(MemSQL co-founder here)

How can I not respond to that!

As far as I know we've only faced off against TimeScaleDB on one small account in the IoT space.

You can't really compare columnstore storage (MemSQL) to rowstore storage (Timescale) for scanning and filtering large amounts of data for analytics use cases (of which time series use cases are a subset). I think this fact is reasonably well established at this point (the idea was popularized by the CStore project a decade ago[1]). Even at the small end scanning compressed data in columnstore format is so much faster then rowstore [2] (the data fits nicely into CPU caches and is well suited for SIMD instructions)

I would be happy to compare public customer references with timescale though. MemSQL is well established in the fortune 100 at this point:

  - https://www.memsql.com/blog/real-time-analytics-at-uber-scale/
  - https://www.memsql.com/blog/pandora/
  - https://www.memsql.com/blog/pinterest-apache-spark-use-case/
  - https://www.memsql.com/releases/akamai-real-time-analytics/
  - https://www.memsql.com/blog/real-time-stream-processing-with-hadoop/
  - https://www.datanami.com/2018/05/14/how-disney-built-a-pipeline-for-streaming-analytics/

  [1]: http://db.csail.mit.edu/projects/cstore/vldb.pdf
  [2]: https://www.memsql.com/blog/memsql-processing-shatters-trillion-rows-per-second-barrier/