this post was submitted on 02 Mar 2025
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This isn't a sophisticated opinion or anything, but personally I find RDBMS to be a bad fit for how data is typically structured in your program. You will usually have an object, often with sub-objects all built up like a tree. If you want to load that into an SQL DB, you need to split it up, equip lots of its parts with IDs and then hope that you can reconstruct it when you take it back out.
On the other hand, JSON was directly designed for serializing programming objects. The chance of you being able to persist and load your object with hardly any structural changes is high.
Of course, this does have other downsides, like the data not being as flexible to access. Similarily, data in an RDBMS is very structured, whereas in many NoSQL databases, you can have individual entries with different fields than the rest.
So, that's perhaps a more general takeaway: SQL makes it hard to put something into the database, but easy to get it out. NoSQL often reverses this.
Right, RDBMS for object permanence is a pain. It’s meant as efficient data storage and retrieval. But I counter that a huge amount of data problems are of that kind, and using object permanence for general database applications seems very contrived. I’m imagining loading a huge amount of data to memory to filter the things you need, essentially rolling your own DBMS. Am I missing something?
Well, for use-cases where an SQL database works well, I would recommend using an SQL database. NoSQL generally tries to provide a better alternative for the use-cases where SQL is suboptimal.
For example, I'm currently building a build system with caching. I need the cache to be persistent on disk between builds, but I just load the cache into memory on startup and if I have a breaking change in the format, I can just wipe the whole cache. So, all the strengths of SQL are irrelevant and the pain points are still there. I mean, truth be told, I'm not using an actual NoSQL DB, but rather just writing a JSON file to disk, but it's still similar.
Another example is that at $DAYJOB, our last project involved making lots of recordings and training a machine learning model on that. The recordings had to be created early on, long before our software was stable and the data scientists who would work with that data, would write all kinds of transformation scripts anyways. In that case, again, I do not think an SQL database would've been the best choice, because we needed the flexibility to just push data into a heap and then later clean it up. If an older format would've really become unusable, we could've just left that data behind, rather than trying to constantly update all the data to the newest database schema.
Gotcha. Thanks!