In my opinion you're correct, it doesn't solve the problem of querying large sets of timeseries data over a long time span.
It doesn't pre-aggregate your data so if you're trying to get aggregates for a large data set, it's not fast.
It doesn't pre-aggregate your data so if you're trying to get aggregates for a large data set, it's not fast.
We also have more in the works for large datasets: eg scaling out storage across multiple machines as well as data tiering for lower storage costs.
Feel free to email me if you'd like more info on either: ajay (at) timescale (dot com)
How is TimescaleDB doing with compression by the way? Meaning if you compare your raw data and the disk usage, what's the ratio?
Also worth noting that TimescaleDB is far more memory-efficient than other time-series databases (eg InfluxDB [1]), and for some folks the memory cost savings is significant (b/c memory cost $$$ >> disk cost $).
[1] https://blog.timescale.com/timescaledb-vs-influxdb-for-time-...