> Building a distributed time-series database on PostgreSQL

Next order of business: Making mud pies.

PostgreSQL is geared towards transactional work. With time series, you basically just append data occasionally, and do analytics. PostgreSQL is terrible for analytics - its architecture is all wrong. 2 or 3 orders of magnitude slower than the state of the art if not more.

If you take a look at any of our benchmarks, you’ll see that this is not the case. PostgreSQL in fact can scale quite well for time-series analytics, if architected correctly.

But why don’t you just try out TimescaleDB and see for yourself?

Please link to those benchmarks, and we'll see. Also, a link to the relevant SIGMOD/VLDB/ICDE/DaMoN/ADMS/etc. submission arguing in favor of TimeScaleDB's design would also be appreciated.

On the linked-to article I only see references to irrelevant transactional DBMSes...

InfluxDB: https://blog.timescale.com/blog/what-is-high-cardinality-how... https://blog.timescale.com/blog/timescaledb-vs-influxdb-for-...

Cassandra: https://blog.timescale.com/blog/time-series-data-cassandra-v...

MongoDB: https://blog.timescale.com/blog/how-to-store-time-series-dat...