djch 0.1.7

Static N+1 query detector for Django ORM – fast Rust-powered checker

django-check

Static N+1 query detection for Django (LSP-based)

django-check is a static analyzer and Language Server that detects N+1 query patterns in Django code before runtime. It inspects queryset construction and related-field access to warn when relations are accessed without proper prefetching (select_related, prefetch_related).

It works inside the editor or directly from CLI.

[!WARNING] This project is in active development and not yet production-ready.

• APIs are unstable
• Diagnostics may be incomplete or incorrect
• Expect breaking changes without notice

Why this exists

Django’s ORM makes it easy to accidentally introduce N+1 queries that:

• pass tests,
• look correct in code review,
• only show up under load.

Runtime tools (django-silk, nplusone) are focuesd in runtime optimiezation. django-check make static analysis before the runtime.

Key properties

• Compute the graph of the Models in the app
• Zero runtime overhead
• LSP-based diagnostics at edit time
• No code instrumentation required
• Works with any editor that supports LSP

What it detects (today)

• Iteration over QuerySets followed by related-field access

• Missing select_related / prefetch_related for:

  • ForeignKey
  • OneToOne
  • ManyToMany
  • Inheritance
  • Reverse relations (with related_name explicit or not)
  • Complex chained relations like model__relation__child_relation__depth
  • Prefetch usage: Prefetch("relation", queryset=Relation.objects.select_related("child")

Installation

pip install djch

Or with uv:

uv pip install djch

This installs the djch binary to your PATH.

CLI

Usage: djch <COMMAND>

Commands:
  server  Start as a Language Server (normally handled by the IDE)
  check   Analyze the current directory tree for N+1 queries
  help    Print this message or the help of the given subcommand(s)

Options:
  -h, --help     Print help (see more with '--help')
  -V, --version  Print version

Check from CLI using djch check. You will get an output like this:

app/foo/bar/views/tier1.py:210:22: [N+1] rp.ticker_benchmark
Potential N+1 query: accessing `rp.ticker_benchmark` inside loop

app/apps/crawler/tasks.py:48:17: [N+1] ticker.industry
Potential N+1 query: accessing `ticker.industry` inside loop

app/apps/crawler/views.py:62:20: [N+1] stream.streamer
Potential N+1 query: accessing `stream.streamer` inside loop

app/apps/foo/selectors/anointed.py:43:19: [N+1] anointed.pattern
Potential N+1 query: accessing `anointed.pattern` inside loop

Editor integration (LSP)

Neovim 0.11+ (native LSP)

Neovim 0.11 ships with a stable built-in LSP client.

Minimal setup:

-- lua/init.lua
vim.lsp.enable("djch")

-- lsp/djch.lua
return {
  cmd = { "djch", "server" },
  filetypes = { "python" },
  root_markers = { 'manage.py', 'pyproject.toml', '.git' }
}

This registers django-check as a first-class LSP server.

If you are already attaching multiple LSPs to Python buffers (e.g. Pyright), Neovim will merge diagnostics correctly.

VS Code (Extension)

You can install the VSCode extension from, or directly in VSCode Extensions Market Place.

https://marketplace.visualstudio.com/items?itemName=richardhapb.Django-Check

Examples

Problematic code

# N+1 query detected
users = User.objects.all()
profiles = [user.profile in user for users]  # N+1

Explanation:

• users is evaluated once
• user.profile triggers one query per iteration

Corrected code

users = User.objects.select_related("profile").all()
profiles = [user.profile in user for users]

---

Problematic code

users = User.objects.all()
for user in users:
    user.profile.bio # N+1

Explanation:

• users is evaluated once
• user.profile triggers one query per iteration

Corrected code

users = User.objects.select_related("profile").all()
for user in users:
    user.profile.bio

django-check will clear the diagnostic once the relation is prefetched.

How it works (high level)

1. Parse Python source into an AST
2. Identify Django model classes and relationships
3. Track QuerySet-producing expressions
4. Track iteration boundaries
5. Detect attribute access that implies ORM resolution
6. Verify whether the required relation is prefetched

Design philosophy

• Zero config
• Just works out of the box
• Editor feedback must be actionable, not noisy

Limitations (work in progress)

• Interprocedural analysis requires type hints on QuerySet parameters
• Limited understanding of:
  • annotate, aggregate
  • complex custom managers

Roadmap

• Custom queryset method summaries
• Templates integration

Contributing

This project lives at the intersection of:

• Python AST
• Django ORM semantics
• LSP protocol design

If you are interested in any of those, contributions are welcome.

Documentation contributions are welcome, the goal is make this tool easy to use.

License

MIT