Mark's IT Blog

In the previous post I described some the general direction and ‘wants’ for the next step of our IT Ops, summarised as:

Start with the basics

Lets start with the simple demo deployment supplied by the CoreOS team.

https://coreos.com/kubernetes/docs/latest/kubernetes-on-vagrant.html

That set up was pretty straight forward (as supplied demos usually are).  Simple verification that the k8s components are up and running:

vagrant global-status 
#expected output assuming 1 etcd, 1 k8s controller and 2 k8s worker as defined in config.rb
id name provider state directory
----------------------------------------------------------------------------------------------------------
2146bec e1 virtualbox running VirtualBox VMs/coreos-kubernetes/multi-node/vagrant
87d498b c1 virtualbox running VirtualBox VMs/coreos-kubernetes/multi-node/vagrant
46bac62 w1 virtualbox running VirtualBox VMs/coreos-kubernetes/multi-node/vagrant
f05e369 w2 virtualbox running VirtualBox VMs/coreos-kubernetes/multi-node/vagrant

#set kubctl config and context
export KUBECONFIG="${KUBECONFIG}:$(pwd)/kubeconfig"
kubectl config use-context vagrant-multi
kubectl get nodes
#expected output
NAME STATUS AGE
172.17.4.101 Ready,SchedulingDisabled 4m
172.17.4.201 Ready 4m
172.17.4.202 Ready 4m

kubectl cluster-info
#expected output
Kubernetes master is running at https://172.17.4.101:443
Heapster is running at https://172.17.4.101:443/api/v1/proxy/namespaces/kube-system/services/heapster
KubeDNS is running at https://172.17.4.101:443/api/v1/proxy/namespaces/kube-system/services/kube-dns
kubernetes-dashboard is running atvagran       

*Note: It can take some time (5 mins or longer if core-os is updating) for the kubernetes cluster to become available. To see status, vagrant ssh c1 (or w1/w2/e1) and run journalctl -f (following service logs).

Accessing the kubernetes dashboard requires tunnelling, which if using the vagrant set up can be accomplished with: https://gist.github.com/iamsortiz/9b802caf7d37f678e1be18a232c3cc08 (note, that is for single node, if using multinode then change line 21 to:

vagrant ssh c1 -c "if [ ! -d /home/$USERNAME ]; then sudo useradd $USERNAME -m -s /bin/bash && echo '$USERNAME:$PASSWORD' | sudo chpasswd; fi"

Now the dashboard can be access on http://localhost:9090/.

Now lets to some simple k8s examples:

Create a load balanced nginx deployment:

# create 2 containers from nginx image (docker hub)
kubectl run my-nginx --image=nginx --replicas=2 --port=80
# expose the service to the internet
kubectl expose deployment my-nginx --target-port=80 --type=LoadBalancer
# list service nodes
kubectl get po
# show service info
kubectl get service my-nginx
kubectl describe service/my-nginx

First interesting point… with simple deployment above, I have already gone awry. Though I have 2 nginx containers (presumably for redundancy and load balancing), they have both been deployed on the same worker node (host). Lets not get bogged down now — will keep working through examples which probably cover how to ensure redundancy across hosts.

1
2
# Delete the service, removes pods and containers

kubectl delete deployment,service my-nginx

Reviewed config file (pod) options: http://kubernetes.io/docs/user-guide/configuring-containers/

Deploy demo application

https://github.com/kubernetes/kubernetes/blob/release-1.3/examples/guestbook/README.md

1. create service for redis master, redis slaves and frontent
2. create a deployment for redis master, redis slaves and frontend

Pretty easy.. now how do we get external traffic to the service? Either NodePort’s, Loadbalancers or ingress resource (?).

Next lets look at how to extend Kubernetes to

• experiment with NodePort’s, LoadBalancers and Ingress Resources
• create an ingress controller (as a pod) – https://github.com/kubernetes/contrib/tree/master/ingress/controllers
• create an ingress resource to route requests
• enable deployments, external load balancing and machine scaling (https://amdatu.org/infra/)

We are currently operating a service oriented architecture that is ‘dockerized’ with both host and containers running CentOS 7 when deployed straight on top of ec2 instances. We also have a deployment pipline with beanstalk + homegrown scripts. I imagine our position/maturity is similar to a lot of SMEs, we have aspirations of being on top of new technologies/practices but are some where in between old school and new school:

This is not about which end of this incomplete spectrum is better… we have decided that for our place in the world, moving further the left is desirable. I know there are a lot of experienced IT operators that take this view:

Why CoreOS for Docker Hosts?

CoreOS: A lightweight Linux operating system designed for clustered deployments providing automation, security, and scalability for your most critical applications – https://coreos.com/why/

Our application and supporting services run in docker, there should not be any dependencies on the host operating system (apart from the docker engine and storage mounts).

Some questions I ask myself now:

• Why do I need to monitor for and stage deployments of updates?
• Why am I managing packages on a host OS that could be immutable (like CoreOS is, kind of)?
• Why am I managing what should be homogeneous machines with puppet?
• Why am I nursing host machines back to health when things go wrong (instead of blowing them away and redeploying)?
• Why do I need to monitor SE Linux events?

I want a Docker Host OS that is/has:

• Smaller, Stricter, Homogeneous and Disposable
• Built in hosts and service clustering
• As little management as possible post deployment

CoreOS looks good for removing the first set of questions and sufficing the wants.

Why Kubernetes?

Kubernetes: “A platform for automating deployment, scaling, and operations of application containers across clusters of hosts” – http://kubernetes.io/docs/whatisk8s/

Some questions I ask myself now:

• Should my deployment, monitoring and scaling completely separate or be a platform?
• Why do I (IT ops) still need to be around for prod deployments (no automatic success criteria for staged deploys and not automatic rollback)?
• Why are our deployment scripts so complex and non-portable
• Do I want a scaling solution outside of AWS Auto-Scaling groups?

I want a tool/platform to:

• Streamline and rationalise our complex deployment process
• Make monitoring, scaling and deployment more manageable without our lines of homebaked scripts
• Generally make our monitoring, scaling and deployment more able to meet changing requirements

Kubernetes looks good for removing the first set of questions and sufficing the wants.

Next steps

• Create a CoreOS cluster
• Install Kubernetes on the cluster
• Deploy an application via Kubernetes
• Assess if CoreOS and Kubernetes take us in a direction we want to go

The rise of single page apps (ie AngularJS) present some interesting problems for Ops. Specifically, the increased dependence on browser executed code means that real user experience monitoring is a must.

To that end I have reviewed some javascript agent monitoring solutions:

• sentry.io
• New Relic Browser
• AppDynamics Browser RUM
• Dynatrace UEM – Didn’t end up testing the SaaS offering 
• Google Analytics with custom event push: http://www.davidverhasselt.com/an-easy-javascript-error-logger-using-ga/

The solution/s must have the following requirements:

• Must have:
  • Detailed javascript error reporting
  • Negligible performance impact
  • Real user performance monitoring
  • Effective single page app (AnglularJS support)
  • Real time alerting
• Nice to have:
  • Low cost
  • Easy to deploy and maintain integration
  • Easy integration with tools we use for notifications (icinga2, Slack)

As our application is a single page Angular app, New Relic Browser requires that we pay US$130 for any single page app capability. The JavaScript error detection was not very impressive as uncaught exceptions outside of the angular app were not reported without angular integration.

Google Analytics with custom event push does not have any real time alerting which disqualifies it as an Ops solution.

AppDynamics Browser was easy to integrate, getting javascript error details in the console was straight forward but getting those errors to communication tools like slack was surprisingly difficult. Alerts are based on health checks which are breaking of metric thresholds – so I can send an alert saying there was more than 0 javascript errors in the last minute. But no details about the error and no direct link to the error.

Sentry.io simple to add monitoring, simple to get alerting with click through to all the javascript error info. No performance monitoring.

Conclusion sticking to the Unix philosophy, using sentry.io for javascript error alerting and AppDynamics Browser Lite for performance alerting. Both have free levels to get started (ongoing, not just 30 day trial).

With the basics of Simulations, Scenarios, Virtual Users, Sessions, Feeders, Checks, Assertions and Reports down –  it’s time to think about what to load test and how.

Will start with a test that tries to mimic the end user experience. That means that all the 3rd party javascript, css, images etc should be loaded. It does not seem reasonable to say our loadtest performance was great but none of our users will get a responsive app because of all those things we depend on (though, yes, most of it will likely already be cached by the user). This increases the complexity of the simulation scripts as there will be lots of additional resource requests cluttering things up. It is very important for maintainability to avoid code duplication and use the singleton object functionality available.

Using the recorder

As I want to include CDN calls, I tried the recorder’s ‘Generate CA’ functionality. This is supposed to generate certs on the fly for each CN. This would be convenient as I could just trust a locally generated CA and not have to track down and trust all sources. Unfortunately I could not get the recorder to generate its own CA, and when using a local CA generated with openssl I could not feed the CA password to the recorder. I only spent 15 mins trying this until reverting to the default self signed cert. Reviewing Firefox’s network panel (Firefox menu -> Developer -> Network ) shows any blocked sources which can then be visited directly and trusted with our fake cert (there are some fairly serious security implications of doing this, I personally only use my testing browser (firefox) with these types of proxy tools and never for normal browsing).

The recorder is very handy for getting the raw code you need into the test script, it is not a complete test though. Next up is:

1. Dealing with authentication headers –  The recorded simulation does not set the header based on response from login attempt
2. Requests dependent on the previous response – The recorder does not capture this dependency it only see the raw outbound requests so there will need to be consideration on parsing results
3. Validating responses

Dealing with authentication headers

The Check API is used for verifying that the response to a request matches expectations and capturing some elements in it.

After half an hour or so of playing around the Check API, it is behaving as I want thanks to good, concise doc.

.exec(http("login-with-creds")
   .post("/cm/login")
   .headers(headers_14)
   .body(RawFileBody("test_user_creds.txt"))
   .check(headerRegex("Set-Cookie", "access_token=(.*);Version=*").saveAs("auth_token"))

The “.check” is looking for the header name “Set-Cookie” then extracting the auth token using a regex and finally saving the token as a key called auth_token.

In subsequent requests I need to include a header containing this value, and some other headers. So instead of listing them out each time a function makes things much neater:

def authHeader (auth_token:String):Map[String, String] = {
   Map("Authorization" -> "Bearer ".concat(auth_token),
       "Origin" -> baseURL)
} 
//...
http("list_irs")
   .get(uri1 + "/information-requests")
   .headers(authHeader("${auth_token}")) // providing the saved key value as a string arg

Its also worth noting that to ensure that all this was working as expected I modified /conf/logback.xml to output all HTTP request response data to stdout.

Requests dependent on the previous response

With many modern applications, the behaviour of the GUI is dictated by responses from an API. For example, when a user logs in, the GUI requests a json file with all (max 50) of the users open requests. When the GUI received this, the requests are rendered. In many cases this rendering process involves many more HTTP requests that depending on the time and state of the users which may vary significantly. So… if we are trying to imitate end user experience instead of requesting the render info for the same open requests all of the time, we should parse the json response and adjust subsequent requests accordingly. Thankfully gatling allows for the use of JsonPath. I got stuck trying to get all of the id vals out of a json return and then create requests for each of them. I had incorrectly assumed that the EL Gatling provided ‘random’ function could be called on a vector. This meant I thought the vector was ‘undefined’ as per the error message. The vector was in fact as expected which was clear by printing it.

//grabs all id values from the response body and puts them in a vector accessible via "${answer_ids}" or sessions.get("answer_ids")
http("list_irs")
.get(uri1 + "/information-requests")
.headers(authHeader("${auth_token}")).check(status.is(200), jsonPath("$..id").findAll.saveAs("answer_ids")) 
//....
//prints all vaules in the answer_ids vector
.exec(session => {
    val maybeId = session.get("answer_ids").asOption[String]
    println(maybeId.getOrElse("no ids found"))
    session
})

To run queries with all of the values pulled out of the json response we can use the foreach component. Again got stuck for a little while here. Was putting the foreach competent within an exec function, where (as below) it should be outside of an exec and reference a chain the contains an exec.

val answer_chain = exec(http("an_answer")
    .get(uri1 + "/information-requests/${item}/stores/answers")
    .headers(authHeader("${auth_token}")).check(status.is(200)))
//...
val scn = scenario("BasicLogin")
/...
.exec(http("list_irs")
    .get(uri1 + "/information-requests")
    .headers(authHeader("${auth_token}")).check(status.is(200), jsonPath("$..id").findAll.saveAs("answer_ids"))),
.foreach("${answer_ids}","item") { answer_chain }

Validating responses

What do we care about in responses?

1. HTTP response headers (generally expecting 200 OK)
2. HTTP response body contents – we can define expectations based on understanding of app behaviour
3. Response time – we may want to define responses taking more than 2000ms as failures (queue application performance sales pitch)

Checking response headers is quite simple and can be seen explicitly above in .check(status.is(200). In fact, there is no need for 200 checks to be explicit as “A status check is automatically added to a request when you don’t specify one. It checks that the HTTP response has a 2XX or 304 status code.” — checks.

HTTP response body content checks are valuable for ensuring the app behaves as expected. They also require a lot of maintenance so it is important to implement tests using code reuse where possible. Gatling is great for this as we can use the scala and all the power that comes with it (ie: reusable objects and functions across all tests).

Next up is response time checks. Note that these response times are specific to the HTTP layer and do not infer a good end user experience. Javascript and other rendering, along with blocking requests mean that performance testing at the HTTP layer is incomplete performance testing (though it is the meat and potatoes).
Gatling provides the Assertions API to conduct checks globally (on all requests). There are numerous scopes, statistics and conditions to choose from there. For specific operations, responseTimeInMillis and latencyInMillis are provided by Gatling – responseTimeInMillis includes the time is takes to fully send the request and fully receive the response (from the test host). As a default I use responseTimeInMillis as it has slightly higher coverage as a test.

These three verifications/tests can be seen here:

package mwc_gatling
import scala.concurrent.duration._
import io.gatling.core.Predef._
import io.gatling.http.Predef._
import io.gatling.jdbc.Predef._

class BasicLogin extends Simulation {
    val baseURL="https://blah.mwclearning.com"
    val httpProtocol = http
        .baseURL(baseURL)
        .acceptHeader("application/json, text/plain, */*")
        .acceptEncodingHeader("gzip, deflate")
        .acceptLanguageHeader("en-US,en;q=0.5")
        .userAgentHeader("Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11; rv:43.0) Gecko/20100101 Firefox/43.0")
    def authHeader (auth_token:String):Map[String, String] = {
        Map("Authorization" -> "Bearer ".concat(auth_token), "Origin" -> baseURL)
    } 

    val answer_chain = exec(http("an_answer")
        .get(uri1 + "/information-requests/${item}/stores/answers")
        .headers(authHeader("${auth_token}")).check(status.is(200), jsonPath("$..status")))
    
    val scn = scenario("BasicLogin")
    .exec(http("get_web_app_deps")
     //... bunch of get requests for JS CSS etc
    .exec(http("login-with-creds")
        .post("/cm/login")
        .body(RawFileBody("test_user_creds.txt"))
           .check(headerRegex("Set-Cookie", "access_token=(.*);Version=*").saveAs("auth_token"))
    //... another bunch of get for post auth deps
        http("list_irs")
            .get(uri1 + "/information-requests")
            .headers(authHeader("${auth_token}")).check(status.is(200), jsonPath("$..id").findAll.saveAs("answer_ids"))
    //... now that we have a vector full of ids we can request those resources
    .foreach("${answer_ids}","item") { answer_chain }
    
  //... finally set the simulation params and assertions
    setUp(scn.inject(atOnceUsers(10))).protocols(httpProtocol).assertions(
        global.responseTime.max.lessThan(2000),
        global.successfulRequests.percent.greaterThan(99))
}

That’s about all I need to get started with Gatling! The next steps are:

1. extending coverage (more tests!)
2. putting processes in place to notify and act on identified issues
3. refining tests to provide more information about the likely problem domain
4. making a modular and maintainable test library that can be updated in one place to deal with changes to app
5. aggregating results for trending and correlation with changes
6. spin up and spin down environments specifically for load testing
7. jenkins integration

With the need to do some more effective load testing I am getting started with Gatling. Why Gatling and not JMeter? I have not used either so I don’t have a valid opinion. I made my choice based on:

• reduced GUI dependency
• newer code base
• documentation conciseness
• personal preference to scala vs straight java
• some blog posts including:
  • https://blog.flood.io/stress-testing-jmeter-and-gatling/
  • https://octoperf.com/blog/2015/06/08/jmeter-vs-gatling/
  • http://badmoodperf.blogspot.com.au/2014/01/gatling-vs-jmeter-fact-checking.html

Working through the Gatling Quickstart

Next step is working through the basic doc: http://gatling.io/docs/2.2.1/quickstart.html#quickstart. Pretty simple and straightforward.

Moving on to the more advanced tutorial: http://gatling.io/docs/2.2.1/advanced_tutorial.html#advanced-tutorial. This included:

• creating objects for process isolation
• virtual users
• dynamic data with Feeders and Checks
• First usage of Gatling’s Expression Language (not rly a language o_O)

The most interesting function:

object Search {
    val feeder = csv("search.csv").random
    val search = exec(http("Home")
                .get("/"))
                .pause(1)
                .feed(feeder)
                .exec(http("Search")
                .get("/computers?f=${searchCriterion}")
                .check(css("a:contains('${searchComputerName}')", "href").saveAs("computerURL")))
                .pause(2)
                .exec(http("Select")
                .get("${computerURL}"))
                .pause(3)
}

…Simulation‘s are plain Scala classes so we can use all the power of the language if needed.

Next covered off the key concepts in Gatling:

• Virtual User -> logical grouping of behaviours ie: Administrator(login, update user, add user, logout)
• Scenario -> define Virtual Users behaviours ie: (login, update user, add user, logout)
• Simulation -> is a description of the load test (group of scenarios, users – how many and what rampup)
• Session -> Each virtual user is back by a Session this can allow for sharing of data between operations (see above)
• Feeders -> Method for getting input data for tests ie: login values, search and response values
• Checks -> Can verify HTTP response codes and capture elements of the response body
• Assertions -> Define acceptance criteria (slower than x means failure)
• Reports -> Aggregated output

Last review for today was of presentation by Stephane Landelle and Romain Sertelon,  the authors of Gatling:

Next step is to implement some test and figure out a good way to separate simulations/scenarios and reports.