1. Lighthouse

Level 3 · Year-to-date revenue

The request

From Brian Mitchell, Finance Analyst

“Next to the monthly revenue for 2025, I'd like a year-to-date column so the board can see where we stand against the annual target at each month end.”

Your goal

For completed orders placed in 2025 and lines with quantity > 0, return one row per month with: month (first day of the month, as a DATE), net_revenue (sum of quantity * unit_price * (1 - discount_pct / 100.0) for the month, rounded to 2) and ytd_revenue (running total of monthly net revenue from January through that month, 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 net_revenue ytd_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

    Aggregates like SUM, AVG, COUNT, MIN and MAX can all be used as window functions. With an ORDER BY inside OVER, they become cumulative:

    SUM(net_revenue) OVER (ORDER BY month) AS ytd_revenue

    For each month, this sums the revenue of every month up to and including it: January’s YTD is January, February’s is January plus February, and so on. The rows are not collapsed, so you get the monthly number and the running total side by side.

    Aggregate first, then window

    Revenue lives on order lines, but the running total is over months. Aggregate to one row per month in a CTE, then apply the window on top. If you put the window directly over line-level rows, you would get a running total per line, which is not what anyone wants on a slide.

    Frames: what “up to the current row” means

    Every window with an ORDER BY has a frame: the set of rows the function sees for the current row. The default frame when you write only ORDER BY is:

    RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW

    RANGE treats rows with the same ORDER BY value as peers and includes all of them. With one row per month that makes no difference. But if you ran a cumulative sum over orders ordered by date and two orders shared a date, both would show the same running total, including each other. That surprises people. Writing the frame explicitly with ROWS makes it row-by-row:

    SUM(net_revenue) OVER (ORDER BY month ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)

    Being explicit costs nothing and documents intent. This default applies in Postgres, DuckDB, MySQL 8, BigQuery and Snowflake alike.

    Resetting the total

    PARTITION BY restarts the running total per group. To have a YTD that resets each year over several years of data:

    SUM(net_revenue) OVER (
      PARTITION BY EXTRACT(YEAR FROM month)
      ORDER BY month
      ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
    ) AS ytd_revenue

    Or per channel, per category, per customer: whatever the business tracks separately.

    Share of total

    The same function with an empty OVER () gives the grand total on every row, which is the standard way to compute a share of total without a subquery:

    ROUND(100.0 * net_revenue / SUM(net_revenue) OVER (), 1) AS pct_of_year

    Common mistakes

    • Forgetting ORDER BY inside OVER: SUM(x) OVER () is the grand total, not a running total.
    • Relying on the outer ORDER BY. The outer one sorts the output; only the ORDER BY inside OVER defines the running order.
    • Rounding monthly values before summing them, which makes the YTD drift by cents from the true total.