1. Lighthouse

Level 2 · ROW_NUMBER vs RANK vs DENSE_RANK

The request

From Ashley Rivera, Web Content Lead

“On the Lighting page we show a 'price rank' badge. Several lamps have the same price and the developer isn't sure which ranking to use. Can you show me all three options side by side?”

Your goal

For products in category 'Lighting', return product_id, name, unit_price and three rankings by unit_price, highest first: row_num (ROW_NUMBER, breaking ties by product_id ascending), price_rank (RANK) and dense_price_rank (DENSE_RANK).

  • Read from the products tablenot done yet
  • Show exactly product_id name unit_price row_num price_rank dense_price_ranknot done yet
  • 20 rowsnot done yet

Your query

SQL

Loading the editor…

    ResultPreview of products · press Run to see yours
    Preview of products
    product_idnamecategorysubcategoryunit_price
    101Vale Velvet LoveseatFurnitureSofas1659.00
    102Alder Oak Coffee TableFurnitureTables919.00
    103Orchard Rattan Dining ChairFurnitureChairs789.00
    104Upland Oak BookcaseFurnitureStorage429.00

    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.

    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
    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
    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
    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

    The three ranking functions agree until there is a tie. Then they disagree in ways that matter. You write them the same way:

    SELECT
      name,
      unit_price,
      ROW_NUMBER() OVER (ORDER BY unit_price DESC, product_id) AS row_num,
      RANK()       OVER (ORDER BY unit_price DESC)             AS price_rank,
      DENSE_RANK() OVER (ORDER BY unit_price DESC)             AS dense_price_rank
    FROM products
    WHERE category = 'Lighting';

    There is no PARTITION BY here, so the whole result is one window: every Lighting product is ranked against every other. The WHERE runs first, so only Lighting products reach the window.

    These are the top Lighting prices at Harbor & Pine:

    unit_price ROW_NUMBER RANK DENSE_RANK
    409.00 1 1 1
    379.00 2 2 2
    349.00 3 3 3
    349.00 4 3 3
    329.00 5 5 4
    …
    189.00 9 9 8
    189.00 10 9 8
    189.00 11 9 8
    159.00 12 12 9
    • ROW_NUMBER gives every row a unique number. Ties are broken arbitrarily unless you add a tie-breaker to the ORDER BY.
    • RANK gives tied rows the same rank, then skips: after two products tied at 3 comes 5. It is the “Olympic” ranking: two golds means no silver.
    • DENSE_RANK gives tied rows the same rank and does not skip: after two tied at 3 comes 4.

    Which one to use

    question function
    Exactly N rows per group, or pick one row per key (dedupe) ROW_NUMBER
    Top N including everyone tied at the cutoff RANK (WHERE rnk <= N)
    The Nth highest distinct value (“second highest salary”) DENSE_RANK (WHERE drnk = N)

    For a price badge like Ashley’s, DENSE_RANK reads most naturally (“2nd highest price point”), while RANK answers “how many products are pricier than this one, plus one”. Neither is wrong; they answer different questions, which is the point to make to the developer.

    The second highest value

    “Find the second highest salary” is a famous interview question. The DENSE_RANK answer handles ties and generalizes to any N:

    WITH ranked AS (
      SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS drnk
      FROM employees
    )
    SELECT DISTINCT salary
    FROM ranked
    WHERE drnk = 2;

    Using ROW_NUMBER here would return the highest salary again if two people share the top salary. Using ORDER BY salary DESC LIMIT 1 OFFSET 1 has the same bug.

    Ties are real

    Prices like $189.00 repeat across products, salaries repeat within departments, order counts repeat across customers. Assume ties exist and decide how to treat them, rather than discovering them when a “top 5” list has six names in one tool and five in another.

    NULLs in the ORDER BY

    Ranking functions follow the sort’s NULL placement (chapter 3). In DuckDB, NULLs sort last by default, so they get the last ranks; in Postgres, ORDER BY x DESC puts NULLs first, so a NULL could be ranked

    1. Add NULLS LAST inside the OVER clause when the ranked column can be NULL.