Skip to main content

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

Project description

Yara ORM

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

CI PyPI Python Coverage Docs License: MIT Sponsor

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

✍️ Deep dive: How the GIL, PyO3 & asyncio cooperate — how the Rust engine bridges Python's event loop without the GIL collapsing it.

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())

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. Coming from Tortoise? Most code moves across unchanged — see Migrating from Tortoise ORM.
  • 🗄️ 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/.

Yara ORM vs Tortoise, SQLAlchemy and Pony on PostgreSQL — latency per operation, log scale, lower is better

operation yara-orm vs Tortoise vs SQLAlchemy vs Pony
bulk_insert 18.5 ms 1.3× 3.9× 12.1×
single_insert 33.7 ms 2.5× 4.7× 1.8×
fetch_all 3.8 ms 4.6× 6.1× 9.4×
count 0.3 ms 1.7× 3.0× 1.4×
group_by 0.8 ms 1.4× 2.0× 2.9×
filter 2.3 ms 4.1× 3.6× 7.8×
get_by_pk 65.8 ms 3.1× 4.7× 1.3×
update 3.4 ms 1.1× 1.2× 35.0×
delete 0.7 ms 1.2× 1.5× 130.2×

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.

Sponsor

Yara ORM is MIT-licensed and developed in the open. If it saves your project time — or you'd like to support continued work on the Rust engine, backends and docs — please consider sponsoring on GitHub. Every bit helps and is hugely appreciated. ❤️

License

MIT © Yara ORM contributors

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

yara_orm-1.11.0.tar.gz (491.5 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

yara_orm-1.11.0-cp314-cp314-win_amd64.whl (3.3 MB view details)

Uploaded CPython 3.14Windows x86-64

yara_orm-1.11.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ x86-64

yara_orm-1.11.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (3.7 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ ARM64

yara_orm-1.11.0-cp314-cp314-macosx_11_0_arm64.whl (3.4 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

yara_orm-1.11.0-cp313-cp313-win_amd64.whl (3.3 MB view details)

Uploaded CPython 3.13Windows x86-64

yara_orm-1.11.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

yara_orm-1.11.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (3.7 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ ARM64

yara_orm-1.11.0-cp313-cp313-macosx_11_0_arm64.whl (3.4 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

yara_orm-1.11.0-cp312-cp312-win_amd64.whl (3.3 MB view details)

Uploaded CPython 3.12Windows x86-64

yara_orm-1.11.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

yara_orm-1.11.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (3.7 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ ARM64

yara_orm-1.11.0-cp312-cp312-macosx_11_0_arm64.whl (3.4 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

yara_orm-1.11.0-cp311-cp311-win_amd64.whl (3.3 MB view details)

Uploaded CPython 3.11Windows x86-64

yara_orm-1.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

yara_orm-1.11.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (3.7 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

yara_orm-1.11.0-cp311-cp311-macosx_11_0_arm64.whl (3.4 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

yara_orm-1.11.0-cp310-cp310-win_amd64.whl (3.3 MB view details)

Uploaded CPython 3.10Windows x86-64

yara_orm-1.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

yara_orm-1.11.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (3.7 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

yara_orm-1.11.0-cp310-cp310-macosx_11_0_arm64.whl (3.4 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

yara_orm-1.11.0-cp39-cp39-win_amd64.whl (3.3 MB view details)

Uploaded CPython 3.9Windows x86-64

yara_orm-1.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

yara_orm-1.11.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (3.7 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

yara_orm-1.11.0-cp39-cp39-macosx_11_0_arm64.whl (3.4 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

File details

Details for the file yara_orm-1.11.0.tar.gz.

File metadata

  • Download URL: yara_orm-1.11.0.tar.gz
  • Upload date:
  • Size: 491.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for yara_orm-1.11.0.tar.gz
Algorithm Hash digest
SHA256 075d1e47ae04fa107ef0d0abd026ee557e59abc85c8f33fa3e701cb5d3de545f
MD5 b1c65688c55f84894279d88a3aa886ff
BLAKE2b-256 4a197031d70d96d3b4977253c9220bb4b2bff26f5bab9a62c6d2f6dbffffc42f

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.11.0-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.14, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for yara_orm-1.11.0-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 d36c423b2c315cbc941f3847430b8fbacbc9ab2b28a200ebbd95c53afdb2454c
MD5 8cf25a6f30b9ae6b3f04575fbf7a50bd
BLAKE2b-256 bce4050c8173b5675bd5f32b06914fcf87cf8003b73c91ea7f71476f334d36fa

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c294c7fb89c51a8e14031c886a793bbb5a534dc6beb98d1457c55d180c90c38f
MD5 7a276f28f2d9611adc92b212c7dcacdf
BLAKE2b-256 e3f93e7d37ff58501879fb83e4027d60b4152dc678186b05b25f1943f2f25bce

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 63162d4b853dc9705a418b230889531c9066628a4df83e5ca346deadf50da613
MD5 6cde218e0ada74263a082de0a44c9e12
BLAKE2b-256 07ade50dc9a8d308aaed9e5ace63e55c8d594d364387e036d3bf41fd2d260926

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 31217992a21683810a1e1d892d4ff814ac04b7d4e7b0f10e3131b5f9a40f59fe
MD5 d3295311ea233f228ba449b9c0a77f0e
BLAKE2b-256 2281fdb339b97397fc7c4c8da0d9e8cf618eae94db552adfe1ec3aaa639738f3

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.11.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for yara_orm-1.11.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 cf37c609c24a5da8d391f44e2913d8d1420e581c459fd7f717dcd6549fd1b9f7
MD5 2316d471d5a1bb94f495ce2a0b5aaf5e
BLAKE2b-256 b570c483fa0fcdeb3947e3c45ebfa28a424a3f29295fe4d54e1cc9855c75e5f5

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c18c274e248caf7f74c9f6d862625c5c1914d4194dff9cb48ce4a5df45a1a89b
MD5 7872118dfe0c1865ac6125a2af96dbb7
BLAKE2b-256 82e85143425041797169aefacf2446b003eb7b012121fda5381a009dff8c424d

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 811dbc71780302cb2e05afcdf7d3b48caa16db5dea2119320e5c53dab8c5e9f3
MD5 98d4a52fc7ebd540a2091722727f5457
BLAKE2b-256 df0de3f5d81b93056a0c07263158b49778bde921e6e2f5c0d14562518c73c179

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 688de1b4e6e43b3d4d68bef9e43518007eaf297f7342edd9e7aabcc34d305046
MD5 5575d36882076d1cd2445c20b5b27a02
BLAKE2b-256 496492d29ce16aa0d638d00076fc28718220f674df2c71b1253f70afa8a67dc2

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.11.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for yara_orm-1.11.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 b33f4764e7980fb67fb3672d7c16b2db6b3aeecbba76a2430274fcfc97616252
MD5 a06d9f3e18cb6daf924e5912eb434d91
BLAKE2b-256 930b8dbef6fe913e5bf73cbb9374b64d349b8909773917626534136d14fe93f4

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 71f81dd751f75e7021dcbc810d8bf0d9a3a017de4d4017b1a371709883f64f40
MD5 901f0f007ab9f99b82b805a8c050896e
BLAKE2b-256 d02830af85d15b3b7ce06274ac6d7629b093d9c04d94d003a059b2e16b932c7a

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0746500a56eadbc2f4bf35b2a587ed27006d6fec2d9d9873b6e7f70a772362fc
MD5 e101ca424d350189c329ede0345df27f
BLAKE2b-256 7ac14b3a46c53f5083976d47299ce55b83377a7c6e1e0da94211ab5136518481

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b70f5a5dfd4aad1597f2373b946b85dc7374bf8a1d93180d1ead0fd324122089
MD5 7a5e4498e948d5d7706eda4395c80747
BLAKE2b-256 95bd412122051e7bedb27c4c8209d536036d9c6d92846928240604fcce18d7a2

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.11.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for yara_orm-1.11.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 edacc363cef96c9370ac6b65fef28556c2c7f9560533101a73d02d3d90b9dd76
MD5 38f927ce8799cfe5d8b6afcddddc4e6c
BLAKE2b-256 7eaf76213e3f344f118cb222285f2b2bef6ef5908006714db13a6f1c685aee51

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 db9b9681e8890bb9893f610bff93720c1adeaac4fa4ad9db1c3b1cf53dcd9568
MD5 cfe8a90b1eafb638ce36732635e1e802
BLAKE2b-256 00e6b2eb8f3d0b16bfce4e2b919ba65a2033505ea9bc714653811d13f59d6015

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 9e1dfb1e977c6601c2d5b47ec7f989eafa8786443b6d6d82c8c0234aebca8fae
MD5 3fec7b7203abe43de6aa35e48a15cbfe
BLAKE2b-256 c2c446a2025b07f0d7779d4afaa4d9c28d89b96b5e7b7db5edfbadfdab6c77d5

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 729a66e1daa0758b411b0fa69bec1e5513f3a59b540cfbccc9bb845221e75906
MD5 03e37b66247314435c9a26443e2a228f
BLAKE2b-256 20ba2b889cb5b1ad2a2ad737d1a556c155a545fc505b3757885b187fa7752b62

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.11.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for yara_orm-1.11.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 7e0801458a2ad1913aa593f1a9b63023597e1fd4f9163a63397e75267b9375dc
MD5 fe7b06ea29fdf217fd860e7f82b60d24
BLAKE2b-256 e39045376fa8046fa9dea9326b8fff8fe944d9bd0d039f39fb32effd353c5ea0

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 941b041c794db28a2cfc4fe45013df0bfab541ae80846ec14d3c2a1d65b1a928
MD5 321b7fe6ba71a49b5947207f904ed666
BLAKE2b-256 282ec3d1cf71dc7d2f8add4e2b3f34c8a12e93ef04b341661cc5e21827ed05be

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5121ecc8074f0faf2a4607640f74024eeacb3661e1233392a73f3a6318292ba4
MD5 9d624e11170795e7700ad6a08e65786c
BLAKE2b-256 46113b9d95e220a6554b7e03651644ea0b3585b65ca911bdf8468086d24c3a3e

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a78f053f032856e7e8a329032b74033c52e130f971f3387bb789464568e90570
MD5 206c126b2f987ffd74cf4b95dc182246
BLAKE2b-256 119e2c5e92edd6df1265cf25d4fce898704ecc3725e2e16bca05ebbf4bd0d5f4

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.11.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for yara_orm-1.11.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 8540defea84adac10c6191068a7f2cedb5dd24bba086dc7e165fc01540b4250d
MD5 e2ae8ffaa4acf28a8c623f7d3cf597ed
BLAKE2b-256 6e0c54c0656f5a357e5db7e0e1a73a7d4c696c057a316e9b2e1df71e97c6ffa3

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 54a6d8eec8ced11947b806ad0f74e14915089211edea323e50d3c8d3d1f244a4
MD5 b88f6c147f66868e1f04c47008ce62bc
BLAKE2b-256 19bbb3451a202659941666f74d506a6bd4575ea11209bf5e5b0c56d56db66527

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 59c729562994c2c10defc5631c64cd5e2d2c83cb75d1fe219212c3dc9680ce6f
MD5 cb1e7f1738fa6cbb4f0704514479d107
BLAKE2b-256 e609a8f9a8852985d35fd39dc873a660dc78265ca0f0e8a361acb6f91e695f0a

See more details on using hashes here.

File details

Details for the file yara_orm-1.11.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.11.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7ad7e3f3a87a53e7ca5a79c04563d365e8e86e01f84889a56dc70558f607c5ff
MD5 59d412b75cbac06aef01f18204bbfc13
BLAKE2b-256 f99b4630878496f909e9b512d4f7827a0b575029b19e780567335bdff558d550

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page