1. Lighthouse

Level 7 · Bestsellers that rarely get discounted

The request

From Thomas Whitaker, Head of Merchandising

“I want to protect full-price sellers in next year's promo plan. Find me the products that sell a lot, at least 500 order lines, but whose average discount is under 2%.”

Your goal

From order_items, considering only rows with quantity > 0, return one row per product with: product_id, lines (number of order lines), units (sum of quantity) and avg_discount_pct (average discount_pct, rounded to 2). Keep only products with at least 500 lines and an average discount_pct below 2.

  • Read from the order_items tablenot done yet
  • Show exactly product_id lines units avg_discount_pctnot done yet
  • 6 rowsnot done yet

Your query

SQL

Loading the editor…

    ResultPreview of order_items · press Run to see yours
    Preview of order_items
    order_item_idorder_idproduct_idquantityunit_price
    11001121931.99
    21001154311.99
    31002154411.99
    410021941149.00

    Mote checks your result

    Press Run. Each row that matches the goal becomes a firefly in your lantern.

    Tables

    Harbor & Pine store data, ready to query.

    order_items30,338 rows · 6 columns
    • order_item_idinteger
    • order_idinteger
    • product_idinteger
    • quantityinteger
    • unit_pricedecimal
    • discount_pctdecimal
    First 5 rows of order_items
    order_item_idorder_idproduct_idquantityunit_pricediscount_pct
    11001121931.990.00
    21001154311.990.00
    31002154411.990.00
    410021941149.000.00
    51002193219.990.00
    customers2,018 rows · 9 columns
    • customer_idinteger
    • first_namevarchar
    • last_namevarchar
    • emailvarchar
    • cityvarchar
    • statevarchar
    • signup_datedate
    • segmentvarchar
    • referral_sourcevarchar
    First 5 rows of customers
    customer_idfirst_namelast_nameemailcitystatesignup_datesegmentreferral_source
    1OwenHudsonowen.hudson@icloud.comSan FranciscoCA2022-06-02consumersearch
    2DouglasMendozadmendoza69@gmail.comAtlantaGA2022-06-02consumerNULL
    3HenryBrookshbrooks@lakesidecafe.comLos AngelesCA2022-06-04businessNULL
    4BrianGrahambgraham@bluecedarhomes.comSeattleWA2022-06-06businesspinterest
    5KyleJimenezkjimenez27@yahoo.comIndianapolisIN2022-06-07consumerNULL
    products120 rows · 8 columns
    • product_idinteger
    • namevarchar
    • categoryvarchar
    • subcategoryvarchar
    • unit_pricedecimal
    • unit_costdecimal
    • launched_ondate
    • is_activeboolean
    First 5 rows of products
    product_idnamecategorysubcategoryunit_priceunit_costlaunched_onis_active
    101Vale Velvet LoveseatFurnitureSofas1659.00NULL2023-09-11true
    102Alder Oak Coffee TableFurnitureTables919.00503.502024-05-16true
    103Orchard Rattan Dining ChairFurnitureChairs789.00429.872023-05-27true
    104Upland Oak BookcaseFurnitureStorage429.00214.702022-12-20true
    105Avery Leather Bed FrameFurnitureBeds449.00232.702021-08-31true
    orders12,000 rows · 7 columns
    • order_idinteger
    • customer_idinteger
    • order_datetimestamp
    • statusvarchar
    • channelvarchar
    • shipping_feedecimal
    • coupon_codevarchar
    First 5 rows of orders
    order_idcustomer_idorder_datestatuschannelshipping_feecoupon_code
    10011252023-01-01 20:51:19completedweb0.00NULL
    10021562023-01-02 07:58:46completedweb0.00NULL
    1003222023-01-02 10:25:27completedmarketplace4.99WELCOME10
    1004482023-01-02 11:21:51completedmarketplace4.99WELCOME10
    1005822023-01-02 11:43:36completedweb0.00WELCOME10
    returns1,673 rows · 5 columns
    • return_idinteger
    • order_item_idinteger
    • returned_ondate
    • reasonvarchar
    • refund_amountdecimal
    First 5 rows of returns
    return_idorder_item_idreturned_onreasonrefund_amount
    1142023-01-10damaged83.69
    2422023-01-10wrong_item19.99
    31052023-01-14wrong_item10.99
    4242023-01-16changed_mind26.99
    51262023-01-16late20.99
    employees60 rows · 8 columns
    • employee_idinteger
    • first_namevarchar
    • last_namevarchar
    • departmentvarchar
    • titlevarchar
    • manager_idinteger
    • hire_datedate
    • salaryinteger
    First 5 rows of employees
    employee_idfirst_namelast_namedepartmenttitlemanager_idhire_datesalary
    1CatherineHaleExecutiveChief Executive OfficerNULL2016-03-01285000
    2RobertKimExecutiveChief Operating Officer12017-01-09235000
    3MeganCollinsSalesHead of Sales12018-04-16172000
    4ChloeGutierrezSalesAccount Executive32020-01-2172000
    5FrankBellSalesAccount Executive32023-05-2173000
    web_sessions40,000 rows · 7 columns
    • session_idinteger
    • customer_idinteger
    • started_attimestamp
    • devicevarchar
    • sourcevarchar
    • pages_viewedinteger
    • convertedboolean
    First 5 rows of web_sessions
    session_idcustomer_idstarted_atdevicesourcepages_viewedconverted
    1NULL2023-01-01 23:02:24mobilepaid1false
    2182023-01-01 15:07:49desktoppaid1false
    3NULL2023-01-01 16:55:46desktopdirect7false
    4NULL2023-01-01 12:43:28mobileorganic3false
    5NULL2023-01-01 13:06:21mobilesocial2false
    marketing_spend3,276 rows · 3 columns
    • spend_datedate
    • channelvarchar
    • amountdecimal
    First 5 rows of marketing_spend
    spend_datechannelamount
    2023-01-01paid492.99
    2023-01-01email37.38
    2023-01-01social200.53
    2023-01-02paid709.13
    2023-01-02email64.13

    The lesson

    HAVING filters groups the way WHERE filters rows. This ticket needs both: WHERE to clean the input rows, HAVING with two aggregate conditions to pick the products.

    WHERE first, HAVING after

    SELECT product_id, COUNT(*) AS lines, ROUND(AVG(discount_pct), 2) AS avg_discount_pct
    FROM order_items
    WHERE quantity > 0
    GROUP BY product_id
    HAVING COUNT(*) >= 500;

    The query runs in this order: FROM, WHERE (drop the 24 lines with a zero or negative quantity), GROUP BY, then HAVING. That returns 14 products with at least 500 lines. Their average discounts sit between 1.58% and 2.45%: most lines carry no coupon at all, so the average stays low.

    Two conditions in one HAVING

    HAVING accepts the same AND, OR and parentheses as WHERE, as long as each condition is about the group: an aggregate, or a column you grouped by.

    HAVING COUNT(*) >= 500
       AND AVG(discount_pct) < 2

    Six of the 14 also have an average discount under 2%. The busiest of them is product 154 with 836 lines.

    You can repeat the aggregate in HAVING rather than using the alias. DuckDB also accepts HAVING lines >= 500, but many databases do not, so writing the aggregate again is the portable habit.

    Common mistakes

    • Putting AVG(discount_pct) < 2 in WHERE. Aggregates are not allowed in WHERE, because WHERE runs before the groups exist.
    • Moving quantity > 0 into HAVING. It is a condition on single lines, so it belongs in WHERE; HAVING only sees groups.
    • Joining the two conditions with OR. “At least 500 lines or a low discount” keeps every small seller with no coupons, which is the opposite of a bestseller list.
    • Filtering on the rounded column. 1.99 rounds to 1.99 here, but a value like 1.996 would show as 2.00 and still pass, which looks like a bug to the reader.

    In SQL Server

    Same clauses and the same order. SQL Server never accepts a SELECT alias in HAVING, so repeat the aggregate. Watch the average too: AVG of an INT column returns an INT there, but discount_pct is a decimal, so 1.99 stays 1.99:

    HAVING COUNT(*) >= 500 AND AVG(discount_pct) < 2

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