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Comment by georgewfraser | Hacker News Reader

I would love to run a time-series benchmark against a good column store like Snowflake to see if purpose-built time series databases are actually faster. I have a sneaking suspicion the time scale databases are just reinventing the column store, and that an appropriate non-sabotaged benchmark would show this.

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> "timescale ... are just reinventing the column store"

Not reinventing but reimplementing it for Postgres, which didn't have serious OLAP capabilities before. Lots of "newsql" systems are combining OLTP and OLAP by starting at one side and adding the other.

So far Timescale has column-oriented compressed storage and scale-out partitioning, and they're working on matching the compute part.

the main selling point is developer experience I think, rather than building out a bunch of stuff on top of a more general purpose tool you use a specialized DB and save time. They also have some benchmarks against Clickhouse for example

I have just looked up those charts on Timescales website [1] and I am a bit surprised. Never extensively used any of those DBs, but I have seen their sources and must say that expected bigger gaps [2]. Also worth looking: the Taxi Rides Benchmark on Postgres vs Clickhouse [3].

[1]: https://www.timescale.com/blog/what-is-clickhouse-how-does-i... [2]: https://www.pradeepchhetri.xyz/clickhousevstimescaledb/ [3]: https://tech.marksblogg.com/benchmarks.html

There is a lot of value in having a columnar storage that is fully ANSI SQL and supports all of the goodies that you get in the Postgres ecosystem.

NoSQL databases with their half assed SQL grammar implementations are a real pain to use in real applications where they often have to be handled differently in code vs the RDBMS because either their syntax is slightly different or their connection stack is incompatible.

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