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Online Boutique is a cloud-native microservices demo application. Online Boutique consists of a 10-tier microservices application. The application is a web-based e-commerce app where users can browse items, add them to the cart, and purchase them.

Google uses this application to demonstrate use of technologies like Kubernetes/GKE, Istio, Stackdriver, gRPC and OpenCensus. This application works on any Kubernetes cluster (such as a local one), as well as Google Kubernetes Engine. It’s easy to deploy with little to no configuration.

If you’re using this demo, please ★Star this repository to show your interest!

👓Note to Googlers: Please fill out the form at go/microservices-demo if you are using this application.

Looking for the old Hipster Shop frontend interface? Use the manifests in release v0.1.5.

Screenshots

Home Page Checkout Screen
Screenshot of store homepage Screenshot of checkout screen

Service Architecture

Online Boutique is composed of many microservices written in different languages that talk to each other over gRPC.

Architecture of microservices

Find Protocol Buffers Descriptions at the ./pb directory.

Service Language Description
frontend Go Exposes an HTTP server to serve the website. Does not require signup/login and generates session IDs for all users automatically.
cartservice C# Stores the items in the user's shopping cart in Redis and retrieves it.
productcatalogservice Go Provides the list of products from a JSON file and ability to search products and get individual products.
currencyservice Node.js Converts one money amount to another currency. Uses real values fetched from European Central Bank. It's the highest QPS service.
paymentservice Node.js Charges the given credit card info (mock) with the given amount and returns a transaction ID.
shippingservice Go Gives shipping cost estimates based on the shopping cart. Ships items to the given address (mock)
emailservice Python Sends users an order confirmation email (mock).
checkoutservice Go Retrieves user cart, prepares order and orchestrates the payment, shipping and the email notification.
recommendationservice Python Recommends other products based on what's given in the cart.
adservice Java Provides text ads based on given context words.
loadgenerator Python/Locust Continuously sends requests imitating realistic user shopping flows to the frontend.

Features

  • Kubernetes/GKE: The app is designed to run on Kubernetes (both locally on "Docker for Desktop", as well as on the cloud with GKE).
  • gRPC: Microservices use a high volume of gRPC calls to communicate to each other.
  • Istio: Application works on Istio service mesh.
  • OpenCensus Tracing: Most services are instrumented using OpenCensus trace interceptors for gRPC/HTTP.
  • Stackdriver APM: Many services are instrumented with Profiling, Tracing and Debugging. In addition to these, using Istio enables features like Request/Response Metrics and Context Graph out of the box. When it is running out of Google Cloud, this code path remains inactive.
  • Skaffold: Application is deployed to Kubernetes with a single command using Skaffold.
  • Synthetic Load Generation: The application demo comes with a background job that creates realistic usage patterns on the website using Locust load generator.

Installation

We offer the following installation methods:

  1. Running locally (~20 minutes) You will build and deploy microservices images to a single-node Kubernetes cluster running on your development machine. There are three options to run a Kubernetes cluster locally for this demo:

    • Minikube. Recommended for Linux hosts (also supports Mac/Windows).
    • Docker for Desktop. Recommended for Mac/Windows.
    • Kind. Supports Mac/Windows/Linux.

  2. Running on Google Kubernetes Engine (GKE)” (~30 minutes) You will build, upload and deploy the container images to a Kubernetes cluster on Google Cloud.

  3. Using pre-built container images: (~10 minutes, you will still need to follow one of the steps above up until skaffold run command). With this option, you will use pre-built container images that are available publicly, instead of building them yourself, which takes a long time).

Option 1: Running locally

💡 Recommended if you're planning to develop the application or giving it a try on your local cluster.

  1. Install tools to run a Kubernetes cluster locally:

  2. Launch the local Kubernetes cluster with one of the following tools:

    • To launch Minikube (tested with Ubuntu Linux). Please, ensure that the local Kubernetes cluster has at least:

      • 4 CPU's
      • 4.0 GiB memory
      minikube start --cpus=4 --memory 4096

    • To launch Docker for Desktop (tested with Mac/Windows). Go to Preferences:

      • choose “Enable Kubernetes”,
      • set CPUs to at least 3, and Memory to at least 6.0 GiB
      • on the "Disk" tab, set at least 32 GB disk space

    • To launch a Kind cluster:

      kind create cluster

  3. Run kubectl get nodes to verify you're connected to “Kubernetes on Docker”.

  4. Run skaffold run (first time will be slow, it can take ~20 minutes). This will build and deploy the application. If you need to rebuild the images automatically as you refactor the code, run skaffold dev command.

  5. Run kubectl get pods to verify the Pods are ready and running.

  6. Access the web frontend through your browser

    • Minikube requires you to run a command to access the frontend service:
    minikube service frontend-external

    • Docker For Desktop should automatically provide the frontend at http://localhost:80

    • Kind does not provision an IP address for the service. You must run a port-forwarding process to access the frontend at http://localhost:8080:

    kubectl port-forward deployment/frontend 8080:8080

Option 2: Running on Google Kubernetes Engine (GKE)

💡 Recommended if you're using Google Cloud Platform and want to try it on a realistic cluster.

  1. Install tools specified in the previous section (Docker, kubectl, skaffold)

  2. Create a Google Kubernetes Engine cluster and make sure kubectl is pointing to the cluster.

    gcloud services enable container.googleapis.com
    gcloud container clusters create demo --enable-autoupgrade \
    --enable-autoscaling --min-nodes=3 --max-nodes=10 --num-nodes=5 --zone=us-central1-a
    kubectl get nodes

  3. Enable Google Container Registry (GCR) on your GCP project and configure the docker CLI to authenticate to GCR:

    gcloud services enable containerregistry.googleapis.com
    gcloud auth configure-docker -q

  4. In the root of this repository, run skaffold run --default-repo=gcr.io/[PROJECT_ID], where [PROJECT_ID] is your GCP project ID.

    This command:

    • builds the container images
    • pushes them to GCR
    • applies the ./kubernetes-manifests deploying the application to Kubernetes.

    Troubleshooting: If you get "No space left on device" error on Google Cloud Shell, you can build the images on Google Cloud Build: Enable the Cloud Build API, then run skaffold run -p gcb --default-repo=gcr.io/[PROJECT_ID] instead.

  5. Find the IP address of your application, then visit the application on your browser to confirm installation.

    kubectl get service frontend-external

    Troubleshooting: A Kubernetes bug (will be fixed in 1.12) combined with a Skaffold bug causes load balancer to not to work even after getting an IP address. If you are seeing this, run kubectl get service frontend-external -o=yaml | kubectl apply -f- to trigger load balancer reconfiguration.

Option 3: Using Pre-Built Container Images

💡 Recommended if you want to deploy the app faster in fewer steps to an existing cluster.

NOTE: If you need to create a Kubernetes cluster locally or on the cloud, follow "Option 1" or "Option 2" until you reach the skaffold run step.

This option offers you pre-built public container images that are easy to deploy by deploying the release manifest directly to an existing cluster.

Prerequisite: a running Kubernetes cluster (either local or on cloud).

  1. Clone this repository, and go to the repository directory

  2. Run kubectl apply -f ./release/kubernetes-manifests.yaml to deploy the app.

  3. Run kubectl get pods to see pods are in a Ready state.

  4. Find the IP address of your application, then visit the application on your browser to confirm installation.

    kubectl get service/frontend-external

(Optional) Deploying on a Istio-installed GKE cluster

Note: you followed GKE deployment steps above, run skaffold delete first to delete what's deployed.

  1. Create a GKE cluster (described in "Option 2").

  2. Use Istio on GKE add-on to install Istio to your existing GKE cluster.

    gcloud beta container clusters update demo \
    --zone=us-central1-a \
    --update-addons=Istio=ENABLED \
    --istio-config=auth=MTLS_PERMISSIVE

  3. (Optional) Enable Stackdriver Tracing/Logging with Istio Stackdriver Adapter by following this guide.

  4. Install the automatic sidecar injection (annotate the default namespace with the label):

    kubectl label namespace default istio-injection=enabled

  5. Apply the manifests in ./istio-manifests directory. (This is required only once.)

    kubectl apply -f ./istio-manifests

  6. In the root of this repository, run skaffold run --default-repo=gcr.io/[PROJECT_ID], where [PROJECT_ID] is your GCP project ID.

    This command:

    • builds the container images
    • pushes them to GCR
    • applies the ./kubernetes-manifests deploying the application to Kubernetes.

    Troubleshooting: If you get "No space left on device" error on Google Cloud Shell, you can build the images on Google Cloud Build: Enable the Cloud Build API, then run skaffold run -p gcb --default-repo=gcr.io/[PROJECT_ID] instead.

  7. Run kubectl get pods to see pods are in a healthy and ready state.

  8. Find the IP address of your Istio gateway Ingress or Service, and visit the application.

    INGRESS_HOST="$(kubectl -n istio-system get service istio-ingressgateway \
   -o jsonpath='{.status.loadBalancer.ingress[0].ip}')"
echo "$INGRESS_HOST"
    curl -v "http://$INGRESS_HOST"

Cleanup

If you've deployed the application with skaffold run command, you can run skaffold delete to clean up the deployed resources.

If you've deployed the application with kubectl apply -f [...], you can run kubectl delete -f [...] with the same argument to clean up the deployed resources.

Conferences featuring Online Boutique

This is not an official Google project.