Running Serverless Deployments With Jenkins X, Gloo, And Knative

Serverless deployments are gaining traction. Today, we have quite a few choices for converting our applications into serverless inside Kubernetes cluster. One of those, my favorite, is Knative. We’ll explore how we can combine it with Jenkins X to create a fully automated continuous deployment pipeline that deploys serverless applications.

Setting Up The Prerequisites

At the time of this writing (August 2019), serverless deployments with Knative work out-of-the-box only in GKE (issue 4668). That does not mean that Knative does not work in other Kubernetes flavors, but rather that Jenkins X installation of Knative works only in GKE. I encourage you to set up Knative yourself and follow along in your Kubernetes flavor. If you cannot run Knative, I still suggest you stick around even if you cannot run the examples. I’ll do my best to be brief and to make the examples clear even for those not running them.

If you’re like to follow along the examples from this article, you’ll need create a Jenkins X cluster in GKE (if you don’t have one already) and you’ll need to install Gloo. The instructions can be found in the Gist.

Please fork the vfarcic/go-demo-6 repository. The commands that follow will clone the repository and restore the branch that already contains everything you’ll need to start.

GH_USER=[...] # Replace `[...]` with your GitHub username.

git clone$GH_USER/go-demo-6

cd go-demo-6

git checkout knative-cd

git merge -s ours master --no-edit

git checkout master

git merge knative-cd

git push

Finally, we’ll import the sample repository into Jenkins X.

jx import --pack go --batch-mode

Some of the commands assume that Jenkins X is running inside the Namespace cd. If that’s not your case, whenever you see --namespace, you’ll have to change the value.

Now we’re ready to explore serverless deployments with Jenkins X, Knative, and Gloo.

Running Serverless Applications

Let’s take a quick look at the definition that makes the application serverless.

cat charts/go-demo-6/templates/ksvc.yaml

We won’t go into details of Knative specification. The details can be found in the official docs. What matters in the context of the current discussion is that the YAML you see in front of you defined a serverless deployment using Knative.

By now, if you created a new cluster, the application we imported should be up-and-running. But, to be on the safe side, we’ll confirm that by taking a quick look at the go-demo-6 activities.

jx get activities \
    --filter go-demo-6 \

Once you confirm that the build is finished press ctrl+c to stop watching the activities.

Similarly, we’ll confirm that the pipeline that will deploy the application to staging was executed successfully as well.

jx get activities \
    --filter environment-tekton-staging/master \

Just as before, feel free to press ctrl+c once you confirm that the build was finished.

Finally, the last verification we’ll do is to confirm that the Pods are running.

kubectl \
    --namespace cd-staging \
    get pods

The output is as follows.

NAME                           READY STATUS  RESTARTS AGE
go-demo-6-lbxwr-deployment-... 2/2   Running 0        26s
jx-go-demo-6-db-arbiter-0      1/1   Running 0        27s
jx-go-demo-6-db-primary-0      1/1   Running 0        27s
jx-go-demo-6-db-secondary-0    1/1   Running 0        27s

On the first look, everything looks "normal". It as if the application was deployed like any other. The only "strange" thing we can observe by looking at the Pods is the name of the one created through the go-demo-6 Deployment and that it contains two containers instead of one. We’ll ignore the "naming strangeness" and focus on the latter observation.

Knative injected a container into our Pod. It contains queue-proxy that, as the name suggests, serves as a proxy responsible for request queue parameters. It also reports metrics to the Autoscaler through which it might scale up or down depending on the number of different parameters. Requests are not going directly to our application but through this container.

Besides the Pod controlled by Knative, we can also observe that MongoDB is there as well. It is not serverless but running in the same way it was running throughout the rest of the book. While we could (and probably should) make it serverless as well, I wanted to demonstrate that different types of deployments can coexist happily.

Now, let’s confirm that the application is indeed accessible from outside the cluster.

STAGING_ADDR=$(kubectl \
    --namespace cd-staging \
    get ksvc go-demo-6 \
    --output jsonpath="{.status.domain}")

curl "http://$STAGING_ADDR/demo/hello"

We retrieved the address through which we can reach the application running in the staging environment, and we used curl to send a request. The output should be hello, PR!

So far, the significant difference when compared with "normal" Kubernetes deployments is that the access to the application is not controlled through Ingress any more. Instead, it goes through a new resource type abbreviated as ksvc (short for Knative Service). Apart from that, everything else seems to be the same, except if we left the application unused for a while. If that’s the case, we still got the same output, but there was a slight delay between sending the request and receiving the response. The reason for such a delay lies in Knative’s scaling capabilities. It saw that the application is not used and scaled it to zero replicas. But, the moment we sent a request, it noticed that zero replicas is not the desired state and scaled it back to one replica. All in all, the request entered into a gateway (in our case served by Gloo Envoy) and waited there until a new replica was created and initialized, unless one was already running. After that, it forwarded the request to it, and the rest is the "standard" process of our application responding and that response being forwarded to us (back to curl).

I cannot be sure whether your serverless deployment indeed scaled to zero or it didn’t. So, we’ll use a bit of patience to validate that it does indeed scale to nothing after a bit of inactivity. All we have to do is wait for five to ten minutes. Get a coffee or some snack.

kubectl \
    --namespace cd-staging \
    get pods

Assuming that sufficient time passed, the output should be as follows.

NAME                        READY STATUS  RESTARTS AGE
jx-go-demo-6-db-arbiter-0   1/1   Running 0        6m21s
jx-go-demo-6-db-primary-0   1/1   Running 0        6m21s
jx-go-demo-6-db-secondary-0 1/1   Running 0        6m21s

The database is there, but the application is now gone. If we ignore other resources and focus only on Pods, it seems like the application is wiped out completely. That is true in terms that nothing application-specific is running. All that’s left are a few Knative definitions and the common resources used for all applications (not specific to go-demo-6).

I> If you still see the go-demo-6 Pod, all I can say is that you are impatient and you did not wait long enough. If that’s what happened, wait for a while longer and repeat the get pods command.

Using telemetry collected from all the Pods deployed as Knative applications, Gloo detected that no requests were sent to go-demo-6 for a while and decided that the time has come to scale it down. It sent a notification to Knative that executed a series of actions which resulted in our application being scaled to zero replicas.

Bear in mind that the actual process is more complicated than that and that there are quite a few other components involved. Nevertheless, for the sake of brevity, the simplistic view we presented should suffice. I’ll leave it up to you to go deeper into Gloo and Knative or accept it as magic. In either case, our application was successfully scaled to zero replicas. We started saving resources that could be better used by other applications and save us some costs in the process.

If you never used serverless deployments and if you never worked with Knative, you might think that your users would not be able to access it anymore since the application is not running. Or you might think that it will be scaled up once requests start coming in, but you might be scared that you’ll lose those sent before the new replica starts running. In any case, we’ll put that to the test by sending three hundred concurrent requests for twenty seconds.

kubectl run siege \
    --image yokogawa/siege \
    --generator "run-pod/v1" \
    -it --rm \
    -- --concurrent 300 --time 20S \
    "http://$STAGING_ADDR/demo/hello" \
    && kubectl \
    --namespace cd-staging \
    get pods

We won’t go into details about Siege. What matters is that we finished sending a lot of requests and that the previous command retrieved the Pods in the staging namespace. That output is as follows.

NAME                           READY STATUS  RESTARTS AGE
go-demo-6-lbxwr-deployment-... 2/2   Running 0        20s
go-demo-6-lbxwr-deployment-... 2/2   Running 0        14s
go-demo-6-lbxwr-deployment-... 2/2   Running 0        14s
jx-go-demo-6-db-arbiter-0      1/1   Running 0        6m59s
jx-go-demo-6-db-primary-0      1/1   Running 0        6m59s
jx-go-demo-6-db-secondary-0    1/1   Running 0        6m59s

Our application is up-and-running again. A few moments ago, the application was not running, and now it is. Not only that, but it was scaled to three replicas to accommodate the high number of concurrent requests.

What did we learn from serverless deployments in the context of our quest to find one that fits our needs the best?

Highly availability is easy in Kubernetes, as long as our applications are designed with that in mind. What that means is that our apps should be scalable and should not contain state. If they cannot be scaled, they cannot be highly available. When a replica fails (note that I did not say if but when), no matter how fast Kubernetes will reschedule it somewhere else, there will be downtime, unless other replicas take over its load. If there are no other replicas, we are bound to have downtime both due to failures but also whenever we deploy a new release. So, scalability (running more than one replica) is the prerequisite for high availability. At least, that’s what logic might make us think.

In the case of serverless deployments with Knative, not having replicas that can respond to user requests is not an issue, at least not from the high availability point of view. While in a "normal" situation, the requests would fail to receive a response, in our case, they were queued in the gateway and forwarded after the application is up-and-running. So, even if the application is scaled to zero replicas (if nothing is running), we are still highly available. The major downside is in potential delays between receiving the first requests and until the first replica of the application is responsive.

The problem we might have with serverless deployments, at least when used in Kubernetes, is responsiveness. If we keep the default settings, our application will scale to zero if there are no incoming requests. As a result, when someone does send a request to our app, it might take longer than usual until the response is received. That could be a couple of milliseconds, a few seconds, or much longer. It all depends on the size of the container image, whether it is already cached on the node where the Pod is scheduled, the amount of time the application needs to initialize, and quite a few other criteria. If we do things right, that delay can be short. Still, any delay reduces the responsiveness of our application, no matter how short or long it is. What we need to do is compare the pros and cons. The results will differ from one app to another.

Let’s take the static Jenkins as an example. In many organizations, it is under heavy usage throughout working hours, and with low or no usage at nights. We can say that half of the day it is not used. What that means is that we are paying double to our hosting vendor. We could have shut it down overnight and potentially remove a node from the cluster due to decreased resource usage. Even if the price is not an issue, surely those resources reserved by inactive Jenkins could be better used by some other processes. Shutting down the application would be an improvement, but it would also produce potentially very adverse effects.

What if someone is working overnight and pushes a change to Git. A webhook would fire trying to notify Jenkins that it should run a build. But, such webhook would fail if there is no Jenkins to handle the request. A build would never be executed. Unless we set up a policy that says "you are never allowed to work after 6 pm, even if the whole system crashed", having a non-responsive system is unacceptable.

Another issue would be to figure out when is our system not in use. If we continue using the "traditional" Jenkins as an example, we could say that it should shut-down at 9 pm. If our official working hours end at 6 pm, that will provide three hours margin for those who do stay in the office longer. But, that would still be a suboptimal solution. During much of those three hours, Jenkins would not be used, and it would continue wasting resources. On the other hand, there is still no guarantee that no one will ever push a change after 9 pm.

Knative solves those and quite a few other problems. Instead of shutting down our applications at predefined hours and hoping that no one is using them while they are unavailable, we can let Knative (together with Gloo or Istio) monitor requests. It would scale down if a certain period of inactivity passed. On the other hand, it would scale back up if a request is sent to it. Such requests would not be lost but queued until the application becomes available again.

All in all, I cannot say that Knative might result in non-responsiveness. What I can say is that it might produce slower responses in some cases (between having none and having some replicas). Such periodical slower responsiveness might produce less negative effect than the good it brings. Is it such a bad thing if static Jenkins takes an additional ten seconds to start building something after a whole night of inactivity? Even a minute or two of delay is not a big deal. On the other hand, in that particular case, the upside outweighs the downsides. Still, there are even better examples of the advantages of serverless deployments than Jenkins.

Preview environments might be the best example of wasted resources. Every time we create a pull request, a release is deployed into a temporary environment. That, by itself, is not a waste. The benefits of being able to test and review an application before merging it to master outweigh the fact that most of the time we are not using those applications. Nevertheless, we can do better. We can use Knative to deploy to preview environments, no matter whether we use it for permanent environments like staging and production. After all, preview environments are not meant to provide a place to test something before promoting it to production (staging does that). Instead, they provide us with relative certainty that what we’ll merge to the master branch is likely code that works well.

If the response delay caused by scaling up from zero replicas is unacceptable in certain situations, we can still configure Knative to have one or more replicas as a minimum. In such a case, we’d still benefit from Knative capabilities. For example, the metrics it uses to decide when to scale might be easier or better than those provided by HorizontalPodAutoscaler (HPA). Nevertheless, the result of having Knative deployment with a minimum number of replicas above zero is similar to the one we’d have with using HPA. So, we’ll ignore such situations since our applications would not be serverless. That is not to say that Knative is not useful if it doesn’t scale to zero. What it means is that we’ll treat those situations separately and stick to serverless features in this section.

What’s next in our list of deployment requirements?

Even though we did not demonstrate it through examples, serverless deployments with Knative do not produce downtime when deploying new releases. During the process, all new requests are handled by the new release. At the same time, the old ones are still available to process all those requests that were initiated before the new deployment started rolling out. Similarly, if we have health checks, it will stop the rollout if they fail. In that aspect, we can say that rollout is progressive.

On the other hand, it is not "true" progressive rollout but similar to those we get with rolling updates. Knative, by itself, cannot choose whether to continue progressing with a deployment based on arbitrary metrics. Similarly, it cannot roll back automatically if predefined criteria are met. Just like rolling updates, it will stop the rollout if health checks fail, and not much more. If those health checks fail with the first replica, even though there is no rollback, all the requests will continue being served with the old release. Still, there are too many ifs in those statements. We can only say that serverless deployments with Knative (without additional tooling) partially fulfills the progressive rollout requirement and that they are incapable of automated rollbacks.

Finally, the last requirement is that our deployment strategy should be cost-effective. Serverless deployments, at least those implemented with Knative, are probably the most cost-effective deployments we can have. Unlike vendor-specific serverless implementations like AWS Lambda, Azure Functions, and Google Cloud’s serverless platform, we are in (almost) full control. We can define how many requests are served by a single replica. We control the size of our applications given that anything that can run in a container can be serverless (but is not necessarily a good candidate). We control which metrics are used to make decisions and what are the thresholds. Truth be told, that is likely more complicated than using vendor-specific serverless implementations. It’s up to us to decide whether additional complications with Knative outweigh the benefits it brings. I’ll leave such a decision in your hands.

So, what did we conclude? Do serverless deployments with Knative fulfill all our requirements? The answer to that question is a resounding "no". No deployment strategy is perfect. Serverless deployments provide huge benefits with high-availability and cost-effectiveness. They are relatively responsive and offer a certain level of progressive rollouts. The major drawback is the lack of automated rollbacks.

Requirement Fullfilled
High-availability Fully
Responsiveness Partly
Progressive rollout Partly
Rollback Not
Cost-effectiveness Fully

Please note that we used Gloo in conjunction with Knative to perform serverless deployments. We could have used Istio instead of Gloo. Similarly, we could have used OpenFaaS instead of Knative. Or we could have opted for something completely different. There are many different solutions we could assemble to make our applications serverless. Still, the goal was not to compare them all and choose the best one. Instead, we explored serverless deployments in general as one possible strategy we could employ. I do believe that Knative is the most promising one, but we are still in early stages with serverless in general and especially in Kubernetes. It would be impossible to be sure of what will prevail. Similarly, for many engineers, Istio would be the service mesh of choice due to its high popularity. I chose Gloo mostly because of its simplicity and its small footprint.

Finally, I decided to present only one serverless implementation mostly because it would take much more than a single article to compare all those that are popular. The same can be said for service mesh (Gloo). Both are fascinating subjects that I might explore in the next book. But, at this moment I cannot make that promise because I do not plan a new book before the one I’m writing (this one) is finished.

What matters is that we’re finished with a very high-level exploration of the pros and cons of using serverless deployments and now we can move into the next one. But, before we do that, we’ll revert our chart to the good old Kubernetes Deployment.

The DevOps 2.6 Toolkit: Jenkins X

The article you just read is an extract from The DevOps 2.6 Toolkit: Jenkins X.

You can get the book from Amazon, LeanPub, or look for it through your favorite book seller.

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