TypedDicts are better than you think

Original Article ↗ Hacker News Discussion ↗

Title wording and tone

  • Several dislike “better than you think” / “you” headlines as condescending or click‑baity.
  • Others argue readers understand it’s generic, find the meta‑complaints unproductive, or see it as normal copywriting.
  • Some note irony: provocative titles drive upvotes, but also provoke guideline‑violating meta‑threads.

TypedDict vs dataclasses / slots / Pydantic

  • Pro‑dataclass arguments: faster attribute access, lower memory with slots=True, nicer .attr syntax, and runtime attribute‑name errors.
  • Counterpoints: Python’s general slowness can dwarf L1/cache details; algorithmic choices matter more; __slots__ especially pays off with many tiny objects.
  • Some strongly recommend Pydantic (or msgspec) as “transformative” for modeling and validating data, but others note:
    • TypedDicts (especially total=False) are more ergonomic for modeling partial/patch data, including function kwargs (PEP 692).
    • Pydantic is heavier and slower; good at boundaries, not necessarily for everything.

Patch semantics, None, and modeling optionality

  • Discussion around representing HTTP PATCH‑style updates where a field can be:
    • Left unchanged
    • Set to a value
    • Explicitly set to None / “unset”
  • None alone can’t encode both “leave as is” and “remove”, so people propose:
    • Using sentinels or tagged unions (e.g. set/unset/ignore actions).
    • TypedDicts with optional keys to model “field omitted” vs “present and null”.

Static typing, enforcement, and tooling

  • One side: annotations “do nothing” without a runtime enforcer like Pydantic or decorators; Python happily ignores them.
  • Other side: static type checkers (mypy, Pyright) plus CI can give strong guarantees inside the codebase once external inputs are validated.
  • Disagreement over whether optional typing is “worst of both worlds” or a sweet spot:
    • Critics: no global guarantees, types can lie, external tools aren’t exhaustive.
    • Supporters: any static checking is better than none; hints improve editor support, docs, refactoring.

Static vs dynamic languages and Python’s type system

  • Many argue static types improve readability, correctness, and refactoring even in small scripts; others find them verbose, ugly, and slowing down experimentation.
  • Comparisons arise to Go, TypeScript, Java, C#, Rust, Scala, Haskell, etc., with mixed views:
    • Some think Python’s typing is weaker and fragmented (multiple checkers, subtle differences).
    • Others value its gradual, opt‑in nature and use hints primarily as lightweight metadata.

Real‑world TypedDict use

  • Several recount messy “dict‑everywhere” Python codebases where TypedDicts made it possible to reason about data flow without large refactors.
  • TypedDicts are seen as a low‑risk, drop‑in way to retrofit structure on string‑keyed dicts, whereas dataclasses or full refactors feel too invasive.