Design distributed parallel job processing

Quick Overview

This question evaluates understanding of distributed systems architecture, parallel job scheduling, fault tolerance, coordination and concurrency control, API and data model design, idempotency, observability, and capacity planning for multi-tenant, heterogeneous workloads.

Design distributed parallel job processing

Company: LinkedIn

Role: Software Engineer

Category: System Design

Difficulty: hard

Interview Round: Technical Screen

Design a distributed system to execute many independent jobs in parallel across a cluster. Specify the job model and APIs, task partitioning/sharding strategy, scheduler and worker architecture, coordination (e.g., leases or heartbeats), fault tolerance and retries with backoff, idempotency and deduplication, progress tracking and result aggregation, scaling and resource management, backpressure and prioritization, and ordering guarantees. Discuss consistency trade-offs (at-least-once vs exactly-once), monitoring, and capacity planning.

Quick Answer: This question evaluates understanding of distributed systems architecture, parallel job scheduling, fault tolerance, coordination and concurrency control, API and data model design, idempotency, observability, and capacity planning for multi-tenant, heterogeneous workloads.

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Sep 6, 2025, 12:00 AM
hardSoftware EngineerTechnical ScreenSystem Design
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Design a Distributed System for Parallel Job Execution

Context

You are asked to design a highly available, horizontally scalable service that executes many independent tasks (embarrassingly parallel jobs) across a compute cluster. Assume multi-tenant usage, heterogeneous task durations (milliseconds to hours), and the need for strong operational visibility and robust fault tolerance.

Requirements

Specify the following, with clear APIs, data models, and rationale:

  1. Job model and external APIs
  2. Task partitioning and sharding strategy
  3. Scheduler and worker architecture
  4. Coordination and concurrency control (leases, heartbeats, visibility timeouts)
  5. Fault tolerance, retries, backoff, and dead-letter handling
  6. Idempotency and deduplication approach
  7. Progress tracking and result aggregation
  8. Scaling and resource management (autoscaling, quotas, bin-packing)
  9. Backpressure and prioritization
  10. Ordering guarantees and trade-offs
  11. Consistency semantics (at-least-once vs exactly-once) and trade-offs
  12. Monitoring, alerting, and observability
  13. Capacity planning assumptions and calculations

State any minimal assumptions you need to make the design concrete.

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