1. Lighthouse

Level 3 · Orders from March 2024

The request

From Kevin Brooks, Operations Manager

“A carrier is disputing some March 2024 shipments. Pull every order placed in March 2024, any status, with the date and shipping fee.”

Your goal

From orders, return order_id, order_date and shipping_fee for every order whose order_date falls in March 2024 (from 2024-03-01 00:00 up to, but not including, 2024-04-01 00:00).

  • Read from the orders tablenot done yet
  • Show exactly order_id order_date shipping_feenot done yet
  • 300 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

    Date filters are everywhere in analytics: last month, this quarter, year to date. They are also where a lot of quietly wrong numbers come from.

    Dates vs timestamps

    signup_date in customers is a DATE: just a day, like 2024-03-31. order_date in orders is a TIMESTAMP: a day plus a time, like 2024-03-31 18:42:10. When you compare a timestamp to a date literal such as '2024-03-31', the literal is treated as midnight at the start of that day, 2024-03-31 00:00:00.

    The BETWEEN trap

    BETWEEN a AND b means >= a AND <= b. It looks perfect for “March”:

    -- bug: misses almost all of March 31
    WHERE order_date BETWEEN '2024-03-01' AND '2024-03-31'

    The upper bound is 2024-03-31 00:00:00, so an order at 9am on March 31 is excluded. People “fix” this with '2024-03-31 23:59:59', which still misses anything in the last second and breaks when timestamps have milliseconds.

    The half-open range

    The standard analyst pattern is: on or after the first moment, strictly before the first moment of the next period.

    WHERE order_date >= '2024-03-01'
      AND order_date <  '2024-04-01'

    It works for dates and timestamps, never double-counts a boundary when you stack consecutive months, and lets the database use an index on order_date.

    You will also see WHERE CAST(order_date AS DATE) BETWEEN '2024-03-01' AND '2024-03-31' or WHERE DATE_TRUNC('month', order_date) = '2024-03-01'. Both return the right rows, but wrapping the column in a function usually prevents index use, which matters on big tables in Postgres and MySQL. Prefer the half-open range.

    Dialect notes

    • Postgres and DuckDB accept '2024-03-01' and convert it automatically. You can be explicit with DATE '2024-03-01' or TIMESTAMP '2024-03-01 00:00:00'.
    • BigQuery separates DATE, DATETIME and TIMESTAMP strictly and may need TIMESTAMP('2024-03-01').
    • MySQL accepts the quoted string the same way as Postgres.
    • Time zones: Harbor & Pine stores timestamps without a time zone. In real warehouses, check whether “March” means UTC or the business’s local time. It changes the numbers at the edges.

    Common mistakes

    • BETWEEN on timestamps, as above.
    • Using <= '2024-03-31' on a timestamp, which is the same bug in a different form.
    • Writing dates in a regional format like '03/01/2024'. Always use ISO YYYY-MM-DD.