Part 2: Deploying Envoy with a Python Flask webapp and Kubernetes

In the first post in this series, Getting Started with Lyft Envoy for microservice resilience, we explored Envoy a bit, dug into how it worked and deployed an application using Kubernetes, Postgres, Flask, and Envoy.

The Application

In this tutorial, we will deploy a straightforward REST-based user service: it can create users, read information about a user, and process simple logins. Obviously, this isn’t terribly interesting by itself, but it brings several real-world concerns together:

• It requires persistent storage, so we’ll have to tackle that early.
• It will let us explore scaling the different pieces of the application.
• It will let us explore Envoy at the edge, where the user’s client talks to our application, and
• It will let us explore Envoy internally, brokering communications between the various parts of the application.

Since Envoy is language-agnostic, we can use anything we like for the service itself. For this serious, we’ll pick on Flask, both because it’s simple and because I like Python. On the database side, we’ll use PostgreSQL – it has good Python support, and it’s easy to get running both locally and in the cloud. And we’ll manage the whole thing with Kubernetes.

Kubernetes

Kubernetes is Ambassador Labs' go-to container orchestrator these days, mostly because it lets you use the same tools, whether you're doing local development or deploying into the cloud for production. So to get rolling today, we'll need a Kubernetes cluster in which we'll work. Within our cluster, we'll create deployments that run the individual pieces of our application and then expose services provided by those deployments (and when we do that, we get to decide whether to expose the service to the world outside the cluster or only to other cluster members).

We’ll start out using Minikube to create a simple Kubernetes cluster running locally. The existence of Minikube is one of the things I really like about Kubernetes – it gives me an environment that’s almost like running Kubernetes somewhere out in the cloud. Still, it’s entirely local, and (with some care) it can keep working at 30000 feet on an airplane with no WiFi.

Note, though, that I said almost like running in the cloud. In principle, Kubernetes is Kubernetes, and where you’re running doesn’t matter. In reality, of course, it does matter: networking, in particular, varies a bit depending on how you’re running your cluster. So getting running in Minikube is a great first step, but we’ll have to be aware that things will probably break a little bit as we move into the cloud

Setting Up

Minikube

Of course you’ll need Minikube installed. See https://github.com/kubernetes/minikube/releases for more here. Mac users might also consider

Once Minikube is installed, you’ll need to start it. Mac users may want the

Alternately

will fire things up with the default driver.

Kubernetes

To be able to work with Minikube, you’ll need the Kubernetes CLI,

Docker

You'll also need the Docker CLI,

The Application

All the code and configuration we’ll use in this demo is in GitHub at

https://github.com/datawire/envoy-steps

Grab a clone of that, and

or

Obviously I’d prefer to simply include everything you need in this blog post, but between Python code, all the Kubernetes config, docs, etc, there’s just too much. So we’ll hit the highlights here, and you can look at the details to your heart’s content in your clone of the repo.

The Docker Registry

Minikube starts a Docker daemon when it starts up, which we'll need to use it for our Docker image builds so that the Minikube containers can load our images. To set up your Docker command-line tools for that:

One of the benefits of Minikube is that a container can always pull your images from the local Docker daemon started by Minikube, so we don't need to push Docker images to any registry -- just building them using the Minikube Docker daemon is good enough. To tell the scripting we'll be using that we're using Minikube and nothing more is needed, run

If you want to reset to a pristine condition later, you can use

Database Matters

Our database can be straightforward — we need a single table to store our user information. We can start by writing the Flask app to check at boot time and create our table if it doesn’t exist, relying on Postgres to ensure that only one table exists. (Later, as we look into multiple Postgres servers, we may need to change this — but let’s keep it simple for now.)

So the only thing we really need is a way to spin up a Postgres server in our Kubernetes cluster. Fortunately there’s a published Postgres 9.6 Docker image readily accessible, so creating the Postgres deployment is pretty easy. The relevant config file is

spec:
                  containers:
                  - name: postgres
                    image: postgres:9.6
                

Given the deployment, we also need to expose the Postgres service within our cluster. That’s defined in

 spec:
                  type: ClusterIP
                  ports:
                  - name: postgres
                    port: 5432
                  selector:
                    service: postgres

Note that we mark this with type

To fire this up, just run

Once that’s done,

NAME                       READY  STATUS   RESTARTS AGE
                postgres-1385931004-p3szz  1/1    Running  0        5s

and

NAME      CLUSTER-IP     EXTERNAL-IP  PORT(S)   AGE
                postgres  10.107.246.55  <none>       5432/TCP  5s

So we now have a running Postgres server, reachable from anywhere in the cluster at

The Flask App

Our Flask app is really simple: basically it just responds to

The only real gotcha is that by default, Flask will listen only on the loopback address, which will prevent any connections from outside the Flask app’s container. We set the Flask app to explicitly listen on

To get the app running in Kubernetes, we’ll need a Docker image that contains our app. We’ll build this on top of the

FROM lyft/envoy:latest
                RUN apt-get update && apt-get -q install -y
                    curl
                    python-pip
                    dnsutils
                WORKDIR /application
                COPY requirements.txt .
                RUN pip install -r requirements.txt
                COPY service.py .
                COPY entrypoint.sh .
                RUN chmod +x entrypoint.sh
                ENTRYPOINT [ "./entrypoint.sh" ]

We’ll build that into a Docker image, then fire up a Kubernetes deployment and service with it. The deployment, in

spec:
                  containers:
                  - name: usersvc
                    image: usersvc:step1

Likewise,

spec:
                  type: LoadBalancer
                  ports:
                  - name: usersvc
                    port: 5000
                    targetPort: 5000
                  selector:
                    service: usersvc

It may seem odd to be starting with

To build the Docker image and crank up the service, run

At this point,

NAME                       READY  STATUS   RESTARTS AGE
                postgres-1385931004-p3szz  1/1    Running  0        5m
                usersvc-1941676296-kmglv   1/1    Running  0        5s

First Test!

And now for the moment of truth: let’s see if it works without Envoy before moving on! This will require us to get the IP address and mapped port number for the

to get a neatly-formed URL to our

Let’s start with a basic health check using

If all goes well, the health check should return something like

{
                  "hostname": "usersvc-1941676296-kmglv",
                  "msg": "user health check OK",
                  "ok": true,
                  "resolvedname": "172.17.0.10"
                }

Next up we can try saving and retrieving a user:

curl -X PUT -H "Content-Type: application/json" \
                     -d '{ "fullname": "Alice", "password": "alicerules" }' \
                     $(minikube service --url usersvc)/user/alice

This should give us a user record for Alice, including her UUID but not her password:

{
                  "fullname": "Alice",
                  "hostname": "usersvc-1941676296-kmglv",
                  "ok": true,
                  "resolvedname": "172.17.0.10",
                  "uuid": "44FD5687B15B4AF78753E33E6A2B033B"
                }

If we repeat it for Bob, we should get much the same:

curl -X PUT -H "Content-Type: application/json" \
                     -d '{ "fullname": "Bob", "password": "bobrules" }' \
                     $(minikube service --url usersvc)/user/bob

Note, of course, that Bob should have a different UUID:

{
                  "fullname": "Bob",
                  "hostname": "usersvc-1941676296-kmglv",
                  "ok": true,
                  "resolvedname": "172.17.0.10",
                  "uuid": "72C77A08942D4EADA61B6A0713C1624F"
                }
                

Finally, we should be able to read both users back (again, minus passwords!) with

curl $(minikube service --url usersvc)/user/alice
                curl $(minikube service --url usersvc)/user/bob
                

Enter Envoy

Given that all of that is working, it’s time to stick Envoy in front of everything, so it can manage to route when we start scaling the front end. As we discussed in the previous article, we have an edge Envoy and an application Envoy, each of which needs its own configuration. So we’ll crank up the edge Envoy first

Since the edge Envoy runs in its own container, we’ll need a separate Docker image for it. Here’s the Dockerfile:

FROM lyft/envoy:latest
                RUN apt-get update && apt-get -q install -y
                    curl
                    dnsutils
                COPY envoy.json /etc/envoy.json
                CMD /usr/local/bin/envoy -c /etc/envoy.json

which is to say, we take

Our edge Envoy’s config is fairly simple, too, since it only needs to proxy any URL starting with

"virtual_hosts": [
                  {
                    "name": "service",
                    "domains": [ "*" ],
                    "routes": [
                      {
                        "timeout_ms": 0,
                        "prefix": "/user",
                        "cluster": “usersvc"
                      }
                    ]
                  }
                ]
 

and here’s the related

"clusters": [
                  {
                    "name": “usersvc”,
                    "type": "strict_dns",
                    "lb_type": "round_robin",
                    "hosts": [
                      {
                        "url": “tcp://usersvc:80”
                      }
                    ]
                  }
                ]
                

Note that we’re using

As usual, you can get the edge Envoy running with a single command:

Sadly we can’t really test anything yet, since the edge Envoy is going to try to talk to application Envoys that aren’t running yet.

App Changes for Envoy

Once the edge Envoy is running, we need to switch our Flask app to use an application Envoy. We needn’t change the database at all, but the Flask app needs a few tweaks:

• We need to have the Dockerfile copy in an Envoy config file.
• We need to have the
  entrypoint.sh
  script start Envoy as well as the Flask app.
• While we’re at it, we can switch back to having Flask listen only on the loopback interface, and
• We’ll switch the service from a
  LoadBalancer
  to a
  ClusterIP
  .

The effect here is that we’ll have a running Envoy through which we can talk to the Flask app — but also that Envoy will be the only way to talk to the Flask app. Trying to go direct will be blocked in the network layer.

The application Envoy’s config, while we’re at it, is very similar to the edge Envoy’s. The

"clusters": [
                  {
                    "name": “usersvc”,
                    "type": "static",
                    "lb_type": "round_robin",
                    "hosts": [
                      {
                        "url": “tcp://127.0.0.1:80”
                      }
                    ]
                  }
                ]
 

Basically we just use a static single-member cluster, with only

All the changes to the Flask side of the world can be found in the

and then bring up the new one:

Second Test!

Once all that is done, voilà: you should be able to retrieve Alice and Bob from before:

curl $(minikube service --url edge-envoy)/user/alice
                curl $(minikube service --url edge-envoy)/user/bob
 

…but note that we’re using the

Scaling the Flask App

One of the promises of Envoy is helping with scaling applications. Let’s see how well it handles that by scaling up to multiple instances of our Flask app:

Once that’s done,

NAME                         READY STATUS   RESTARTS  AGE
                edge-envoy-2874730579-7vrp4  1/1   Running  0         3m
                postgres-1385931004-p3szz    1/1   Running  0         5m
                usersvc-2016583945-h7hqz     1/1   Running  0         6s
                usersvc-2016583945-hxvrn     1/1   Running  0         6s
                usersvc-2016583945-pzq2x     1/1   Running  0         3m

and we should then be able to see

multiple times, and look at the

But it’s not. Uhoh. What’s going on here?

Remembering that we’re running Envoy in

(Make sure to use one of your pod names when you run this! Just pasting the line above is extremely unlikely to work.)

Running this check, we find that only one address comes back — so Envoy’s DNS-based service discovery simply isn’t going to work. Envoy can’t round-robin among our three service instances if it never hears about two of them.

The Service Discovery Service

What's going on here is that Kubernetes puts each service into its DNS, but it doesn't put each service endpoint into its DNS — and we need Envoy to know about the endpoints to load balance. Thankfully, Kubernetes knows each service's service endpoints, and Envoy knows how to query a REST service for discovery information. So we can make this work with a simple Python shim that bridges the Envoy "Service Discovery Service" (SDS) to the Kubernetes API.

There’s also the Istio project, which is digging into a more full-featured solution here.

Our SDS is in the

We also need to modify the edge Envoy’s config slightly: rather than using

"cluster_manager": {
                  "sds": {
                    "cluster": {
                      "name": "usersvc-sds",
                      "connect_timeout_ms": 250,
                      "type": "strict_dns",
                      "lb_type": "round_robin",
                      "hosts": [
                        {
                          "url": "tcp://usersvc-sds:5000"
                        }
                      ]
                    },
                    "refresh_delay_ms": 15000
                  },
                  "clusters": [
                    {
                      "name": "usersvc",
                      "connect_timeout_ms": 250,
                      "type": "sds",
                      "service_name": "usersvc",
                      "lb_type": "round_robin",
                      "features": "http2"
                    }
                  ]
                }
                

Look carefully: the

The

bash up.sh usersvc-sds
                bash down.sh edge-envoy
                bash up.sh edge-envoy2
                

and now, repeating our health check should show you the round-robin around the hosts. But, of course, asking for the details of user Alice should always give the same results, no matter which host the database lookup

If you repeat that a few times, the host information should change, but the user information should not.

Up Next

We have everything working, including using Envoy to handle round-robin traffic between our several Flask apps. With

Next up: Google Container Engine and AWS. One of the promises of Kubernetes is being able to easily get stuff deployed in multiple environments, so we’re going to see whether that actually works.