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

SeniorPrefer not to say

Jun 2026

Summary

System design round at Microsoft for a software engineering role. One meaty question about centralized logging that took up the whole session. Came out feeling okay but unsure if I covered enough on the reliability side.

Questions Asked (1)

Design a centralized logging system for a large company where many services and batch jobs across multiple hosts need to emit logs. The system should handle ingestion at scale, durable storage with retention policies, querying by time range, service, host, log level, and trace ID, burst tolerance, partial outage resilience, and multi-tenant access control per team or environment.

System DesignData ModelingTechnical Trade-offs

Author's notes

I started with the data flow, which felt like the right move.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., log volume, retention, query patterns), then design a pipeline with ingestion, buffering, storage, and query layers. Focus on trade-offs between durability, latency, and cost, and address multi-tenancy and fault tolerance explicitly.

Pro tip: Emphasize decoupling components with a durable buffer like Kafka to handle bursts and partial outages, and discuss how to enforce tenant isolation at every layer to prevent noisy neighbors.

1. Clarify Requirements and Scale

Ask questions to understand expected log volume (e.g., TB/day), retention periods, query latency SLAs, and tenant isolation needs. This ensures the design meets actual needs without over-engineering.

2. Design Ingestion and Buffering

Propose a scalable ingestion layer (e.g., HTTP endpoints, agents) that writes to a durable, partitioned buffer like Kafka to absorb bursts and decouple producers from consumers.

3. Design Storage and Indexing

Choose a storage solution (e.g., Elasticsearch, ClickHouse, or object storage with indexing) that supports efficient queries by time, service, host, level, and trace ID, and implement retention policies via tiering or TTL.

4. Address Query and Multi-Tenancy

Design a query service that enforces access control per tenant and optimizes queries with appropriate partitioning and indexing. Ensure isolation to prevent cross-tenant data leaks and performance interference.

5. Ensure Resilience and Scalability

Discuss how the system handles partial outages (e.g., buffer replication, retries) and scales horizontally at each layer. Mention monitoring and backpressure mechanisms.

Key Points to Mention

Use of a durable, partitioned message queue (e.g., Kafka) for burst tolerance and decoupling.
Storage choice balancing query flexibility and cost (e.g., hot vs. cold storage, indexing strategies).
Retention policies implemented via time-based partitioning, TTL, or tiered storage.
Multi-tenant access control using authentication, authorization, and resource isolation (e.g., separate indices per tenant).
Query optimization with appropriate indexing (e.g., inverted index for text, columnar for analytics) and partitioning by time and tenant.
Fault tolerance through replication, idempotent processing, and graceful degradation during partial outages.

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