(Not affiliated with TimescaleDB, just trying to understand your critique)
TimescaleDB is a Postgres extension, which means they have to play PG's rules, which rather implies a complicated mesh of functions, views, and "internal-use-only" tables.
But once they're there, you can pretty much pretend they don't exist (until they break, of course, but this is true for everything in your software stack).
Is your complaint targeting the size of these scripts, or their contents? Because everything in there appears pretty reasonable to me.
My problem is what these scripts do. When working with timeseries you want to be able to filter on dimensions/tags/labels.
TimescaleDB doesn't support this out of the box, the reason being Postgres' limitations on multi column indexes when you have data types like jsonb.
What they have to do to work around this is to have a mapping table from dimensions/tags/labels which are stored in a jsonb field to integers which they then use to look the data up in the metrics table.
I would expect that a timeseries database is optimized for this type of lookup without hacks involving two tables.
(TimescaleDB engineer) Actually, the reason we break out the Jsonb like this is because json blobs are big and take up a lot of space and so we normalize it out. This is the same thing that InfluxDB or Prometheus do under the hood. We also do this under the hood for Timescale-Prometheus.
If you have a data model that is slightly more fixed than Prometheus, you'd create a column per tag and have a foreign-key to the labels table. That's a pretty simple schema which would avoid a whole lot of complexity in here. So if you are developing most apps this isn't a problem and filtering on dimensions/tags/labels isn't a problem. Again, this is only an issue of you have very dynamic user-defined tags, as Prometheus does. In which case you can just use Timescale-Prometheus.
I'll echo the sibling comment by the TDB engineer, having read and contributed to similar systems myself.
Most of these custom databases (Influx) use techniques similar to TDB's under the hood, in order to persist the information in an read/write optimized way; they just don't "expose" it as clearly to the end-user, because they aren't offering a relational database.
A critical difference between the two, is that custom databases don't benefit from the fine-tuned performance characteristics of Postgres. So even though they're doing the same thing, they'll generally be doing it slower.
Which kinda confirms the parent’s issues... InfluxDB is really easy to deploy and forget. With TimescaleDB you should be ready to know ins-and-outs of PG to secure and maintain correctly.
Sure, for scaling and high loads TDB might be good but InfluxDB is easier and suitable for most loads and maintainability.
> With TimescaleDB you should be ready to know ins-and-outs of PG to secure and maintain correctly.
Gotcha, this makes sense. To this, I'd pose the question: is the same not true for Influx? (i.e. With IFDB, you should be ready to know its ins and outs to secure and maintain it correctly). I guess I think about choosing a database like buying a house. I want it to be as good in X years as it is today, maybe better.
From this perspective, PG has been around for a long time, and will continue to be around for a long time. When it comes time to make tweaks to your 5 year old database system, it will be easier to find engineers with PG experience than Influx experience. Not to mention all of the security flaws that have been uncovered and solved by the PG community, that will need to be retrodden by InfluxDB etc.
Anyways, it's just an opinion, and it's good to have a diversity of them. FWIW, I think your perspective is totally valid. It's always interesting to find points where reasonable people differ.
Think if InfluxDB is "move-in ready" when buying a house. Sure you won't know the electrical and plumbing as well as you will a "fixer-uppper" in the long run, but you'll have a place to sleep a lot faster and easier.
I think it's a good analogy; very reasonable people come to different conclusions on this problem IRL too.
I prefer older houses (at least pre-80s). They have problems that are very predictable and minor. If they had major problems, they would show them quite clearly in 2020.
My friends prefer newer properties, but this is an act of faith, that the builders today have built something that won't have severe structural/electrical/plumbing problems in 20 years.
Of course, if you only plan on living some place for 3-5 years, none of this matters; you want to live where you're comfortable.
TimescaleDB is a Postgres extension, which means they have to play PG's rules, which rather implies a complicated mesh of functions, views, and "internal-use-only" tables.
But once they're there, you can pretty much pretend they don't exist (until they break, of course, but this is true for everything in your software stack).
Is your complaint targeting the size of these scripts, or their contents? Because everything in there appears pretty reasonable to me.