Users With Purchase Events

An easy SQL interview practice problem on DataDriven. Write the query and run it against a real database, with instant feedback.

Domain: SQL
Difficulty: easy
Seniority: L3
Asked in: Meta

Problem

Return the IDs of every user who has triggered at least one purchase event, listed from the lowest ID to the highest.

At least one purchase. That changes everything.

Tables

event_data

Columns: event_id INTEGER, event_type TEXT, user_id INTEGER, event_timestamp TEXT, payload TEXT, tags TEXT, properties TEXT

event_data: 10 of 200 rows
event_id	event_type	user_id	event_timestamp	payload	tags	properties
3059	button_click	100	2026-02-02 01:07:00	{"button_id": "cancel", "element_class": "btn-primary", "position": {"x": 13, "y": 7}}	["desktop", "web", "returning"]	{"os": "Edge", "language": "US", "screen_size": "en", "utm_source": "mobile"}
3118	purchase	197	2026-03-03 02:14:00	{"amount": 56.93, "currency": "GBP", "items": [{"sku": "SKU1002", "qty": 3}]}	["ios", "premium", "new_user", "organic"]	{"country": "Windows", "device_type": "FR", "referrer": "fr", "utm_medium": "1920x1080", "browser": "twitter"}
3177	signup	294	2026-04-04 03:21:00	{"signup_method": "apple", "referral_code": "REF3X", "accepted_terms": true}	["android", "free", "campaign_a", "referral", "experiment_v1"]	{"language": "iOS", "screen_size": "es", "utm_source": "desktop", "session_id": "bing", "os": "organic"}
3236	login	391	2026-05-05 04:28:00	{"login_method": "password", "remember_me": true, "mfa_used": false}	["web", "returning"]	{"device_type": "JP", "referrer": "ja", "utm_medium": "1440x900"}
3295	logout	488	2026-06-06 05:35:00	{"action": "logout", "value": 50}	["premium", "new_user", "organic"]	{"screen_size": "de", "utm_source": "tablet", "session_id": "amazon", "os": "Chrome"}
3354	search	585	2026-07-07 06:42:00	{"query": "phone", "results_count": 102, "filters": {"category": "electronics"}}	["free", "campaign_a", "referral", "experiment_v1"]	{"referrer": "mobile", "utm_medium": "375x812", "browser": "email", "country": "Edge", "device_type": "US"}
3413	add_to_cart	NULL	2026-08-08 07:49:00	{"product_id": 1007, "quantity": 2, "price": 58.49, "variant": "medium"}	["returning", "campaign_b", "featured", "experiment_v2", "ios"]	{"utm_source": "fr", "session_id": "twitter", "os": "Safari", "language": "Windows", "screen_size": "FR"}
3472	open	779	2026-09-09 08:56:00	{"action": "open", "value": 80}	["new_user", "organic"]	{"utm_medium": "bing", "browser": "organic", "country": "iOS"}
3531	crash	876	2026-01-10 09:03:00	{"action": "crash", "value": 90}	["campaign_a", "referral", "experiment_v1"]	{"session_id": "direct", "os": "Firefox", "language": "macOS", "screen_size": "JP"}
3590	error	973	2026-02-11 10:10:00	{"action": "error", "value": 100}	["campaign_b", "featured", "experiment_v2", "ios"]	{"browser": "Chrome", "country": "Android", "device_type": "DE", "referrer": "de", "utm_medium": "tablet"}

users

Columns: user_id INTEGER, username TEXT, email TEXT, signup_date TEXT, account_status TEXT, age_bucket TEXT

users: 10 of 200 rows
user_id	username	email	signup_date	account_status	age_bucket
100	alice	alice@example.com	2025-02-02	inactive	25-34
197	aaron42	aaron42@example.com	2026-03-03	suspended	35-44
294	amelia	amelia@example.com	2024-04-04	pending_verification	45-54
391	arjun	arjun@example.com	2025-05-05	active	55-64
488	ava99	ava99@example.com	2026-06-06	inactive	65+
585	andrew_k	andrew_k@example.com	2024-07-07	suspended	NULL
682	anika	anika@example.com	2025-08-08 16:34:00	pending_verification	18-24
779	aiden	aiden@example.com	2026-09-09 14:53:00	active	25-34
876	aria_b	aria_b@example.com	2024-10-10	inactive	35-44
973	brian	brian@example.com	2025-11-11	suspended	45-54

Expected output

First 5 of 13 rows
user_id
197
1555
2913
4271
5629

Worked solution and explanation

Why this problem exists in real interviews

This tests filtering by event type and deduplicating user IDs. Interviewers check whether you can extract a unique set of entities matching a condition and sort them deterministically.

Break down the requirements

Step 1: Filter to purchase events

WHERE event_type = 'purchase' restricts the 200M row table to purchase events only.

Step 2: Return distinct user IDs ordered

SELECT DISTINCT user_id ORDER BY user_id produces a deduplicated, sorted list.

The solution

Filter and deduplicate by event type

SELECT DISTINCT user_id
FROM event_data
WHERE event_type = 'purchase'
ORDER BY user_id

Common follow-up questions

How would you also return the count of purchases per user? (Replace DISTINCT with GROUP BY `user_id` and add COUNT(*).)
What if `event_type` had mixed casing ('Purchase', 'PURCHASE')? (Use LOWER(`event_type`) = 'purchase' for case-insensitive matching.)
How would you find users who had both purchase and view events? (Intersect two filtered DISTINCT sets, or use GROUP BY with HAVING.)