Tuesday, August 19, 2025

Kubernetes pods to run microservices that are launched from user generated blog

 Q: In my blogger.com blog I want users to be able to upload pics and create a collage, but I can't have a hosted server do this.. I want Kubernetes pods to be launched when any service request comes, the collage images are sent to the pods and result can be downloaded by the user. Is this possible?

Yes, it is possible to implement the functionality you described using Kubernetes and serverless principles. Here’s an outline of how you can design such a system:

Architecture Overview

  1. Frontend (Blogger Blog)

    • Your Blogger site acts as the frontend where users upload images and request the collage.

    • JavaScript on the blog can handle image uploads and send them to a backend API.

  2. Backend API Gateway

    • Use a cloud-hosted API gateway (e.g., AWS API Gateway, Google Cloud Endpoints) to accept requests.

    • The API gateway triggers the creation of a Kubernetes pod for processing.

  3. Kubernetes Cluster

    • Kubernetes is configured to launch pods on demand.

    • Each pod contains a containerized application that processes the collage request.

  4. Collage Service (Inside Pods)

    • This service receives uploaded images, processes the collage, and stores the result in cloud storage (e.g., AWS S3, Google Cloud Storage).

    • A signed download URL is sent back to the user for retrieving the collage.

  5. Cloud Storage

    • Temporary storage for user-uploaded images and the final collage.

    • Ensures scalability and reduces dependency on persistent storage within pods.

Steps to Implement

1. Blogger Integration

  • Add a custom HTML/JavaScript form to your Blogger blog.

  • Use JavaScript to handle file uploads and communicate with the API gateway.

  • Example (AJAX):

    const formData = new FormData();
formData.append("image1", file1);
formData.append("image2", file2);

fetch('https://your-api-gateway-url/upload', {
    method: 'POST',
    body: formData
})
.then(response => response.json())
.then(data => {
    console.log('Download link:', data.download_url);
    // Show the link to the user
});

2. API Gateway

  • Choose a cloud provider for the API gateway:

    • AWS: API Gateway with AWS Lambda to trigger Kubernetes jobs.

    • Google Cloud: Cloud Endpoints or Cloud Functions.

    • Azure: Azure API Management.

3. Kubernetes Pod Auto-Scaling

  • Set up Kubernetes pods with:

    • A containerized application that processes images (e.g., using Python’s PIL library).

    • Horizontal Pod Autoscaler (HPA) to launch new pods based on demand.

  • Use Kubernetes Jobs to run processing tasks.

Example Deployment (YAML):
apiVersion: batch/v1
kind: Job
metadata:
  name: collage-job
spec:
  template:
    spec:
      containers:
      - name: collage-processor
        image: your-collage-processor-image
        env:
        - name: STORAGE_BUCKET
          value: "your-bucket-name"
      restartPolicy: Never
  backoffLimit: 4

4. Collage Service

  • Use a lightweight library like:

    • Python: PIL (Pillow), OpenCV.

    • Node.js: Sharp, Jimp.

  • The service should:

    • Receive image files.

    • Process the images into a collage.

    • Upload the result to cloud storage.

5. Cloud Storage Integration

  • Store images and the final collage in a cloud bucket.

  • Generate pre-signed URLs for:

    • Uploading images to the pod.

    • Downloading the collage.

Challenges and Solutions

  1. Latency:

    • Cold starts of Kubernetes pods can add latency.

    • Use pre-warmed pods or a managed serverless container service (e.g., AWS Fargate, Google Cloud Run).

  2. Security:

    • Authenticate requests using API keys or OAuth.

    • Validate and sanitize user inputs to prevent abuse.

  3. Cost Management:

    • Set limits on pod resource usage and configure auto-scaling policies.

    • Implement a cleanup process for unused files.

This setup avoids the need for a traditional hosted server while leveraging Kubernetes for scalability and cloud storage for persistence. If you’d like, I can help you with detailed YAML configurations or code for specific parts!


Q: Thanks! considering that my blog hardly gets 100 unique visitors in a day, a couple would be interested in making a collage, how much would making one collage cost?

The cost of processing a single collage depends on several factors, including the cloud provider, the configuration of your Kubernetes cluster, and the resources allocated for each pod. Since your blog has low traffic and only a few users would use the collage feature, the costs can be kept minimal by using a pay-as-you-go model. Here’s a breakdown:

Cost Components

  1. Cloud Provider Costs
    You’ll primarily pay for:

    • Compute (CPU & Memory): For the time your pod is running.

    • Storage: For temporary storage of images and collages.

    • API Gateway: For handling requests.

    • Data Transfer: For uploading and downloading images.

  2. Kubernetes Costs

    • If you’re using a managed Kubernetes service (like GKE, EKS, or AKS), you’ll incur costs for:

      • The control plane (often free for low usage in some tiers).

      • The worker nodes, charged by the resources used.

  3. Processing Requirements
    For a simple collage generator:

    • Assume 0.25 vCPU and 256MB RAM for a single pod.

    • Average processing time: ~10-30 seconds per collage.

  4. Storage Costs

    • Temporary storage of uploaded images: Minimal (~1-10MB per collage).

    • Output storage for collages: ~1-2MB per collage.

Estimated Costs per Collage

Component Details Estimated Cost
Compute 0.25 vCPU, 256MB RAM for 30 seconds (~0.0001 hours). ~$0.0000025 per collage (AWS/GCP).
API Gateway One request to trigger the job. ~$0.0004 per request.
Storage (Temporary) 10MB upload + 2MB result stored for <1 day. ~$0.0002 per collage.
Data Transfer 12MB total transfer (upload/download). ~$0.00012 per collage.

Total Estimated Cost per Collage: $0.00072 ($0.001).

Monthly Cost for Your Traffic

Assume:

  • 5 collages/day (max traffic).

  • 30 days/month.

Monthly Cost:

5×30×0.001=0.15 USD/month5 \times 30 \times 0.001 = 0.15 \, \text{USD/month}

Cost Optimization Tips

  1. Use Preemptible/Spot Instances:
    If the pod takes only a few seconds, spot instances can reduce costs significantly.

  2. Free Tiers:
    Many cloud providers offer free-tier usage:

    • Google Cloud: 50GB storage free/month, 1 vCPU/month for GKE.

    • AWS: 1 million API Gateway requests and 750 hours of t2.micro free for 12 months.

  3. Minimize Cold Starts:
    Use small and efficient Docker images to reduce startup times.

  4. Batch Processing:
    Queue requests and process multiple collages together to maximize resource usage.

Given your low traffic and minimal resource requirements, you can likely run this setup for less than $1/month in total. It's an extremely cost-effective solution! If you'd like, I can help analyze costs for a specific cloud provider you're considering.

Q: Is this kind of architecture called microservices?

Yes, the architecture you're considering has characteristics of microservices, but it also aligns closely with serverless and event-driven architecture principles. Let's break it down to clarify:

What is Microservices Architecture?

Microservices architecture involves designing an application as a collection of small, independent, and loosely coupled services. Each service focuses on a specific business capability and communicates with others via well-defined APIs.

Key Features of Microservices:

  1. Small, Single-Purpose Services: Each service handles a distinct functionality (e.g., image processing, user authentication).

  2. Independence: Services can be developed, deployed, and scaled independently.

  3. API Communication: Services communicate over APIs (usually HTTP or message queues).

  4. Containerization: Often deployed using Docker or Kubernetes.

Your architecture includes some of these elements:

  • The collage generation pod could be considered a "microservice" that performs one task (image processing).

  • Kubernetes enables independent scaling and containerization.

  • The system communicates via APIs (e.g., API Gateway).

How It Also Aligns with Serverless Architecture

Your approach more closely resembles serverless principles:

  1. Event-Driven: A Kubernetes pod (or job) is launched only in response to an event (user request).

  2. Ephemeral Services: Pods exist only for the duration of the task, which is a hallmark of serverless computing.

  3. Cost Efficiency: You only pay for the compute resources used during the task, avoiding idle costs.

This serverless-style deployment is supported by Kubernetes when configured with event-driven triggers like Kubernetes Jobs or integrations with cloud event systems (e.g., Cloud Run, AWS Lambda with EKS).

Comparison: Microservices vs. Serverless

Aspect Microservices Serverless (Your Case)
Granularity Many small services, each running continuously. Short-lived tasks triggered by events.
Deployment Runs on Kubernetes, VMs, or similar. Uses serverless compute (e.g., pods/jobs).
Scaling Scaled independently based on load. Automatically scaled, no idle resources.
Resource Usage Resources allocated even during idle times. Pay-per-use, zero cost when idle.
Communication Requires inter-service communication. Minimal; tasks are isolated.

Your Architecture

Your design doesn't fully implement microservices since you aren't creating multiple interacting services. Instead, it leverages serverless principles on Kubernetes, with event-driven pods acting as disposable "workers" for a specific task.

Terminology:

  • It can be described as event-driven microservices or serverless workloads on Kubernetes.

Would Full Microservices Be Better?

For your use case (low traffic, simple processing task), microservices might be overkill. Serverless-style architecture is simpler and more cost-effective. However, if your system evolves to include multiple independent features (e.g., collage generation, video editing, user management), adopting a true microservices approach might make sense.

Would you like guidance on scaling this system or adapting it to include more features?

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