1. Lighthouse

Level 5 · The ten biggest refunds we caused

The request

From Rachel Adams, Customer Support Lead

“For the carrier meeting I want a short list: the ten largest refunds we paid for damaged or wrong items, biggest first.”

Your goal

From returns, for returns whose reason is 'damaged' or 'wrong_item', return return_id, returned_on, reason and refund_amount for the 10 largest refunds. Sort by refund_amount descending, then by return_id ascending when refunds tie. Return exactly 10 rows.

  • Read from the returns tablenot done yet
  • Show exactly return_id returned_on reason refund_amountnot done yet
  • 10 rows, sorted as askednot done yet

Your query

SQL

Loading the editor…

    ResultPreview of returns · press Run to see yours
    Preview 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

    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.

    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
    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
    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
    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 top-N list is three clauses working together: filter, sort, cut. You used all three on the price list; here they run on a new table, returns.

    Filter, sort, cut

    SELECT return_id, reason, refund_amount
    FROM returns
    WHERE reason IN ('damaged', 'wrong_item')
    ORDER BY refund_amount DESC, return_id ASC
    LIMIT 3;

    The top three are return 802 (damaged, $4,827.00), return 1200 (wrong item, $3,996.00) and return 641 (damaged, $3,318.00). The database applies the steps in this order: WHERE removes the other reasons, ORDER BY sorts what is left, and LIMIT keeps the first rows of the sorted result. LIMIT without ORDER BY returns “some” rows, not the top ones.

    ASC is the default, so return_id ASC and plain return_id are the same. Writing it out makes mixed directions easy to read: largest refund first, but lowest id first among ties.

    Why the tie-breaker matters here

    Returns 31 and 317 both refunded exactly $3,198.00. Both fit inside the top ten, but which one is listed first is up to the database unless you say so. If a tie fell right at the cut-off, between rows 10 and 11, the database could even pick a different row each time. A unique column as the last sort key, here return_id, removes the doubt.

    Common mistakes

    • Sorting ascending by accident, which gives the ten smallest refunds instead of the largest.
    • Putting LIMIT before ORDER BY. SQL expects ORDER BY first, then LIMIT, and reports a syntax error otherwise.
    • Filtering with reason = 'damaged' OR reason = 'wrong_item' and then adding more AND conditions without parentheses. IN keeps it safe.
    • Reading the result without the sort column. Keep refund_amount in the SELECT list, so anyone can see at a glance that the list really runs from largest to smallest.

    In SQL Server

    SQL Server has no LIMIT; it puts TOP n right after SELECT. TOP 10 WITH TIES would also keep any extra rows that tie with the tenth one:

    SELECT TOP 10 return_id, refund_amount
    FROM returns
    WHERE reason IN ('damaged', 'wrong_item')
    ORDER BY refund_amount DESC, return_id ASC;

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