How do I approach System Design interview questions?

System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

What difficulty level is this interview question?

This is a hard difficulty System Design question, commonly asked during Onsite rounds at Applied Intuition.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Applied Intuition during technical interviews.

Design a job scheduler | Applied Intuition Interview Question

Quick Overview

This question evaluates competency in designing scalable, durable distributed job schedulers, covering multi-tenant architecture, queuing and metadata persistence, execution semantics, idempotency, retries/dead-lettering, failure handling, and observability.

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.

Quick Overview

Requirements

Functional

Job types: immediate, delayed (schedule at/after time), recurring (cron-like), and jobs with dependencies (DAG).
Priorities (e.g., high, medium, low) with fair-share across tenants.
Retries with configurable backoff and jitter; dead-lettering.
Core APIs: submit, cancel, pause, resume, status (and list/filter optional).
Execution semantics: at-least-once by default; discuss how to approach exactly-once.
Idempotency, failure handling, deduplication.

Non-Functional

Persistence and durability of both metadata and queues.
Monitoring, alerting, and observability.
Multi-tenant isolation, quotas, and security.
Horizontal scaling to high throughput and low scheduler latency.
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.

Design a job scheduler

Quick Overview

Design a Scalable Distributed Job Scheduler

Context

Requirements

Deliverables

Solution

Comments (0)

Design a job scheduler

Quick Overview

Design a Scalable Distributed Job Scheduler

Context

Requirements

Deliverables

Solution

Comments (0)