1. Lighthouse

Level 5 · Boss: homepage merchandising lists

The request

From Ashley Rivera, Web Content Lead

“The new homepage has three product strips and I need the data for each: our priciest lighting pieces, our newest active launches, and the second row of budget picks.”

Your goal

Step 1 of 3. From products, return product_id, name and unit_price for the 5 most expensive active products in category 'Lighting'. Sort by unit_price descending, then product_id ascending.

  • Read from the products tablenot done yet
  • Show exactly product_id name unit_pricenot done yet
  • 5 rows, sorted as askednot done yet

Step 1 · 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.

    Step 2 · 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.

      Step 3 · 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

        Each homepage strip is a top-N list, which means three decisions every time: which rows qualify, how to sort them, and how many to keep. Getting the clause order right matters:

        SELECT ...
        FROM products
        WHERE ...        -- 1. which rows qualify
        ORDER BY ...     -- 2. sort, with a tie-breaker
        LIMIT ... OFFSET ...;  -- 3. how many, starting where

        The database filters first, then sorts, then cuts. So “the 5 most expensive Lighting products” is not “the 5 most expensive products, then keep the Lighting ones”. If you sorted all products and limited before filtering, you could end up with zero Lighting rows. SQL’s clause order does the right thing as long as the condition is in WHERE.

        Step 1: priciest lighting

        Filter to active Lighting products, sort by price descending, keep 5. The tie-breaker product_id ASC matters if two lamps share a price at the 5th position.

        Step 2: newest launches

        launched_on is a DATE, and several products can launch on the same day, so ties are likely. product_id DESC breaks them, a reasonable choice since higher IDs were created later. In a real ticket, say which tie-breaker you used so Ashley is not surprised.

        Step 3: the second strip

        The strip shows 6 items, and Ashley wants the second one, so skip 6 and take 6. Apply the price filter before paging. A product exactly at $40 is not “under 40”, so < 40 is right.

        Inactive products

        All three strips filter on is_active. Discontinued products should never be promoted, and forgetting the flag is one of the most common reasons a homepage links to an out-of-stock page. When you build any customer-facing list, ask whether inactive or hidden items need excluding.

        Check your own work

        Before sending results like these, spend ten seconds on sanity checks:

        • Does step 1 show only Lighting, and are prices falling?
        • Does step 2 show only active products, with dates falling?
        • Does the first row of step 3 cost at least as much as the 6th row of the first strip would?

        Interviewers love candidates who check their output without being asked. On the job, it is what keeps your name off the “who sent this?” thread.