The request

From Ashley Rivera, Web Content Lead
Your goal
For orders with status 'completed' placed in 2025, return one row per day of the week with: weekday_num (ISO day number, 1 = Monday to 7 = Sunday), day_name (the weekday's name, e.g. Monday), orders, evening_orders (orders placed at 18:00 or later) and evening_pct (evening_orders / orders * 100, rounded to 1). Sort by weekday_num ascending.
- Read from the
orderstablenot done yet - Show exactly
weekday_numday_nameordersevening_ordersevening_pctnot done yet - 7 rows, sorted as askednot done yet
Your query
Loading the editor…
Reference solution
| order_id | customer_id | order_date | status | channel |
|---|---|---|---|---|
| 1001 | 125 | 2023-01-01 20:51:19 | completed | web |
| 1002 | 156 | 2023-01-02 07:58:46 | completed | web |
| 1003 | 22 | 2023-01-02 10:25:27 | completed | marketplace |
| 1004 | 48 | 2023-01-02 11:21:51 | completed | marketplace |
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.
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 |
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 |
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 |
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
Dates hide many useful parts: the year, the month, the day of the week, the hour. Pulling one out and grouping by it is a fast way to find patterns.
Recap: date parts in DuckDB
| function | returns | example for Saturday 2025-11-29 20:15 |
|---|---|---|
ISODOW(ts) |
1 = Monday … 7 = Sunday | 6 |
DAYOFWEEK(ts) |
0 = Sunday … 6 = Saturday | 6 |
DAYNAME(ts) |
the name | Saturday |
HOUR(ts) |
0 to 23 | 20 |
Names sort alphabetically, so keep a number next to them to sort by. ISO numbering (Monday first) is what most business calendars use.
SELECT HOUR(order_date) AS hour, COUNT(*) AS orders
FROM orders
WHERE status = 'completed'
AND order_date >= '2025-01-01'
AND order_date < '2026-01-01'
GROUP BY HOUR(order_date)
ORDER BY orders DESC
LIMIT 3;
Grouping by hour like this shows the evening peak the ticket asks about: the three busiest hours of 2025
are 20:00 (339 completed orders), 19:00 (328) and 21:00 (292). An hour value of 20 covers 20:00:00 up to
20:59:59, so HOUR(order_date) >= 18 means “from 6 PM until midnight”. If the ticket had defined the
evening as 6 PM to 10 PM, the condition would need an upper bound too: HOUR(order_date) BETWEEN 18 AND 21.
In the full answer, Saturday (704) and Sunday (682) bring the most completed orders in 2025, and on every weekday roughly a third or more of orders arrive at 18:00 or later.
When you group by an expression, repeat the expression in GROUP BY (or use the column position). Both
ISODOW and DAYNAME go into the grouping; they always agree, so the row count stays seven.
Common mistakes
- Alphabetical days.
ORDER BY day_namegives Friday, Monday, Saturday, Sunday … - Mixing numbering systems.
DAYOFWEEKstarts at Sunday = 0,ISODOWat Monday = 1. - Forgetting the year filter. Weekday patterns change as the business grows; compare like with like.
In SQL Server
Use DATENAME(weekday, order_date) for the name and DATEPART(hour, order_date) for the hour.
DATEPART(weekday, ...) depends on the DATEFIRST setting (Sunday = 1 by default in US English), so
(DATEPART(weekday, order_date) + @@DATEFIRST - 2) % 7 + 1 is the usual way to get Monday = 1.
Lantern lit!
Practised: date partsisodowdaynamehourconditional aggregation
- Solved
- No hints
- First run
