1. Lighthouse

Level 3 · Compute margin per product

The request

From Brian Mitchell, Finance Analyst

“Can you get me the margin on every product? I need the dollar margin per unit, and the margin as a percent of price, so I can spot thin-margin items.”

Your goal

From products, return product_id, name, unit_margin (unit_price minus unit_cost, rounded to 2 decimals) and margin_pct (unit_margin divided by unit_price times 100, rounded to 1 decimal). Include every product; products with a missing cost will show NULL margins, which is expected.

  • Read from the products tablenot done yet
  • Show exactly product_id name unit_margin margin_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

    The SELECT list is not limited to existing columns. Any expression works: arithmetic, functions, or constants. Analysts do this constantly to derive metrics such as margin, tax or price per unit.

    SELECT
      name,
      unit_price,
      unit_price * 1.08 AS price_with_tax
    FROM products;

    Aliases with AS

    AS price_with_tax gives the computed column a name, called an alias. Without it the database invents a header such as (unit_price * 1.08), which is ugly in a spreadsheet and impossible to reference cleanly later. Use lowercase snake_case aliases that describe the metric. AS is optional in most databases (unit_price * 1.08 price_with_tax also works), but writing it makes the intent obvious and avoids the missing-comma bug from the previous level.

    Rounding

    ROUND(x, 2) rounds to two decimals. Round at the very end, on the final number. If you round an intermediate value and then divide it, the error compounds:

    -- good: compute from raw values, round once
    ROUND((unit_price - unit_cost) / unit_price * 100, 1) AS margin_pct

    NULL in arithmetic

    Some products have no unit_cost recorded. Any arithmetic with NULL returns NULL: 49.99 - NULL is NULL, not 49.99. That is usually what you want here: an unknown cost means an unknown margin, and reporting it as a 100% margin would be wrong. You will learn to replace NULLs deliberately with COALESCE in chapter 6, but only do it when a default really makes sense.

    Integer division

    In this dataset prices are decimals, so division behaves as expected. Be careful with integer columns though. In Postgres, 7 / 2 returns 3 because both sides are integers. DuckDB and MySQL return 3.5, and BigQuery requires DIV() for integer division. A safe portable habit when dividing counts is to multiply by 1.0 first, or cast: CAST(a AS DECIMAL) / b.

    Common mistakes

    • Forgetting parentheses: unit_price - unit_cost / unit_price divides first, then subtracts. Multiplication and division bind tighter than addition and subtraction, same as in math class.
    • Referring to an alias in the same SELECT list: SELECT unit_price - unit_cost AS m, m / unit_price fails in Postgres, MySQL and BigQuery. DuckDB allows it, but you should repeat the expression or use a subquery so your SQL runs everywhere.