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 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.

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-1.3.0.tar.gz (336.0 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-1.3.0-cp314-cp314-win_amd64.whl (2.4 MB view details)

Uploaded CPython 3.14Windows x86-64

yara_orm-1.3.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ x86-64

yara_orm-1.3.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ ARM64

yara_orm-1.3.0-cp314-cp314-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

yara_orm-1.3.0-cp313-cp313-win_amd64.whl (2.4 MB view details)

Uploaded CPython 3.13Windows x86-64

yara_orm-1.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

yara_orm-1.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ ARM64

yara_orm-1.3.0-cp313-cp313-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

yara_orm-1.3.0-cp312-cp312-win_amd64.whl (2.4 MB view details)

Uploaded CPython 3.12Windows x86-64

yara_orm-1.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

yara_orm-1.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ ARM64

yara_orm-1.3.0-cp312-cp312-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

yara_orm-1.3.0-cp311-cp311-win_amd64.whl (2.4 MB view details)

Uploaded CPython 3.11Windows x86-64

yara_orm-1.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

yara_orm-1.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

yara_orm-1.3.0-cp311-cp311-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

yara_orm-1.3.0-cp310-cp310-win_amd64.whl (2.4 MB view details)

Uploaded CPython 3.10Windows x86-64

yara_orm-1.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

yara_orm-1.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

yara_orm-1.3.0-cp310-cp310-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

yara_orm-1.3.0-cp39-cp39-win_amd64.whl (2.4 MB view details)

Uploaded CPython 3.9Windows x86-64

yara_orm-1.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

yara_orm-1.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

yara_orm-1.3.0-cp39-cp39-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

File details

Details for the file yara_orm-1.3.0.tar.gz.

File metadata

  • Download URL: yara_orm-1.3.0.tar.gz
  • Upload date:
  • Size: 336.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for yara_orm-1.3.0.tar.gz
Algorithm Hash digest
SHA256 8c5c478f6f1366337172f508ad163682fe1f9940cb52a45795990b183a0c168f
MD5 7b56686e65c14f531449b65b5984b620
BLAKE2b-256 0b73e4c970dcfd97086d518742fcf4b62a6afd09684570662350067aaa8c8dc7

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.3.0-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 2.4 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-1.3.0-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 c037059fd4039d689b4b94cfadb28214056070e357ed71418164502ca021ef30
MD5 9418981c2691effc92700d3b18529fd0
BLAKE2b-256 57caea35c60bdffb0ce5aec163b4f7f133d9f8be03432fafe54d568586993c29

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7b07269f759222d74f79c822a8448081328e33a9c09c7975076e2aa27f257cc8
MD5 7033c36bc64dea786ca6d70405990f09
BLAKE2b-256 8747734cca54f844cdd87266152c0e34b6b6a1906d1f404722d2878891f7a604

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8fcebbcf777574d73eee99c2b764d0f50e95c94308f81fd221db6366b7657da9
MD5 102b8c9c61dc5c08011b4ccf66e8e21e
BLAKE2b-256 58d955f95be64d88d78f3bb54ddc8fa7c31da59f86a558438ca823f36f411812

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4c0eb99d44f6acb5c10b1b191a7a8261882af41e0147fb154a9b8209544041cf
MD5 65f59bb3f9f058ddd1f0a56b3f1f81d8
BLAKE2b-256 e1caa7e99b18dc0967bbbc23c8e059c776a194cd3165b7152fe1ab96580807fe

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.3.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 2.4 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-1.3.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 4aca2de291f61382f39ddc125aad65bf14747cc5685a2a65901a7f72649f0dff
MD5 f02e705f6a40b7bc756bca1769a852ed
BLAKE2b-256 c90f7b99f21aeddc55ba6373630d4e48e83d203979509726f1379ea17398d849

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a8373480784d14606219d308a4da7472abe2f07e3d446aa01ed9da9f146f6e35
MD5 b8d48339d98ead909b038543cb59e888
BLAKE2b-256 16eb4d169aa9d9057ae9c9eac2dd74558a688cb5366298701238a596def16b3c

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 fd5d4f60fa1eea48d102d09dff00ab2e58ed6d7193f31f7865ece260a9c73af7
MD5 a4c5761ea93de1985369c1e200a9d278
BLAKE2b-256 16ea3ff8ceb30d3dca2b715b8a15267f190447825a15fce8c6a87def168df31f

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e82735ad55436b4d04677d7d2cc3395bae79b25107cc09de88f73fa03aa31999
MD5 b81a770019edd8278382c8da204812b7
BLAKE2b-256 9fae08e82cca5735e2e48baad8c6fda46f3da12c8d9c8116f5273e626a144483

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.3.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 2.4 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-1.3.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 6eb6df325d7a55c06a728de914f258488477fc1055cca1aea711a2c14406a764
MD5 0bada39f6c0e61586af19bf182aac9a6
BLAKE2b-256 234ac2b66ec5e87360263fb7eaf4ea4a69de15fdf8d59a3587d2a7c8b08e3d24

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 df22a847b10a74602d128da5ca021b7d581137c1a18b5f9e3d35f3ccc599c2d0
MD5 fa8acd5b228165766b9656fc1b65f5bb
BLAKE2b-256 69ba34eae3ddcaa91e628331d3df95cafadad6601d9f7dd400b6351d44334c95

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 fd79cff8a4779af9a12ceca424d7df38af867097d9eee893fdd253f8a43a3beb
MD5 326581cfe1504828610c4e416c6fae1c
BLAKE2b-256 21045e74436179df229a43d539de4115f8cc381b8dc028ded9291c5d3a86e5bf

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cee2ae24e59f2bd0bc834d9cc8f49d529a3424a9f2b3aee7e3460d5d71ed989b
MD5 085ed56e9460146f730da224a3f79159
BLAKE2b-256 167d44f63e7c9e7d5700c21301e6fa9e0a5112a3e4633d3a6937043f26a900bd

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.3.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 2.4 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-1.3.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 4ab527ae6397a6da8bf92fcc3295f678160fd4449a3d2138e60589039934a08c
MD5 24c4694c897274edd7a6b6c3b1269ddd
BLAKE2b-256 053ef4dbcee5dd32373c0dee4709bc11c8a44b6d1cd3761e2a593a1e7baf243a

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3b5f3616a1cc4149ecb679f092fd6a862f4defd0729041b956f3f957f90121e5
MD5 5abb5efabadfeff7309b9bd08d390422
BLAKE2b-256 3711aedc1772d74d108480b4d173258d654992f4a8135fcaa81782cf045c0247

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f98c008593a1c4dfbd7ededc65cce56e29084dfc238b0b3e00d15a375a3f9fce
MD5 ab6945526044b3c55ee4a735039edd06
BLAKE2b-256 c737a2bc821f4754cebe03279deb4e89443ec37c886f96bf3b5accb646343bb7

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 bb56ec75cc3ad740d15f0c2dc300f3d32fc76337cffc386d5d87bf9eb1d4efee
MD5 476d960d52c7047532c223f53be72daf
BLAKE2b-256 e0ff2282f08bb255991e120faea67fc5b17d17dfa966adc52289c22679a18d46

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.3.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 2.4 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-1.3.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 4c067da8c2c313d245cc7cc350d1893bd1c24b72eb9641c76137885cbcf5586d
MD5 af5a12b90abf43cebfb5cd772d4bd36f
BLAKE2b-256 6af427dd3c7d11f5368af5bb3e9b5cf6705d63e208ef3d9fde53ea034b7078d7

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b232bea10fd495ce8f53bf0044b152432aae2d71455b2823ecc2a6bc82918af8
MD5 3793a68148bdddac190499d4839d9f97
BLAKE2b-256 dd5707494b9bdfc7a12c314efd18e55116109569a5f4c3fb72414235453c49d9

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e65973af8d0e0ce01e4ce07670390b508cb074993b190af91a8c426d43fdec43
MD5 384c6001b7721c4533e8824384303ce3
BLAKE2b-256 122a14d275156150af100ef0816420010d2493022ca17b9bbac9303745b95f4a

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8f6dc36f883b671e332b00e82dcfcfb900280fde85437275e647fcba76436113
MD5 6b5d5a38901035cc3e787cb608565acd
BLAKE2b-256 7052c8d30fa02d1395c282f0acfe172a4b36cfbf5d2e99af4068fb561befded5

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: yara_orm-1.3.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 2.4 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-1.3.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 3c5fc82c543cbc57b87bcb1550e7ba81d91984776d381b04eca0759d27934b8f
MD5 0d82d489e375c3e72971525dd925f977
BLAKE2b-256 46aabcfcdd461a8c5d30fcac78bc99542222a5a67089a1b293d924c1c2186e8b

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6b6d848e9ec226e964d25a21e030a60d9278c6fbe49513e0b3d57cff0340d869
MD5 0d0846b9121861acf0071c0e9b1d3e1a
BLAKE2b-256 5d74ccb1245866926e33edab4160b84d37bd713c37a11d9abb8c75e079746901

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 3309f4c9bf2c1f24f86c4590ebf7a076850288884f565c592cd8cdb874e8978b
MD5 4776def6703671acd15036e740e33f55
BLAKE2b-256 ce23dd2cdaa20c093286537e127b2f8945f3fa0fa14bedaec88748000f38726a

See more details on using hashes here.

File details

Details for the file yara_orm-1.3.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yara_orm-1.3.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ba88c169f2210e2592679eab75810cd72d367a56ea984be26b9ec95a8e150745
MD5 644da8a7c4ba81d7257fe456cb072f6f
BLAKE2b-256 a3c5614a8f1cc21caede8b4b8e4b45ce077bc26844e326da2b7a67f151fa43f9

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