1. Lighthouse

Level 8 · Debug: January looks twice as big

The request

From Lauren Hayes, Email Marketing Manager

“My seasonality chart for 2024 and 2025 says we had 481 completed orders in January. Finance says January 2025 was 268. Only 12 bars show up, too. The numbers look off. What's wrong with this query?”

Your goal

Fix the query. For orders with status 'completed' placed in 2024 or 2025, return one row per calendar month (24 rows) with: month (the first day of the month, as a DATE), orders and coupon_orders (orders with a coupon_code). Sort by month ascending.

  • Read from the orders tablenot done yet
  • Show exactly month orders coupon_ordersnot done yet
  • 24 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

    This query runs, the chart renders, and every number is wrong. The tell is in the ticket: 12 bars for a two-year range.

    Spotting the bug

    EXTRACT(month FROM order_date) returns a number from 1 to 12 and nothing else. Grouping by it puts every January in the data into one bucket. With 2024 and 2025 in the filter, the January bar is January 2024 (213 completed orders) plus January 2025 (268): 481.

    Two quick checks catch this kind of bug:

    1. Row count. A monthly series over two years has 24 rows. Twelve means years were merged.
    2. Compare one number with a trusted source. Finance’s January 2025 figure did not match.

    The fix: truncate, don’t extract

    DATE_TRUNC('month', ts) keeps the year and month and sets the rest to the start of the month:

    SELECT CAST(DATE_TRUNC('month', order_date) AS DATE) AS month,
           COUNT(*) AS orders
    FROM orders
    WHERE status = 'completed'
      AND EXTRACT(month FROM order_date) = 1
    GROUP BY CAST(DATE_TRUNC('month', order_date) AS DATE)
    ORDER BY month;

    This returns 178, 213 and 268 completed January orders for 2023, 2024 and 2025. Notice that EXTRACT is fine in the WHERE clause here: picking Januaries from every year is exactly what it is for.

    A real DATE in the month column also helps whoever consumes the result. Charting tools put dates on a proper time axis, and a later join to another monthly table (marketing spend, targets) can match on the date directly instead of on a year and a month number.

    EXTRACT(month) does have honest uses: a seasonality view that averages “all Januaries” on purpose. In that case divide by the number of years, and label the axis “month of year”, not a date.

    Common mistakes

    • Grouping by EXTRACT(year ...) and EXTRACT(month ...) as two columns works, but returns two numbers instead of one date, which charts and joins handle less well.
    • Forgetting the cast: DATE_TRUNC on a timestamp returns a timestamp such as 2025-01-01 00:00:00.

    In SQL Server

    DATETRUNC(month, order_date) exists from SQL Server 2022. On older versions use DATEFROMPARTS(YEAR(order_date), MONTH(order_date), 1). MONTH(order_date) and DATEPART(month, order_date) behave like EXTRACT: month number only.

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