[mfreed]

This is a great observation. As you point out, this was designed for the workload patterns we typically see with time-series, events, and analytical data, where the query (& insert) patterns differ across time.

So I agree that it's good for cold storage, but it's a bit nuanced. For example, you rarely see small random queries to old historical data, but you do often see larger scans over historical data. And in those cases, the throughput you get from S3 is actually quite high (especially that we've engineered with with proper columnar compression and row group/columnar exclusion). Which is very different from a latency bounded workload where you have a lot of small random reads, which is much more common in CRUD-like workloads.

Also, with Timescale, you have the ability to build continuous aggregates (incrementally materialized views). So you can have the raw data (or even lower levels of rollups) that get tiered into S3, while the more frequently accessed rollups can remain in hot storage.

(Timescale co-founder)