(TimescaleDB co-founder)

Thanks for the mention, and I completely agree :-)

Personally, there is a lot in this article that is misguided.

For example, it essentially defines "time-series database" as "metric store." As TimescaleDB users know, TimescaleDB handles a lot more than just metrics. In fact, we handle any of the data types that Postgres can handle, which I suspect is more than what Honeycomb's custom store supports.

  TSDBs are good at what they do, but high cardinality is not built into the design. The wrong tag (or simply having too many tags) leads to a combinatorial explosion of storage requirements.

This is a broad generalization. Some time-series databases are better at high cardinality than others. Also, what is "high-cardinality" - 100K? 1M? 10M? (We in fact are designed for _higher cardinalities_ than most other time-series databases [0])

  In contrast, our distributed column store optimizes for storing raw, high-cardinality data from which you can derive contextual traces. This design is flexible and performant enough that we can support metrics and tracing using the same backend. The same cannot be said of time series databases, though, which are hyper-specialized for a specific type of data. 

We just launched tracing and metrics support in the same backend - in Promscale, built on TimescaleDB [1]

I do commend the folks at Honeycomb for having a good product loved by some of my colleagues (at other companies). I also commend them for attempting to write an article aimed to educate. But I wish they had done more research - because without it, this article (IMO) ends up confusing more than educating.

For anyone curious on our definition of "time-series data" and "time-series databases": https://blog.timescale.com/blog/what-the-heck-is-time-series...

[0] https://blog.timescale.com/blog/what-is-high-cardinality-how...

[1] https://blog.timescale.com/blog/what-are-traces-and-how-sql-...