2025
Day 10
Day 10
SELECT
TRIM(LOWER(raw_wish)) AS wish,
COUNT(*) AS count
FROM wish_list
GROUP BY wish
ORDER BY count DESC
LIMIT 20; wish count
1 lego city f1 car 32893
2 barbie dreamhouse 32785
3 nerf blaster 32746
4 lego star wars set 32611
5 beyblade battle arena 29564
6 magna-tiles pet playhouse 29529
7 bluey supermarket play set 26292
8 lego friends amusement park 25982
9 pokemon trainer box 25968
10 duplo building set 23005
11 mini brands fill the fridge 22965
12 electric toy train set 22885
13 toniebox audio player 19529
14 scooter 19496
15 vr headset 16468
16 squishmallows 16304
17 shaved ice machine 16263
18 drone for kids 13151
19 coding robot 13025
20 headphones 130060.144 sec elapsedDay 11
Day 11
SELECT
cat.official_category,
SUM(inv.quantity) AS total_usable_snowballs
FROM snowball_categories AS cat
LEFT JOIN snowball_inventory AS inv
ON
cat.official_category = inv.category_name
AND inv.quantity >= 0
GROUP BY cat.official_category
ORDER BY total_usable_snowballs ASC; official_category total_usable_snowballs
1 arctic blast premium 19335
2 everfrost training round 39612
3 polar precision microball 117912
4 glacier sphere (xl) 275503
5 north ridge compact 991208
6 frost-flight deluxe 15860290.117 sec elapsedDay 12
Day 12
UPDATE hotline_messages
SET status = 'approved'
WHERE
1 = 1
AND LOWER(transcript) LIKE '%sorry%'
AND status IS NULL;
DELETE FROM hotline_messages
WHERE
1 = 1
AND tag IN (
'penguin prank',
'time-loop advisory',
'possible dragon',
'nonsense alert'
)
OR LOWER(caller_name) = 'test caller';
SELECT
COALESCE(status, 'needs review') AS imputed_status,
COUNT(*) AS n_messages
FROM hotline_messages
GROUP BY imputed_status;UPDATE successfully executed. 68 rows were affected.
DELETE successfully executed. 89 rows were affected.
imputed_status n_messages
1 needs review 501
2 approved 4770.014 sec elapsedDay 13
Day 13
WITH os AS (
SELECT
volunteer_name,
role AS assigned_role,
CASE
WHEN shift_time LIKE '%10%' THEN '10:00'
WHEN shift_time LIKE '%12%' THEN '12:00'
WHEN shift_time LIKE '%2%' THEN '14:00'
END AS shift_time
FROM official_shifts
),
lms AS (
SELECT
volunteer_name,
CASE
WHEN LOWER(assigned_task) LIKE '%stage%' THEN 'stage_setup'
WHEN LOWER(assigned_task) LIKE '%cocoa%' THEN 'cocoa_station'
WHEN LOWER(assigned_task) LIKE '%parking%' THEN 'parking_support'
WHEN LOWER(assigned_task) LIKE '%choir%' THEN 'choir_assistant'
WHEN
LOWER(assigned_task) LIKE '%handwarmer%'
THEN 'handwarmer_handout'
WHEN LOWER(assigned_task) LIKE '%snow%' THEN 'snow_shoveling'
WHEN LOWER(assigned_task) LIKE '%shovel%' THEN 'snow_shoveling'
END AS assigned_task_clean,
CASE
WHEN LOWER(time_slot) LIKE '%noon%' THEN '12:00'
WHEN time_slot LIKE '%10%' THEN '10:00'
WHEN time_slot LIKE '%2%' THEN '14:00'
END AS time_slot_clean
FROM last_minute_signups
),
merged_table AS (
SELECT
COALESCE(lms.volunteer_name, os.volunteer_name) AS volunteer_name,
COALESCE(lms.assigned_task_clean, os.assigned_role) AS assigned_role,
COALESCE(lms.time_slot_clean, os.shift_time) AS shift_time
FROM lms
FULL OUTER JOIN os ON lms.volunteer_name = os.volunteer_name
)
SELECT DISTINCT
volunteer_name,
assigned_role,
shift_time
FROM merged_table
ORDER BY volunteer_name ASC
LIMIT 15; volunteer_name assigned_role shift_time
1 Aaron Carter parking_support 14:00
2 Aaron Diaz choir_assistant 14:00
3 Aaron Evans cocoa_station 14:00
4 Aaron Francis handwarmer_handout 14:00
5 Abigail Hernandez stage_setup 10:00
6 Adam King stage_setup 10:00
7 Adil Foster stage_setup 14:00
8 Adil Rahman stage_setup 14:00
9 Adrian Cox cocoa_station 10:00
10 Aisha Bennett cocoa_station 12:00
11 Aisha Khan choir_assistant 12:00
12 Aisha Mohammed cocoa_station 14:00
13 Alina Popov cocoa_station 12:00
14 Alina Powell parking_support 12:00
15 Amina Diallo parking_support 14:000.002 sec elapsedDay 14
Day 14
WITH total_counts AS (
SELECT
user_name,
artist,
COUNT(artist) AS count
FROM listening_logs
WHERE played_at > '2025-01-01'
GROUP BY user_name, artist
)
SELECT
user_name,
artist,
count
FROM
(
SELECT
*,
ROW_NUMBER() OVER (PARTITION BY
user_name
ORDER BY count DESC) AS user_count
FROM total_counts
) AS tc
WHERE user_count < 4
ORDER BY user_name ASC, count DESC
LIMIT 24; user_name artist count
1 Abigail Hernandez Ed Sheeran 78
2 Abigail Hernandez Rotten Mango 15
3 Abigail Hernandez Billie Eilish 4
4 Adrian Cox Kendrick Lamar 128
5 Adrian Cox Stuff You Should Know 30
6 Adrian Cox Pentatonix 6
7 Alex Rivera Ed Sheeran 274
8 Alex Rivera Call Her Daddy (Alex Cooper) 42
9 Alex Rivera Green Day 11
10 Anders Nilsson Snow Patrol 101
11 Anders Nilsson SmartLess 29
12 Anders Nilsson Blink-182 5
13 Anthony King Pentatonix 114
14 Anthony King The Tucker Carlson Show 14
15 Anthony King The Beatles 5
16 Anya Pavlov Coldplay 135
17 Anya Pavlov Just Creepy: Scary Stories 19
18 Anya Pavlov Ariana Grande 5
19 Ava Morales Angels & Airwaves 90
20 Ava Morales Just Creepy: Scary Stories 23
21 Ava Morales Foo Fighters 5
22 Benjamin Davis Coldplay 93
23 Benjamin Davis Billie Eilish 6
24 Benjamin Davis Mostly Technical 60.009 sec elapsedDay 15
Day 15
WITH dates AS (
SELECT DISTINCT gift_date AS delivery_date
FROM deliveries_assigned
WHERE gift_date > '2025-12-14'
ORDER BY delivery_date
),
expected_delivery_dates AS (
SELECT
fam.id AS family_id,
fam.family_name,
dt.delivery_date
FROM families AS fam
CROSS JOIN dates AS dt
)
SELECT
edd.delivery_date AS unassigned_date,
edd.family_name
FROM expected_delivery_dates AS edd
LEFT JOIN deliveries_assigned AS del
ON
edd.delivery_date = del.gift_date
AND edd.family_id = del.family_id
WHERE del.id IS NULL
GROUP BY unassigned_date, edd.family_name
ORDER BY unassigned_date ASC, edd.family_name ASC
LIMIT 25; unassigned_date family_name
1 2025-12-15 Adil Rossi
2 2025-12-15 Aisha Connor
3 2025-12-15 Amina Perez
4 2025-12-15 Amina Wong
5 2025-12-15 Andre Flores
6 2025-12-15 Anya Singh
7 2025-12-15 Arjun Wong
8 2025-12-15 Bianca Connor
9 2025-12-15 Caleb Petrov
10 2025-12-15 Caleb Roberts
11 2025-12-15 Carmen Carter
12 2025-12-15 Carmen Garcia
13 2025-12-15 Casey Flores
14 2025-12-15 Chi Hughes
15 2025-12-15 Clara Johnson
16 2025-12-15 Dara Bautista
17 2025-12-15 David Ramirez
18 2025-12-15 Elias Petrov
19 2025-12-15 Ethan Flores
20 2025-12-15 Eva Gonzalez
21 2025-12-15 Eva Turner
22 2025-12-15 Haruto Diaz
23 2025-12-15 Hugo Perez
24 2025-12-15 Hugo Wong
25 2025-12-15 Isaac Andersson0.006 sec elapsedDay 16
Day 16
WITH available_cars AS (
SELECT
car_id,
available_mixins
FROM cocoa_cars
ORDER BY total_stock DESC
LIMIT 3
),
available_mixins AS (
SELECT
car_id,
UNNEST(available_mixins) AS available_mixins
FROM available_cars
),
passengers_mixins AS (
SELECT
passenger_name,
UNNEST(favorite_mixins) AS favorite_mixins
FROM passengers
)
SELECT
pm.passenger_name,
ARRAY_AGG(DISTINCT am.car_id) AS cars_available
FROM passengers_mixins AS pm
INNER JOIN available_mixins AS am
ON pm.favorite_mixins = am.available_mixins
GROUP BY pm.passenger_name
ORDER BY pm.passenger_name ASC
LIMIT 25; passenger_name cars_available
1 Aaron Ashcroft {2}
2 Abigail Worthington {2,9}
3 Aiden Stanfield {9}
4 Alexander Stratton {5,9}
5 Alexis Thorndale {9}
6 Alice Merriweather {2,5,9}
7 Amanda Fitzroy {2,5,9}
8 Amelia Rosewood {9}
9 Anna Westfield {2}
10 Aria Blackwood {2,5,9}
11 Ashley Fairhaven {2,5,9}
12 August Blackwell {2,5,9}
13 Aurora Whitfield {2,5,9}
14 Benjamin Fairweather {9}
15 Benjamin Patel {9}
16 Bianca Pereira {9}
17 Blake Dunwood {5,9}
18 Brandon Locklear {2,5,9}
19 Caleb Osei {5,9}
20 Cameron Gladstone {2,9}
21 Cameron Rogers {2,5,9}
22 Carlos Mendez {5,9}
23 Caroline Bannister {2,9}
24 Carter Brookfield {2}
25 Charlotte Singh {2,5,9}0.003 sec elapsedDay 17
Day 17
WITH last_score AS (
SELECT MAX(id) AS last_id
FROM price_changes
GROUP BY product_id
),
price_calculations AS (
SELECT
pid.id,
pd.product_name,
pid.price AS current_price,
LAG(pid.price)
OVER (
PARTITION BY pd.product_name
ORDER BY pid.effective_timestamp ASC
)
AS previous_price
FROM price_changes AS pid
INNER JOIN products AS pd
ON pid.product_id = pd.product_id
)
SELECT
product_name,
current_price,
previous_price,
current_price - previous_price AS price_diff
FROM price_calculations
WHERE
id IN (SELECT ls.last_id FROM last_score AS ls)
ORDER BY product_name ASC
LIMIT 25; product_name current_price previous_price price_diff
1 Classic Candle 49.55 57.05 -7.50
2 Classic Cookie Decorating Kit 243.09 214.65 28.44
3 Classic Cookware Set 66.24 79.06 -12.82
4 Classic Festive Scarf 67.10 61.20 5.90
5 Classic Holiday Jam Trio 15.99 18.89 -2.90
6 Classic Mug 173.05 157.02 16.03
7 Classic Notepad 120.49 122.23 -1.74
8 Classic Ornament 35.05 30.25 4.80
9 Classic Pecan Praline Bites 45.42 41.74 3.68
10 Classic Roasted Cashews 257.33 223.63 33.70
11 Classic Sled 193.40 166.11 27.29
12 Classic White Chocolate Popcorn 40.38 47.51 -7.13
13 Cozy Almond Brittle 107.21 116.42 -9.21
14 Cozy Board Game 39.20 45.96 -6.76
15 Cozy Candle 105.68 91.49 14.19
16 Cozy Cookware Set 106.87 89.35 17.52
17 Cozy Fruit Assortment 30.08 25.79 4.29
18 Cozy Lamp 138.36 170.15 -31.79
19 Cozy Notepad 102.78 106.04 -3.26
20 Cozy Pecan Praline Bites 22.24 18.69 3.55
21 Cozy Puzzle 209.20 227.24 -18.04
22 Cozy Reindeer Headband 215.40 196.31 19.09
23 Cozy Scented Pine Sachet 152.76 159.88 -7.12
24 Cozy Trail Mix Trio 107.81 101.09 6.72
25 Cozy White Chocolate Popcorn 74.78 71.58 3.200.006 sec elapsedDay 18
Day 18
WITH latest_order AS (
SELECT
customer_id,
MAX(created_at) AS latest_date
FROM orders
GROUP BY customer_id
)
SELECT
ord.customer_id,
((ord.order_data::json ->> 'gift')::json ->> 'wrapped')::boolean
AS gift_wrap,
(ord.order_data::json ->> 'shipping')::json ->> 'method' AS shipping_method,
(ord.order_data::json ->> 'risk')::json ->> 'flag' AS risk
FROM orders AS ord
INNER JOIN
latest_order AS lo
ON
ord.customer_id = lo.customer_id
AND ord.created_at = lo.latest_date customer_id gift_wrap shipping_method risk
1 1 FALSE express <NA>
2 2 FALSE standard <NA>
3 3 TRUE standard high
4 4 FALSE express <NA>
5 5 TRUE overnight <NA>
6 6 TRUE overnight <NA>
7 7 FALSE express <NA>
8 8 FALSE overnight <NA>
9 9 TRUE express <NA>
10 10 FALSE standard medium
11 11 TRUE standard medium
12 12 TRUE standard medium
13 13 TRUE standard <NA>
14 14 TRUE standard <NA>
15 15 FALSE express medium
16 16 FALSE express <NA>
17 17 FALSE standard <NA>
18 18 FALSE overnight low
19 19 FALSE standard <NA>
20 20 FALSE overnight <NA>
21 21 TRUE overnight <NA>
22 22 FALSE standard medium
23 23 FALSE express <NA>
24 24 FALSE overnight <NA>
25 25 TRUE overnight high
26 26 FALSE standard low
27 27 TRUE standard <NA>
28 28 FALSE standard low
29 29 TRUE overnight high
30 30 FALSE overnight medium
31 31 FALSE express <NA>
32 32 FALSE overnight <NA>
33 33 TRUE express <NA>
34 34 FALSE express <NA>
35 35 TRUE express high
36 36 TRUE overnight <NA>
37 37 TRUE overnight <NA>
38 38 TRUE standard <NA>
39 39 TRUE overnight <NA>
40 40 FALSE express <NA>
41 41 FALSE standard high
42 42 FALSE overnight medium
43 43 TRUE express <NA>
44 44 TRUE standard <NA>
45 45 FALSE standard <NA>
46 46 FALSE standard <NA>
47 47 FALSE standard <NA>
48 48 TRUE standard <NA>
49 49 TRUE express high
50 50 TRUE express low0.003 sec elapsedDay 19
Day 19
INSERT INTO misdelivered_presents
SELECT
id,
child_name,
delivery_location,
gift_name,
scheduled_at,
DATE_TRUNC('minute', NOW()) AS flagged_at,
'Invalid delivery location' AS reason
FROM deliveries
WHERE
delivery_location IN (
'Volcano Rim', 'Drifting Igloo', 'Abandoned Lighthouse', 'The Vibes'
);
SELECT
id,
child_name,
delivery_location,
gift_name,
scheduled_at,
flagged_at,
reason
FROM misdelivered_presents
WHERE flagged_at = DATE_TRUNC('minute', NOW())
ORDER BY id
LIMIT 25;INSERT successfully executed. 103 rows were affected.
id child_name delivery_location gift_name scheduled_at
1 3 Mila N. The Vibes storybook collection 2025-12-24 21:09:00
2 22 Lena F. Abandoned Lighthouse plush reindeer 2025-12-24 19:08:00
3 23 Mila N. Abandoned Lighthouse storybook collection 2025-12-24 20:42:00
4 29 Mateo C. Volcano Rim plush reindeer 2025-12-24 21:44:00
5 31 Nia G. Drifting Igloo robot toy 2025-12-24 19:57:00
6 38 Omar Q. The Vibes science kit 2025-12-24 19:48:00
7 41 Hiro T. The Vibes wooden train set 2025-12-24 21:04:00
8 47 Omar Q. Volcano Rim art supplies 2025-12-24 20:09:00
9 53 Ava J. Volcano Rim plush reindeer 2025-12-24 18:20:00
10 59 Sofia K. Abandoned Lighthouse robot toy 2025-12-24 20:46:00
11 62 Lena F. Drifting Igloo wooden train set 2025-12-24 20:21:00
12 68 Mila N. Volcano Rim teddy bear 2025-12-24 20:01:00
13 70 Mateo C. Abandoned Lighthouse storybook collection 2025-12-24 19:50:00
14 72 Zara S. The Vibes wooden train set 2025-12-24 20:47:00
15 85 Mila N. Abandoned Lighthouse board game 2025-12-24 21:03:00
16 87 Jonah W. Drifting Igloo wooden train set 2025-12-24 21:08:00
17 92 Layla B. The Vibes storybook collection 2025-12-24 21:40:00
18 94 Layla B. Drifting Igloo plush reindeer 2025-12-24 20:32:00
19 96 Omar Q. Volcano Rim teddy bear 2025-12-24 20:13:00
20 99 Ravi P. Abandoned Lighthouse science kit 2025-12-24 20:45:00
21 111 Sofia K. Abandoned Lighthouse art supplies 2025-12-24 18:53:00
22 116 Lena F. Volcano Rim science kit 2025-12-24 19:07:00
23 120 Zara S. Drifting Igloo puzzle box 2025-12-24 20:21:00
24 123 Lena F. The Vibes plush reindeer 2025-12-24 19:03:00
25 125 Hiro T. The Vibes robot toy 2025-12-24 21:11:00
flagged_at reason
1 2026-01-02 13:04:00 Invalid delivery location
2 2026-01-02 13:04:00 Invalid delivery location
3 2026-01-02 13:04:00 Invalid delivery location
4 2026-01-02 13:04:00 Invalid delivery location
5 2026-01-02 13:04:00 Invalid delivery location
6 2026-01-02 13:04:00 Invalid delivery location
7 2026-01-02 13:04:00 Invalid delivery location
8 2026-01-02 13:04:00 Invalid delivery location
9 2026-01-02 13:04:00 Invalid delivery location
10 2026-01-02 13:04:00 Invalid delivery location
11 2026-01-02 13:04:00 Invalid delivery location
12 2026-01-02 13:04:00 Invalid delivery location
13 2026-01-02 13:04:00 Invalid delivery location
14 2026-01-02 13:04:00 Invalid delivery location
15 2026-01-02 13:04:00 Invalid delivery location
16 2026-01-02 13:04:00 Invalid delivery location
17 2026-01-02 13:04:00 Invalid delivery location
18 2026-01-02 13:04:00 Invalid delivery location
19 2026-01-02 13:04:00 Invalid delivery location
20 2026-01-02 13:04:00 Invalid delivery location
21 2026-01-02 13:04:00 Invalid delivery location
22 2026-01-02 13:04:00 Invalid delivery location
23 2026-01-02 13:04:00 Invalid delivery location
24 2026-01-02 13:04:00 Invalid delivery location
25 2026-01-02 13:04:00 Invalid delivery location0.008 sec elapsedDay 20
Day 20
WITH roll_avg AS (
SELECT
child_id,
child_name,
behavior_date,
AVG(score) OVER (
PARTITION BY child_id
ORDER BY
child_id,
behavior_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
) AS rolling_avg_score
FROM behavior_logs
)
SELECT
child_id,
child_name,
behavior_date,
rolling_avg_score
FROM roll_avg
WHERE
1 = 1
AND behavior_date >= '2025-12-07' AND rolling_avg_score < 0
ORDER BY behavior_date, child_name
LIMIT 25; child_id child_name behavior_date rolling_avg_score
1 99 Ava X. 2025-12-07 -0.1428571
2 5 Ethan D. 2025-12-07 -0.8571429
3 27 Jonah L. 2025-12-07 -0.2857143
4 49 Liam I. 2025-12-07 -0.1428571
5 117 Mateo H. 2025-12-07 -0.5714286
6 79 Nia E. 2025-12-07 -0.2857143
7 7 Nolan R. 2025-12-07 -1.1428571
8 25 Omar D. 2025-12-07 -0.7142857
9 65 Priya G. 2025-12-07 -0.5714286
10 99 Ava X. 2025-12-08 -0.2857143
11 5 Ethan D. 2025-12-08 -1.2857143
12 114 Jonah L. 2025-12-08 -0.4285714
13 27 Jonah L. 2025-12-08 -0.5714286
14 49 Liam I. 2025-12-08 -0.5714286
15 80 Lucas P. 2025-12-08 -0.4285714
16 117 Mateo H. 2025-12-08 -0.7142857
17 79 Nia E. 2025-12-08 -1.0000000
18 7 Nolan R. 2025-12-08 -0.8571429
19 100 Nolan X. 2025-12-08 -0.4285714
20 25 Omar D. 2025-12-08 -0.8571429
21 65 Priya G. 2025-12-08 -0.7142857
22 5 Ethan D. 2025-12-09 -1.1428571
23 114 Jonah L. 2025-12-09 -0.7142857
24 97 Layla N. 2025-12-09 -0.5714286
25 49 Liam I. 2025-12-09 -0.57142860.005 sec elapsedDay 21
Day 21
This isn’t entirely what he wanted, but this one at least has some practical usecase for me personally.
WITH scored AS (
SELECT
id,
title,
description,
CASE
WHEN
(LOWER(title) LIKE '%flight%' OR LOWER(title) LIKE '%fly%')
AND (
LOWER(description) LIKE '%flight%'
OR LOWER(description) LIKE '%fly%'
)
THEN 2
WHEN
(LOWER(title) LIKE '%flight%' OR LOWER(title) LIKE '%fly%')
OR (
LOWER(description) LIKE '%flight%'
OR LOWER(description) LIKE '%fly%'
)
THEN 1
ELSE 0
END AS score
FROM archive_records
)
SELECT
id,
title,
description,
score
FROM scored
WHERE score > 0
ORDER BY score DESC
LIMIT 5; id title
1 393 Elven Flight Proficiency Program
2 187 Flight Stabilization Mechanism Enhancements
3 63 Memorandum: Flight Stabilization Techniques
4 10 Sleigh Skim Mechanism Upgrade
5 33 Reindeer Workload Assessment Checklist
description
1 This comprehensive training module equips elves with advanced airborne maneuvering skills essential for diurnal navigation. Emphasizing agility and precision, trainees will engage in simulated flight drills using enchanted winged harnesses to ensure they master aerial dynamics.
2 Through careful recalibration of the guidance fins, we have achieved unprecedented stability during aerial maneuvers. Preliminary tests indicate a 30% reduction in turbulence susceptibility, paving the way for smoother flights across unpredictable terrains.
3 In our quest to enhance airborne toy functionality, we have devised a set of stabilization methods to mitigate erratic flight patterns. Through meticulous experimentation with weight distribution and magical energy fluctuations, we aim to achieve unprecedented aerial balance in our sleigh prototypes.
4 The experiment involved enchanting the underside of the sleigh with a whispering wind charm to achieve unprecedented speeds. Results were alarming, leading to uncontrollable flight trajectories and sudden descents—definitely do not attempt again.
5 Ensure all reindeer are properly rested before the study begins. Maintain a balance between flying practice and nutrition to prevent overwhelm and ensure optimal performance outcomes.
score
1 2
2 2
3 2
4 1
5 10.007 sec elapsedDay 22
Day 22
SELECT
vehicle_id,
departure_time,
SUM(ARRAY_LENGTH(XPATH('//passenger/name/text()', manifest_xml), 1)) AS n_passengers --noqa: LT05
FROM travel_manifests
WHERE UPPER(vehicle_id) LIKE 'CARGO%'
GROUP BY vehicle_id, departure_time
HAVING SUM(ARRAY_LENGTH(XPATH('//passenger/name/text()', manifest_xml), 1)) > 20
ORDER BY departure_time; vehicle_id departure_time n_passengers
1 CARGO-12 2025-12-22 18:00:00 28
2 CARGO-12 2025-12-22 23:00:00 24
3 CARGO-12 2025-12-23 04:00:00 38
4 CARGO-12 2025-12-23 07:00:00 21
5 CARGO-12 2025-12-23 22:00:00 210.006 sec elapsedDay 23
Day 23
Nope, not even going to try this one.
Day 24
Day 24
WITH dispatches AS (
SELECT
marker_letter,
ROW_NUMBER()
OVER (PARTITION BY system_id ORDER BY dispatched_at DESC)
AS row_num
FROM system_dispatches
WHERE payload ->> 'source' = 'primary'
)
SELECT STRING_AGG(marker_letter, '') AS confirmation_phrase
FROM dispatches
WHERE row_num = 1; confirmation_phrase
1 AYVENTOFSQJ0.002 sec elapsedI suppose the answer was supposed to be 'ADVENTOFSQL', but there wasn’t a row labeled 'primary_source' in the input file I downloaded with the letter ‘D’, so I think it’s a data issue. I got the same result when I tried the online playground thing.
Last challenge, was a nice Advent of SQL this year! 👏🏻 Lot of work went into this once again.