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The business review wants the platform's play volume by month with the change against the previous month already computed, rather than a column of numbers to subtract by eye.
Return one row per month, ordered by play_month.
Result columns · in this order
play_month | Month as YYYY-MM. |
plays | Plays in that month. |
listeners | Distinct listeners active that month. |
plays_change | Plays minus the previous month's plays; NULL for the first month. |
How to approach it
Aggregate to months first, then reach back one row with LAG.
Sample input
| play_id | user_id | track_id | played_at | ms_played | device |
|---|---|---|---|---|---|
| 1 | u1 | t1 | 2026-01-05 20:00:00 | 210000 | mobile |
| 2 | u1 | t2 | 2026-01-05 20:04:00 | 180000 | mobile |
| 3 | u1 | t1 | 2026-01-05 20:08:00 | 150000 | mobile |
| 4 | u1 | t9 | 2026-01-05 21:10:00 | 200000 | mobile |
| 5 | u2 | t3 | 2026-01-06 07:00:00 | 240000 | desktop |
| 6 | u2 | t4 | 2026-01-06 07:10:00 | 195000 | desktop |
| 7 | u2 | t3 | 2026-01-06 09:00:00 | 30000 | desktop |
| 8 | u3 | t5 | 2026-01-09 22:00:00 | 300000 | mobile |
| 9 | u3 | t6 | 2026-01-09 22:06:00 | 270000 | mobile |
| 10 | u3 | t5 | 2026-01-10 22:00:00 | 300000 | mobile |
| 11 | u4 | t7 | 2026-01-15 17:00:00 | 225000 | mobile |
| 12 | u4 | t8 | 2026-01-15 17:04:00 | 250000 | mobile |
| 13 | u5 | t1 | 2026-01-20 06:30:00 | 210000 | desktop |
| 14 | u5 | t3 | 2026-01-20 06:34:00 | 240000 | desktop |
| 15 | u4 | t7 | 2026-02-02 17:00:00 | 225000 | mobile |
| 16 | u1 | t1 | 2026-02-03 19:00:00 | 210000 | desktop |
| 17 | u1 | t5 | 2026-02-03 19:05:00 | 300000 | desktop |
| 18 | u5 | t5 | 2026-02-05 06:30:00 | 300000 | desktop |
| 19 | u5 | t7 | 2026-02-05 07:15:00 | 225000 | desktop |
| 20 | u2 | t10 | 2026-02-11 12:00:00 | 165000 | mobile |
| 21 | u2 | t3 | 2026-02-11 12:03:00 | 240000 | mobile |
| 22 | u3 | t6 | 2026-02-14 21:00:00 | 270000 | desktop |
| 23 | u3 | t5 | 2026-02-14 21:05:00 | 120000 | desktop |
| 24 | u6 | t2 | 2026-02-18 13:00:00 | 180000 | mobile |
| 25 | u6 | t1 | 2026-02-18 13:03:00 | 210000 | mobile |
| 26 | u5 | t2 | 2026-03-01 09:00:00 | 180000 | mobile |
| 27 | u5 | t9 | 2026-03-01 09:03:00 | 200000 | mobile |
| 28 | u1 | t9 | 2026-03-02 08:30:00 | 60000 | mobile |
| 29 | u2 | t4 | 2026-03-05 18:00:00 | 100000 | mobile |
| 30 | u3 | t6 | 2026-03-08 20:00:00 | 270000 | mobile |
| 31 | u4 | t8 | 2026-03-11 16:00:00 | 20000 | mobile |
| 32 | u6 | t2 | 2026-03-15 13:00:00 | 90000 | mobile |
| 33 | u8 | t10 | 2026-03-20 11:00:00 | 165000 | mobile |
| 34 | u8 | t4 | 2026-03-20 11:03:00 | 195000 | mobile |
| 35 | u8 | t10 | 2026-03-20 11:04:00 | 165000 | mobile |
| 36 | u8 | t9 | 2026-03-20 11:07:00 | 200000 | mobile |
36 rows — scroll inside the table to see them all.
Expected output
| play_month | plays | listeners | plays_change |
|---|---|---|---|
| 2026-01 | 14 | 5 | null |
| 2026-02 | 11 | 6 | -3 |
| 2026-03 | 11 | 7 | 0 |
3 rows — all rows shown.
Constraints
play_month is the YYYY-MM prefix of played_at.plays counts rows. listeners counts distinct user_id within that month — someone active in two months counts in both.plays_change is this month's plays minus the previous month's. The first month has no previous month, so it is NULL. Do not report 0.play_month.Worked example
January has 14 plays, February 11 and March 11, so plays_change reads NULL, -3, 0.
Reporting 0 for January instead of NULL would state that January was flat against a month that does not exist in the data. NULL says 'unknown', which is the truth, and it is also what makes the genuine 0 in March meaningful.
What this tests
Reading the previous row's value with LAG over an aggregate, and the discipline of leaving the first period NULL rather than implying it was flat.
Submit for review to find out what your query gets right, what it gets wrong, and how it compares with the best working query for this exercise.
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