Design a Merchandise Ranking Backend

1. Problem Statement

2. Architecture Discussion Map

Use this as one discussion aid, not a single correct answer. Your design should follow from the requirements, scale, and trade-offs you establish.

graph LR
    A["Design a Merchandise Ranking Backend"]
    A --> F1["Event schema and ingestion"]
    A --> F2["Windowed popularity scoring"]
    A --> F3["Top-K materialization"]
    A --> F4["Catalog storage and browse API"]
    A --> F5["Freshness, abuse, and backfills"]

3. Key Focus Areas

• 1
  Event schema and ingestion
• 2
  Windowed popularity scoring
• 3
  Top-K materialization
• 4
  Catalog storage and browse API
• 5
  Freshness, abuse, and backfills

4. What the Interviewer Looks For

The interviewer probes these areas, and the report grades the interview against them.

• Event model and ranking semantics
• Ingestion and stream aggregation
• Top-K materialization and browse serving
• Data quality: late, duplicate, and fraudulent events
• Operations, backfills, and evolution

5. What Strong Candidates Should Demonstrate

• Model behavioral events and ranking windows explicitly.
• Separate event ingestion and aggregation from low-latency browsing.
• Handle late, duplicate, and fraudulent popularity signals.