Hey r/DarkInterview — sharing a free Stripe coding question from https://darkinterview.com .

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Shipping Cost Calculator

Design a shipping cost engine for an e-commerce platform where pricing depends on country, product, and quantity-based pricing rules.
The problem progresses from simple fixed pricing to tiered and mixed pricing models, similar to real-world logistics/payment infra tradeoffs.

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Part 1: Fixed Rate Shipping

Implement calculate_shipping_cost(order, shipping_cost) where each product has a fixed per-unit shipping cost by country.

Compact example (US + CA):

• US: mouse(20*550) + laptop(5*1000) = 16000
• CA: mouse(20*750) + laptop(5*1100) = 20500

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Part 2: Tiered Incremental Pricing

Now each product has quantity tiers with {minQuantity, maxQuantity, cost}.
You must split quantity across tiers and sum tier-by-tier costs.

Compact example (laptop qty=5):

• US tiers: 0-2 @1000, 3+ @900
  Cost: (2*1000) + (3*900) = 4700
  Total order: 15700
• CA tiers: 0-2 @1100, 3+ @1000
  Cost: (2*1100) + (3*1000) = 5200
  Total order: 20200

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Part 3: Mixed Pricing Models (fixed + incremental)

Support tier type:

• incremental: per-unit (units * cost)
• fixed: flat fee once that tier is used

Compact example (laptop qty=5):

• US: fixed 1000 for first tier + (3*900) = 3700
  Total order: 14700
• CA: fixed 1100 for first tier + (3*1000) = 4100
  Total order: 19100

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Edge Cases (Important)

• Missing country or product config: error vs skip vs default?
• Tier boundary semantics: explicitly define [min, max) behavior.
• Unsorted/overlapping/gapped tiers: reject config or normalize first?
• Zero quantity / invalid input (negative quantity, bad tier ranges): validation policy.

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Key Design Decisions to Discuss

• Input validation policy: strict fail-fast vs permissive handling.
• Tier normalization: sort tiers and detect overlap/gaps before calculation.
• Integer/currency precision: keep all values in integer minor units.
• Extensibility: add new pricing types without rewriting core calculator logic.

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Follow-up Discussion Topics

1. Performance at scale: precomputed lookups, tier compilation, batch order evaluation.
2. Config hot reload/versioning: safely roll out pricing updates.
3. Testing strategy: boundary tests, malformed config tests, regression fixtures.
4. Monitoring: calculation latency, config error rate, pricing mismatch alerts.

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Full question + Python solution: https://darkinterview.com/collections/t4y7u1i8/questions/2a15f417-c9bb-4ab2-b606-24568b9f30c7