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NO.35 An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and call the company's Python API to execute trades. You need to efficiently implement a solution. What should you do?
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Explanation:
Explanation
For this high-volume trade processing scenario, Cloud Tasks + Cloud Functions is the most appropriate solution because:
Why Cloud Tasks?
- Task-based queuing: Cloud Tasks is specifically designed for executing discrete tasks/jobs with guaranteed delivery
- Rate limiting and retries: Built-in support for controlling execution rates and automatic retries for failed tasks
- Secure: Integrates well with IAM for secure access control
- Reliable delivery: Ensures trade requests are processed exactly once
Why Cloud Functions?
- Python API integration: Cloud Functions natively supports Python, which matches the company's Python API requirement
- Event-driven: Automatically triggers when tasks are added to the queue
- Scalable: Automatically scales based on queue depth
- Cost-effective: Only runs when there are tasks to process
Why not the other options?
- Cloud Pub/Sub: Better for streaming/messaging scenarios, not task execution with guaranteed processing
- Cloud Scheduler: Designed for cron-like scheduled jobs, not real-time queuing
- Workflows: Better for orchestrating complex workflows, not simple task execution
- Cloud Run: Could work but Cloud Functions is more lightweight for this specific Python API use case
This combination provides a secure, scalable, and efficient solution for processing high-volume trade requests with the company's Python API.
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