1. Lighthouse

Level 6 · Parse a carrier feed with mixed date formats

The request

From Kevin Brooks, Operations Manager

“Our carrier sends delivery dates as free text and every depot writes them differently. For the Halloween orders in this sample, I need delivery time in days, a reference code for each shipment, and a flag on anything you can't read so I can chase it.”

Your goal

Keep the delivery_feed CTE from the starter. Parse delivered_text, which uses the formats '%Y-%m-%d', '%m/%d/%Y' and '%b %d, %Y', into delivered_on (a DATE, NULL when no format matches). Join to orders and return: order_id, ship_ref (the text 'ORD-', the order_id, '-', and the order's month and day as '%m%d', e.g. 'ORD-11500-1031'), order_day (order_date as a DATE), delivered_on, days_to_deliver (delivered_on minus order_day, in days) and needs_review (true when delivered_on is NULL). Sort by order_id.

  • Read from the orders tablenot done yet
  • Show exactly order_id ship_ref order_day delivered_on days_to_deliver needs_reviewnot done yet
  • 9 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

    A date stored as text is just characters. Before you can subtract, group by month or compare, you have to parse it, and that means telling the database which format the text uses.

    strptime and format codes

    strptime(text, format) reads text into a TIMESTAMP using format codes:

    code means example
    %Y 4-digit year 2025
    %m month number 11 or 3
    %d day of month 05 or 6
    %b short month name Nov
    SELECT strptime('11/05/2025', '%m/%d/%Y');   -- 2025-11-05 00:00:00

    DuckDB’s parser accepts single-digit months and days for %m and %d, so '11/7/2025' also works.

    Several formats in one column

    strptime raises an error when the text does not match. try_strptime returns NULL instead, which lets you try formats one after another:

    CAST(COALESCE(
      try_strptime(delivered_text, '%Y-%m-%d'),
      try_strptime(delivered_text, '%m/%d/%Y'),
      try_strptime(delivered_text, '%b %d, %Y')
    ) AS DATE) AS delivered_on

    DuckDB also takes a list: try_strptime(delivered_text, ['%Y-%m-%d', '%m/%d/%Y']). Whatever is still NULL after the last format is your review list. In the sample, order 11500 was placed on Halloween, 2025-10-31, and delivered on 2025-11-04: 4 days.

    Formatting dates back into text

    strftime goes the other way, from a date to text in the format you choose. Reference codes often embed a date: CONCAT('ORD-', order_id, '-', strftime(order_date, '%m%d')) gives ORD-11500-1031. CONCAT turns the number into text for you.

    Common mistakes

    • Ambiguous day and month. '05/11/2025' is May 11 in the US and November 5 in Europe. No query can tell which was meant. Ask the sender for their format, then parse each source with its own rule. '13/11/2025' fails as month/day, a hint that this depot writes the day first.
    • Comparing text dates. '11/05/2025' < '2025-11-04' compares characters, not dates.
    • Dropping the failures. Filtering out NULL dates hides the deliveries that most need a follow-up.

    Dialects and SQL Server

    Postgres and Snowflake use TO_DATE(text, 'MM/DD/YYYY') (Snowflake also has TRY_TO_DATE), BigQuery SAFE.PARSE_DATE('%m/%d/%Y', text), MySQL STR_TO_DATE. SQL Server uses style numbers: 101 is mm/dd/yyyy, 103 is dd/mm/yyyy, 23 is yyyy-mm-dd. ISDATE and TRY_PARSE help with checks.

    SELECT CONCAT('ORD-', o.order_id, '-', FORMAT(o.order_date, 'MMdd')) AS ship_ref,
           COALESCE(TRY_CONVERT(date, f.delivered_text, 23),
                    TRY_CONVERT(date, f.delivered_text, 101)) AS delivered_on
    FROM delivery_feed AS f
    JOIN orders AS o ON o.order_id = f.order_id;

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