`yggdrasil.arrow.tabular`¶

tabular ¶

In-memory :class:Tabular holding Arrow record batches.

Hot path is fully in-memory: reads yield held batches as-is and writes mutate the held batch list in place subject to options.mode (AUTO / OVERWRITE / TRUNCATE → replace, APPEND → append, IGNORE → no-op when non-empty). Use this when you want a :class:Tabular over Arrow data you already have on the driver and don't want the IPC serialization round-trip.

Auto-spill via :class:`ArrowIPCFile`¶

Same shape as :class:yggdrasil.io.holder.IO: when the in-memory footprint crosses spill_bytes (default 128 MiB), the holder spills the current in-memory tail to a fresh part file inside a per-holder spill folder under tempfile.gettempdir(). Spills are append-only — each consolidation writes only the new tail, so an ingest-heavy workload pays O(tail) per spill instead of O(total). Each part is mmap'd separately, so reads remain zero-copy from the OS page cache and the live state is just a list of :class:pa.Table chunks (concat'd on demand inside the C++ runtime).

Layout::

{spill_dir}/                             # tmp-{start}-{end}-{seed}/
    part-000000-{seed}.arrow             # Arrow IPC file (one per spill)
    part-000001-{seed}.arrow
    ...

The folder name carries the same tmp-{start}-{end}-{seed} prefix the existing janitor convention expects, so any cross- process sweeper that reaps stale spill state finds the folder by name. Cleanup is one :func:shutil.rmtree — no per-file unlink loops, no half-deleted state on partial failure.

Writes go through :class:yggdrasil.io.arrow_ipc_file.ArrowIPCFile over a :class:yggdrasil.io.path.local_path.LocalPath, so the spill picks up the same OSFile streaming, codec knob, and legacy-format toggle the format leaf already manages. Spill compression defaults to None because the spill is throwaway local cache, where the codec overhead would hurt re-read latency without buying anything we'd keep. Override per-instance via spill_compression= when on-disk size matters more than read-back speed.

Skip-when-cached: if a consolidate is requested but the in-memory tail is empty, the call short-circuits with no I/O — the spill state is already on disk and there's nothing new to flush.

Flip the spill threshold off with spill_bytes=0 (or None). Pass an explicit spill_path= to use a caller-owned folder; the caller's folder is left intact on :meth:unpersist / :meth:_release (mirrors the IO "external spill path" branch — we still mint our own part files under it).

What we ingest¶

:meth:_ingest accepts the shapes a real caller actually has on hand without forcing a manual conversion to Arrow:

:class:pyarrow.Table / :class:pyarrow.RecordBatch / :class:pyarrow.RecordBatchReader / :class:pyarrow.ChunkedArray
another :class:Tabular (drained as an Arrow batch stream)
polars :class:DataFrame / :class:LazyFrame (LazyFrame collects on ingest — the holder is in-memory by design)
pandas :class:DataFrame
pyspark :class:DataFrame (driver-side via toArrow on Spark 4+, toPandas otherwise)
list[dict] rows / dict[str, list] columns
any iterable yielding the above
multiple positional sources: ArrowTabular(t1, t2, t3) is equivalent to ingesting each in order.

That keeps the most common conversion glue on this side of the API instead of every caller writing the same five-line isinstance ladder.

ArrowTabular ¶

ArrowTabular(
    data: ArrowSource = None,
    *more: ArrowSource,
    schema: Optional[StructField] = None,
    spill_bytes: Optional[int] = _DEFAULT_SPILL_BYTES,
    spill_ttl: int = _DEFAULT_SPILL_TTL,
    spill_path: "Any | None" = None,
    spill_compression: "str | None" = None,
    **kwargs: Any
)

Bases: Tabular[CastOptions]

In-memory Arrow batch holder with auto-spill to local IPC.

Schema is tracked separately so an empty buffer still answers :meth:collect_schema correctly when one was supplied at construction (or carried over from a write that was later overwritten).

State machine¶

Three states the holder can be in at any time:

All in-memory. _spilled_tables empty; _batches holds every record batch. The default for small payloads.
Spilled, empty tail. _spilled_tables holds one or more mmap-backed tables (one per spill part); _batches is empty. Reads stream from the parts in order.
Spilled, non-empty tail. Both sides populated. Reads concat the parts with the in-memory tail. Crossing the threshold again writes the tail as a new part file — previous parts stay untouched (append-only spill).

batches `property` ¶

batches: list[RecordBatch]

Defensive copy of every held batch — spilled + in-memory.

spilled `property` ¶

spilled: bool

Whether any data is currently mmap-backed by an IPC file.

spill_dir `property` ¶

spill_dir: 'pathlib.Path | None'

Folder under which spill part files are minted, or None.

spill_parts `property` ¶

spill_parts: 'list[pathlib.Path]'

Defensive copy of the spill part file list (oldest first).

spill_bytes `property` `writable` ¶

spill_bytes: int

Current spill threshold in bytes (0 disables auto-spill).

opened `property` ¶

opened: bool

True iff :meth:_acquire has run and :meth:_release hasn't.

closed `property` ¶

closed: bool

Inverse of :attr:opened.

unpersist ¶

unpersist() -> None

Drop in-memory + spilled state and remove the owned spill folder.

open ¶

open() -> 'Disposable'

Acquire the resource and cascade into owned children.

Order:

Run our own :meth:_acquire (subclass body).
Flip :attr:opened to True and mark _self_opened.
For each owned child, in registration order:
If the child is already opened, just :meth:_claim it. It stays self-opened — the existing self-open is what keeps it alive after we let go.
Otherwise, call :meth:open on the child (which recursively cascades into ITS owned children), then clear the child's _self_opened flag so the child knows its open is parent-driven, then :meth:_claim it. Without that flag clear, the eventual :meth:_unclaim would refuse to close — it would see "I'm self-opened, someone explicitly opened me, leave me alone."

Both branches record the child in our per-frame scratch list so :meth:_release knows what to unclaim.

Transactional rollback: if any child's open or claim raises, we walk back through the children we already touched (in reverse), unclaim each, then call our own :meth:_release with committed=False and re-raise the original exception. From the caller's view, the open atomically either succeeded with the whole graph live, or failed with nothing changed.

Not reentrant: raises :class:RuntimeError if already opened. Nesting is expressed via with self: blocks, not via paired :meth:open calls.

commit ¶

commit()

Commit current state

rollback ¶

rollback()

Rollback current state

close ¶

close(force: bool = False) -> None

Drop the schema cache and forward to any cooperative close.

Tabular itself has no resources to release — the schema cache is the only state it owns. Subclasses that mix Tabular with a lifecycle (Disposable-derived IO, holders, …) inherit this hook through cooperative super().close(); pure Tabular subclasses without a lifecycle peer get a harmless no-op forward.

mark_dirty ¶

mark_dirty() -> None

Signal pending mutations — commit on next clean :meth:close.

for_scheme `classmethod` ¶

for_scheme(scheme: Any) -> 'type[URLBased]'

Return the :class:URLBased subclass registered for scheme.

Lazy: if no subclass is registered yet, this routes through :meth:Scheme.path_class which imports the backend module on demand (firing :meth:__init_subclass__ as a side effect).

Raises :class:ValueError for an unknown scheme and :class:ImportError when the backend's optional dependencies aren't installed.

dispatch `classmethod` ¶

dispatch(url: Any, **kwargs: Any) -> 'URLBased'

Build the right :class:URLBased subclass from url.

Looks up the subclass via :meth:for_scheme, then delegates to that subclass's :meth:from_url. Used as the cross-cutting entry point when the caller has a URL but doesn't know (or care) which concrete class owns its scheme.

URL.from_(url).scheme drives the lookup; an empty scheme falls back to the file:// handler so bare paths work.

to_singleton ¶

to_singleton(ttl: Any = ...) -> 'Singleton'

Promote this instance into the per-class _INSTANCES cache.

Hot listing paths (iterdir / _ls / glob) build children with singleton_ttl=False so the bounded cache doesn't fill up with thousands of short-lived entries. When a caller decides one of those children is worth keeping around (handing it to a long-running worker, returning it from an API), :meth:to_singleton registers self into the cache so the next constructor call with the same key collapses to the same instance.

ttl defaults to the subclass's _SINGLETON_TTL (... = no caching, None = process lifetime, or a seconds count). When a different instance is already cached under this key, that pre-existing one wins and is returned unchanged — the cache is the source of truth.

invalidate_singleton ¶

invalidate_singleton(remove_global: bool = True) -> None

Pop self from the per-class _INSTANCES cache.

Mutating ops on a Singleton-cached object (writes, deletes, schema invalidations on a Databricks table, put_object on an :class:S3Path) want to make sure the next caller asking for the same key gets a fresh build rather than collapsing onto this stale handle — that's what remove_global=True (the default) does. The pop is :meth:identity-guarded: only an entry that still points at self is removed, so a concurrent re-construction that already raced past this thread is left alone.

remove_global=False is a no-op. The keyword exists so subclass invalidators (invalidate_singleton, _invalidate_entity_tag_cache, …) can offer the same switch without branching at the call site.

matches_static ¶

matches_static(
    predicate: "Predicate", *, free_cols: "tuple[str, ...] | None" = None
) -> bool

True iff predicate could match any row given :attr:static_values. Conservative on undecidables (column not in static values, predicate evaluator failure) so the caller still reads.

Builds a one-row pyarrow Table from the predicate's free columns that we have static values for, then evaluates the predicate against it — generalises the partition-only prune so any aggregator (folder read, future warehouse file skip) reuses the one helper.

free_cols lets a caller that's about to prune the same predicate against N children precompute the free-column tuple once and reuse it — :func:free_columns walks the AST every call, so on a 64-OR predicate (the cache batch lookup shape) the saving is N-1 full walks per iter_children loop. Default None keeps the call site short for one-off prune checks.

from_ `classmethod` ¶

from_(
    obj: Any,
    *,
    media_type: "MediaType | MimeType | str | None" = None,
    default: Any = ...,
    as_folder: bool = False,
    **kwargs: Any
) -> "Tabular | None"

Coerce obj into a :class:Tabular.

Routes:

None — returns default (None when default=None).
:class:Tabular — returned as-is. When as_folder is True and obj is a local :class:Path, wraps it in a :class:Folder.
str / :class:os.PathLike — coerced via :class:Path.from_. When as_folder is True, wraps in :class:Folder.
File-like objects — drained into :class:Memory; media_type required.

Falls back to default on unrecognised shapes when supplied; otherwise raises :class:TypeError.

options_class `classmethod` ¶

options_class() -> 'type[O]'

The :class:CastOptions subclass this implementer consumes.

Default :class:CastOptions. Format-specific leaves with their own knobs (Parquet compression, CSV delimiter, …) override.

check_options `classmethod` ¶

check_options(
    options: "O | None" = None, overrides: "dict | None" = None, **kwargs: Any
) -> O

Validate and merge caller kwargs into a resolved options.

Canonical pattern: a public method passes overrides=locals() and the ...-defaulted entries are stripped, the rest merged.

cleanup ¶

cleanup(wait: 'Any' = False) -> int

Garbage-collect stale state on this backend.

Default no-op (returns 0) — single-file leaves and warehouse-backed tables don't have a sweep concept the client owns. Folder-shaped subclasses override to unlink stale part-* files, throttled by TTL.

wait controls sync vs async dispatch on backends that support it: a truthy :class:yggdrasil.dataclasses.waiting.WaitingConfig (or True / a positive timeout) blocks until the sweep finishes; a falsy value (the default) hands the work off to a background thread. Backends without an async path treat both the same.

Returns the number of files / rows removed when known; 0 for fire-and-forget async dispatch or a no-op backend.

optimize ¶

optimize(byte_size: 'int | None' = None, **kwargs: Any) -> int

Repartition / compact this Tabular's storage.

Default implementation is a no-op and returns 0 — single-file leaves (parquet, csv, arrow IPC, …) don't have a compaction concept. Aggregator subclasses (:class:Folder) override this to walk their child leaves and bin-pack small part files into bundles near byte_size. Files already close to the target size are left alone so a repeated call is cheap.

byte_size=None keeps the legacy "collapse every leaf with more than one part into a single file" behavior, which is what the local-cache compaction loop in :class:Session expects. Any extra keyword arguments are accepted and ignored so upstream callers can pass forward-compatible knobs without the base raising.

delete ¶

delete(
    predicate: "PredicateLike" = None,
    *,
    wait: "WaitingConfigArg" = True,
    missing_ok: bool = False,
    delete_staging: bool = True,
    **kwargs: Any
) -> "Table"

Delete rows matching predicate; return this tabular.

predicate is a :class:Predicate from :mod:yggdrasil.execution.expr or a SQL string that parses into one ("id IN (1,2,3)", "price > 100 AND region = 'EU'"). None means "no filter" — every row is removed (DELETE FROM t with no WHERE).

wait / missing_ok / delete_staging are honoured by resource-backed subclasses (e.g. :class:yggdrasil.databricks.table.table.Table, which drops the table asset); the generic row-rewrite path ignores them. Any extra **kwargs (e.g. options=DeltaOptions(...)) flow through to :meth:_delete.

The default implementation reads every batch, drops rows the predicate accepts, and rewrites the leaf with the survivors. Aggregator subclasses (:class:yggdrasil.path.folder.Folder) override to walk children, prune subtrees whose partition bounds make the predicate trivially false, and only rewrite the leaves that actually hold matched rows.

collect_schema ¶

collect_schema(options: 'O | None' = None, **kwargs: Any) -> Schema

Return this Tabular's :class:Schema, caching the first hit.

The cache slot is :attr:_schema_cache; on first call this method stamps the resolved schema into it so subsequent collect_schema calls short-circuit. Writers overwrite the slot via :meth:_persist_schema; lifecycle hooks clear it via :meth:_unpersist_schema.

count ¶

count(options: 'O | None' = None, **kwargs: Any) -> int

Return the number of rows in this tabular.

scan_arrow_batches ¶

scan_arrow_batches(
    options: "O | None" = None, **kwargs: Any
) -> Iterator[pa.RecordBatch]

Zero-copy scan — yield the source's :class:pa.RecordBatch views verbatim.

The lazy / zero-copy counterpart to :meth:read_arrow_batches, mirroring :meth:read_polars_frame vs :meth:scan_polars_frame. Where read_arrow_batches layers the full options pipeline on every batch — target cast, projection, resample, dedup, row-limit slicing, each of which can copy or re-encode — scan_arrow_batches hands back exactly what the leaf produced, untouched. For an in-memory source (:class:~yggdrasil.arrow.tabular.ArrowTabular) those batches are views over the held buffers (no copy); for a byte-backed leaf they're the freshly-decoded batches with none of the extra processing copies layered on. Use it when you want the raw Arrow stream and will project / filter downstream yourself.

scan_arrow_table ¶

scan_arrow_table(options: 'O | None' = None, **kwargs: Any) -> pa.Table

Zero-copy scan into one chunked :class:pa.Table (no rechunk, no cast).

The zero-copy counterpart to :meth:read_arrow_table. Assembles the source batches with :func:pa.Table.from_batches, which references the batch buffers as table chunks rather than copying them — so no cast, no projection, no rechunk memcpy that read_arrow_table performs to coalesce + conform the result. An empty source yields an empty table carrying the bound schema.

The batches must share one schema (the zero-copy contract): read_arrow_table reconciles parts that drifted across writes, scan_arrow_table does not — reach for read_arrow_table when a source's parts are known to be heterogeneous.

scan_arrow_batch_reader ¶

scan_arrow_batch_reader(
    options: "O | None" = None, **kwargs: Any
) -> "pa.RecordBatchReader"

Zero-copy scan as a streaming :class:pa.RecordBatchReader view.

The raw-reader counterpart to :meth:read_arrow_batch_reader: wraps the source batch stream in a reader without the per-batch conform / target-cast pass, so batches flow through as views over the source buffers. The reader's schema is the source's own — taken from the first batch, so it matches the raw views exactly (no collect_schema probe, which on a byte cursor would consume the stream out from under the read). Only the first batch is pulled up front to seed the schema; the rest stay lazy behind the reader.

read_table ¶

read_table(options: 'O | None' = None, **kwargs: Any) -> 'Tabular | None'

Read into an in-memory :class:Tabular.

When options.spark_session is set, reads via :meth:_read_spark_frame and wraps in a :class:Dataset. Otherwise materializes Arrow batches into :class:ArrowTabular. Returns None when empty.

write_table ¶

write_table(obj: Any, options: 'O | None' = None, **kwargs: Any) -> None

Dispatch obj to the best _write_* hook based on its runtime type.

Recognizes another :class:Tabular (drained as a pyarrow record-batch stream), pa.Table / pa.RecordBatch / pa.RecordBatchReader, polars DataFrame / LazyFrame, pandas DataFrame, pyspark DataFrame, list[dict], dict[str, list], and iterables of any of the above. Module-name sniffing keeps optional engine deps out of the import graph — we only touch a frame's API once we've confirmed it's an instance of one we know how to drain.

union ¶

union(other: 'Any', *, mode: 'ModeLike | None' = None) -> 'Tabular'

Return a Tabular representing self UNION ALL other.

mode controls how mismatched schemas are reconciled:

Mode.IGNORE (default) — keep self's schema; extra columns in other are dropped, missing ones are filled null.
Mode.APPEND — widen to the superset schema (every field from both sides survives).

Concrete subclasses override :meth:_union for in-place mutation (Arrow batch append, Spark unionByName).

Accepts :class:Tabular, pa.RecordBatch, pa.Table, list[Response], or a Spark DataFrame. None returns self unchanged.

read_spark_dataset ¶

read_spark_dataset(options: 'O | None' = None, **kwargs: Any) -> 'SparkDataset'

Read into a :class:Dataset holder.

Mirrors :meth:read_arrow_dataset for the Spark engine: the return type is a yggdrasil holder rather than the bare engine frame, so callers keep the Tabular surface (chained transforms, persist / insert / schema, …) without an extra wrap at the call site. :class:Dataset overrides :meth:_read_spark_dataset to return itself in place — no materialise round trip when the source already speaks Spark.

read_record_iterator ¶

read_record_iterator(
    options: "O | None" = None, **kwargs: Any
) -> "Iterator[Mapping[str, Any]]"

Stream rows as plain dict. True streaming — the full table never materializes; batch.to_pylist() does the column→row rotation in pyarrow C++ once per batch.

read_records ¶

read_records(options: 'O | None' = None, **kwargs: Any) -> 'Iterator[Any]'

Stream rows as :class:yggdrasil.data.record.Record. Lower per-row allocation than :meth:read_pylist for stable-schema sources — the underlying :class:Schema is materialized once and shared by reference across every record.

unique ¶

unique(by: 'str | Any | Iterable[Any]') -> 'Tabular'

Drop duplicate rows on by; keep first occurrence per key tuple.

Parameters¶

by One or more column references — :class:str column names, :class:yggdrasil.data.Field instances (resolved via :attr:Field.name), or any iterable mixing the two. Empty / None is a no-op — returns self.

Returns¶

Tabular A new holder carrying the deduped rows. Spark-shaped inputs (anything whose :meth:_native_spark_frame exposes a :class:pyspark.sql.DataFrame) return a fresh :class:yggdrasil.spark.tabular.Dataset over the spark-side dedup; everything else collects through Arrow and returns an :class:yggdrasil.arrow.tabular.ArrowTabular.

resample ¶

resample(
    on: "str | Any",
    sampling: "int | float | Any",
    *,
    partition_by: "str | Any | Iterable[Any] | None" = None,
    fill_strategy: "str | None" = "ffill"
) -> "Tabular"

Align rows to a fixed time grid on on; one row per bucket.

Parameters¶

on The time column to resample on — column name (:class:str) or :class:yggdrasil.data.Field. sampling Bucket size. Accepted shapes:

* :class:`int` / :class:`float` — seconds (floats are
  rounded to the nearest integer second).
* :class:`datetime.timedelta` — total seconds.
* :class:`str` — ISO-8601 duration (``"PT1H"``,
  ``"P1D"``, ``"PT15M"``) parsed via
  :func:`yggdrasil.data.types.primitive.temporal._parse_iso_duration`.

``sampling <= 0`` is a short-circuit — returns ``self``.

partition_by Entity columns the resample is independent on. None / empty → flat global timeline. Same coercion as :meth:unique's by. fill_strategy How to fill nulls left by the bucket's "first" aggregation. "ffill" (default), "bfill", or "none" / None to disable. See :func:yggdrasil.arrow.ops.fill_arrow_table for the full semantics.

Returns¶

Tabular Spark-shaped holders return a :class:Dataset over the spark-side resample; everything else returns an :class:ArrowTabular over the arrow-side resample.

select ¶

select(*columns: 'str | Any') -> 'Tabular'

Project to columns and return a new Tabular.

Each entry is a column reference — :class:str, a :class:yggdrasil.data.Field (resolved via :attr:Field.name), or an iterable mixing both. The result preserves the caller's order, which matches both :meth:pyarrow.Table.select and :meth:pyspark.sql.DataFrame.select semantics.

Raises :class:ValueError on an empty selection — a zero- column projection is almost always a caller mistake; pass :class:Schema.empty projections through the cast surface instead.

drop ¶

drop(*columns: 'str | Any') -> 'Tabular'

Return a new Tabular with the named columns removed.

Columns missing from the source are silently ignored — matches Spark's :meth:DataFrame.drop and pyarrow's :meth:Table.drop_columns (when filtered to existing names). An empty argument list is a no-op that returns self.

filter ¶

filter(predicate: 'PredicateLike') -> 'Tabular'

Drop rows where predicate is false.

predicate accepts every shape :meth:yggdrasil.execution.expr.Expression.from_ recognises:

a SQL predicate string ("x > 0 AND y IS NOT NULL"), parsed by the in-tree SQL parser;
a yggdrasil :class:Predicate node (col("x") > 0, :func:is_in, :func:between, …);
a native engine expression — :class:pyarrow.compute.Expression, :class:polars.Expr, or :class:pyspark.sql.Column — lifted via the matching backend.

The predicate is parsed once and dispatched to the typed :meth:_filter hook; the engine-side filter then runs in its native kernel (Arrow C++, Spark Catalyst) so the row scan stays vectorised.

cast ¶

cast(options: 'O | None' = None, **kwargs) -> 'Tabular'

Cast rows, returning a new :class:Tabular.

Accepts a :class:Schema or :class:CastOptions. When options is given, reads to arrow and casts each batch through :meth:CastOptions.cast_arrow_batch.

display ¶

display(n: int = 10, *, max_width: int = 32) -> str

Render the first n rows as an aligned, typed text table.

Columns and their types come from this Tabular's own :meth:collect_schema — the header is two rows: the column names, then their type tags (the project :class:~yggdrasil.data.Field's :meth:Field.short → :meth:DataType.short, recursive for nested types — i64 / str / list<str> / struct<name:str, age:i64>). Columns are separated by │ with a ─┼─ rule; numbers/booleans right-align; nested cell values are compacted to one line. Long values and headers are clipped (cells to max_width, type/name tags to a slightly larger cap) so one long string or column name can't balloon the table. The n rows are pushed down as a row_limit so no more than that is ever read.

print(dbc.sql.execute("SELECT * FROM t").display())
print(IO.from_("data.parquet").display(5))

lazy ¶

lazy() -> 'LazyTabular'

Return a :class:LazyTabular wrapping this source.

Transformations on the returned object (select, filter, join, …) accumulate in an :class:ExecutionPlan without touching data. Any read_* call materialises the plan.

yggdrasil.arrow.tabular¶

tabular ¶

Auto-spill via :class:ArrowIPCFile¶

What we ingest¶

ArrowTabular ¶

State machine¶

batches property ¶

spilled property ¶

spill_dir property ¶

spill_parts property ¶

spill_bytes property writable ¶

opened property ¶

closed property ¶

unpersist ¶

open ¶

commit ¶

rollback ¶

close ¶

mark_dirty ¶

for_scheme classmethod ¶

dispatch classmethod ¶

to_singleton ¶

invalidate_singleton ¶

matches_static ¶

from_ classmethod ¶

options_class classmethod ¶

check_options classmethod ¶

cleanup ¶

optimize ¶

delete ¶

collect_schema ¶

count ¶

scan_arrow_batches ¶

scan_arrow_table ¶

scan_arrow_batch_reader ¶

read_table ¶

write_table ¶

union ¶

read_spark_dataset ¶

read_record_iterator ¶

read_records ¶

unique ¶

Parameters¶

Returns¶

resample ¶

Parameters¶

Returns¶

select ¶

drop ¶

filter ¶

cast ¶

display ¶

lazy ¶

`yggdrasil.arrow.tabular`¶

Auto-spill via :class:`ArrowIPCFile`¶

batches `property` ¶

spilled `property` ¶

spill_dir `property` ¶

spill_parts `property` ¶

spill_bytes `property` `writable` ¶

opened `property` ¶

closed `property` ¶

for_scheme `classmethod` ¶

dispatch `classmethod` ¶

from_ `classmethod` ¶

options_class `classmethod` ¶

check_options `classmethod` ¶