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Contributing

See CONTRIBUTING.md.

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.
  • OpenTelemetry Tracing: Most services are instrumented using OpenTelemetry trace interceptors for gRPC/HTTP.
  • 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.

Quickstart

  1. Make sure that you have access to a kubernetes cluster. It can be provisioned using any cloud provider or you can setup a local cluster.

    There are three options to run a Kubernetes cluster locally for this demo:

    • Minikube (recommended). Please, ensure that the local Kubernetes cluster has at least 4 CPU's and 4.0 GiB of memory. 
      minikube start --cpus=4 --memory 4096
      Also run minikube tunnel in a separate console to be able to access app's UI.
    • Docker for Desktop
      • 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.
    • Kind 
      kind create cluster

  2. Create kubernetes manifest files:

    ./hack/make-release-artifacts.sh

    If you want to enable Splunk RUM instrumentation, set RUM_REALM and RUM_AUTH env variables before creating the manifest files, for example

    export RUM_REALM=<YOUR_RUM_SPLUNK_REALM> RUM_AUTH=<YOUR_RUM_AUTH_TOKEN>

    Optional RUM parameters can be used as well: RUM_APP_NAME, RUM_ENVIRONMENT and RUM_DEBUG.

  3. Now you can see kubernetes manifest in ./release/kubernetes-manifests.yaml. To apply it in your kubernetes cluster, run:

    kubectl apply -f release/kubernetes-manifests.yaml

  4. Run kubectl get pods to verify the Pods are ready and running. It may take up to 2 minutes.

  5. Now you can see web frontend by looking for EXTERNAL_IP of the frontend kubernetes service:

    kubectl get service frontend-external | awk '{print $4}'

    The same way you can access the load generator UI:

    kubectl get service loadgenerator | awk '{print $4}'

Local development

  1. Launch a 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 CPUs
      • 4.0 GiB memory
      • 32 GB disk space
      minikube start --cpus=4 --memory 4096 --disk-size 32g

    • 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

  2. Run kubectl get nodes to verify you're connected to the respective control plane.

  3. 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.

  4. Run kubectl get pods to verify that all the pods are ready and running.

  5. 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

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.