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Design a metrics collection and alerting system

Last updated: Mar 29, 2026

Quick Overview

This question evaluates system design competencies such as scalable time-series data modeling, ingestion and storage architecture, query latency versus retention trade-offs, and alert evaluation logic within the System Design domain.

  • medium
  • Snapchat
  • System Design
  • Software Engineer

Design a metrics collection and alerting system

Company: Snapchat

Role: Software Engineer

Category: System Design

Difficulty: medium

Interview Round: Onsite

Design a metrics collection and alerting system (like a simplified monitoring platform). Functional requirements: - Collect time-series metrics from many services/hosts (e.g., counters, gauges, timers). - Support **near real-time querying/dashboards** (low-latency queries over recent data). - Support **offline/analytical queries** over long time ranges (heavier aggregations, historical analysis). - Support **alerting**: user-defined rules (e.g., threshold, rate of change) with notifications. Non-functional requirements: - High write throughput, horizontal scalability, high availability. - Handle spikes, backpressure, and partial failures. - Reasonable multi-tenancy and access control. Provide an end-to-end architecture, key data models, storage choices, and how alert evaluation works. Discuss tradeoffs (e.g., Lambda/Kappa style), retention, and handling high-cardinality metrics.

Quick Answer: This question evaluates system design competencies such as scalable time-series data modeling, ingestion and storage architecture, query latency versus retention trade-offs, and alert evaluation logic within the System Design domain.

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Snapchat
Jan 26, 2026, 12:00 AM
Software Engineer
Onsite
System Design
1
0

Design a metrics collection and alerting system (like a simplified monitoring platform).

Functional requirements:

  • Collect time-series metrics from many services/hosts (e.g., counters, gauges, timers).
  • Support near real-time querying/dashboards (low-latency queries over recent data).
  • Support offline/analytical queries over long time ranges (heavier aggregations, historical analysis).
  • Support alerting : user-defined rules (e.g., threshold, rate of change) with notifications.

Non-functional requirements:

  • High write throughput, horizontal scalability, high availability.
  • Handle spikes, backpressure, and partial failures.
  • Reasonable multi-tenancy and access control.

Provide an end-to-end architecture, key data models, storage choices, and how alert evaluation works. Discuss tradeoffs (e.g., Lambda/Kappa style), retention, and handling high-cardinality metrics.

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