This application is based on this repository by Google™

The application is currently deployed here.

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 Online Boutique to demonstrate use of technologies like Kubernetes/GKE, Istio, Stackdriver, gRPC and OpenCensus. This repository contains a revamped version of Online Boutique, that can be run on Google Kubernetes Engine. It's easy to deploy and tear down. Complete step-by-step to deploy and tear down this application can be found on Installation section below.

Home Page	Checkout Screen

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

Find Protocol Buffers Descriptions at the ./pb directory.

• Kubernetes/GKE: The app is designed to run on Google Kubernetes Engine.
• gRPC: Microservices use a high volume of gRPC calls to communicate to each other.
• 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. Uses Faker to help generate random email and street addresses.

1. Firebase Authentication: The application provides user authentication by Sign in with Google Account and autofill the user's email when checkout.
2. Firestore & Cloud Storage: The application fetches its products from Firestore, and the product images can be stored on Cloud Storage.
3. Cloud SQL: The application uses Cloud SQL to store Order information.
4. Terraform: The application use Terraform to automate creation and teardown of various GCP resources such as GKE cluster, Cloud Storage, and Cloud SQL instance.
5. Faker & Google Geocoding API: The application generates random users and addresses for load testing, then converts the addresses to geolocation coordinates.
6. Dataflow: The application uses Dataflow, a fully managed data processing service, to load data from Cloud SQL (transactional database) to BigQuery (analytical database).
7. BigQuery: The application uses BigQuery, a serverless data warehouse, to enable scalable data analysis.
8. Data Studio: The application uses Data Studio to quickly visualize the data that have been processed in BigQuery.

We offer the following installation method:

Running on Google Kubernetes Engine (GKE)” (~30 minutes)

• You will build, upload and deploy the container images to a Kubernetes cluster on Google Cloud.

• kubectl (can be installed via gcloud components install kubectl)
• skaffold (ensure version ≥v1.10)
• Enable GCP APIs for Cloud Monitoring, Tracing, Debugger:

  gcloud services enable monitoring.googleapis.com \
      cloudtrace.googleapis.com \
      clouddebugger.googleapis.com

💡 You can try it on a realistic cluster using Google Cloud Platform.

1. Enable Google Kubernetes Engine API

    gcloud services enable container.googleapis.com
   

2. Run terraform apply in the directory /terraform. Enter your GCP Project ID and then yes.

   It will create GCP resources used in the application:

   Resources	Explanation
   GKE cluster	For deployment
   Cloud Storage Buckets	Product images, Firestore backup
   Cloud Storage Objects	Product images
   Cloud SQL Instance	Store Order information
   Service Accounts	For Firestore, Cloud SQL

3. Connect kubectl with cluster demo

    gcloud container clusters get-credentials demo --zone asia-east1-a --project <GCP_project_ID>
   

4. Configuring Firebase Authentication

   • Create a new Firebase Project on Firebase Console and connect it with your GCP Project.
   • Go to Project Settings on the Firebase Console to get your firebaseConfig.
   • Update firebaseConfig here accordingly (necessary fields only).

5. Configuring Cloud Firestore

   • On the GCP Console, go to Firestore and select Native Mode.
   • Copy firestore-backup to the Cloud Storage Bucket created by Terraform

     gsutil cp -r firestore-backup/content gs://<Project ID>-firestore-backup

   • Import Firestore backup from the Cloud Storage Bucket

     gcloud firestore import gs://<Project ID>-firestore-backup/content/

   • On the Firestore interface, update the value of picture field on each products because your product image links are different. Change them to https://storage.googleapis.com/<Project ID>-product-image/xxx.jpg.
   • Update projectID for the Firestore client here.
   • Create a new JSON key of the service account firestore-sa created by Terraform, and save it on directory /src/productcatalogservice.
   • Update your JSON key file name on .gitignore, Dockerfile, and code.

6. Configuring Cloud SQL

   • Terraform has created Cloud SQL instance and service account needed by the application.
   • Create Kubernetes secret

     kubectl create secret generic order-secret \
         --from-literal=db_user=root \
         --from-literal=db_password=<GCP_project_ID> \
         --from-literal=db_name=order
     

   • Update project ID for Kubernetes service account annotation on yaml.
   • Update Cloud SQL instance connection name on yaml and code.

7. 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

8. You can turn off order load generator by setting tasks > checkout here to 0. Else, the generated addresses are processed with Geocoding API. Don't forget to enable it.

9. 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.

10. 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.

11. Authorize the domain for Firebase Authentication

    • Go to Authentication section on the Firebase Console and enable Sign-in method: Google.
    • Add the IP address of your deployed application to the Authorized domains.

You can run skaffold delete to clean up the deployed resources.

• Google Cloud Next'18 London – Keynote showing Stackdriver Incident Response Management
• Google Cloud Next'18 SF
  • Day 1 Keynote showing GKE On-Prem
  • Day 3 – Keynote showing Stackdriver APM (Tracing, Code Search, Profiler, Google Cloud Build)
  • Introduction to Service Management with Istio
• KubeCon EU 2019 - Reinventing Networking: A Deep Dive into Istio's Multicluster Gateways - Steve Dake, Independent

This is not an official Google project.