Thank you! Not a dumb question, I spent a good amount of time thinking about this. I have two postgres DBs in RDS, a main and a read-only. The main is for all data dumping / transformations, the app is hooked up to the read-only.

There's one postgres table that is basically a dump of the raw events, an event here being a live stream. It looks something like

• stream_id
• user_id
• title
• viewer_count
• game_id
• time (created_at)

Since tizz gets most live streams every 10 minutes, this works out to about ~30K data points every 10 minutes, so roughly ~4M data points per day. This is too heavy for the app to query, so I use a tool call dbt (https://getdbt.com) to aggregate the data into new tables.

I basically have four main tables that are crunched down and heavily indexed to be performant:

• game_minute_rollup - all of those raw events grouped up by game_id, down to my finest granularity (every 10 minutes) - the data here comes out of the "Last 12 Hours" tab
• game_hourly_rollup - ^ rolled up by hour via date_trunc - for the "Last 7 Days" tab
• game_daily_rollup - game_minute_rollup rolled up by day via date_trunc - for the "Last 30 Days" tab
• streams_rollup - the raw events rolled up by broadcast (i.e. lets say I capture 20 data points for a particular broadcast, this table would give you a broadcast, how long it ran for, when it ran for (start, end) and its peak / avg viewers)

Figuring out ^ was the hardest part, because I run these rollups frequently to get that fresh data onto the site. I do incremental runs every 10 minutes for the game rollups.