Design scalable worker pool for template jobs

You have implemented a function that takes a template string (or many template strings) and replaces placeholders like {{db_host}} and {{db_port}} using a key–value dictionary.

Now consider that you run this in production as a backend service and must process a very large volume of such replacement jobs (e.g., millions of template strings per hour).

Each job consists of:

• An identifier
• A template string (or a small set of template strings)
• A dictionary of substitution values

The system must:

• Handle high throughput and scale horizontally.
• Avoid being blocked by slow or heavy jobs.
• Use a worker pool model to process jobs concurrently.

Design Tasks

1. High-level architecture
   Design a system that can process a large number of template-substitution jobs reliably. Describe the main components (e.g., API layer, queues, workers, data stores) and how they interact.
2. Worker pool mechanism
   Explain in detail how the worker pool works. In particular:
   • How are jobs produced and put into the system? (Describe the producer side.)
   • How are jobs consumed by workers? (Describe the consumer side.)
   • How does this implement the classic producer–consumer model ?
   • How do you control concurrency and avoid overloading the system?
3. Work distribution among workers
   Suppose you want to distribute jobs across multiple workers. Discuss:
   • Different strategies for assigning jobs to workers (e.g., round-robin, random, hashing by key, multiple queues vs. a single shared queue).
   • When and why you might choose hash-based assignment on some key (for example, to keep all jobs related to a given customer or resource on the same worker).
   • Trade-offs between fairness, load balancing, and preserving ordering for related jobs.
4. Scalability and reliability
   Explain how your design:
   • Scales out when job volume increases (e.g., adding more workers, sharding queues).
   • Handles failures (e.g., worker crashes in the middle of a job, retry logic, idempotency).
   • Provides backpressure so that producers do not overwhelm the system.
5. Implementation considerations
   Briefly discuss:
   • What technologies you might use for the queue (e.g., Kafka, RabbitMQ, cloud message queues) and why.
   • Metrics and monitoring you would put in place (e.g., queue length, worker utilization, job latency).
   • Any specific optimizations for this string-template-replacement domain (e.g., caching parsed templates, batching jobs).

Provide a step-by-step, detailed design explaining your reasoning and the trade-offs you are making.