1. Lighthouse

Level 6 · Premium picks in every category

The request

From Jessica Turner, Merchandising Manager

“For the holiday gift guide I want a premium shelf in every category: the products priced above their category's average. Comparing a $179 vase with a $1,659 sofa makes no sense, so each product should be measured against its own category.”

Your goal

From products, return every product whose unit_price is greater than the average unit_price of products in the same category, with: category, product_id, name, unit_price and category_avg (that category's average unit_price, rounded to 2; compare with the unrounded average). Sort by category ascending, then unit_price descending, then product_id ascending.

  • Read from the products tablenot done yet
  • Show exactly category product_id name unit_price category_avgnot done yet
  • 48 rows, sorted as askednot 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

    A subquery that stands alone runs once. A correlated subquery refers to a column of the outer query, so its value changes from row to row. That is what “compare with your own group” needs.

    Uncorrelated versus correlated

    The starter runs the subquery once:

    SELECT COUNT(*) AS products
    FROM products
    WHERE unit_price > (SELECT AVG(unit_price) FROM products);

    The store-wide average is $270.16 and 33 products beat it: 16 in Furniture, 10 in Outdoor, 6 in Lighting and 1 in Kitchen. No Decor or Textiles product comes close, because the expensive categories pull the average up.

    The correlated version ties the inner query to the current outer row:

    WHERE p.unit_price > (
      SELECT AVG(p2.unit_price)
      FROM products AS p2
      WHERE p2.category = p.category
    )

    Read it as “for this product p, average the prices of the products p2 in the same category”. The aliases are not decoration: without them, category = category inside the subquery refers to the inner table on both sides and is always true, so you silently get the global average back.

    With the correlation, every category has a premium shelf: 9 Decor products beat the Decor average of $84.79, and 8 Furniture products beat the Furniture average of $740.00.

    Performance and alternatives

    Logically, a correlated subquery runs once per outer row. Modern engines, DuckDB included, usually rewrite it into a join behind the scenes, so it is fast on tables like this. Two equivalent formulations you will meet: join products to a grouped subquery of category averages, or use a window function, AVG(unit_price) OVER (PARTITION BY category), which comes in the next chapter.

    Common mistakes

    • Comparing with the rounded average. $84.79 is rounded; compare with the raw AVG and round only for display.
    • Writing GROUP BY category in the subquery without correlation. That returns six rows and the comparison fails with “more than one row returned”.

    In SQL Server

    Correlated subqueries work the same way. If unit_price were an integer column, AVG would return an integer in SQL Server (truncated), so write AVG(CAST(unit_price AS decimal(10,2))) or AVG(unit_price * 1.0).

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