Deploy Angular separately; scale Node.js API with Puppeteer concurrently. Dockerfile:
FROM node:14-slim; WORKDIR /api; COPY . .; RUN npm install && npm install puppeteer; EXPOSE 3000; CMD ["npm","start"]
I have experimented with deploying the Angular frontend and Node.js API on separate containers, and the experience highlights the importance of isolating services for smoother scaling. In my setup, I used Cloud Storage to serve the Angular static files while the Node.js API worked seamlessly behind a load balancer. This separation allowed rapid deployment of updates without affecting the entire system. I also found that managing environment-specific configurations and resource allocation in Google Cloud greatly improves overall device efficiency and system reliability upon scaling.
i used cloud run for node js api and firebase hosting for angular, which simplified deployments. scaling was responsve but i had occasional latency issues with puppeteer. adjusting timeouts help fix those quirk issues.
In my experience, deploying these applications on Google Cloud requires proper separation of responsibilities, which I achieved by leveraging Kubernetes for container orchestration. I deployed the Angular frontend using a service like Google Cloud Storage with a CDN fronting it to enhance performance and ensure rapid content delivery. Meanwhile, the Node.js API with Puppeteer was containerized and run in a dedicated pod for better resource isolation and scaling flexibility. This approach allowed me to tune resource allocation precisely, troubleshoot issues more effectively, and seamlessly roll out updates without major downtime.
I have used Google Compute Engine to deploy an Angular frontend alongside a Node.js backend that utilizes Puppeteer. In my experience, segregating the deployment environments allowed for more granular control over resource allocation. By configuring separate virtual machine instances, I was able to fine-tune scalability and performance without affecting the overall system. I made sure that environment variables and network configurations were well documented, which simplified the troubleshooting process. This method also eased the process of updating each component independently.