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The designboard Playground

Practice the hard part of System Design — defending your design.

Draw your architecture on our interactive canvas, then defend it against a focused AI interviewer who probes your bottlenecks, data flows, and trade-offs.

Design & Practice

Product, in action

It doesn't take your first answer at face value.

  1. Interviewer

    How would you design the database layer for a URL shortener?

  2. Candidate

    “I would use PostgreSQL and create a table mapping short codes to long URLs.”

  3. Following up

    PostgreSQL holds up for writes, but what happens under a sudden spike of 100k writes/sec?

  4. Candidate

    “I would add Redis as a cache in front of it.”

  5. Following up

    A cache is for reads. How does a cache absorb write spikes?

  6. Candidate

    “Ah… I suppose I would need a message queue like Kafka to buffer the writes.”

Three questions in, the architectural bottleneck is obvious. The AI interviewer finds the soft spots in your design that simple diagrams hide.

How it works

Ask. Probe. Then tell you the truth.

01

Design & Ask

Draw your architecture on our playground canvas. The AI reviews it and opens with a tailored question.

02

Probe & Drill

The interviewer drills into your connections, bottlenecks, scaling trade-offs, and failure modes.

03

Architect Report

Leave with scored systems engineering competencies and an honest read on where your design cracked.

The report

An honest read, not a participation trophy.

Interview report

A promising design with strong database fundamentals, but cracked under high-throughput write scaling and replication edge cases.

7.2/ 10
Overall Rating
8 Strengths8 Gaps
Performance TrendHover to inspect, click to jump to feedback
Rating (1-10)
0246810Q1Q2Q3Q4

Strengths

  • •Solid understanding of relational database indexes and constraints
  • •Good application of message queuing for asynchronous write buffering
  • •Proper application of trade-offs regarding SQL scaling limitations

Improvements

  • •Address write buffering mechanisms precisely rather than assuming caches absorb writes
  • •Study distributed consensus and split-brain scenarios in coordinator nodes
  • •Deepen knowledge of database replication lag and stale reads

Detailed Feedback

4 Questions evaluated

Q1: How would you design the database layer for a URL shortener?

Rating & Follow-upsScore: 8.5/10 — 1 Follow-up

Strengths

  • •Correctly identified that writes are the primary bottleneck
  • •Chosen database engine aligns well with schema simplicity

Gaps

  • •Did not immediately account for index size growth

Q2: What happens under a sudden write spike of 100k requests/sec?

Rating & Follow-upsScore: 6/10 — 2 Follow-ups

Strengths

  • •Understood the need to buffer incoming writes asynchronously

Gaps

  • •Initially proposed a cache (Redis) which does not absorb write spikes
  • •Failed to explain consumer offset committing strategy in Kafka

Q3: How do you handle database write replication and consistency?

Rating & Follow-upsScore: 7.5/10 — 1 Follow-up

Strengths

  • •Correctly applied the CAP theorem to justify eventual consistency

Gaps

  • •Vague on conflict resolution strategies when multiple nodes receive writes

Q4: How would you prevent duplicate short code generation?

Rating & Follow-upsScore: 6.8/10 — 2 Follow-ups

Strengths

  • •Proposed a range partition server approach to distribute IDs

Gaps

  • •Did not discuss single-point-of-failure mitigation for the coordinator

Step up to the board. Let's test your architecture.

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