1. Lighthouse

Level 5 · December is short by eleven orders

The request

From Brian Mitchell, Finance Analyst

“Your Q4 2025 report has 623 completed orders in December. The order system says 634. October and November match. What's going on?”

Your goal

Fix the query. For completed orders placed from 2025-10-01 through 2025-12-31 (every time of day), return one row per month with: month (a DATE, first of the month), orders (number of distinct orders) and net_revenue (sum of quantity * unit_price * (1 - discount_pct / 100.0) over lines with quantity > 0, rounded to 2). Sort by month ascending.

  • Read from the orders tablenot done yet
  • Read from the order_items tablenot done yet
  • Show exactly month orders net_revenuenot 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
    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
    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
    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

    October and November match the order system, December is short. When only the edge of a date range is wrong, look at how the range ends.

    What BETWEEN means on a timestamp

    order_date is a TIMESTAMP. Comparing it with the string '2025-12-31' turns the string into 2025-12-31 00:00:00, the very first second of the day. BETWEEN is inclusive on both ends, but the end is midnight, so it keeps only orders placed at exactly 00:00:00 on Dec 31.

    On Dec 31, 2025 there were 25 orders between 05:18 and 23:23. Eleven of them were completed, and they are exactly the gap: 623 versus 634 orders, and $281,531.76 versus $288,591.06 of revenue. Over the full year, BETWEEN '2025-01-01' AND '2025-12-31' finds 4,758 orders of every status instead of 4,783.

    The equality version of the same bug

    SELECT COUNT(*) FROM orders WHERE order_date = '2025-11-28';

    This returns 0 for Black Friday, which had 56 orders. Equality with a date string only matches timestamps at midnight.

    The fix: half-open ranges

    WHERE o.order_date >= '2025-10-01'
      AND o.order_date < '2026-01-01'

    Start inclusive, end exclusive, at the first moment you do not want. It works for dates and timestamps alike, needs no 23:59:59 guesswork, and month ranges chain without gaps or overlaps. For a single day, >= '2025-11-28' AND < '2025-11-29' returns the 56 orders.

    Casting the column, CAST(order_date AS DATE) BETWEEN ..., also gives the right rows, but it applies a function to every row and stops many databases from using an index on order_date.

    Common mistakes

    • <= '2025-12-31 23:59:59': misses anything with fractional seconds after 23:59:59.
    • BETWEEN '2025-10-01' AND '2026-01-01': inclusive, so it would pull in orders placed at exactly midnight on New Year’s Day.
    • Fixing the WHERE but not checking the GROUP BY: here the month truncation was already right.

    In SQL Server

    DATETIME and DATETIME2 behave the same way: BETWEEN '2025-10-01' AND '2025-12-31' stops at midnight. Use the same half-open range, or compare on CAST(order_date AS date) for one-off checks:

    WHERE order_date >= '2025-10-01' AND order_date < '2026-01-01'

    On the job

    “The last day is short” is so common that many teams ban BETWEEN on timestamps in code review.

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