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Palo Alto Networks·Software Engineer·Onsite - System Design / Architecture·Senior

Senior
Apr 2026

Summary

Did a system design round for a software engineering role at Palo Alto Networks. The prompt was to design a metrics monitoring system, which sounds scoped until you actually start pulling on the threads.

Questions Asked (1)

Q1

Design a metrics monitoring system.

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

Spent the first few minutes just trying to nail down what kind of metrics we're talking about.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scope, then outline a high-level architecture covering data collection, storage, processing, and visualization. Dive into key components like scalability, reliability, and trade-offs, and wrap up with how you'd handle failures and ensure data accuracy.

Pro tip: Emphasize the importance of defining clear SLIs/SLOs and how they drive the design; also discuss how you'd prevent alert fatigue by using intelligent alerting and aggregation.

1. Clarify Requirements

Ask questions to understand the scale (number of metrics, data points per second), latency requirements, retention period, and query patterns. Also clarify if it's for internal use or a product feature.

2. High-Level Architecture

Sketch the main components: data collection (agents, push vs pull), ingestion pipeline (message queue), storage (time-series database), processing (aggregation, alerting), and visualization (dashboards).

3. Deep Dive into Key Components

Discuss the choice of time-series database (e.g., Prometheus, InfluxDB), data model (tags, fields), and how to handle high cardinality. Explain aggregation and downsampling strategies.

4. Scalability and Reliability

Address partitioning, replication, and fault tolerance. Discuss how to scale ingestion and query layers horizontally, and how to ensure data durability and availability.

5. Trade-offs and Wrap-up

Summarize key trade-offs (e.g., consistency vs. availability, cost vs. retention). Mention monitoring the monitoring system itself and future extensions.

Key Points to Mention

  • Time-series data characteristics: high write throughput, time-based queries, and retention policies.
  • Data collection methods: push (e.g., StatsD) vs. pull (e.g., Prometheus), and their trade-offs.
  • Storage options: specialized TSDBs vs. general-purpose databases, and indexing strategies for efficient queries.
  • Alerting and anomaly detection: threshold-based vs. machine learning, and reducing false positives.
  • Scalability techniques: sharding, replication, and using distributed message queues like Kafka.
  • Security and multi-tenancy: isolation of data, access control, and encryption.

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