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

SeniorPrefer not to say

May 2026

Summary

System design round at Grammarly with two engineers in the room, one leading. The problem was designing a clicker aggregator, which sounds deceptively simple but has some real distributed systems wrinkles once you start pulling on it.

Questions Asked (1)

Design a clicker aggregator system.

System DesignTechnical Trade-offsData Modeling

Author's notes

First instinct was to just slap a counter in a database and call it done.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and scale of the clicker aggregator, such as the number of events per second, expected latency, and consistency needs. Then propose a high-level architecture that ingests click events, aggregates them in real-time or near-real-time, and stores results for querying, while discussing trade-offs between accuracy, latency, and cost. Finally, dive into data modeling and partitioning strategies to handle high throughput and ensure scalability.

Pro tip: Demonstrate awareness of the CAP theorem and how it applies to your design choices—explicitly state whether you prioritize availability or consistency for different components (e.g., ingestion vs. aggregation). Also, mention how you would handle late-arriving data and ensure idempotency to avoid double-counting.

1. Clarify Requirements

Ask questions to understand the scale (events per second, number of unique clickers), latency requirements (real-time vs. batch), data retention, and query patterns. This ensures the design meets actual needs.

2. High-Level Architecture

Propose a pipeline: ingestion layer (e.g., API gateway, message queue), processing layer (stream processing or batch), storage layer (OLAP database, time-series DB), and query layer. Discuss how data flows through the system.

3. Data Modeling and Partitioning

Design the data schema for click events and aggregated counts, considering dimensions like time, user, and campaign. Explain partitioning and sharding strategies to distribute load and enable efficient queries.

4. Trade-offs and Scalability

Discuss trade-offs between different approaches: Lambda vs. Kappa architecture, exactly-once vs. at-least-once processing, and SQL vs. NoSQL storage. Explain how the system scales horizontally and handles failures.

5. Monitoring and Evolution

Mention how to monitor system health, detect anomalies, and evolve the design as requirements change (e.g., adding new dimensions or increasing retention).

Key Points to Mention

Use of a distributed message queue (e.g., Kafka) for reliable ingestion and buffering.
Stream processing frameworks (e.g., Flink, Spark Streaming) for real-time aggregation with windowing.
Data storage options: time-series databases (e.g., InfluxDB) or columnar stores (e.g., ClickHouse) for fast analytical queries.
Idempotency and deduplication strategies to handle retries and ensure accurate counts.
Partitioning by time and/or clicker ID to distribute load and enable parallel processing.
Trade-offs between latency and accuracy: e.g., approximate counts using HyperLogLog vs. exact counts.

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