What to Expect in the eBay System Design Interview

The eBay system design interview is a 45 to 60 minute round in the final loop. You design a large-scale system on a virtual whiteboard while explaining your choices. Questions usually mirror eBay's own products: marketplace listings, bidding, search, payments, fraud detection, and notifications. Candidates report that eBay weighs this round heavily when deciding the seniority level of an offer. Interviewers probe your capacity estimates, database choices, caching plan, and failure handling.

A system design interview tests structure, not code. You define requirements, sketch the parts, choose data stores, and defend trade-offs. The sections below cover the common eBay question types and one full walkthrough.

The Question Types eBay Asks

  • Marketplace core. Design a listings service, a bidding system, or a shopping cart. These test data modeling and correctness under many concurrent writes.
  • Search and discovery. Design product search or a recommendation feed. These test indexing, ranking, and caching for read-heavy traffic.
  • Payments and settlement. Design a checkout or payout flow. These test consistency, meaning all parts of the system agree on the data, and exactly-once behavior.
  • Trust and fraud. Design a fraud detection pipeline or a review system. These test streaming data and rule evaluation at scale.
  • Platform pieces. Candidates also report smaller questions, such as a feature flag service or a clickstream pipeline.

Signature Walkthrough: Design a Bidding System

The auction question fits eBay exactly, so prepare it first. Here is the high-level shape.

Requirements. Users place bids on an item until a deadline. Everyone sees the current highest bid quickly. When time expires, exactly one winner exists. Traffic spikes hard in the final minute of an auction.

API and data model. Expose two main calls: place a bid, and read auction state. Store auctions and bids in separate tables. Keep the current highest bid as a single row per auction for fast reads.

The hard part: concurrent bids. Many users bid on one item in the same second. Two safe options exist. First, optimistic locking: each update checks a version number, and stale updates fail and retry. Second, a queue per auction: a message queue (a buffer that stores requests in order) serializes bids so one worker applies them in sequence. Say the trade-off: locking is simpler, the queue handles hotter items.

Reads and caching. Bid reads outnumber writes heavily. Cache the current price in a fast memory store and update it on every accepted bid. Push live updates to open pages through WebSockets, which are persistent two-way connections.

Closing the auction. A scheduler closes each auction at its deadline. Make the close operation idempotent, meaning running it twice changes nothing the second time. Then notify the winner and losers through an events pipeline.

Scale and failure. Shard the database by auction ID, which means splitting data across servers by that key. Replicate for failover. State your estimates out loud: bids per second, storage per day, cache size.

A note on hot auctions. One popular auction can overload a single shard. Mention this without being asked. A per-item queue plus an in-memory current price handles the spike. This detail separates senior answers from average ones.

What Interviewers Probe

  • Numbers. Expect "how many servers" and "how much storage" follow-ups. Practice quick estimation.
  • Consistency choices. Know where strong consistency is required (bids, payments) and where eventual consistency is fine (view counts, search freshness).
  • Failure plans. Say what happens when the cache dies or a shard is slow. A design without failure handling reads as junior.
  • Trade-off language. Never present one option as free. Name the cost of every choice you make.

How to Prepare

TAGS
System Design Interview
CONTRIBUTOR
Arslan Ahmad
Arslan Ahmad
ex-FAANG engineering manager and author or Grokking series.
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