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Java (Spring Boot and Vert.x) + Istio on Kubernetes/OpenShift

There are three different and super simple microservices in this system and they are chained together in the following sequence:

customer -> preference -> recommendation

For 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

Prerequisite CLI tools

You will need in this tutorial

Setup minishift

Assumes minishift, tested with minishift v1.12.0+daa0943

Minishift creation script

#!/bin/bash

# add the location of minishift execuatable to PATH
# I also keep other handy tools like kubectl and kubetail.sh
# in that directory

export MINISHIFT_HOME=~/minishift_1.13.1
export PATH=$MINISHIFT_HOME:$PATH

minishift profile set tutorial
minishift config set memory 8GB
minishift config set cpus 3
minishift config set vm-driver virtualbox
minishift config set image-caching true
minishift addon enable admin-user

minishift start

Setup environment

eval $(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.

Istio installation script

#!/bin/bash
curl -L https://github.com/istio/istio/releases/download/0.6.0/istio-0.6.0-osx.tar.gz | tar xz
cd istio-0.6.0
oc 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
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          4m

And if you need quick access to the OpenShift console

minishift console

Note: on your first launch of the OpenShift console via minishift, you will like receive a warning with "Your connection is not private", it depends on your browser type and settings. Simply select "Proceed to 192.168.99.100 (unsafe)" to bypass the warning.

For minishift, with the admin-user addon, the user is "admin" and the password is "admin"

Deploy customer

Make sure you have are logged in

oc whoami

and you have setup the project/namespace

oc new-project tutorial
oc adm policy add-scc-to-user privileged -z default -n tutorial

Then clone the git repository

git clone https://github.com/redhat-developer-demos/istio-tutorial
cd istio-tutorial

Start deploying the microservice projects, starting with customer

cd customer

mvn clean package

docker build -t example/customer .

docker images | grep customer

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 jar/app layer will be very fast.

Add istioctl to your $PATH, you downloaded it a few steps back. An example

export ISTIO_HOME=~/istio-0.6.0
export PATH=$ISTIO_HOME/bin:$PATH

istioctl version

Version: 0.6.0
GitRevision: 2cb09cdf040a8573330a127947b11e5082619895
User: root@a28f609ab931
Hub: docker.io/istio
GolangVersion: go1.9
BuildStatus: Clean

Now let's deploy the customer pod with its sidecar

oc apply -f <(istioctl kube-inject -f src/main/kubernetes/Deployment.yml) -n tutorial

oc create -f src/main/kubernetes/Service.yml -n tutorial

Since customer is the forward most microservice (customer -> preference -> recommendation), let's add an OpenShift Route that exposes that endpoint.

oc expose service customer

oc get route

oc get pods -w

Waiting for Ready 2/2, to break out of the waiting use "ctrl-c"

Then test the customer endpoint

curl customer-tutorial.$(minishift ip).nip.io

You should see the following error because 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 customer

You 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: preference

Back to the main istio-tutorial directory

cd ..

Deploy preference

cd preference

mvn clean package

docker build -t example/preference .

docker images | grep preference

oc apply -f <(istioctl kube-inject -f src/main/kubernetes/Deployment.yml) -n tutorial

oc create -f src/main/kubernetes/Service.yml

oc get pods -w

Wait for the Ready 2/2

curl customer-tutorial.$(minishift ip).nip.io

It will respond with an error since 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: recommendation

and check out the logs

stern preference -c preference

You 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: recommendation

Back to the main istio-tutorial directory

cd ..

Deploy recommendation

Note: The tag "v1" at the end of the image name is important. 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

mvn clean package

docker build -t example/recommendation:v1 .

docker images | grep recommendation

oc apply -f <(istioctl kube-inject -f src/main/kubernetes/Deployment.yml) -n tutorial

oc create -f src/main/kubernetes/Service.yml

oc get pods -w

curl customer-tutorial.$(minishift ip).nip.io

it returns

customer => preference => recommendation v1 from '99634814-sf4cl': 1

and you can monitor the recommendation logs with

stern recommendation -c recommendation

Back to the main istio-tutorial directory

cd ..

Updating Redeploying Code

When you wish to change code (e.g. editing the .java files) and wish to "redeploy", simply:

cd {servicename}

vi src/main/java/com/redhat/developer/demos/{servicename}/{Servicename}{Controller|Verticle}.java

Make your edits and esc-w-q

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=v1

Why 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 Replicas

Monitoring

Out of the box, you get monitoring via Prometheus and Grafana.

open "$(minishift openshift service grafana -u)/d/1/istio-dashboard?refresh=5s&orgId=1"

alt text

Scroll-down to see the stats for customer, preference and recommendation

alt text

Custom Metrics

Istio also allows you to specify custom metrics which can be seen inside of the Prometheus dashboard

minishift openshift service prometheus --in-browser

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-system

In the Prometheus dashboard, add the following

round(increase(istio_recommendation_request_count{destination="recommendation.tutorial.svc.cluster.local" }[60m]))

and select Execute

alt text

Then run several requests through the system

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.

Tracing

Tracing requires a bit of work on the Java side. Each microservice needs to pass on the headers which are used to enable the traces.

https://github.com/redhat-developer-demos/istio-tutorial/blob/master/customer/src/main/java/com/redhat/developer/demos/customer/tracing/HttpHeaderForwarderHandlerInterceptor.java

and

https://github.com/redhat-developer-demos/istio-tutorial/blob/master/customer/src/main/java/com/redhat/developer/demos/customer/CustomerApplication.java#L21-L31

To open the Jaeger console, select customer from the list of services and Find Traces

minishift openshift service jaeger-query --in-browser

alt text

Istio RouteRule Changes

recommendation:v2

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 2nd deployment.yml file to label things correctly

cd recommendation

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      438MB

Important: We have a 2nd Deployment to manage the v2 version of recommendation.

oc apply -f <(istioctl kube-inject -f src/main/kubernetes/Deployment-v2.yml) -n tutorial

oc get pods -w

Wait 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         15s

and test the customer endpoint

curl customer-tutorial.$(minishift ip).nip.io

you 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.io

you 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
done

The 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-v2

Now, 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': 38

Scale back to a single replica of the recommendation-v2 Deployment

oc scale --replicas=1 deployment/recommendation-v2

and back to the main directory

cd ..

Changing Istio RouteRules

All users to recommendation:v2

From the main istio-tutorial directory,

istioctl create -f istiofiles/route-rule-recommendation-v2.yml -n tutorial

curl customer-tutorial.$(minishift ip).nip.io

you should only see v2 being returned

All users to recommendation:v1

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 tutorial

All users to recommendation v1 and v2

By simply removing the rule

istioctl delete routerule recommendation-default -n tutorial

and you should see the default behavior of load-balancing between v1 and v2

curl customer-tutorial.$(minishift ip).nip.io

Split traffic between v1 and v2

Canary 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          1h

Create 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 tutorial

and send in several requests

#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done

In another terminal, change the mixture to be 75/25

istioctl replace -f istiofiles/route-rule-recommendation-v1_and_v2_75_25.yml -n tutorial

Clean up

istioctl delete routerule recommendation-v1-v2 -n tutorial

Fault Injection

Apply 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)

HTTP Error 503

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          3h

You 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': 51

Clean up

istioctl delete routerule recommendation-503 -n tutorial

Delay

The 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 tutorial

And hit the customer endpoint

#!/bin/bash
while true
do
time curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done

You 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 tutorial

or

./kubetail.sh recommendation -n tutorial

Clean up

istioctl delete routerule recommendation-delay -n tutorial

Retry

Instead 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 tutorial

Now, 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 abort

Now add the retry rule

istioctl create -f istiofiles/route-rule-recommendation-v2_retry.yml -n tutorial

and 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': 198

You can see the active RouteRules via

istioctl get routerules -n tutorial

Now, 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 abort

Now, 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': 130

Timeout

Wait 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

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
done

Then add the timeout rule

istioctl create -f istiofiles/route-rule-recommendation-timeout.yml -n tutorial

You 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 total

Clean up, delete the timeout rule

istioctl delete routerule recommendation-timeout -n tutorial

Smart routing based on user-agent header (Canary Deployment)

What is your user-agent?

https://www.whoishostingthis.com/tools/user-agent/

Note: the "user-agent" header being forwarded in the Customer and Preferences controllers in order for route rule modications around recommendation

Set recommendation to all v1

istioctl create -f istiofiles/route-rule-recommendation-v1.yml -n tutorial

Set Safari users to v2

istioctl create -f istiofiles/route-rule-safari-recommendation-v2.yml -n tutorial

istioctl get routerules -n tutorial

and 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-browser

That 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.io

You 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.io

You can describe the routerule to see its configuration

istioctl get routerule recommendation-safari -o yaml -n tutorial

Remove the Safari rule

istioctl delete routerule recommendation-safari -n tutorial

Set mobile users to v2

istioctl 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.io

Clean up

istioctl delete routerule recommendation-mobile -n tutorial

Mirroring Traffic (Dark Launch)

oc get pods -l app=recommendation -n tutorial

You should have 2 pods for recommendation based on the steps above

istioctl get routerules -n tutorial

You 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.io

Check the logs of recommendation-v2

oc logs -f `oc get pods|grep recommendation-v2|awk '{ print $1 }'` -c recommendation

Clean up

istioctl delete routerule recommendation-mirror -n tutorial

Access Control

Whitelist

We'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 tutorial
curl customer-tutorial.$(minishift ip).nip.io
customer => 404 NOT_FOUND:preferencewhitelist.listchecker.tutorial:customer is not whitelisted
To reset the environment:
istioctl delete -f istiofiles/acl-whitelist.yml -n tutorial

Blacklist

We'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 tutorial
curl customer-tutorial.$(minishift ip).nip.io
customer => 403 PERMISSION_DENIED:denycustomerhandler.denier.tutorial:Not allowed
To reset the environment:
istioctl delete -f istiofiles/acl-blacklist.yml -n tutorial

Load Balancer

By 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 tutorial

Wait a bit (oc get pods -w to watch) and curl the customer endpoint many times

curl customer-tutorial.$(minishift ip).nip.io

Add 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          33m

Wait 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
done

The 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': 182

Now, add the Random LB DestinationPolicy

istioctl create -f istiofiles/recommendation_lb_policy_app.yml -n tutorial

And 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': 14

Clean up

istioctl delete -f istiofiles/recommendation_lb_policy_app.yml -n tutorial

oc scale deployment recommendation-v2 --replicas=1 -n tutorial

Rate Limiting

Note: 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.yml

Now setup the requestcount quota

istioctl create -f istiofiles/rate_limit_rule.yml

Throw some requests at customer

#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done

You 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': 112

Clean up

istioctl delete -f istiofiles/rate_limit_rule.yml

istioctl delete -f istiofiles/recommendation_rate_limit_handler.yml

Circuit Breaker

Fail Fast with Max Connections and Max Pending Requests

First, 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 tutorial

Load test without circuit breaker

Let'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.io

You should see an output similar to this:

siege output with all successful requests

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 tutorial

More information on the fields for the simple circuit-breaker https://istio.io/docs/reference/config/istio.routing.v1alpha1.html#CircuitBreaker.SimpleCircuitBreakerPolicy

Load test with circuit breaker

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

siege output with some 503 requests due to open circuit breaker

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.

Clean up

istioctl delete routerule recommendation-v1-v2 -n tutorial
istioctl delete -f istiofiles/recommendation_cb_policy_version_v2.yml -n tutorial

Pool Ejection

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 tutorial

Scale number of instances of v2 deployment

oc scale deployment recommendation-v2 --replicas=2 -n tutorial
oc get pods -w

Wait for all the pods to be in the ready state.

Test behavior without failing instances

Throw some requests at the customer endpoint:

#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done

You 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': 220

Test behavior with failing instance and without pool ejection

Let's get the name of the pods from recommendation v2:

oc get pods -l app=recommendation,version=v2

You should see something like this:

recommendation-v2-2036617847-hdjv2   2/2       Running   0          1h
recommendation-v2-2036617847-spdrb   2/2       Running   0          7m

Now 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/bash

You will be inside the application container of your pod recommendation-v2-2036617847-spdrb. Now execute:

curl localhost:8080/misbehave
exit

This is a special endpoint that will make our application return only 503s.

Throw some requests at the customer endpoint:

#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done

You'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'

Test behavior with failing instance and with pool ejection

Now let's add the pool ejection behavior:

istioctl create -f istiofiles/recommendation_cb_policy_pool_ejection.yml -n tutorial

Throw some requests at the customer endpoint:

#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done

You 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': 270

Ultimate resilience with retries, circuit breaker, and pool ejection

Even 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 503s 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 tutorial

Throw some requests at the customer endpoint:

#!/bin/bash
while true
do curl customer-tutorial.$(minishift ip).nip.io
sleep .1
done

You won't receive 503s 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': 554

Our misbehaving pod recommendation-v2-2036617847-spdrb never shows up in the console, thanks to pool ejection and retry.

Clean up

oc scale deployment recommendation-v2 --replicas=1 -n tutorial
oc delete pod -l app=recommendation,version=v2
istioctl delete routerule recommendation-v1-v2 -n tutorial
istioctl delete -f istiofiles/recommendation_cb_policy_pool_ejection.yml -n tutorial

Egress

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 istioegress

Create HTTPBin Java App

cd 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.io

Note: It does not work...yet, more to come.

Back to the main istio-tutorial directory

cd ../..

Create the Github Java App

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):8080

Note: 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 ../..

Istio-ize Egress

istioctl create -f istiofiles/egress_httpbin.yml -n istioegress

istioctl get egressrules

curl egresshttpbin-istioegress.$(minishift ip).nip.io

or 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

exit

add 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
EOF

and 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

exit

Now, 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.io

Clean up

istioctl delete egressrule httpbin-egress-rule google-egress-rule github-egress-rule -n istioegress

and if you need some memory back, just delete the project

oc delete project istioegress

Tips & Tricks

Some 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 tutorial

From these images

oc get pods -o jsonpath="{.items[*].spec.containers[*].image}" -l app=customer -n tutorial

Get 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:8080
oc exec $CPOD -c customer -n tutorial curl http://localhost:15000/routes > afile.json

Look 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.yml

The review the routes again

oc exec $CPOD -c customer -n tutorial curl http://localhost:15000/routes > bfile.json

Here 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 tutorial

Dive into the istio-proxy container

oc exec -it $CPOD -c istio-proxy -n tutorial /bin/bash
cd /etc/istio/proxy
ls
cat envoy-rev3.json

Snowdrop Troubleshooting

https://github.com/snowdrop/spring-boot-quickstart-istio/blob/master/TROUBLESHOOT.md

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