Setup distributed job processing system with dynamic worker scaling

[irfanghat]

System Overview

1. Client requests a long-running job via an API.
2. The request is pushed to a queue (e.g., RabbitMQ, SQS, Kafka).
3. A Worker Manager Service picks jobs from the queue.
4. The Worker Manager scales workers dynamically based on queue length.
5. Workers process jobs asynchronously.
6. Once done, the worker sends a notification (Webhook, WebSocket, Slack, Email).
7. The client can poll the status or get the final response.

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** Key Components**

1️⃣ Client API (Request Submission)

• A REST API or GraphQL endpoint for clients.
• Pushes job details to a queue (e.g., AWS SQS, Redis Queue, Kafka).
• Returns a job ID for tracking.

2️⃣ Message Queue

• Purpose: Decouples job requests from execution.
• Options:
  • AWS SQS (Managed)
  • RabbitMQ (Self-hosted)
  • Apache Kafka (Streaming & event-driven)
  • Redis (Simple in-memory queue)

3️⃣ Worker Manager Service

• Listens to the queue and dynamically scales workers.

• Scaling logic:

  • More jobs in the queue → Spawn more workers.
  • Few jobs → Scale down workers to save costs.

• Deployment options:

  • Nomad (Alternative to Kubernetes)

4️⃣ Worker Nodes

• Execute tasks asynchronously.

• Process the job based on workload type (CPU-bound, I/O-bound).

• Returns results to a database, object storage, or the client directly.

5️⃣ Notifications System

• When a job completes, notify the client via:

  • WebSockets (Realtime UI updates)

  • Webhooks (Client provides a callback URL)

  • Email/SMS/Slack (For user notifications)

6️⃣ Status Tracking & Response

• A database stores job progress (Redis, PostgreSQL, DynamoDB).

• Clients can poll for job status via API.