Microsoft·Software Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026

Summary

Microsoft system design round, focused entirely on a log ingestion pipeline. Pretty intense for a single question but they kept pulling on threads for the full hour.

Questions Asked (1)

Design a streaming system that ingests logs from multiple services, filters for errors, tracks real-time stats like error counts and moving averages per service, and triggers alarms when thresholds are exceeded. The solution should include an efficient sliding window for computing moving averages.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with a basic pub/sub setup and they seemed fine with that, but the sliding window piece is where I fumbled.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, latency, accuracy) and then present a high-level architecture covering ingestion, processing, storage, and alerting. Dive into the sliding window algorithm for moving averages, discussing trade-offs between tumbling, sliding, and session windows, and how to implement efficiently with ring buffers or exponential moving averages.

Pro tip: Demonstrate awareness of real-world constraints: mention that exact sliding windows can be memory-intensive, so consider approximate algorithms like t-digest or exponential moving averages when perfect accuracy isn't required, and always discuss backpressure and fault tolerance.

1. Clarify Requirements and Constraints

Ask about scale (events/sec, number of services), latency requirements (real-time vs near-real-time), accuracy of moving averages, and alarm thresholds. This shows you prioritize understanding before designing.

2. High-Level Architecture

Propose a pipeline: ingestion (e.g., Kafka), stream processing (e.g., Flink, Spark Streaming), storage (e.g., time-series DB), and alerting (e.g., PagerDuty). Explain how components interact and scale.

3. Sliding Window Design

Detail the sliding window algorithm: choose between count-based or time-based windows, discuss data structures (ring buffer, deque), and handle out-of-order events with watermarks. Compare exact vs approximate methods.

4. Real-Time Stats and Alarms

Explain how to compute error counts and moving averages per service, and how to evaluate thresholds. Discuss state management, window expiration, and triggering alarms with deduplication and suppression.

5. Trade-offs and Scalability

Discuss trade-offs: latency vs accuracy, memory vs computation, and centralized vs distributed processing. Address fault tolerance, exactly-once semantics, and monitoring of the system itself.

Key Points to Mention

Use of stream processing frameworks like Kafka Streams, Flink, or Spark Streaming for scalable ingestion and processing.
Sliding window implementation: ring buffer for fixed-size windows, or time-based windows with watermarks for out-of-order events.
Approximate algorithms (e.g., exponential moving average, t-digest) when exact moving averages are too resource-intensive.
State management and fault tolerance: checkpointing, exactly-once processing, and handling late data.
Alarm triggering: threshold evaluation, deduplication, and integration with alerting systems.
Scalability considerations: partitioning by service, horizontal scaling, and backpressure handling.

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