It's not like PostgreSQL hasn't been in development for close to 30 years, covering basically every use case imaginable just through millions of deployments.

In addition, SQL in itself is a proven technology. The reality is that most problems you might think about solving with specialized databases (Big Data TM etc) could probably easily be solved with your run-of-the-mill RDBMS anyway, if more than five minutes are spent on designing the schema. It's extremely versatile, despite just being one layer above key-value storage.

Depends.

If you want to fully embrace the vibe tables are difficult.

Even before LLMs, I was at a certain company that preferred MongoDB so we didn’t need migrations.

Sometimes you don’t care about data structure and you just want to toss something up there and worry about it later.

Postgres is the best answer if you have a solid team and you know what you’re doing.

If you want to ride solo and get something done fast, Firebase and its NoSQL cousins might be easier .

I really enjoy this comment.

> Postgres is the best answer if you have a solid team and you know what you’re doing.

Not every type of data simply fits into relational model.

Example: time series data.

So depending on your model - pick your poison.

But for relational models, there is hardly anything better than postgres now.

It makes me happy coz I always rooted for the project from earily 2000s.

Even for timeseries there is https://github.com/timescale/timescaledb. Haven't used it, just knew it existed.

It's very good. Postgres by itself can handle a very high volume of inserts (I did over 100,000 rows/s on very modest hardware). But timescale makes it easier to deal with that data. It's not strictly necessary but it's very time series friendly (good compression, good indexing and partitioning etc). Nothing a pg expert can't accomplish with a vanilla postgres but very, very handy.

I haven’t tried timescale, but I have found postgres with time-based partitions works very well for timeseries data. Unless you’ve got really heavy indexes, the insert speed is phenomenal, like you said, and you’ve got the freedom to split your partitions up into whatever size buckets makes the most sense for your ingestion and query patterns.

A really nice pattern has been to use change data capture and kafka to ship data off to clickhouse for long-term storage and analytics, which allows us to simply drop old partitions in postgres after some time.

I think timescale will compress them heavily on your schedule so if that's acceptable to your use case you might be able to do away with clickhouse. Hard to say of course, without knowing details around your insertion and query patterns, retention requirements and aggregations you need. But timescale can do a lot of that with pretty straightforward syntax.

I have used TimescaleDB in my last work place. We needed a easy way to store and visualize 500hz sensor data for few 10s of devices. We used it and Grafana to build a internal R&D tool and it worked way better than I imagined. Before I left I think the DB was using ~200GB on a compressed btrfs volume in DigitalOcean droplet and still performed fine for interactive Grafana usage.

Don't get me wrong, Postgres is awesome when things work.

But, for example I was working on a .net project and entity framework decided it couldn't migrate Postgres tables correctly.

I'm not a SQL person, at this point my options are to drop tables, and let .net recreate them or try and write my own migrations.

This just isn't an issue with Firebase. I can add all the fields I want directly on the client.

What situations do you encounter where you don't care about the structure of the data? The only ones I've ever encountered have been logging, where it's only purpose is to be manually text searchable, and something like OpenStreetMap where everything is just a key value store and the structure is loosely community defined.

As soon as you have a loosely defined object you can't access any specific keys which makes it useless for 99% of times you want to store and retrieve data.

You define the data schema client side.

That's the entire idea behind Firebase. It makes prototyping much faster. I don't know how well it scales, but it works for most smaller projects.

Wait until you hear about ORMs.

I still have to create tables. I still have to migrate tables, these are all things I don't need to worry about with firebase.

It all depends on what you need to actually do. The only real weakness of Firebase is the Google lock in.

You still have to worry about data migrations without a schema though. Unless you just never access old records.

Depends.

Let's say you have an object called box.

It has 3 properties, the id, it's name and it's weight.

4 months later you add a 4th property. Cost, odd records that don't have a cost just return null for that field.

PG requires a lot of expertise to keep running when you get to a billion rows or massive ingest. It can do it, but it doesn't just do it out of box running the defaults.

Hopefully at 1B records, you have a business model that allows you to spend some money on either hardware or talent to solve this problem.

Unfortunately, most places go with hardware first, so the problem grows larger and larger. When they do finally hire someone who knows how to address it, it’s a monstrous undertaking that dev teams are reluctant to do, because it’s a lengthy pause on feature development (so really, PMs are reluctant), and also future velocity is typically somewhat slower – turns out proper RDBMS data modeling is rigid, and doesn’t suffer fools.

There's a gist that shows up in these threads https://gist.github.com/cpursley/c8fb81fe8a7e5df038158bdfe0f...

But while digging that up it seems there is one with more colors: https://postgresforeverything.com/

And one for the AI crowd https://github.com/dannybellion/postgres-is-all-you-need#pos...

I hear MySQL can be better for some workloads?

Till about yesterday, MySQL was little more than a CSV file on wheels, and even current popularity is mostly driven by two decades of LAMP.

You can be sure that PostgreSQL will be applicable for any work load that MySQL can handle, but not necessarily the other way round, so if you actually have the freedom to make a choice, go with PostgreSQL.

In particular, because PostgreSQL has a lot more features, people imply that the other one, for the lack of those features and its associated complexity, must automatically be faster. Which isn't true, neither generally, nor in special cases, since the latter one can go either way - your particular query might just run 4x on PostgreSQL. There is also no universal approach to even benchmark performance, since every database and query will have completely different characteristics.

> Till about yesterday, MySQL was little more than a CSV file on wheels

That's utter nonsense. What are you even referring to here?

InnoDB is a rock-solid OLTP storage engine, and it's been MySQL's default since 2010. A very large percentage of the global economy relies on InnoDB, and has for quite some time.

<Not intending to start a flamewar...> MySQL shines for simple OLTP. My PostgreSQL friends all have "paged at 3am to vacuum the database" war stories. I like simple and dumb for transactional data and MySQL delivers.