- About Car Demo
- Key technical benefits
- System functionality
- Architecture
- Get the source code
- Getting Started for Local
- GCP Infrastructure and Deployment
- Data Analytics and Visualization
- Documentation
- How to merge a feature
- Next Features to be implemented
We are developing an eCommerce platform dedicated to the sale of cars. Users will have the capability to search for products, place orders through a shopping cart, and complete payments. The platform will also incorporate inventory management for efficient tracking. Additionally, a secure centralized data platform will be established to support data analytics and facilitate the implementation of AI/ML use cases.
- Developed the platform by following Domain Driven Design principle.
- Developed microservices with Axon Framework. Each microservice solve a business problem or representing a domain for example order, product, cart, inventory etc.
- Front end Angular - Angular
- The WebFlux framework facilitates non-blocking, event-driven processes for car user interfaces (Dashboards).
- The data platform is built on Databricks, a unified platform for data engineering, data governance, data analytics, and AI/ML.
- Established a secure integration of Microsoft powerBI with databricks for visualization and reporting.
- Ready to deploy in the GCP
- Run from Docker containers
Car-demo system provides the following functionality:
- Ordering: receives a customer order, order details including items, item quantities
- Payment: Involves the user verification, and settlement of transactions
- Shipping: Streamline outgoing shipments to customers with pricing on shipments and confirmation notifications
- Shopping cart: Allowing customers to select items from the store's website
- Inventory management: Manage car [product] that are available for sale
- Databricks: The Databricks Pipeline ingests data from a GCP Pub/Sub topic, stores it in a Databricks Delta Lake table, and utilizes Unity Catalog as a comprehensive governance solution for data and AI within the lakehouse environment.
- PowerBI: Integrating Databricks with PowerBI to enhance visualization and reporting capabilities.
Clone the repository:
$ git clone https://github.com/NashTech-Labs/car-demo.git
Let's execute the application using the default local settings, which include a separate .env file for each service, and configurations. In the root of the car-demo, there is a local-dev/docker-compose.yml file that enables us to launch necessary resources such as MySQL, Axon Server, and Elastic Server. The following steps outline the setup and launch process for the applications.
1) Run docker-compose
Resource Launches
$ docker-compose up -d
Resource Down
$ docker-compose down
2) Set GOOGLE_APPLICATION_CREDENTIALS as an environment variable
This property is utilized for connecting and authenticating GCP resources, and the following services rely on it:
- Elasticsearch service
- Inventory service
- Shipment service
You can include this environment property in IntelliJ by navigating to the upper right corner, selecting Edit Configurations, and then going to Environmental Variables.
3) Run Services
The listed services are operate sequentially.
Phase-1
- admin-service README.md
- inventory-service README.md
- order-service README.md
- payment-service README.md
- shipment-service README.md
- elasticsearch README.md
Phase-2
- cart-service README.md
Phase-3
- car-ui README.md
Now open a browser on this urls
| Application | Url |
|---|---|
| Headless admin service | localhost:8080 |
| Headless cart service | localhost:9094 |
| Headless elasticsearch service | localhost:9095 |
| Headless inventory service | localhost:9091 |
| Headless order service | localhost:9090 |
| Headless payment service | localhost:9092 |
| Headless shipment service | localhost:9093 |
| Car Ui | localhost:8080 |
To deploy the GCP infrastructure required, follow the steps in deployment readme: readme.md deployment/gcpresources/readme.md
-
Add required secrets in https://github.com/NashTech-Labs/car-demo/settings/secrets/actions
-
GKE_KEY : the service account json key having these permission:
Cloud Datastore Owner Cloud Functions Admin Cloud SQL Admin Compute Admin Container Registry Service Agent Create Service Accounts Firestore Service Agent Kubernetes Engine Admin Project IAM Admin Pub/Sub Admin Secret Manager Admin Secret Manager Secret Accessor Security Reviewer Service Account Admin Service Account User Service Usage Admin Storage Admin Storage Object Admin -
GKE_PROJECT: Project name github workflow for CI/CD can be found here: car-demo-pipeline.yml
-
Keep the service name in projects-changes-deploy.txt which you want to deploy.
When a commit push to main branch it will start the deployment of the services/project mentioned in file projects-changes-deploy.txt. deployments steps for each project are mentioned in apps-deployment-script.sh
Note:
common: It consists of common utility, events, commands and models used across multiple projects.
you will find the deployment step of common in apps-deployment-script.sh
it will do clean package and push the package on github.
so, Each time you make the changes in common upgrade the version in its pom.xml and also
update same version of common in each projects pom.xml
The process to obtain a JSON key for a Google Cloud Platform (GCP) service account involves the following steps:
1. Open the Google Cloud Console: Navigate to the Google Cloud Console at https://console.cloud.google.com/.
2. Access the IAM & Admin Section: In the console, locate and click on the "IAM & Admin" section.
3. Choose the Project: Select the project for which you want to create a service account or ensure that you are working within the correct project.
4. Navigate to Service Accounts: In the IAM & Admin section, find and click on "Service accounts" to view the existing service accounts for the selected project.
5. Create a New Service Account: Click on the "Create Service Account" button to initiate the process of creating a new service account.
6. Provide Details: Enter a name for your service account, add a description (optional), and choose the appropriate role(s) for the service account based on your project's needs.
7. Generate a Key: After creating the service account, click on the "Create Key" button. Choose the key type as JSON, and a JSON key file will be generated and downloaded to your device.
Keep the JSON Key Secure: Ensure that you securely store the downloaded JSON key file, as it contains sensitive information and grants access to your GCP resources.
The ReadMe.md file contains the steps for deploying the system.
To verify the deployment of Kubernetes on Google Cloud Platform (GCP), follow these steps:
1. Access GCP Console:
Log in to the Google Cloud Console at https://console.cloud.google.com/.
2. Navigate to Kubernetes Engine:
In the console, go to the "Kubernetes Engine" section.
3. Select Cluster:
Choose the Kubernetes cluster you want to check.
4. Review Cluster Details:
Examine the cluster details, including the status, version, and other relevant information.
5. Verify Nodes:
Confirm that the nodes in your cluster are in the "RUNNING" state.
6. Check Workloads:
Examine the workloads running on the cluster to ensure that your applications and services are deployed as expected.
7. Inspect Pods:
Review the status of individual pods within the cluster to ensure they are running without errors.
8. Examine Services:
Confirm that the Kubernetes services associated with your applications are accessible and running correctly.
9. Monitor Events:
Check the cluster events for any warnings or errors that might indicate deployment issues.
10. Test Connectivity:
Verify that you can connect to your deployed applications and services from external sources if applicable.
11. Check External Access:
If your applications are supposed to be externally accessible, confirm that external access is properly configured.
12. Review Logs:
Examine logs for applications and services to identify any issues or error messages.
For the analytical purpose, the insightful data generated by microservices brings to the databricks Lakehouse platform. find the details here README.md
- Create a branch from main branch and name it based on the feature to be implemented
- Implement feature in new feature branch
- Raise a PR against the main branch
- Get it approved from Approver and merge the feature branch to master
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