This is absolute key advice.

Another way to look at it is the functional core, imperative shell pattern.

Wrapping up your dict in a value object (dataclass or whatever that is in you language) early on means you handle the ugly stuff first. Parse don't validate. Resist the temptation of optional fields. Is there really anything you can do if the field is null? No, then don't make it optional. Let it crash early on. Clearly define you data.

If you have put your data in a neat value objects you know what is in it. You know the types. You know all required fields are there. You will be so much happier. No checking for null throughout the code, no checking for empty strings. You can just focus on the business logic.

Seriously so much suffering can be avoided by just following this pattern.

I'm a big fan of using Protobuf for the third-party API validation task. After some slightly finniky initial schema definition (helped by things like json-to-proto.github.io), I can be sure the data I'm consuming from an external API is strongly typed, and the functions included in Protobuf which convert JSON to a Proto message instance blows up by default if there's an unexpected field in the API data it's consuming.

I use it to parse and validate incoming webhook data in my Python AWS Lambda functions, then re-use the protobuf types when I later ship the webhook data to our Flutter-based frontend. Adding extensions to the protobuf fields gives me a nice, structured way to add flags and metadata to different fields in the webhook message. For example, I can add table & column names to the protobuf message fields, and have them automatically be populated from the DB with some simple helper functions. Avoids me needing to write many lines of code that look like:

MyProtoClass.field1 = DB.table.column1.val

MyProtoClass.field2 = DB.table.column2.val

Here’s an out-there take, but one I’ve held loosely for a long time and haven’t shed yet: dicts are not appropriate for what people mostly use them for, which is named access to member attributes.

dict is an implementation of a hash table. Hash table are designed for o(1) lookup of items. As such, they are arrays which are much bigger than the number of items they store, to allow hashing items into integers and sidestep collisions. They’re meant to act like an index that contains many records, not a single record.

A single record is more like a tuple, except you want named access instead of, title = movie[0], release_year = movie[1], etc. And Python had that, in NamedTuple, but it was kinda magical and no one used it (shoutout Raymond Hettinger).

Granted, this rant is pretty much the meme with the guy explaining something to a brick wall, in that dicts are so firmly entrenched as the "record" type of choice in Python (but not so in other languages: struct, case class, etc. and JSON doesn’t just deserialize to a weak type but I digress).

For better or for worse, Python doesn't do typing well. I don't disagree that I prefer well defined types, but if that is your desire then I think Python is perhaps not the correct choice of language.

Seems like the issue is less using dicts than not treating external APIs as input that needs to be sanitized.

I generally support this. When dealing with API endpoints especially I like to wrap them in a class that ends up being. I also like having nested data structures as their own class sometimes too. Depends on complexity & need of course.

    class GetThingResult
      def initialize(json)
        @json = json
      end
    
      # single thing
      def thing_id
        @json.dig('wrapper', 'metadata', 'id')
      end
    
      # multiple things
      def history
        @json['history'].map { |h| ThingHistory.new(h) }
      end
      ... two dozen more things
    end

Dicts can be a problem, but this particular example isn't that great, like in this diagram from the article:

  External API <--dict--> Ser/De <--model--> Business Logic

There's config for this in Pydantic, but it's not the default, and isn't for most ser/de frameworks (TypeScript is a notable exception here).

Closed enums have a similar tradeoff.

I've cleaned up code where input parameters came in a dict form. Absolute shit show.

- The only way to figure out which parameters are even possible was to search through the code for the uses of the dict.

- Default values were decided on the spot all over the place (input.getOrDefault(..)).

- Parameter names had to be typed out each time, so better be careful with correct spelling.

- Having a concise overview how the input is handled (sanitized) was practically impossible.

0/10 design decision, would not recommend.

Debatable. Here's a counter-point:

https://www.youtube.com/watch?v=aSEQfqNYNAc

But ok, it's less bad in Python since objects are dicts anyway and you don't need getters.

Big structs as params in rust have similar issues

FYI, posted in 2020, updated in 2021.

dicts are OK, because at least they do have a `key` and it does mean something.

un-annotated tuples and too many func params are cancer.

Less important in Elixir (where they are "maps") due to the immutable nature of them as well as the Struct type which is a structured map.

I think one really nice thing about Python is duck typing. Your interfaces are rarely asking for a dict as much as they’re asking for a dict-like. It’s pretty great how often you can worry about this kind of problem at the appropriate time (now, later, never) without much pain.

There’s useful ideas in this post but I’d be careful not to throw the baby out with the bath water. Dicts are right there. There’s dict literals and dict comprehensions. Reach for more specific dict-likes when it really matters.

It's a bit of an odd article because the second part kind of shows why dicts aren't a problem. You basically just need to apply the most old school of OO doctrines: "recipients of messages are responsible for how they interpret them", and that's exactly what the author advocates when he talks about treating dict data akin to data over the wire, which is correct.

If you're programming correctly and take encapsulation seriously, then whatever shape incoming data in a dict has isn't something you should take an issue with, you just need to make sure if what you care about is in it (or not) and handle that within your own context appropriately.

Rich Hickey once gave a talk about something like this talking about maps in Clojure and I think he made the analogy of the DHL truck stopping at your door. You don't care what every package in the truck is, you just care if your package is in there. If some other data changes, which data always does, that's not your concern, you should be decoupled from it. It's just equivalent to how we program networked applications. There are no global semantics or guarantees on the state of data, there can't be because the world isn't in sync or static, there is no global state. There's actually another Hickey-ism along the lines of "program on the inside the same way you program on the outside". Dicts are cool, just make sure that you're always responsible for what you do with one.

Lists and sets suffer the same drawbacks. If the advice is to not use any of the batteries included if the language, why are we using Python?

If you want an immutable mapping, why not use an enum?

Personally I find it is often helpful to keep Dicts in a BigBag ie:

BigBag<Dict>