Design a Scalable Distributed Job Scheduler

Context

Design a multi-tenant, horizontally scalable job scheduler for backend services. The system must persist jobs durably, survive failures, and support high throughput. It will run in the cloud and integrate with a durable queue and a replicated metadata store.

Requirements

• Functional
  1. Job types: immediate, delayed (schedule at/after time), recurring (cron-like), and jobs with dependencies (DAG).
  2. Priorities (e.g., high, medium, low) with fair-share across tenants.
  3. Retries with configurable backoff and jitter; dead-lettering.
  4. Core APIs: submit, cancel, pause, resume, status (and list/filter optional).
  5. Execution semantics: at-least-once by default; discuss how to approach exactly-once.
  6. Idempotency, failure handling, deduplication.
• Non-Functional
  1. Persistence and durability of both metadata and queues.
  2. Monitoring, alerting, and observability.
  3. Multi-tenant isolation, quotas, and security.
  4. Horizontal scaling to high throughput and low scheduler latency.
  5. Multi-region deployment and failover.

Deliverables

• System components: API layer, scheduler/dispatcher, queues, workers/executors, metadata store.
• Data model for jobs, attempts, dependencies, and queues.
• API design for submit, cancel, pause, resume, status.
• Discussion of execution semantics, idempotency, failure handling/dedup, monitoring/alerting, multi-tenant isolation, horizontal scaling, durability, and multi-region strategies.