90th Percentile Daily Sales by Region

SQL practice question · Hard · Window Functions · Walmart

Compute the 90th percentile of daily store sales per region using the NEAREST-RANK method and without percentile functions: sort the region's non-NULL `sales` values ascending; the p90 is the value at position CEIL(0.9 * n), where n is the number of non-NULL values. Return `region`, `sales_days` (n), `p90_sales` (ROUND 2). Regions with no non-NULL sales are excluded. Order by `region`.

Table: store_daily_sales

Schema of store_daily_sales
Column	Type
store_id	INTEGER
region	TEXT
sales_date	DATE
sales	REAL

Schema of store_daily_sales

Column

Type

store_id

INTEGER

region

TEXT

sales_date

DATE

sales

REAL

Sample rows of store_daily_sales
store_id	region	sales_date	sales
1	North	2026-09-01	100
1	North	2026-09-02	140
2	North	2026-09-01	90
2	North	2026-09-02	300
3	North	2026-09-01	120
4	South	2026-09-01	70
4	South	2026-09-02	75
5	South	2026-09-01	50

Sample rows of store_daily_sales

store_id

region

sales_date

sales

North

2026-09-01

100

North

2026-09-02

140

North

2026-09-01

North

2026-09-02

300

North

2026-09-01

120

South

2026-09-01

South

2026-09-02

South

2026-09-01

Expected output on the sample data
region	sales_days	p90_sales
North	5	300
South	6	80

Expected output on the sample data

region

sales_days

p90_sales

North

300

South

Solve it in the live editor on this page — run on sample data, then submit against hidden tests.

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