Design Schema and Logic for Subscription Event Tracking

user_subscription_events

+----------+-------------+---------------------+-----------+---------+
| user_id  | event_type  | event_time          | plan_type | source  |
+----------+-------------+---------------------+-----------+---------+
| 101      | signup      | 2023-05-01 09:03:12 | paid      | web     |
| 101      | cancel      | 2023-06-10 14:27:45 | paid      | web     |
| 101      | signup      | 2023-07-02 08:11:06 | paid      | mobile  |
| 202      | signup      | 2023-05-05 11:55:20 | paid      | email   |
| 303      | cancel      | 2023-05-07 16:02:09 | paid      | support |
+----------+-------------+---------------------+-----------+---------+

Scenario

Subscription event pipeline feeding the experiment: users can signup, cancel, then signup again; downstream tables drive metrics and triggering code.

Question

Design the raw event table schema needed to derive a user's current subscription status and historical states. 2. Write SQL or Python logic that produces one row per user with their latest status, handling the edge case where a user signs up, cancels, and then signs up again. 3. Review the provided Python snippet that assigns variants and delivers the free-trial offer; identify at least three improvements or safeguards you would add.

Hints

Think append-only event logs, window functions, deduping by latest timestamp, idempotent triggers.

Design subscription events schema

Create SQL DDL for an append-only user_subscription_events table to capture subscription signups and cancels so downstream jobs can derive both current status and history. Enforce NOT NULL constraints and restrict event_type to 'signup' or 'cancel'. Add useful indexes for windowing by user and event_time using the provided columns. For this executable answer, return the sample rows that conform to the proposed schema, ordered by user_id and event_time.

Table user_subscription_events

column	type	constraints
user_id	INTEGER	NOT NULL
event_type	VARCHAR(20)	NOT NULL, CHECK (event_type IN ('signup','cancel'))
event_time	TIMESTAMP	NOT NULL
plan_type	VARCHAR(20)	NOT NULL
source	VARCHAR(20)	NOT NULL

Sample data:

event_time	event_type	plan_type	source	user_id
2023-05-01 09:03:12	signup	paid	web	101
2023-06-10 14:27:45	cancel	paid	web	101
2023-07-02 08:11:06	signup	paid	mobile	101
2023-05-05 11:55:20	signup	paid	email	202
2023-05-07 16:02:09	cancel	paid	support	303

Expected output

A CREATE TABLE statement defining the raw event schema with constraints and supporting indexes. The table holds append-only rows for signups and cancels. Example rows consistent with the schema are shown.

event_time	event_type	plan_type	source	user_id
2023-05-01 09:03:12	signup	paid	web	101
2023-06-10 14:27:45	cancel	paid	web	101
2023-07-02 08:11:06	signup	paid	mobile	101
2023-05-05 11:55:20	signup	paid	email	202
2023-05-07 16:02:09	cancel	paid	support	303

Hints

1. Use a CHECK constraint to restrict event_type to allowed values.
2. Index by (user_id, event_time) to speed up latest-event window functions.
3. Append-only is typically enforced by ingestion process and auditing.

Latest Subscription Status Per User

Return one row per user from user_subscription_events with the user's most recent event details and derived current_status. Use active when the latest event is signup and canceled when it is cancel. If a user has multiple events at the same timestamp, prefer signup over cancel, then use source alphabetically as a deterministic final tie-breaker.

Table user_subscription_events

column	type	constraints
user_id	INTEGER	NOT NULL
event_type	VARCHAR(20)	NOT NULL, CHECK (event_type IN ('signup','cancel'))
event_time	TIMESTAMP	NOT NULL
plan_type	VARCHAR(20)	NOT NULL
source	VARCHAR(20)	NOT NULL

Sample data:

event_time	event_type	plan_type	source	user_id
2023-05-01 09:03:12	signup	paid	web	101
2023-06-10 14:27:45	cancel	paid	web	101
2023-07-02 08:11:06	signup	paid	mobile	101
2023-05-05 11:55:20	signup	paid	email	202
2023-05-07 16:02:09	cancel	paid	support	303

Expected output

One row per user showing their latest event details and derived current_status. If two events share the same event_time, prefer 'signup' over 'cancel'.

current_status	latest_event_time	latest_event_type	latest_plan_type	latest_source	user_id
active	2023-07-02 08:11:06	signup	paid	mobile	101
active	2023-05-05 11:55:20	signup	paid	email	202
canceled	2023-05-07 16:02:09	cancel	paid	support	303

Hints

1. Use ROW_NUMBER() partitioned by user_id.
2. Order by event_time DESC, then prefer signup before cancel.
3. Use TO_CHAR in PostgreSQL when a formatted timestamp string is required.

Improve offer-delivery logic

List at least three concrete improvements or safeguards for a Python service that assigns experiment variants and delivers a free-trial offer, focusing on idempotency, deterministic assignment, eligibility, late events, I/O robustness, and data validation.

Table user_subscription_events

column	type	constraints
user_id	INTEGER	NOT NULL
event_type	VARCHAR(20)	NOT NULL, CHECK event_type IN ('signup','cancel')
event_time	TIMESTAMP	NOT NULL
plan_type	VARCHAR(20)	NOT NULL
source	VARCHAR(20)	NOT NULL

Sample data:

event_time	event_type	plan_type	source	user_id
2023-05-01 09:03:12	signup	paid	web	101
2023-06-10 14:27:45	cancel	paid	web	101
2023-07-02 08:11:06	signup	paid	mobile	101
2023-05-05 11:55:20	signup	paid	email	202
2023-05-07 16:02:09	cancel	paid	support	303

Expected output

A set of concrete recommendations that improve safety and correctness when assigning variants and sending offers.

recommendation
Idempotency: check a delivery/exposure log keyed by (user_id, experiment_id, variant, offer_id) and wrap assignment+send in a transaction to avoid duplicates.
Deterministic variant assignment: use hash-based bucketing on (user_id, experiment_id) and persist the assignment to prevent re-randomization.
Eligibility gating: only trigger for eligible users (e.g., not active, no prior free trial, correct region/plan) and enforce a cool-off window from last event.
Handle late/out-of-order events: compute status by event_time with stable tie-breakers and schedule reconciliations to correct prior decisions.
Robust I/O: add timeouts, retries with exponential backoff, and a dead-letter queue; emit structured logs and metrics for monitoring.
Data validation: enforce allowed event_type values; quarantine malformed rows; default to a safe no-send behavior on unknowns.

Hints

1. Think about deduplication and transactionality.
2. Use stable hashing to assign variants deterministically.
3. Gate offers by eligibility and timing; plan for late data and transient failures.