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

Design task scheduler with dependencies | Google Interview Question

Quick Overview

This question evaluates a candidate's ability to design distributed task scheduling and orchestration systems, covering competencies in data modeling for tasks and DAGs, API design, execution engines, fault tolerance, backpressure, worker liveness, scaling, multi-tenancy, and observability.

Design a Distributed Task Scheduling Infrastructure

Context

Design a distributed task scheduling and orchestration system that can run at scale, supports task dependencies (DAGs), persists state, and handles both short and long-running tasks.

Requirements

Data models
- Define schemas/models for Tasks and DAGs, including runs/attempts and state transitions.
APIs
- Submit a task or DAG, cancel, and query status/history.
Execution engine
- How tasks are dispatched, leased, executed, and acknowledged.
Fault tolerance
- Retries with backoff, idempotency, exactly-once vs at-least-once semantics.
Backpressure
- Prevent overload and provide fairness.
Worker heartbeats
- Registration, lease extensions, and liveness detection.
Stuck/straggler handling
- Detection and mitigation (e.g., timeouts, speculative execution).
Scaling & multi-tenancy
- Horizontal scale, isolation, quotas, fairness, and preemption.
Observability
- Metrics, logs, tracing, audits, and a minimal UI model.

Assume a heterogeneous worker fleet (containers/VMs), a durable message bus, and a persistent metadata store.

Quick Overview

Requirements

Data models

Define schemas/models for Tasks and DAGs, including runs/attempts and state transitions.

APIs

Submit a task or DAG, cancel, and query status/history.

Execution engine

How tasks are dispatched, leased, executed, and acknowledged.

Fault tolerance

Retries with backoff, idempotency, exactly-once vs at-least-once semantics.

Backpressure

Prevent overload and provide fairness.

Worker heartbeats

Registration, lease extensions, and liveness detection.

Stuck/straggler handling

Detection and mitigation (e.g., timeouts, speculative execution).

Scaling & multi-tenancy

Horizontal scale, isolation, quotas, fairness, and preemption.

Observability

Metrics, logs, tracing, audits, and a minimal UI model.

Assume a heterogeneous worker fleet (containers/VMs), a durable message bus, and a persistent metadata store.

Design task scheduler with dependencies

Quick Overview

Design a Distributed Task Scheduling Infrastructure

Context

Requirements

Solution

Comments (0)

Design task scheduler with dependencies

Quick Overview

Design a Distributed Task Scheduling Infrastructure

Context

Requirements

Solution

Comments (0)