1. Lighthouse

Level 7 · Which weekday sells most?

The request

From Ashley Rivera, Web Content Lead

“I schedule homepage refreshes and promo emails by weekday. For 2025, which days bring the most completed orders, and how many of each day's orders come in the evening?”

Your goal

For orders with status 'completed' placed in 2025, return one row per day of the week with: weekday_num (ISO day number, 1 = Monday to 7 = Sunday), day_name (the weekday's name, e.g. Monday), orders, evening_orders (orders placed at 18:00 or later) and evening_pct (evening_orders / orders * 100, rounded to 1). Sort by weekday_num ascending.

  • Read from the orders tablenot done yet
  • Show exactly weekday_num day_name orders evening_orders evening_pctnot done yet
  • 7 rows, sorted as askednot 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

    Dates hide many useful parts: the year, the month, the day of the week, the hour. Pulling one out and grouping by it is a fast way to find patterns.

    Recap: date parts in DuckDB

    function returns example for Saturday 2025-11-29 20:15
    ISODOW(ts) 1 = Monday … 7 = Sunday 6
    DAYOFWEEK(ts) 0 = Sunday … 6 = Saturday 6
    DAYNAME(ts) the name Saturday
    HOUR(ts) 0 to 23 20

    Names sort alphabetically, so keep a number next to them to sort by. ISO numbering (Monday first) is what most business calendars use.

    SELECT HOUR(order_date) AS hour, COUNT(*) AS orders
    FROM orders
    WHERE status = 'completed'
      AND order_date >= '2025-01-01'
      AND order_date < '2026-01-01'
    GROUP BY HOUR(order_date)
    ORDER BY orders DESC
    LIMIT 3;

    Grouping by hour like this shows the evening peak the ticket asks about: the three busiest hours of 2025 are 20:00 (339 completed orders), 19:00 (328) and 21:00 (292). An hour value of 20 covers 20:00:00 up to 20:59:59, so HOUR(order_date) >= 18 means “from 6 PM until midnight”. If the ticket had defined the evening as 6 PM to 10 PM, the condition would need an upper bound too: HOUR(order_date) BETWEEN 18 AND 21.

    In the full answer, Saturday (704) and Sunday (682) bring the most completed orders in 2025, and on every weekday roughly a third or more of orders arrive at 18:00 or later.

    When you group by an expression, repeat the expression in GROUP BY (or use the column position). Both ISODOW and DAYNAME go into the grouping; they always agree, so the row count stays seven.

    Common mistakes

    • Alphabetical days. ORDER BY day_name gives Friday, Monday, Saturday, Sunday …
    • Mixing numbering systems. DAYOFWEEK starts at Sunday = 0, ISODOW at Monday = 1.
    • Forgetting the year filter. Weekday patterns change as the business grows; compare like with like.

    In SQL Server

    Use DATENAME(weekday, order_date) for the name and DATEPART(hour, order_date) for the hour. DATEPART(weekday, ...) depends on the DATEFIRST setting (Sunday = 1 by default in US English), so (DATEPART(weekday, order_date) + @@DATEFIRST - 2) % 7 + 1 is the usual way to get Monday = 1.

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