl create -f -
apiVersion: config.istio.io/v1alpha2
kind: EgressRule
metadata:
name: google-egress-rule
namespace: istioegress
spec:
destination:
service: www.google.com
ports:
- port: 443
protocol: https
EOFand shell into the github pod for testing google access
oc exec -it $(oc get pods -o jsonpath="{.items[*].metadata.name}" -l app=egressgithub,version=v1) -c egressgithub /bin/bash
curl http://www.google.com:443
exitNow, apply the egressrule for github and execute the Java code that hits api.github.com/users
istioctl create -f istiofiles/egress_github.yml -n istioegress
curl egressgithub-istioegress.$(minishift ip).nip.ioSome tips and tricks that you might find handy
You have two containers in a pod
oc get pods -o jsonpath="{.items[*].spec.containers[*].name}" -l app=customer -n tutorialFrom these images
oc get pods -o jsonpath="{.items[*].spec.containers[*].image}" -l app=customer -n tutorialGet the pod ids
CPOD=$(oc get pods -o jsonpath='{.items[*].metadata.name}' -l app=customer -n tutorial)
PPOD=$(oc get pods -o jsonpath='{.items[*].metadata.name}' -l app=preference -n tutorial)
RPOD1=$(oc get pods -o jsonpath='{.items[*].metadata.name}' -l app=recommendation,version=v1 -n tutorial)
RPOD2=$(oc get pods -o jsonpath='{.items[*].metadata.name}' -l app=recommendation,version=v2 -n tutorial)The pods all see each other’s services
oc exec $CPOD -c customer -n tutorial curl http://preference:8080
oc exec $CPOD -c customer -n tutorial curl http://recommendation:8080
oc exec $RPOD2 -c recommendation -n tutorial curl http://customer:8080oc exec $CPOD -c customer -n tutorial curl http://localhost:15000/routes > afile.jsonLook for "route_config_name": "8080", you should see 3 entries for customer, preference and recommendation
{
"name": "8080",
"virtual_hosts": [{
"name": "customer.springistio.svc.cluster.local|http",
"domains": ["customer:8080", "customer", "customer.springistio:8080", "customer.springistio", "customer.springistio.svc:8080", "customer.springistio.svc", "customer.springistio.svc.cluster:8080", "customer.springistio.svc.cluster", "customer.springistio.svc.cluster.local:8080", "customer.springistio.svc.cluster.local", "172.30.176.159:8080", "172.30.176.159"],
"routes": [{
"match": {
"prefix": "/"
},
"route": {
"cluster": "out.customer.springistio.svc.cluster.local|http",
"timeout": "0s"
},
"decorator": {
"operation": "default-route"
}
}]
}, {
"name": "preference.springistio.svc.cluster.local|http",
"domains": ["preference:8080", "preference", "preference.springistio:8080", "preference.springistio", "preference.springistio.svc:8080", "preference.springistio.svc", "preference.springistio.svc.cluster:8080", "preference.springistio.svc.cluster", "preference.springistio.svc.cluster.local:8080", "preference.springistio.svc.cluster.local", "172.30.249.133:8080", "172.30.249.133"],
"routes": [{
"match": {
"prefix": "/"
},
"route": {
"cluster": "out.preference.springistio.svc.cluster.local|http",
"timeout": "0s"
},
"decorator": {
"operation": "default-route"
}
}]
}, {
"name": "recommendation.springistio.svc.cluster.local|http",
"domains": ["recommendation:8080", "recommendation", "recommendation.springistio:8080", "recommendation.springistio", "recommendation.springistio.svc:8080", "recommendation.springistio.svc", "recommendation.springistio.svc.cluster:8080", "recommendation.springistio.svc.cluster", "recommendation.springistio.svc.cluster.local:8080", "recommendation.springistio.svc.cluster.local", "172.30.209.113:8080", "172.30.209.113"],
"routes": [{
"match": {
"prefix": "/"
},
"route": {
"cluster": "out.recommendation.springistio.svc.cluster.local|http",
"timeout": "0s"
},
"decorator": {
"operation": "default-route"
}
}]
}]
}Now add a new routerule
oc create -f istiofiles/route-rule-recommendation-v2.ymlThe review the routes again
oc exec $CPOD -c customer -n tutorial curl http://localhost:15000/routes > bfile.jsonHere is the Before:
"route": {
"cluster": "out.recommendation.springistio.svc.cluster.local|http",
"timeout": "0s"
},and
"decorator": {
"operation": "default-route"
}And the After:
"route": {
"cluster": "out.recommendation.springistio.svc.cluster.local|http|version=v2",
"timeout": "0s"
},and
"decorator": {
"operation": "recommendation-default"
}If you need the Pod IP
oc get pods -o jsonpath='{.items[*].status.podIP}' -l app=customer -n tutorialDive into the istio-proxy container
oc exec -it $CPOD -c istio-proxy -n tutorial /bin/bash
cd /etc/istio/proxy
ls
cat envoy-rev3.jsonSnowdrop Troubleshooting
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