yggdryl Python bindings

import os
from decimal import Decimal

import pyarrow as pa

from yggdryl import DataType, Field, MediaType, MimeType, Uri

price = DataType.decimal("18", 4)
clock = DataType.time("microseconds")
text = DataType(str)
field = Field("price", price, nullable=False)
field.set_table_name("bars")
field.set_parquet_field_id(17)
field.set_location("s3://warehouse/bars/data.arrow")
field.set_property("postgres", "type", "numeric(18,4)")
scalar = field.arrow_scalar(Decimal("12.5000"))
file = Uri.from_path(r"C:\data\prices.arrow")
encoded = Uri("https://example.test/prices.csv.gz")

assert str(price) == "decimal128(18,4)"
assert str(clock) == "time64(us)"
assert text == DataType("utf8")
assert field.to_arrow().name == "price"
assert field.parquet_field_id == 17
assert field["PARQUET:field_id"] == "17"
assert field.location.scheme == "s3"
assert field.get_property("postgres", "type") == "numeric(18,4)"
assert scalar == pa.scalar(Decimal("12.5000"), type=pa.decimal128(18, 4))
assert str(file) == "file:///C:/data/prices.arrow"
assert os.fspath(file) == "C:/data/prices.arrow"
assert encoded.media_type.base == MimeType("text/csv")
encoded.set_media_type(
    MediaType.from_parts(MimeType("application/json"), [MimeType("application/gzip")])
)
assert encoded.file_name == "prices.json.gz"

The package provides native Python views over Yggdryl's DataType, Field, MimeType, MediaType, Uri, Url, and Urn, plus dataclass-compatible records. Parsing, validation, Arrow conversion, ordering, stable hashing, and path normalization remain owned by Rust. Python 3.10 and PyArrow 18 are the minimum supported versions; 15 supplied the generic Arrow C-stream reader boundary, and 18 is where the run-end-encoded map and extension-type scalar paths became correct. DataType.arrow_scalar(value, *, safe=True) builds a Scalar of the projected physical type. Field.arrow_scalar adds top-level nullability enforcement and returns an already matching Scalar by identity; safe=False explicitly requests PyArrow's unsafe cast rules. Conversion from arbitrary Python objects into a PyArrow Scalar is Python-specific; native scalar validation and materialization are shared through the default-enabled core yggdryl::arrow runtime.

from yggdryl import Field, MediaType, MimeType

media = MediaType.from_parts(MimeType.CSV, [MimeType.GZIP, MimeType.ZSTD])
field = Field(
    "payload",
    "binary",
    metadata={"HTTPS:Content-Type": "text/csv; charset=utf-8"},
)
field.set_media_type(media)

assert field.mime_type == MimeType.CSV
assert field.content_encoding == "gzip, zstd"
assert field.get("HTTPS:CONTENT-TYPE") == "text/csv"

MimeType exposes the complete native known vocabulary as immutable class constants and accepts custom validated MIME names. MediaType() defaults to octet-stream, consumes iterable encodings once, and returns a detached tuple from encodings; it is mutable and intentionally unhashable. Field HTTP and HTTPS input shares canonical lowercase http:* Arrow metadata. Raw header properties retain parameters, while typed MIME/media, unsigned content length, and absolute HTTP Location accessors validate without a binding-side parser. Pair media updates are atomic and cache-aware.

Precise Arrow layout can stay beside the logical Python annotation. Reserved Annotated options work as (key, value) pairs or entries in one mapping:

from decimal import Decimal
from typing import Annotated

import pyarrow as pa
from yggdryl import Field

price = Field.from_pyhint(
    "price",
    Annotated[
        Decimal,
        ("arrow_type", pa.decimal128(9, 0)),
        {"nullable": False, "metadata": {"unit": "EUR"}, "id": 7},
    ],
)

assert price.to_arrow().type == pa.decimal128(9, 0)
assert price.parquet_field_id == 7

from yggdryl import Field, fields
from yggdryl.fields import Int32Field, ListField

trade_id: Int32Field = fields.int32("trade_id", nullable=False)
tags: ListField[str] = fields.list("tags", fields.utf8("item"))

assert type(trade_id) is Field
assert tags.data_type.kind == "list"
assert trade_id.show_diff(trade_id) == "✓ equal"

The categorized yggdryl.fields package covers every native datatype variant. Its aliases preserve kind/value information for static typing while factories return the same generic native Field. equals(..., with_metadata=False) can ignore metadata recursively; show_diffs returns readable UTF-8 difference lines and show_diff joins them.

from decimal import Decimal

from yggdryl.records import record

@record
class Order:
    order_id: int
    price: Decimal

order = Order(42, Decimal("12.50"))
payload = order.into_toml()  # UTF-8 bytes with a collision-safe envelope
assert Order.from_toml(payload) == order

yggdryl.json, yggdryl.toml, and yggdryl.yaml expose byte-first dumps/loads plus path and typed text/binary file-object dump/load. JSON Lines and YAML also expose document-stream dump_all/load_all; TOML is deliberately one document. String content uses the borrowed native text path; paths and named or explicitly formatted I/O redirect to the native reader/writer paths without staging a whole encoded document. A document carries shapes, never class names: a set reads back as a list and a uuid.UUID as its text, and a class is constructed only through an explicit target such as cls=.

import pyarrow as pa

from yggdryl import Record

Trade = Record.from_arrow_schema(
    pa.schema([
        pa.field("trade_id", pa.uint32(), nullable=False),
        pa.field("tags", pa.list_(pa.string()), nullable=False),
    ]),
    class_name="Trade",
    module=__name__,
)

records = tuple(Trade.from_dicts([
    {"trade_id": "1", "tags": ["new"]},
    {"trade_id": "2", "tags": ["filled"]},
]))
batch = Trade.into_arrow_record_batch(records)

assert tuple(Trade.from_arrow_record_batch(batch)) == records
assert Trade.into_arrow_schema().field("trade_id").type == pa.uint32()

Dynamic records import Arrow Fields/Schemas through native Field and DataType values, preserving exact physical widths and nested layout. from_arrow lazily accepts batches, tables, readers, C-stream exporters, and batch iterables; bounded output is available through into_arrow_record_batches and into_arrow_record_batch_reader.

DataType.cast_arrow_array and Field.cast_arrow_array use the native Arrow kernel plan; their cast_arrow_batch forms reconcile Struct columns by ASCII-case-insensitive name, target order, and Field null/default policy. yggdryl.Tabular is the open resource protocol, while the growable in-memory ArrowTable is convenient for tests and local batch work. TabularMixin derives RecordBatchReader, non-consuming Dataset getters, positional append/upsert, atomic overwrite, and cached Record conveniences from a concrete adapter's private cursor-read, snapshot, and overwrite-all hooks. See the Python tabular guide and the reproducible ArrowTable benchmark.

DataType(value) infers native wrappers, datatype expression strings, PyArrow datatypes, and Python annotations such as str or list[int]. DataType.decimal(precision, scale=0) also accepts integer-like objects and base-10 numeric strings, selecting Decimal128 through precision 38 and Decimal256 through precision 76. DataType.time(unit) accepts the shared native unit aliases and selects Time32 for seconds/milliseconds or Time64 for microseconds/nanoseconds. DataType.variant(fields) builds the canonical dense Union with sequential native type IDs; fields.variant adds the containing Field state, while explicit fields.union remains available for custom IDs or sparse layout.

Start with the Python guide and the records guide.

Development Status
- 3 - Alpha
Programming Language
- Python :: 3
- Rust
Typing
- Typed

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yggdryl 0.1.1

yggdryl Python bindings

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Algorithm	Hash digest
SHA256	`d50580366d81e869406df07a76bcf663592b1c6100a5134e5264467146e69dbb`
MD5	`1d7db28b5962be96484d12b676f512c2`
BLAKE2b-256	`9203e3fa8d0eda2c3a45b6ecc1824db3abd0fe426cf7c8b040cc39c3e0f622b5`

Algorithm	Hash digest
SHA256	`8591f48eefd19f2f448c05ce7afffb9db66045641e88103e524fce6e7734de28`
MD5	`41f719949ab1c022d45cd168fc0314ae`
BLAKE2b-256	`a2d25fd8aea0d1b0539b184d4e2d99b2aaef33f90e58271efb73a5e1ea2fe179`

Algorithm	Hash digest
SHA256	`5f00af05f70ecb813ad72a89ac13cb7069afeadbe97499fb150525230071d683`
MD5	`cd29157ea26e043b98f7d8ca10821674`
BLAKE2b-256	`a544c2cc1a4f031dfd8ab9d4b7de6771d4d901a4b46f859eb4a8282f984f46db`

Algorithm	Hash digest
SHA256	`94b91621f90411064bd1bc198b4ce7ce4a4684ca5b1ec3b01e40b221c9a17b43`
MD5	`d2a81b4c7b00a46d85c001089e5f4099`
BLAKE2b-256	`65dcbf14bb4dc17a89d0be0cc72866d1b9d8e9ccda6fffe87a6e575a39c856ff`

Algorithm	Hash digest
SHA256	`bf5cb40770a7407c76c535d5077a4de851ae2be92ceb1507b30a84bb933cb4d1`
MD5	`37d8fb16730a426ef493399c94195843`
BLAKE2b-256	`5eadac8bf79274dcda341882ade6cc47752ce38a6e763005bea4a4c908b0c848`

Algorithm	Hash digest
SHA256	`aaa0a1a85ba4bf49a85dc19143a156ad54746ba695e98a1c5b9fb1880336cfad`
MD5	`154ad253e7cbc22fb12f923b8537353f`
BLAKE2b-256	`5ba2c5c6d6f058f3630b322ca32af4767344df422a4a8b41b680401aa2d937c5`

Algorithm	Hash digest
SHA256	`1c39e5f98de0c46e1ba7d5518ecbc6fbe0537db93c0af0305109144282142399`
MD5	`448fc942d9de83667a6d308894003bce`
BLAKE2b-256	`b49fe7411c687ce0f22f68b729457f225e56f9c8f9c894379a3e7d50c42ab54d`

Algorithm	Hash digest
SHA256	`9fb4612b0bd9c95076e953cde9b986d54bd68a3253b7b9d6076cbe7b53a16376`
MD5	`6e2986cecb0e6d316fcf00a93007157d`
BLAKE2b-256	`042ac6c763acc0e5cdb9a872e5c385d2256e04307702322073db641aee5c673a`

Algorithm	Hash digest
SHA256	`2f95c6ee08172fb2355bf1a386db3949411cf88dac9a11fac8ecfa413ecc189e`
MD5	`4b66cad03e9f314ecdb060eb2967b1ed`
BLAKE2b-256	`87c8571003c0a7505b8d1acd11aa20a532de205f71784280c221666811150585`

Algorithm	Hash digest
SHA256	`23f5f4a63d8b03ce6ca10d1b52a6c2b1900df1162bfacaa83a8d0c569312ef19`
MD5	`ad9faa1dc63d80fb2708a5d5c4ddfde9`
BLAKE2b-256	`bc1a9a87e5d3efb7b5e8aebbd3b1a76474ac0cb1978f5b7d2df39bfc6ea59f4c`