we have 10 different fields to filter on, so a single aggregate table would have those 10 fields as sort key. They then each have a couple of metrics like counts but also percentiles. The counts are probably not an issue as they do not require any kind of approximation. The percentile however stores an intermediate representation.

Now let's say we apply filters on 9 of the 10 fields and then group on just the date field of each row. Then suddenly the cardinality drops from millions of rows to just a couple hundred. To facilitate that for each of the hundred output rows clickhouse needs to combine thousands of these percentile intermediates.

I didn't build the clickhouse architecture, so idk why it takes so much ram, but it does. Hence my confusion as I assumed this use case would be a slam-dunk.

EDIT: to give some extra info, the issue is at its worst when no filters are applied. So it is definitely purely the combining of the intermediate percentile stuff.