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Google·Software Engineer·Onsite - System Design / Architecture·Staff

Staff
Jun 2026

Summary

Google EM interview with a system design question about monitoring infrastructure at scale. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

Q1

How would you design a monitoring system for 1000 web servers?

System DesignTechnical Trade-offsProduct Analytics & Metrics
Author's notes

I started talking about metrics collection and aggregation pipelines but realized mid-sentence I hadn't asked anything about what we're actually monitoring for.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., metrics, frequency, retention), then propose a high-level architecture with data collection, aggregation, storage, and visualization. Discuss trade-offs between push vs pull, centralized vs distributed, and how to ensure scalability, reliability, and cost-efficiency.

Pro tip: Emphasize the importance of designing for failure and graceful degradation—monitoring should not become a single point of failure. Also, mention that you'd start with a simple solution and iterate based on operational needs.

1. Clarify Requirements

Ask about the metrics to monitor (CPU, memory, latency, etc.), data volume, retention period, alerting needs, and budget constraints. This ensures the design meets actual needs.

2. High-Level Architecture

Outline components: agents on each server, a time-series database (e.g., Prometheus, InfluxDB), a message queue for buffering, and a visualization/alerting layer (e.g., Grafana, Alertmanager).

3. Data Collection Strategy

Decide between push (agents send metrics) vs pull (central server scrapes). Consider using a service discovery mechanism to dynamically find servers.

4. Scalability and Reliability

Design for horizontal scaling: shard the time-series database, use distributed message queues, and ensure high availability with replication. Implement backpressure and rate limiting.

5. Trade-offs and Iteration

Discuss trade-offs: push vs pull, open-source vs custom, cost vs performance. Suggest starting with a simple setup and evolving as needs grow.

Key Points to Mention

  • Use of time-series databases optimized for high write throughput and efficient storage.
  • Push vs pull models: pull (e.g., Prometheus) simplifies service discovery but may not scale as well; push (e.g., StatsD) is more scalable but requires a robust queue.
  • Data aggregation and downsampling to reduce storage costs and improve query performance.
  • Alerting and anomaly detection: setting thresholds, using machine learning for dynamic baselines.
  • High availability and fault tolerance: avoid single points of failure, use replication and sharding.
  • Security: authentication, encryption, and access control for monitoring data.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.