The request

From Ashley Rivera, Web Content Lead
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
productstablenot done yet - Show exactly
product_idnameunit_pricerow_numprice_rankdense_price_ranknot done yet - 20 rowsnot done yet
Your query
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Reference solution
| product_id | name | category | subcategory | unit_price |
|---|---|---|---|---|
| 101 | Vale Velvet Loveseat | Furniture | Sofas | 1659.00 |
| 102 | Alder Oak Coffee Table | Furniture | Tables | 919.00 |
| 103 | Orchard Rattan Dining Chair | Furniture | Chairs | 789.00 |
| 104 | Upland Oak Bookcase | Furniture | Storage | 429.00 |
Mote checks your result
Press Run. Each row that matches the goal becomes a firefly in your lantern.
What Ashley gets, and why
Tables
Harbor & Pine store data, ready to query.
products120 rows · 8 columns
product_idintegernamevarcharcategoryvarcharsubcategoryvarcharunit_pricedecimalunit_costdecimallaunched_ondateis_activeboolean
| product_id | name | category | subcategory | unit_price | unit_cost | launched_on | is_active |
|---|---|---|---|---|---|---|---|
| 101 | Vale Velvet Loveseat | Furniture | Sofas | 1659.00 | NULL | 2023-09-11 | true |
| 102 | Alder Oak Coffee Table | Furniture | Tables | 919.00 | 503.50 | 2024-05-16 | true |
| 103 | Orchard Rattan Dining Chair | Furniture | Chairs | 789.00 | 429.87 | 2023-05-27 | true |
| 104 | Upland Oak Bookcase | Furniture | Storage | 429.00 | 214.70 | 2022-12-20 | true |
| 105 | Avery Leather Bed Frame | Furniture | Beds | 449.00 | 232.70 | 2021-08-31 | true |
customers2,018 rows · 9 columns
customer_idintegerfirst_namevarcharlast_namevarcharemailvarcharcityvarcharstatevarcharsignup_datedatesegmentvarcharreferral_sourcevarchar
| customer_id | first_name | last_name | city | state | signup_date | segment | referral_source | |
|---|---|---|---|---|---|---|---|---|
| 1 | Owen | Hudson | owen.hudson@icloud.com | San Francisco | CA | 2022-06-02 | consumer | search |
| 2 | Douglas | Mendoza | dmendoza69@gmail.com | Atlanta | GA | 2022-06-02 | consumer | NULL |
| 3 | Henry | Brooks | hbrooks@lakesidecafe.com | Los Angeles | CA | 2022-06-04 | business | NULL |
| 4 | Brian | Graham | bgraham@bluecedarhomes.com | Seattle | WA | 2022-06-06 | business | |
| 5 | Kyle | Jimenez | kjimenez27@yahoo.com | Indianapolis | IN | 2022-06-07 | consumer | NULL |
orders12,000 rows · 7 columns
order_idintegercustomer_idintegerorder_datetimestampstatusvarcharchannelvarcharshipping_feedecimalcoupon_codevarchar
| order_id | customer_id | order_date | status | channel | shipping_fee | coupon_code |
|---|---|---|---|---|---|---|
| 1001 | 125 | 2023-01-01 20:51:19 | completed | web | 0.00 | NULL |
| 1002 | 156 | 2023-01-02 07:58:46 | completed | web | 0.00 | NULL |
| 1003 | 22 | 2023-01-02 10:25:27 | completed | marketplace | 4.99 | WELCOME10 |
| 1004 | 48 | 2023-01-02 11:21:51 | completed | marketplace | 4.99 | WELCOME10 |
| 1005 | 82 | 2023-01-02 11:43:36 | completed | web | 0.00 | WELCOME10 |
order_items30,338 rows · 6 columns
order_item_idintegerorder_idintegerproduct_idintegerquantityintegerunit_pricedecimaldiscount_pctdecimal
| order_item_id | order_id | product_id | quantity | unit_price | discount_pct |
|---|---|---|---|---|---|
| 1 | 1001 | 121 | 9 | 31.99 | 0.00 |
| 2 | 1001 | 154 | 3 | 11.99 | 0.00 |
| 3 | 1002 | 154 | 4 | 11.99 | 0.00 |
| 4 | 1002 | 194 | 1 | 149.00 | 0.00 |
| 5 | 1002 | 193 | 2 | 19.99 | 0.00 |
returns1,673 rows · 5 columns
return_idintegerorder_item_idintegerreturned_ondatereasonvarcharrefund_amountdecimal
| return_id | order_item_id | returned_on | reason | refund_amount |
|---|---|---|---|---|
| 1 | 14 | 2023-01-10 | damaged | 83.69 |
| 2 | 42 | 2023-01-10 | wrong_item | 19.99 |
| 3 | 105 | 2023-01-14 | wrong_item | 10.99 |
| 4 | 24 | 2023-01-16 | changed_mind | 26.99 |
| 5 | 126 | 2023-01-16 | late | 20.99 |
employees60 rows · 8 columns
employee_idintegerfirst_namevarcharlast_namevarchardepartmentvarchartitlevarcharmanager_idintegerhire_datedatesalaryinteger
| employee_id | first_name | last_name | department | title | manager_id | hire_date | salary |
|---|---|---|---|---|---|---|---|
| 1 | Catherine | Hale | Executive | Chief Executive Officer | NULL | 2016-03-01 | 285000 |
| 2 | Robert | Kim | Executive | Chief Operating Officer | 1 | 2017-01-09 | 235000 |
| 3 | Megan | Collins | Sales | Head of Sales | 1 | 2018-04-16 | 172000 |
| 4 | Chloe | Gutierrez | Sales | Account Executive | 3 | 2020-01-21 | 72000 |
| 5 | Frank | Bell | Sales | Account Executive | 3 | 2023-05-21 | 73000 |
web_sessions40,000 rows · 7 columns
session_idintegercustomer_idintegerstarted_attimestampdevicevarcharsourcevarcharpages_viewedintegerconvertedboolean
| session_id | customer_id | started_at | device | source | pages_viewed | converted |
|---|---|---|---|---|---|---|
| 1 | NULL | 2023-01-01 23:02:24 | mobile | paid | 1 | false |
| 2 | 18 | 2023-01-01 15:07:49 | desktop | paid | 1 | false |
| 3 | NULL | 2023-01-01 16:55:46 | desktop | direct | 7 | false |
| 4 | NULL | 2023-01-01 12:43:28 | mobile | organic | 3 | false |
| 5 | NULL | 2023-01-01 13:06:21 | mobile | social | 2 | false |
marketing_spend3,276 rows · 3 columns
spend_datedatechannelvarcharamountdecimal
| spend_date | channel | amount |
|---|---|---|
| 2023-01-01 | paid | 492.99 |
| 2023-01-01 | 37.38 | |
| 2023-01-01 | social | 200.53 |
| 2023-01-02 | paid | 709.13 |
| 2023-01-02 | 64.13 |
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
- Add
NULLS LASTinside the OVER clause when the ranked column can be NULL.
Lantern lit!
Practised: row numberrankdense ranktieswindow functions
- Solved
- No hints
- First run
