MediumAggregations

Marketplace Merchant 48-Hour Fulfillment SLA Rate

Frequently Asked By:AmazonShopifyUber

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Problem Statement

Group records in `merchant_orders` by `merchant_id`. Calculate: 1. `merchant_id` 2. `total_orders`: Count of all orders 3. `on_time_orders`: Count of orders where `hours_to_dispatch <= 48` 4. `sla_compliance_pct`: Percentage of orders dispatched within 48 hours, rounded to 1 decimal place Filter groups with `HAVING COUNT(*) >= 3` (minimum volume). Order results by `sla_compliance_pct` DESC, then by `merchant_id` ASC.

Why Technical Interviewers Ask This

Operations leadership benchmarks third-party seller logistics by measuring the percentage of merchant orders dispatched within their contractual 48-hour service level agreement (SLA).

Common Pitfalls & Interview Traps

Forgetting required sorting and deterministic tie-breaking.

Not accounting for NULL or zero-value denominators during calculations.

Using inefficient quadratic subqueries instead of single-pass aggregations constructs.

Canonical Modern SQL Solution

PostgreSQL 16 / ANSI SQL

SELECT 
        merchant_id,
        COUNT(*) AS total_orders,
        COUNT(CASE WHEN hours_to_dispatch <= 48 THEN 1 END) AS on_time_orders,
        ROUND(COUNT(CASE WHEN hours_to_dispatch <= 48 THEN 1 END) * 100.0 / COUNT(*), 1) AS sla_compliance_pct
      FROM merchant_orders
      GROUP BY merchant_id
      HAVING COUNT(*) >= 3
      ORDER BY sla_compliance_pct DESC, merchant_id ASC;

Approach Breakdown

This query addresses the problem requirements by filtering and structuring data in Aggregations. It meets the target criteria: Merchant fulfillment SLA compliance rates for active sellers.

Edge Cases to Verbally Clarify with Your Interviewer

The dataset contains zero matching records (must return valid empty result without crashing).

Multiple records tie on sorting criteria (must evaluate tie-breaking order deterministically).

Foreign keys or secondary attributes have NULL values.

Frequently Asked Follow-Up Questions

What makes this question a favorite among Amazon and Shopify interviewers?

Interviewers use this problem to test whether candidates write defensive, scalable Aggregations queries that run efficiently on multi-million row production databases without excessive memory usage.

How does the query optimizer execute this query?

Using single-pass table scans with index lookups or sort buffers. Running EXPLAIN (ANALYZE, BUFFERS) in the DB Exercises workbench visualizes the execution nodes and buffer hit cache.