1. Lighthouse

Level 4 · Return rates that are all whole numbers

The request

From Jessica Turner, Merchandising Manager

“Our return-rate report was migrated from the old Postgres warehouse. Every category now shows a whole number: 6, 6, 6, 5, 5, 4. Textiles and Furniture can't be exactly the same. Can you fix the rate?”

Your goal

Fix the query. Over all order lines with quantity > 0, return one row per product category with: category, lines (number of order lines), returned_lines (lines that have a row in returns) and return_rate_pct (returned_lines / lines * 100, rounded to 2).

  • Read from the products tablenot done yet
  • Read from the order_items tablenot done yet
  • Show exactly category lines returned_lines return_rate_pctnot done yet

Your query

SQL

Loading the editor…

    ResultPreview of products · press Run to see yours
    Preview of products
    product_idnamecategorysubcategoryunit_price
    101Vale Velvet LoveseatFurnitureSofas1659.00
    102Alder Oak Coffee TableFurnitureTables919.00
    103Orchard Rattan Dining ChairFurnitureChairs789.00
    104Upland Oak BookcaseFurnitureStorage429.00

    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.

    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
    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
    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
    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
    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
    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

    Six categories, six whole numbers. Real return rates are never that tidy. The query is fine everywhere except one character.

    Integer division in different databases

    Dividing two integers means different things depending on where the query runs:

    • Postgres, SQL Server, Redshift, SQLite: / between two integers is integer division. 402 / 5824 is 0.
    • DuckDB: / always returns a decimal (402 / 5824 is 0.069...), and // is the integer division operator.
    • MySQL, BigQuery, Snowflake: / returns a decimal.

    COUNT(*) is an integer everywhere. So a Postgres query written as COUNT(r.return_id) * 100 / COUNT(*) produced whole percents there, and a migration tool that faithfully kept that behavior in DuckDB wrote //. The bug was already in the original report.

    What the starter returns

    402 * 100 // 5824 is 6 for Textiles, and 103 * 100 // 1616 is also 6 for Furniture. The real rates are 6.90 and 6.37. Had the multiplication come after the division, 402 // 5824 * 100, every category would show 0.

    The fix

    Make one operand decimal before dividing:

    ROUND(100.0 * COUNT(r.return_id) / COUNT(*), 2) AS return_rate_pct

    100.0 is a decimal literal, so the whole expression is evaluated in decimals from the left. This version gives the same answer in every database above, which is why analysts write it this way even in DuckDB. CAST(COUNT(*) AS DOUBLE) works too, but is longer.

    Rounding too early

    The other way to lose precision is rounding before scaling: ROUND(402 / 5824, 2) * 100 turns 0.069 into 0.07 and then into 7, and Textiles reads 7% instead of 6.90%. Round once, at the end, to the precision the report shows.

    Common mistakes

    • COUNT(r.return_id) / COUNT(*) * 1.0: in Postgres or SQL Server the integer division already happened; multiplying afterward is too late.
    • COUNT(*) for returned lines: after a LEFT JOIN it counts every line. Count a column from returns.
    • An inner join to returns: it drops the lines that were never returned, so every rate becomes 100%.

    In SQL Server

    This is the version most likely to bite you there: / on two INTs truncates.

    SELECT 402 / 5824;           -- 0
    SELECT 100 * 402 / 5824;     -- 6
    SELECT 100.0 * 402 / 5824;   -- 6.902472...

    Write 100.0 * first, exactly as here.

    On the job

    Rates of exactly 0, 100 or whole numbers are a red flag in any report. Check the division before you check the data.

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