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

Senior
Jun 2026

Summary

Google system design round for a software engineer role. The problem was a logger system with frequency tracking, which sounds manageable until you start thinking about concurrency and scale at the same time.

Questions Asked (1)

Q1

Design a logging system that records messages with timestamps and can efficiently return the top k most frequent log messages, while handling high write volume and concurrent access.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

I started with a basic hashmap for frequency counting and a heap for top-k retrieval, which the interviewer seemed fine with initially.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (write throughput, read frequency, latency, consistency) and then propose a scalable architecture that separates the write path (ingestion) from the read path (querying). Use a distributed message queue for buffering writes, a partitioned store for logs, and an efficient algorithm like count-min sketch or heavy hitters for top-k queries. Discuss trade-offs between accuracy, memory, and latency.

Pro tip: Emphasize that top-k is typically an approximate problem at scale, and propose a solution that uses probabilistic data structures (e.g., count-min sketch) with periodic batch recomputation to balance accuracy and performance. This shows you understand real-world constraints and can make pragmatic trade-offs.

1. Clarify Requirements and Constraints

Ask about expected write volume (e.g., millions per second), read frequency, latency requirements, consistency needs, and whether exact or approximate top-k is acceptable. This scopes the problem and guides design choices.

2. High-Level Architecture

Propose a distributed system with an ingestion layer (e.g., Kafka) to handle high write volume, a processing layer to aggregate counts, and a storage layer for logs and counts. Ensure components are horizontally scalable.

3. Data Structures and Algorithms for Top-K

Discuss using a count-min sketch for approximate frequency counting with low memory, combined with a heap for top-k retrieval. Alternatively, use a distributed hash table with per-shard counts and merge results.

4. Concurrency and Consistency

Address concurrent access by partitioning data (e.g., by log message hash) to avoid contention. Use atomic counters or CRDTs for concurrent updates, and consider eventual consistency for counts.

5. Trade-offs and Optimizations

Compare exact vs approximate counting, memory vs accuracy, and latency vs throughput. Suggest optimizations like batching writes, caching frequent queries, and using time-windowed counts for recency.

Key Points to Mention

  • Partitioning by log message hash to distribute load and enable parallel processing
  • Use of probabilistic data structures (count-min sketch, HyperLogLog) for memory-efficient frequency estimation
  • Leveraging a message queue (e.g., Kafka) for buffering and decoupling writes from processing
  • Periodic batch recomputation of top-k to avoid real-time heavy computation
  • Handling concurrency with atomic operations or sharded counters to reduce contention
  • Trade-offs between exact and approximate results, and between latency and consistency

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