1. Lighthouse

Level 6 · Shipping tiers by channel

The request

From Kevin Brooks, Operations Manager

“Our carrier contract is up for renewal. For 2025, how many completed orders per channel shipped free, standard, express or freight? And what share of each channel's orders shipped free?”

Your goal

For orders with status 'completed' placed in 2025, return one row per channel with: channel, orders (all such orders), free_orders (shipping_fee = 0), standard_orders (shipping_fee above 0 and below 10), express_orders (at least 10 and below 40), freight_orders (40 or more) and free_pct (free_orders / orders * 100, rounded to 1).

  • Read from the orders tablenot done yet
  • Show exactly channel orders free_orders standard_orders express_orders freight_orders free_pctnot done yet
  • 3 rowsnot done yet

Your query

SQL

Loading the editor…

    ResultPreview of orders · press Run to see yours
    Preview of orders
    order_idcustomer_idorder_datestatuschannel
    10011252023-01-01 20:51:19completedweb
    10021562023-01-02 07:58:46completedweb
    1003222023-01-02 10:25:27completedmarketplace
    1004482023-01-02 11:21:51completedmarketplace

    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.

    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
    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
    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 practice combines two tools from the chapter: CASE to sort rows into buckets, and conditional aggregation to count each bucket in its own column.

    Recap: one column per bucket

    GROUP BY gives one row per group. To get the buckets as columns rather than extra rows, count each condition separately:

    SELECT channel,
           COUNT(*) AS orders,
           COUNT(CASE WHEN coupon_code IS NOT NULL THEN 1 END) AS coupon_orders
    FROM orders
    WHERE status = 'completed'
      AND order_date >= '2025-01-01'
      AND order_date < '2026-01-01'
    GROUP BY channel;

    For 2025 this gives web 256 coupon orders out of 1,761, app 277 out of 1,904 and marketplace 84 out of 630. Each extra condition is one more column, and the table is still read only once, which is why this pattern beats writing one query per bucket and gluing the results together in a spreadsheet.

    The CASE returns 1 for rows that match and NULL for the rest, so COUNT counts only matches. SUM(CASE WHEN ... THEN 1 ELSE 0 END) gives the same number and is common too.

    The shipping fees on Harbor & Pine orders take few values: 0 when the order qualifies for free shipping, 6.95 standard, 14.95 express and 49.00 freight on web and app, while marketplace orders carry 4.99 or a marked-up fee (7.65, 16.45, 53.90). Ranges in the CASE, rather than exact values, put both price lists into the right tiers.

    In 2025 the web channel had 1,761 completed orders, 1,351 of them shipped free (76.7%). Marketplace had 630 and none shipped free.

    Common mistakes

    • Gaps and overlaps. Write ranges with >= on the lower bound and < on the upper one, so each fee lands in exactly one tier. A quick check: free + standard + express + freight should equal orders.
    • Integer percentages. Multiply by 100.0 first, so the division keeps decimals in every SQL dialect.
    • Counting the CASE with ELSE 0. COUNT(CASE WHEN ... THEN 1 ELSE 0 END) counts every row, because 0 is not NULL. With ELSE 0, use SUM.

    In SQL Server

    Conditional aggregation works the same way. Integer division is a real trap there: 100 * free_orders / orders on two integers truncates to a whole number, so keep the 100.0.

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