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

Senior

May 2026

Summary

System design round at Tradedesk that skipped the usual 'design a social network from scratch' format and instead walked through a scenario incrementally, eventually landing on a read-heavy architecture problem where precomputation was the key lever. Felt more like a conversation than a quiz, which I wasn't totally prepared for.

Questions Asked (1)

Design a system optimized for a read-heavy workload (think something like a social feed), and walk through how you'd handle scale using precomputation strategies.

System DesignTechnical Trade-offsData Modeling

Author's notes

The question evolved gradually so I didn't realize we were heading toward precomputation until we were already knee-deep.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (read/write ratio, latency, consistency) and then propose a high-level architecture with precomputation as the core strategy. Walk through data modeling, precomputation pipelines (batch and stream), storage choices, and scaling techniques like sharding and caching, while discussing trade-offs.

Pro tip: Emphasize that precomputation is about trading write-time complexity for read-time speed, and discuss how to handle updates and deletes in precomputed feeds to show depth.

1. Clarify Requirements and Constraints

Ask about read/write ratio, latency SLAs, consistency needs, and scale (users, QPS). This sets the stage for design decisions.

2. High-Level Architecture

Propose a system with separate read and write paths, using precomputation to serve reads from optimized stores. Mention components like feed service, precomputation workers, and storage layers.

3. Data Modeling and Precomputation Strategies

Design data models for feeds (e.g., fan-out on write vs. read) and explain precomputation techniques: batch (e.g., MapReduce) and stream (e.g., Kafka, Flink) processing to materialize feeds.

4. Storage and Caching

Choose storage optimized for reads (e.g., Redis, Cassandra) and discuss caching layers (CDN, in-memory) to reduce latency and load.

5. Scaling and Trade-offs

Explain scaling via sharding, replication, and partitioning. Discuss trade-offs: precomputation cost vs. read latency, consistency vs. availability, and handling hot keys.

Key Points to Mention

Fan-out on write vs. fan-out on read and when to use each
Batch and stream processing for precomputation (e.g., Lambda architecture)
Caching strategies (write-through, write-behind, TTL) and cache invalidation
Sharding and partitioning strategies for read scalability
Trade-offs: precomputation cost, storage overhead, consistency models
Handling updates/deletes in precomputed feeds (e.g., versioning, tombstones)

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