yara-orm 1.0.0

Fast async Python ORM with a Rust engine — Tortoise-style models, querysets, relations and migrations for PostgreSQL and SQLite

Yara ORM

A fast, async Python ORM with a Rust engine — Tortoise-style models, querysets, relations and migrations for PostgreSQL and SQLite.

📖 Documentation: vsdudakov.github.io/yara-orm

Yara ORM is a high-performance async ORM for Python that pairs the ergonomics of a Django/Tortoise-style API — models, querysets, relations, aggregation and migrations — with a hot path (connection pooling, parameter binding, row decoding) written in compiled Rust (PyO3 + tokio). It is a drop-in-feel alternative to Tortoise ORM and async SQLAlchemy: 2–9× faster than popular pure-Python ORMs on common operations, with first-class PostgreSQL and SQLite backends, full type hints, and 100% test coverage.

from yara_orm import Model, YaraOrm, fields

class User(Model):
    id = fields.IntField(pk=True)
    name = fields.CharField(max_length=120)

await YaraOrm.init("postgres://localhost/app")
await YaraOrm.generate_schemas()
await User.create(name="Ada")
print(await User.filter(name__icontains="ad").count())

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Highlights

• ⚡ Rust engine — pooling, binding and decoding in compiled code; the async bridge (PyO3 + tokio) keeps your event loop free.
• 🧩 Familiar API — Tortoise/Django-style models, lazy chainable querysets, Q objects, aggregation, prefetch_related, transactions, signals.
• 🗄️ Pluggable backends — PostgreSQL and SQLite today, selected by URL; a new database is one Rust trait + one Python dialect.
• 🚚 Migrations — operation-based, auto-generated, backend-portable (makemigrations / upgrade / downgrade).
• 🧪 Quality — fully typed, linted (ruff + ty) and 100% test coverage.

Installation

pip install yara-orm

Prebuilt wheels are published for Linux, macOS and Windows on CPython 3.9–3.14, so installation needs no Rust toolchain. (Installing the source distribution on an unsupported platform compiles the engine and requires a Rust toolchain — see Development.)

Quick start

import asyncio
from yara_orm import Model, YaraOrm, fields


class Tournament(Model):
    id = fields.IntField(pk=True)
    name = fields.CharField(max_length=100)
    created_at = fields.DatetimeField(auto_now_add=True)


class Event(Model):
    id = fields.IntField(pk=True)
    name = fields.CharField(max_length=100, index=True)
    tournament = fields.ForeignKeyField("Tournament", related_name="events")


async def main() -> None:
    await YaraOrm.init("postgres://localhost/app")   # or "sqlite:///app.db"
    await YaraOrm.generate_schemas()

    cup = await Tournament.create(name="World Cup")
    await Event.create(name="Final", tournament=cup)

    # Lazy, chainable queries
    finals = await Event.filter(name__icontains="fin").order_by("-id")
    count = await Event.filter(tournament=cup).count()

    # Relations
    async for event in cup.events:
        print(event.name, "→", (await event.tournament).name)

    await YaraOrm.close()


asyncio.run(main())

Querying

# Lookups: exact, not, gt/gte/lt/lte, in, isnull, contains/icontains,
# startswith/endswith (+ case-insensitive `i` variants)
await User.filter(age__gte=18, name__icontains="a").order_by("-age").limit(10)

# Complex boolean filters with Q
from yara_orm import Q
await User.filter(Q(name="Ada") | Q(age__lt=30)).exclude(active=False)

# Aggregation + group by
from yara_orm import Count, Sum
await Author.annotate(books=Count("books")).filter(books__gte=1)
await Book.annotate(total=Sum("rating")).group_by("author_id").values("author_id", "total")

# Construction-free projections
await User.all().values("id", "name")
await User.all().values_list("name", flat=True)

Model methods: create, bulk_create, get, get_or_none, filter, exclude, all, annotate, prefetch_related, raw; instances save, delete, fetch_related.

Relations

class Author(Model):
    name = fields.CharField(max_length=100)

class Book(Model):
    title = fields.CharField(max_length=200)
    author = fields.ForeignKeyField("Author", related_name="books")
    tags = fields.ManyToManyField("Tag", related_name="books")

book = await Book.create(title="Compilers", author=author)
await book.tags.add(tag1, tag2)        # m2m add / remove / clear

await book.author                       # forward FK (awaitable)
async for b in author.books: ...        # reverse manager
await Author.all().prefetch_related("books")   # no N+1

ForeignKeyField, OneToOneField, ManyToManyField, recursive self-FK, related_name, Prefetch(rel, queryset=...).

Transactions, signals & more

from yara_orm import in_transaction, atomic, pre_save, connections

async with in_transaction():            # commit on success, rollback on error
    await Account.create(name="A")
    async with in_transaction():        # nesting opens a savepoint
        await Account.create(name="B")  # rolls back independently on error

@atomic(isolation="SERIALIZABLE")       # isolation levels (PostgreSQL)
async def transfer(): ...

@pre_save(User)                          # lifecycle signals
async def on_save(sender, instance, using_db, update_fields): ...

await connections.get("default").execute("INSERT ...", [..])   # manual SQL

Also: enum fields (IntEnumField/CharEnumField), column/table comments (description=, Meta.table_description), and multi-database routing via a Router over multiple named connections.

Backends

Backends are selected by the connection URL; the abstraction is a single Rust trait (Backend) plus a Python BaseDialect subclass:

await YaraOrm.init("postgres://user@localhost/db")   # PostgreSQL (tokio-postgres)
await YaraOrm.init("sqlite:///path/to/app.db")        # SQLite (rusqlite)

The SQLite backend maps rich types (uuid/json/datetime/decimal) onto SQLite's storage classes and reconstructs them on read from the declared column type, so the model layer is identical across backends.

Migrations

A Django/Tortoise-style, operation-based migration system. Migrations are auto-generated from model changes and backend-portable — the same operations render to PostgreSQL or SQLite DDL at apply time. Applied migrations are tracked in an orm_migrations table.

# autodetect model changes -> migrations/0001_initial.py
python -m yara_orm --models myapp.models makemigrations --name initial

# preview SQL without running it (per the target dialect)
python -m yara_orm --db sqlite:///app.db --models myapp.models sqlmigrate 0001_initial

# apply / revert / inspect
python -m yara_orm --db postgres://localhost/app --models myapp.models upgrade
python -m yara_orm --db postgres://localhost/app --models myapp.models downgrade
python -m yara_orm --db postgres://localhost/app --models myapp.models history

Each migration is a class Migration(m.Migration) whose operations are built from live field objects: CreateModel, DeleteModel, AddField, RemoveField, AlterField, AddIndex, RemoveIndex, plus hand-written renames (RenameModel / RenameField / RenameIndex), constraints (AddConstraint / RemoveConstraint / RenameConstraint with UniqueConstraint / CheckConstraint) and RunSQL / RunPython for data migrations. makemigrations emits the idempotent analogs (CreateModelIfNotExists, AddFieldIfNotExists, …) and detects AlterField automatically. The same commands are available programmatically via yara_orm.MigrationManager.

Performance

Median of 5 runs, PostgreSQL 18, Python 3.12, 5000 rows — Yara ORM is fastest on every operation measured. Cells show Yara ORM's time and each competitor's slowdown factor (>1 means Yara ORM is faster). Full methodology in benchmarks/.

operation	yara-orm	vs Tortoise	vs SQLAlchemy	vs Pony
bulk_insert	11.0 ms	2.1×	6.2×	18.7×
single_insert	33.4 ms	2.5×	4.7×	1.8×
fetch_all	3.4 ms	4.7×	3.5×	8.9×
count	0.3 ms	2.1×	3.2×	1.7×
filter	2.2 ms	4.1×	10.0×	7.9×
get_by_pk	62.5 ms	3.1×	4.7×	1.3×
update	3.4 ms	1.1×	1.2×	36.5×
delete	0.7 ms	1.3×	1.6×	134.0×

Speed comes from the Rust hot path, positional row decoding (no per-row dict or column-name allocation), compiled-SQL + prepared-statement caching, and connection pooling. Run it yourself with make bench.

Architecture

┌─────────────────────────────────────────────┐
│ Python  (python/yara_orm) ................. │
│   Model / metaclass ....... schema + ORM API│
│   QuerySet ................ lazy SQL builder│
│   fields .................... abstract types│
│   dialects ................ per-DB SQL rules│
└───────────────┬─────────────────────────────┘
                │  sql + params  (PyO3 / asyncio bridge)
┌───────────────▼─────────────────────────────┐
│ Rust  (rust/src)  →  yara_orm._engine ..... │
│   Engine ...................... async facade│
│   Backend trait .............. pluggable DBs│
│     PgBackend ............... tokio-postgres│
│     SqliteBackend ................. rusqlite│
│   Value .................. Py⇆Rust⇆SQL types│
└─────────────────────────────────────────────┘

• Rust owns pooling (deadpool), binding, type conversion and decoding.
• Python owns the model layer and SQL generation.
• Adding a database = a new Backend impl + scheme match in rust/src/backend/mod.rs, plus a BaseDialect subclass in python/yara_orm/dialects.py. The model layer never changes.

Development

git clone https://github.com/vsdudakov/yara-orm
cd yara-orm
make dev        # create .venv313 and install dev tools (maturin, ruff, ty, pytest)
make build      # compile the Rust engine into the venv (maturin develop)
make lint       # ruff check + ruff format --check + ty
make test       # pytest against $DB (default postgres://localhost/orm_demo)
make cov        # tests with the 100% coverage gate
make bench      # 4-way benchmark (needs `make bench-setup` once; Python ≤ 3.12 for Pony)

Requires a Rust toolchain (rustup) and a local PostgreSQL for the Postgres tests; the SQLite tests are self-contained.

Contributing

Issues and pull requests are welcome. Please run make lint and make cov (both must be green — lint clean and 100% coverage) before opening a PR.

License

MIT © Yara ORM contributors