JSON data within a database is perfectly fine and has completely justified use cases. JSON is just a way to structure data. If it’s bespoke data or something that doesn’t need to be structured in a table, a JSON string can keep all that organized.
We use it for intake questionnaire data. It’s something that needs to be on file for record purposes, but it doesn’t need to be queried aside from simply being loaded with the rest of the record.
Edit: and just to add, even MS SQL/Azure SQL has the ability to both query and even index within a JSON object. Of course Postgres’ JSONB data type is far better suited for that.
While I understand your point, there’s a mistake that I see far too often in the industry. Using Relational DBs where the data model is better suited to other sorts of DBs. For example, JSON documents are better stored in document DBs like mongo. I realize that your use case doesn’t involve querying json - in which it can be simply stored as text. Similar mistakes are made for time series data, key-value data and directory type data.
I’m not particularly angry at such (ab)uses of RDB. But you’ll probably get better results with NoSQL DBs. Even in cases that involve multiple data models, you could combine multiple DB software to achieve the best results. Or even better, there are adaptors for RDBMS that make it behave like different types at the same time. For example, ferretdb makes it behave like mongodb, postgis for geographic db, etc.
Using Relational DBs where the data model is better suited to other sorts of DBs.
This is true if most or all of your data is such. But when you have only a few bits of data here and there, it’s still better to use the RDB.
For example, in a surveillance system (think Blue Iris, Zone Minder, or Shinobi) you want to use an RDB, but you’re going to have to store JSON data from alerts as well as other objects within the frame when alerts come in. Something like this:
{ "detection":{ "object":"person", "time":"2024-07-29 11:12:50.123", "camera":"LemmyCam", "coords": { "x":"23", "y":"100", "w":"50", "h":"75" } } }, "other_ojects":{ <repeat above format multipl times> } }While it’s possible to store this in a flat format in a table. The question is why would you want to. Postgres’ JSONB datatype will store the data as efficiently as anything else, while also making it queryable. This gives you the advantage of not having to rework the the table structure if you need to expand the type of data points used in the detection software.
It definitely isn’t a solution for most things, but it’s 100% valid to use.
There’s also the consideration that you just want to store JSON data as it’s generated by whatever source without translating it in any way. Just store the actual data in it’s “raw” form. This allows you to do that also.
Edit: just to add to the example JSON, the other advantage is that it allows a variable number of objects within the array without having to accommodate it in the table. I can’t count how many times I’ve seen tables with “extra1, extra2, extra3, extra4, …” because they knew there would be extra data at some point, but no idea what it would be.
Jsonb in postgres is fine, I’ve been using it for years. Much better than letting mongodb anywhere near the stack.
Postgres handles NoSQL better than many dedicated NoSQL database management systems. I kept telling another team to at least evaluate it for that purpose - but they knew better and now they are stuck with managing the MongoDB stack because they are the only ones that use it. Postgres is able to do everything they use out of the box. It just doesn’t sound as fancy and hip.
But then postgres is basically an OS at this point, enough to compete with emacs for meme potential. And I say that as a happy postgres user.
Wait until people learn about the possibility of putting a web server INSIDE of postgres :)
https://betterprogramming.pub/what-happens-if-you-put-http-server-inside-postgres-a1b259c2ce56
My principle dev asked if we could figure out how to invoke Lambda functions from within postgres trigger functions.
I was like, “Probably. But it’s like putting a diving board at the top of the Empire State building… doable, but a bad plan all around.”
Sounds like someone heard about containers through a bad game of telephone!
Classically, a lot of RDBMSen are. MySQL held back for the most part, though it’s not necessarily better for it.
PostgreSQL can even run WebAssembly (with an extension)
Both Oracle and Postgres have pretty good support for json in SQL.
and the plane in the picture is perfectly capable of transporting a plane. What’s your point?
JSON in the DB isn’t an antipattern. It is frequently used in absolutely terrible designs but it is not itself a bad thing.
I’m a data architect and I approve this message.
Carrying the body of a smaller plane in a larger plane isn’t an antipattern either. Airbus does this between body assembly and attaching the wings.
I think plane people call it a fusilage, because they’re weird and like French.
The alternative is not super exciting though. My experience with NoSQL has been pretty shit so far. Might change this year as the company I’m at has a perfect case for migrating to NoSQL but I’ve been waiting for over a year for things to move forward…
Also, I had a few cases where storing JSON was super appropriate : we had a form and we wanted to store the answers. It made no sense to create tables and shit, since the form itself could change over time! Having JSON was an elegant way to store the answers. Being able to actually query the JSON via Oracle SQL was like dark magic, and my instincts were all screaming at the obvious trap, but I was rather impressed by the ability.
There are valid reasons to do this, of course. But yeah it fits the image.
It’s normal to denormalize data in a relational database. Having a lot of joins can be expensive and non-performant. So it makes sense to use a common structure like JSON for storing the demoralized data. It’s concise, and still human readable and human writable.
Why should I spin up a NoSQL solution when 99% of my data is relational?
Having a lot of joins can be expensive and non-performant.
Only if you don’t know how to do indexing properly. Normalized data is more performant (less duplication of data, less memory and bandwidth is being used) if you know how to index.
It may have been true decades ago that denormalized tables were more performant, I don’t know. But today it’s far more common that the phrase “denormalized tables are more performant” is something that’s said by someone that sucks at indexing and/or is just being lazy.
But I do put JSON into tables sometimes when the data is going to be very inconsistent between different items and there’s no need to index any of the values in there. Like if different vendors provide different kinds of information about their products, I need to store it somewhere, so just serialize it and put it in there to be read by a program that has abstraction layers to deal with it. It’s never going to perform well if I do a query on it, but if all that’s needed is to display details on one item at a time, it’s fine.
At least it’s not XML.
SQL blows for hierarchical data though.
Want to fetch a page of posts AND their tags in normalized SQL? Either do a left join and repeat all the post values for every tag or do two round-trip queries and manually join them in code.
If you have the tags in a JSON blob on the post object, you just fetch and decide that.
If you only join on indexed columns and filter it down to a reasonable number of results it’s easily fast enough.
For true hierarchical structures there’s tricks. Like using an extra Path table, which consists of AncestorId, DescendentId and NumLevel.
If you have this structure:
A -> B -> C
Then you have:
A, A, 0
A, B, 1
A, C, 2
B, B, 0
B, C, 1
C, C, 0
That way you can easily find out all children below a node without any joins in simple queries.
The fact that you’d need to keep this structure in SQL and make sure it’s consistent and updated kinda proves my point.
It’s also not really relevant to my example, which involves a single level parent-child relationship of completely different models (posts and tags).
I’m no expert in JSON, but don’t you lose the ability to filter it before your application receives it all? If you had a reasonable amount of data then in SQL you can add WHERE clause and cut down what you get back so you could end up processing a lot less data than in your JSON example, even with the duplicated top table data. Plus if you’re sensible you can ensure you’re not bringing back more fields than you need.
In a traditional SQL database, yeah. In various document-oriented (NoSQL) databases, though, you can do that.
Document databases are just a big text field with additional index and metadata fields anyway.
