Comment by Alex-Programs | Hacker News Reader

Comment by Alex-Programs | Hacker News Reader

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I use Postgres via its Docker image on a VPS with some other containers.

That's probably a bit abnormal nowadays - I prefer simplicity with my deployment, and it can all fit on a single VPS with plenty of margin. It's not difficult to do, and I haven't had to touch that container since it was started.

I haven't upgraded Postgres from one version to another.

> From the MongoDB point of view, was it hard to get into Postgres

Nope, and I barely know SQL. You probably know more than me.

> What are your favorites features ?

Getting out of my way and being nice and fast. In retrospect I overengineered some aspects of my code around the assumption that postgres is far slower than it actually is.

I really don't use many of its features.

The ARM problems were just that MongoDB didn't like ARM to start with (iirc), and then when I got it running on my raspberry pi they didn't like the lack of SIMD instructions, and then it was a bit of a pain installing an older version that was barely supported anymore. It wasn't particularly difficult, just irritating.

Postgres just worked. Different ARM machine though, so it wasn't quite representative.

Ok so, what you meant by ARM is that MongoDB was not easy to use on ARM architecture machines.

In regard of benchmarks, few months ago, I've seen some benchmarks where Postgres obliterated MySQL, both for read and writes (basic ones), in terms of performances, with Node.js drivers tho' (probably matters too), I did not though that Mongo would be "faster", as it seems from your point of view. Tho' I assume that for a project of my size (tiny), any of the big general purpose database would be largely sufficient, in terms of benchmarks and just a big machines would be good.

Looking at Postgres doc, which is quite nice btw (I did not remember that), I think there is much more features that I can dream of. (More than MySQL!!) The pain point I used to have, trying it, was the "administration" stuff, I'll read more in the doc, probably it was just me that was a noob with it (skill issue), also I did not used docker too.

At the moment, I tried both Cloud things, such a Google cloud, and VPSs, I largely enjoyed more using VPS. Thought I've found Google clound run quite powerful and easy to use.

Speaking about what you said in your previous comments about transforming data beforehand; I've done a PoC for myself about a year ago of that project, where I made multiple scripts (background jobs) which would gather data from each sources, and I tried to normalize everything to my schemas and all the common points of them. It was annoying to update them over the courses of multiple days, because I did not thought everything well from the get go, which is one of the reason I consider documents (I used to put a JSON column where I put any data I did not thought beforehand), but it's eventually a skill issue from me, and now thinking about that, I guess I could just look at the most important sources (more than 10 sources generating data daily, with 2 really big and important ones) and take time to think my schemas properly. In that regards, do you put the raw data somewhere ? (Would you keep it even?) If yes, in the DB or in text form within a S3 like storage ?

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