Viewers Turned Streamers
A hard-tier problem at 43% community acceptance, tagged with Database. Reported in interviews at PhonePe and 0 others.
PhonePe's asking this database problem in their OA and it's a hard-rated SQL or schema design puzzle. If you're preparing for their assessment, you need to know the exact pattern here because this isn't a standard join-and-aggregate drill. The acceptance rate sits at 43%, which means nearly 6 out of 10 candidates who attempt it fail. Most miss the state transitions or temporal logic baked into the problem. If you hit this live and blank on the approach, StealthCoder surfaces a working solution in seconds, invisible to the proctor, so you can move forward without tanking your score.
Companies that ask "Viewers Turned Streamers"
Viewers Turned Streamers is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an Amazon engineer who used it to pass JPMorgan's OA and system design loop.
Get StealthCoderThis problem forces you to track how users move between states: from 'viewer' status to 'streamer' status. The trap is treating it as a simple lookup. You'll need to handle temporal ordering, identify the exact moment the transition happens, and often join user activity tables with metadata or timeline tables. The schema design and query logic both matter. Most candidates either miss a join condition, fail to order by timestamp correctly, or don't account for users who might have already been streamers. The real skill is recognizing this is fundamentally about state changes, not just counting rows. StealthCoder eliminates the guesswork during the live assessment by parsing the exact schema and generating the correct window functions or subquery logic instantly.
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Viewers Turned Streamers recycles across companies for a reason. It's hard-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an Amazon engineer who used it to pass JPMorgan's OA and system design loop. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Viewers Turned Streamers interview FAQ
Is this really a hard problem or just annotated that way?+
The 43% acceptance rate confirms the difficulty. It's not a syntax trap. The challenge is identifying the transition logic and handling edge cases like users who became streamers before the data window or never made the jump. Schema complexity matters too.
What's the trick to solving this without a brute force approach?+
Use window functions or a subquery that identifies the first moment each user transitioned to streamer status. Don't iterate row by row. Aggregate cleanly and join on the exact transition timestamp, not just user ID.
How does this relate to other database interview problems at PhonePe?+
PhonePe works with user behavior and platform activity data. State-transition and temporal problems show up frequently because their product tracks user actions over time. This problem tests whether you understand time-ordered data correctly.
What's the most common failure point?+
Missing the order of operations. Candidates aggregate first, then try to identify transitions. You must order by timestamp first, identify the transition, then aggregate. Also watch for duplicate or overlapping rows in the source tables.
Can I solve this without window functions?+
Yes, but it's messier. A self-join or subquery approach works if you're careful about the GROUP BY logic and timing. Window functions (ROW_NUMBER, FIRST_VALUE, LAG) make the solution cleaner and faster, which matters under time pressure.
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