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 |
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Service Architecture
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.
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:
-
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.
-
Running on Google Kubernetes Engine (GKE) (~30 minutes) You will build, upload and deploy the container images to a Kubernetes cluster on Google Cloud.
-
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).
Prerequisites
- kubectl (can be installed via
gcloud components install kubectl
) - Local Kubernetes cluster deployment tool:
- Minikube (recommended for Linux)
- Docker for Desktop (recommended for Mac/Windows)
- It provides Kubernetes support as noted here
- Kind
- 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
Option 1: Running locally
💡 Recommended if you're planning to develop the application or giving it a try on your local cluster.
-
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 CPU's
- 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
-
-
Run
kubectl get nodes
to verify you're connected to “Kubernetes on Docker”. -
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, runskaffold dev
command. -
Run
kubectl get pods
to verify the Pods are ready and running. -
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. Note: If your cluster has Workload Identity enabled, see these instructions
-
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
-
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
-
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. -
Find the IP address of your application, then visit the application on your browser to confirm installation.
kubectl get service frontend-external
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).
-
Clone this repository, and go to the repository directory
-
Run
kubectl apply -f ./release/kubernetes-manifests.yaml
to deploy the app. -
Run
kubectl get pods
to see pods are in a Ready state. -
Find the IP address of your application, then visit the application on your browser to confirm installation.
kubectl get service/frontend-external
Option 4: Deploying on a Istio-enabled GKE cluster
Note: if you followed GKE deployment steps above, run
skaffold delete
first to delete what's deployed.
-
Create a GKE cluster (described in "Option 2").
-
Use the 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
-
(Optional) Enable Stackdriver Tracing/Logging with Istio Stackdriver Adapter by following this guide.
-
Install the automatic sidecar injection (annotate the
default
namespace with the label):kubectl label namespace default istio-injection=enabled
-
Apply the manifests in
./istio-manifests
directory. (This is required only once.)kubectl apply -f ./istio-manifests
-
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. -
Run
kubectl get pods
to see pods are in a healthy and ready state. -
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"
Option 5: Deploying on a Workload Identity-enabled GKE cluster
See this doc.
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
- 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.