1. Lighthouse

Level 7 · Debug: the markup numbers look off

The request

From Jessica Turner, Merchandising Manager

“Tyler wrote me a markup query, but the numbers look off. The Alder Oak Coffee Table shows a markup of 819%, and it looks like every product is just its price minus 100. Can you fix it?”

Your goal

Fix the query. From products, return product_id, name, unit_price, unit_cost and markup_pct: (unit_price minus unit_cost) divided by unit_cost, times 100, rounded to 1 decimal. Include every product; products with a missing cost keep a NULL markup_pct.

  • Read from the products tablenot done yet
  • Show exactly product_id name unit_price unit_cost markup_pctnot done yet
  • 120 rowsnot 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
    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
    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
    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

    This query runs, returns all 120 products and shows no error message. It is still wrong, which is the most dangerous kind of bug. The ticket gives the clue: every markup equals the price minus 100.

    How SQL reads arithmetic

    SQL follows school math. * and / run before + and -, and operators of the same level run left to right. So the starter’s expression

    unit_price - unit_cost / unit_cost * 100

    is read as unit_price - ((unit_cost / unit_cost) * 100). Any number divided by itself is 1, so the whole thing is unit_price - 100. The Alder Oak Coffee Table costs $919, so it shows 819. Parentheses force the order you mean:

    SELECT
      name,
      unit_price,
      unit_cost,
      ROUND((unit_price - unit_cost) / unit_cost * 100, 1) AS markup_pct
    FROM products;

    Now the coffee table, with a cost of $503.50, has a markup of 82.5%: it sells for 82.5% more than it costs. The Vale Velvet Loveseat has no cost on file, so its markup stays NULL, which is honest.

    Markup or margin?

    The margin level divided the profit by the price. Markup divides the same profit by the cost. For the coffee table that is a 45.2% margin and an 82.5% markup. Both are correct, they answer different questions, and buyers often talk in markup while finance talks in margin. When a ticket says “margin”, ask which one they mean. A quick rule of thumb: margin can never reach 100%, while markup often does, so a “margin” above 100% is really a markup, or a bug.

    Common mistakes

    • Spotting a pattern and not trusting it. “Every value is price minus 100” is a strong hint that one part of the expression collapsed to a constant. Read the formula operator by operator.
    • Adding parentheses in the wrong place: unit_price - (unit_cost / unit_cost) * 100 changes nothing, because it spells out what the database already did.
    • Sanity-checking only the first row. Check one cheap and one expensive product, and make sure a product with a NULL cost still shows NULL rather than a number.

    In SQL Server

    Operator precedence is the same, so the same parentheses fix it. Watch the types too: if both columns were INT there, (price - cost) / cost would truncate to a whole number before the * 100. Multiply first or use 100.0:

    SELECT ROUND((ListPrice - StandardCost) * 100.0 / StandardCost, 1) AS MarkupPct
    FROM Production.Product;

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