I think these are good points about cstore_fdw and real-time analysis (although we don't have personal experience with this). The usual thing that prevents ind

I think these are good points about cstore_fdw and real-time analysis (although we don't have personal experience with this). The usual thing that prevents indexes from scaling with large data is that inserts slow down as soon as the index BTrees can't fit into memory. TimescaleDB gets around this problem by auto-sizing of tables: we start new tables as old tables (and their indexes) grow so large that they can no longer fit in memory. This allows us to have tables that are not so big that inserts become slow but big enough that queries don't have to touch many tables (and are thus efficient). However, as data sizes grow, you may want to convert data to column-stores to save disk-space though, as you allude to. We are looking at how best to do this and the best "archive" format to use. Stay tuned.