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# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
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""":py:class:`SessionContext` — entry point for running DataFusion queries.

A :py:class:`SessionContext` holds registered tables, catalogs, and
configuration for the current session. It is the first object most programs
create: from it you register data, run SQL strings
(:py:meth:`SessionContext.sql`), read files
(:py:meth:`SessionContext.read_csv`,
:py:meth:`SessionContext.read_parquet`, ...), and construct
:py:class:`~datafusion.dataframe.DataFrame` objects in memory
(:py:meth:`SessionContext.from_pydict`,
:py:meth:`SessionContext.from_arrow`).

Session behavior (memory limits, batch size, configured optimizer passes,
...) is controlled by :py:class:`SessionConfig` and
:py:class:`RuntimeEnvBuilder`; SQL dialect limits are controlled by
:py:class:`SQLOptions`.

Examples:
    >>> ctx = dfn.SessionContext()
    >>> df = ctx.from_pydict({"a": [1, 2, 3]})
    >>> ctx.sql("SELECT 1 AS n").to_pydict()
    {'n': [1]}

See :ref:`user_guide_concepts` in the online documentation for the broader
execution model.
"""

from __future__ import annotations

import uuid
import warnings
from typing import TYPE_CHECKING, Any, Protocol

try:
    from warnings import deprecated  # Python 3.13+
except ImportError:
    from typing_extensions import deprecated  # Python 3.12


from urllib.parse import urlparse

import pyarrow as pa

from datafusion.catalog import (
    Catalog,
    CatalogList,
    CatalogProviderExportable,
    CatalogProviderList,
    CatalogProviderListExportable,
    TableProviderFactory,
    TableProviderFactoryExportable,
)
from datafusion.dataframe import DataFrame
from datafusion.expr import sort_list_to_raw_sort_list
from datafusion.options import (
    DEFAULT_MAX_INFER_SCHEMA,
    CsvReadOptions,
    _convert_table_partition_cols,
)
from datafusion.record_batch import RecordBatchStream

from ._internal import RuntimeEnvBuilder as RuntimeEnvBuilderInternal
from ._internal import SessionConfig as SessionConfigInternal
from ._internal import SessionContext as SessionContextInternal
from ._internal import SQLOptions as SQLOptionsInternal
from ._internal import expr as expr_internal

if TYPE_CHECKING:
    import pathlib
    from collections.abc import Iterable, Sequence

    import pandas as pd
    import polars as pl  # type: ignore[import]
    from _typeshed import CapsuleType as _PyCapsule

    from datafusion.catalog import CatalogProvider, Table
    from datafusion.common import DFSchema
    from datafusion.expr import Expr, SortKey
    from datafusion.plan import ExecutionPlan, LogicalPlan
    from datafusion.user_defined import (
        AggregateUDF,
        LogicalExtensionCodecExportable,
        PhysicalExtensionCodecExportable,
        ScalarUDF,
        TableFunction,
        WindowUDF,
    )


class ArrowStreamExportable(Protocol):
    """Type hint for object exporting Arrow C Stream via Arrow PyCapsule Interface.

    https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html
    """

    def __arrow_c_stream__(  # noqa: D105
        self, requested_schema: object | None = None
    ) -> object: ...


class ArrowArrayExportable(Protocol):
    """Type hint for object exporting Arrow C Array via Arrow PyCapsule Interface.

    https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html
    """

    def __arrow_c_array__(  # noqa: D105
        self, requested_schema: object | None = None
    ) -> tuple[object, object]: ...


class TableProviderExportable(Protocol):
    """Type hint for object that has __datafusion_table_provider__ PyCapsule.

    https://datafusion.apache.org/python/user-guide/io/table_provider.html
    """

    def __datafusion_table_provider__(self, session: Any) -> object: ...  # noqa: D105


class PhysicalOptimizerRuleExportable(Protocol):
    """Type hint for object that has __datafusion_physical_optimizer_rule__ PyCapsule.

    The method returns a PyCapsule wrapping an ``FFI_PhysicalOptimizerRule``,
    typically produced by a separate compiled extension.
    """

    def __datafusion_physical_optimizer_rule__(self) -> object: ...  # noqa: D105


class QueryPlannerExportable(Protocol):
    """Type hint for object that has a __datafusion_query_planner__ PyCapsule.

    The method returns a PyCapsule wrapping an ``FFI_QueryPlanner``, typically
    produced by a separate compiled extension. ``session`` is the
    :py:class:`SessionContext` the planner is being installed on; take the
    extension codecs from it rather than building your own.
    """

    def __datafusion_query_planner__(self, session: Any) -> object: ...  # noqa: D105


class SessionConfig:
    """Session configuration options."""

    def __init__(self, config_options: dict[str, str] | None = None) -> None:
        """Create a new :py:class:`SessionConfig` with the given configuration options.

        Args:
            config_options: Configuration options.
        """
        self.config_internal = SessionConfigInternal(config_options)

    def with_create_default_catalog_and_schema(
        self, enabled: bool = True
    ) -> SessionConfig:
        """Control if the default catalog and schema will be automatically created.

        Args:
            enabled: Whether the default catalog and schema will be
                automatically created.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = (
            self.config_internal.with_create_default_catalog_and_schema(enabled)
        )
        return self

    def with_default_catalog_and_schema(
        self, catalog: str, schema: str
    ) -> SessionConfig:
        """Select a name for the default catalog and schema.

        Args:
            catalog: Catalog name.
            schema: Schema name.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_default_catalog_and_schema(
            catalog, schema
        )
        return self

    def with_information_schema(self, enabled: bool = True) -> SessionConfig:
        """Enable or disable the inclusion of ``information_schema`` virtual tables.

        Args:
            enabled: Whether to include ``information_schema`` virtual tables.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_information_schema(enabled)
        return self

    def with_batch_size(self, batch_size: int) -> SessionConfig:
        """Customize batch size.

        Args:
            batch_size: Batch size.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_batch_size(batch_size)
        return self

    def with_target_partitions(self, target_partitions: int) -> SessionConfig:
        """Customize the number of target partitions for query execution.

        Increasing partitions can increase concurrency.

        Args:
            target_partitions: Number of target partitions.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_target_partitions(
            target_partitions
        )
        return self

    def with_repartition_aggregations(self, enabled: bool = True) -> SessionConfig:
        """Enable or disable the use of repartitioning for aggregations.

        Enabling this improves parallelism.

        Args:
            enabled: Whether to use repartitioning for aggregations.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_repartition_aggregations(
            enabled
        )
        return self

    def with_repartition_joins(self, enabled: bool = True) -> SessionConfig:
        """Enable or disable the use of repartitioning for joins to improve parallelism.

        Args:
            enabled: Whether to use repartitioning for joins.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_repartition_joins(enabled)
        return self

    def with_repartition_windows(self, enabled: bool = True) -> SessionConfig:
        """Enable or disable the use of repartitioning for window functions.

        This may improve parallelism.

        Args:
            enabled: Whether to use repartitioning for window functions.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_repartition_windows(enabled)
        return self

    def with_repartition_sorts(self, enabled: bool = True) -> SessionConfig:
        """Enable or disable the use of repartitioning for window functions.

        This may improve parallelism.

        Args:
            enabled: Whether to use repartitioning for window functions.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_repartition_sorts(enabled)
        return self

    def with_repartition_file_scans(self, enabled: bool = True) -> SessionConfig:
        """Enable or disable the use of repartitioning for file scans.

        Args:
            enabled: Whether to use repartitioning for file scans.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_repartition_file_scans(enabled)
        return self

    def with_repartition_file_min_size(self, size: int) -> SessionConfig:
        """Set minimum file range size for repartitioning scans.

        Args:
            size: Minimum file range size.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_repartition_file_min_size(size)
        return self

    def with_parquet_pruning(self, enabled: bool = True) -> SessionConfig:
        """Enable or disable the use of pruning predicate for parquet readers.

        Pruning predicates will enable the reader to skip row groups.

        Args:
            enabled: Whether to use pruning predicate for parquet readers.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_parquet_pruning(enabled)
        return self

    def set(self, key: str, value: str) -> SessionConfig:
        """Set a configuration option.

        Args:
        key: Option key.
        value: Option value.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.set(key, value)
        return self

    def with_extension(self, extension: Any) -> SessionConfig:
        """Create a new configuration using an extension.

        Args:
            extension: A custom configuration extension object. These are
            shared from another DataFusion extension library.

        Returns:
            A new :py:class:`SessionConfig` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_extension(extension)
        return self


class RuntimeEnvBuilder:
    """Runtime configuration options."""

    def __init__(self) -> None:
        """Create a new :py:class:`RuntimeEnvBuilder` with default values."""
        self.config_internal = RuntimeEnvBuilderInternal()

    def with_disk_manager_disabled(self) -> RuntimeEnvBuilder:
        """Disable the disk manager, attempts to create temporary files will error.

        Returns:
            A new :py:class:`RuntimeEnvBuilder` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_disk_manager_disabled()
        return self

    def with_disk_manager_os(self) -> RuntimeEnvBuilder:
        """Use the operating system's temporary directory for disk manager.

        Returns:
            A new :py:class:`RuntimeEnvBuilder` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_disk_manager_os()
        return self

    def with_disk_manager_specified(
        self, *paths: str | pathlib.Path
    ) -> RuntimeEnvBuilder:
        """Use the specified paths for the disk manager's temporary files.

        Args:
            paths: Paths to use for the disk manager's temporary files.

        Returns:
            A new :py:class:`RuntimeEnvBuilder` object with the updated setting.
        """
        paths_list = [str(p) for p in paths]
        self.config_internal = self.config_internal.with_disk_manager_specified(
            paths_list
        )
        return self

    def with_unbounded_memory_pool(self) -> RuntimeEnvBuilder:
        """Use an unbounded memory pool.

        Returns:
            A new :py:class:`RuntimeEnvBuilder` object with the updated setting.
        """
        self.config_internal = self.config_internal.with_unbounded_memory_pool()
        return self

    def with_fair_spill_pool(self, size: int) -> RuntimeEnvBuilder:
        """Use a fair spill pool with the specified size.

        This pool works best when you know beforehand the query has multiple spillable
        operators that will likely all need to spill. Sometimes it will cause spills
        even when there was sufficient memory (reserved for other operators) to avoid
        doing so::

            ┌───────────────────────z──────────────────────z───────────────┐
            │                       z                      z               │
            │                       z                      z               │
            │       Spillable       z       Unspillable    z     Free      │
            │        Memory         z        Memory        z    Memory     │
            │                       z                      z               │
            │                       z                      z               │
            └───────────────────────z──────────────────────z───────────────┘

        Args:
            size: Size of the memory pool in bytes.

        Returns:
            A new :py:class:`RuntimeEnvBuilder` object with the updated setting.

        Examples:
            >>> config = dfn.RuntimeEnvBuilder().with_fair_spill_pool(1024)
        """
        self.config_internal = self.config_internal.with_fair_spill_pool(size)
        return self

    def with_greedy_memory_pool(self, size: int) -> RuntimeEnvBuilder:
        """Use a greedy memory pool with the specified size.

        This pool works well for queries that do not need to spill or have a single
        spillable operator. See :py:func:`with_fair_spill_pool` if there are
        multiple spillable operators that all will spill.

        Args:
            size: Size of the memory pool in bytes.

        Returns:
            A new :py:class:`RuntimeEnvBuilder` object with the updated setting.

        Examples:
            >>> config = dfn.RuntimeEnvBuilder().with_greedy_memory_pool(1024)
        """
        self.config_internal = self.config_internal.with_greedy_memory_pool(size)
        return self

    def with_temp_file_path(self, path: str | pathlib.Path) -> RuntimeEnvBuilder:
        """Use the specified path to create any needed temporary files.

        Args:
            path: Path to use for temporary files.

        Returns:
            A new :py:class:`RuntimeEnvBuilder` object with the updated setting.

        Examples:
            >>> config = dfn.RuntimeEnvBuilder().with_temp_file_path("/tmp")
        """
        self.config_internal = self.config_internal.with_temp_file_path(str(path))
        return self


class SQLOptions:
    """Options to be used when performing SQL queries."""

    def __init__(self) -> None:
        """Create a new :py:class:`SQLOptions` with default values.

        The default values are:
        - DDL commands are allowed
        - DML commands are allowed
        - Statements are allowed
        """
        self.options_internal = SQLOptionsInternal()

    def with_allow_ddl(self, allow: bool = True) -> SQLOptions:
        """Should DDL (Data Definition Language) commands be run?

        Examples of DDL commands include ``CREATE TABLE`` and ``DROP TABLE``.

        Args:
            allow: Allow DDL commands to be run.

        Returns:
            A new :py:class:`SQLOptions` object with the updated setting.

        Examples:
            >>> options = dfn.SQLOptions().with_allow_ddl(True)
        """
        self.options_internal = self.options_internal.with_allow_ddl(allow)
        return self

    def with_allow_dml(self, allow: bool = True) -> SQLOptions:
        """Should DML (Data Manipulation Language) commands be run?

        Examples of DML commands include ``INSERT INTO`` and ``DELETE``.

        Args:
            allow: Allow DML commands to be run.

        Returns:
            A new :py:class:`SQLOptions` object with the updated setting.

        Examples:
            >>> options = dfn.SQLOptions().with_allow_dml(True)
        """
        self.options_internal = self.options_internal.with_allow_dml(allow)
        return self

    def with_allow_statements(self, allow: bool = True) -> SQLOptions:
        """Should statements such as ``SET VARIABLE`` and ``BEGIN TRANSACTION`` be run?

        Args:
            allow: Allow statements to be run.

        Returns:
            A new :py:class:SQLOptions` object with the updated setting.

        Examples:
            >>> options = dfn.SQLOptions().with_allow_statements(True)
        """
        self.options_internal = self.options_internal.with_allow_statements(allow)
        return self


class SessionContext:
    """This is the main interface for executing queries and creating DataFrames.

    See :ref:`user_guide_concepts` in the online documentation for more information.
    """

    def __init__(
        self,
        config: SessionConfig | None = None,
        runtime: RuntimeEnvBuilder | None = None,
    ) -> None:
        """Main interface for executing queries with DataFusion.

        Maintains the state of the connection between a user and an instance
        of the connection between a user and an instance of the DataFusion
        engine.

        Args:
            config: Session configuration options.
            runtime: Runtime configuration options.

        Example usage:

        The following example demonstrates how to use the context to execute
        a query against a CSV data source using the :py:class:`DataFrame` API::

            from datafusion import SessionContext

            ctx = SessionContext()
            df = ctx.read_csv("data.csv")
        """
        config = config.config_internal if config is not None else None
        runtime = runtime.config_internal if runtime is not None else None

        self.ctx = SessionContextInternal(config, runtime)

    def __repr__(self) -> str:
        """Print a string representation of the Session Context."""
        return self.ctx.__repr__()

    @classmethod
    def global_ctx(cls) -> SessionContext:
        """Retrieve the global context as a `SessionContext` wrapper.

        Returns:
            A `SessionContext` object that wraps the global `SessionContextInternal`.
        """
        internal_ctx = SessionContextInternal.global_ctx()
        wrapper = cls()
        wrapper.ctx = internal_ctx
        return wrapper

    def enable_url_table(self) -> SessionContext:
        """Control if local files can be queried as tables.

        Returns:
            A new :py:class:`SessionContext` object with url table enabled.
        """
        klass = self.__class__
        obj = klass.__new__(klass)
        obj.ctx = self.ctx.enable_url_table()
        return obj

    def register_object_store(
        self, schema: str, store: Any, host: str | None = None
    ) -> None:
        """Add a new object store into the session.

        Args:
            schema: The data source schema.
            store: The :py:class:`~datafusion.object_store.ObjectStore` to register.
            host: URL for the host.
        """
        self.ctx.register_object_store(schema, store, host)

    def deregister_object_store(self, schema: str, host: str | None = None) -> None:
        """Remove an object store from the session.

        Args:
            schema: The data source schema (e.g. ``"s3://"``).
            host: URL for the host (e.g. bucket name).
        """
        self.ctx.deregister_object_store(schema, host)

    def _register_object_store_for_path(
        self, path: str | pathlib.Path, store: Any
    ) -> None:
        """Parse a URL path and register the given object store for its scheme and host.

        This is a convenience helper used by methods like
        :py:meth:`register_parquet` and :py:meth:`read_parquet` to
        automatically register an object store when an ``object_store``
        parameter is provided.

        Args:
            path: A URL-style path (e.g. ``"s3://bucket/key.parquet"`` or
                ``"file:///tmp/data.parquet"``).
            store: An object store instance to register.

        Raises:
            ValueError: If the path does not contain a URL scheme, or if
                a non-file scheme is missing a host/bucket component.
        """
        parsed = urlparse(str(path))
        if not parsed.scheme:
            msg = (
                f"Cannot determine object store URL from path {path!r}. "
                "The path must use a URL scheme (e.g. 's3://bucket/key')."
            )
            raise ValueError(msg)
        # file:// URLs typically have an empty netloc (e.g. file:///tmp/a.parquet)
        # For other schemes (s3, gs, az, https) the netloc (bucket/host) is required.
        if parsed.scheme != "file" and not parsed.netloc:
            msg = (
                f"Cannot determine object store URL from path {path!r}. "
                "The path must include a host or bucket "
                "(e.g. 's3://bucket/key')."
            )
            raise ValueError(msg)
        scheme = f"{parsed.scheme}://"
        host = parsed.netloc or None
        self.register_object_store(scheme, store, host=host)

    def register_listing_table(
        self,
        name: str,
        path: str | pathlib.Path,
        table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
        file_extension: str = ".parquet",
        schema: pa.Schema | None = None,
        file_sort_order: Sequence[Sequence[SortKey]] | None = None,
    ) -> None:
        """Register multiple files as a single table.

        Registers a :py:class:`~datafusion.catalog.Table` that can assemble multiple
        files from locations in an :py:class:`~datafusion.object_store.ObjectStore`
        instance.

        Args:
            name: Name of the resultant table.
            path: Path to the file to register.
            table_partition_cols: Partition columns.
            file_extension: File extension of the provided table.
            schema: The data source schema.
            file_sort_order: Sort order for the file. Each sort key can be
                specified as a column name (``str``), an expression
                (``Expr``), or a ``SortExpr``.
        """
        if table_partition_cols is None:
            table_partition_cols = []
        table_partition_cols = _convert_table_partition_cols(table_partition_cols)
        self.ctx.register_listing_table(
            name,
            path,
            table_partition_cols,
            file_extension,
            schema,
            self._convert_file_sort_order(file_sort_order),
        )

    def sql(
        self,
        query: str,
        options: SQLOptions | None = None,
        param_values: dict[str, Any] | None = None,
        **named_params: Any,
    ) -> DataFrame:
        """Create a :py:class:`~datafusion.DataFrame` from SQL query text.

        See the online documentation for a description of how to perform
        parameterized substitution via either the ``param_values`` option
        or passing in ``named_params``.

        Note: This API implements DDL statements such as ``CREATE TABLE`` and
        ``CREATE VIEW`` and DML statements such as ``INSERT INTO`` with in-memory
        default implementation.See
        :py:func:`~datafusion.context.SessionContext.sql_with_options`.

        Args:
            query: SQL query text.
            options: If provided, the query will be validated against these options.
            param_values: Provides substitution of scalar values in the query
                after parsing.
            named_params: Provides string or DataFrame substitution in the query string.

        Returns:
            DataFrame representation of the SQL query.
        """

        def value_to_scalar(value: Any) -> pa.Scalar:
            if isinstance(value, pa.Scalar):
                return value
            return pa.scalar(value)

        def value_to_string(value: Any) -> str:
            if isinstance(value, DataFrame):
                view_name = str(uuid.uuid4()).replace("-", "_")
                view_name = f"view_{view_name}"
                view = value.df.into_view(temporary=True)
                self.ctx.register_table(view_name, view)
                return view_name
            return str(value)

        param_values = (
            {name: value_to_scalar(value) for (name, value) in param_values.items()}
            if param_values is not None
            else {}
        )
        param_strings = (
            {name: value_to_string(value) for (name, value) in named_params.items()}
            if named_params is not None
            else {}
        )

        options_raw = options.options_internal if options is not None else None

        return DataFrame(
            self.ctx.sql_with_options(
                query,
                options=options_raw,
                param_values=param_values,
                param_strings=param_strings,
            )
        )

    def sql_with_options(
        self,
        query: str,
        options: SQLOptions,
        param_values: dict[str, Any] | None = None,
        **named_params: Any,
    ) -> DataFrame:
        """Create a :py:class:`~datafusion.dataframe.DataFrame` from SQL query text.

        This function will first validate that the query is allowed by the
        provided options.

        Args:
            query: SQL query text.
            options: SQL options.
            param_values: Provides substitution of scalar values in the query
                after parsing.
            named_params: Provides string or DataFrame substitution in the query string.

        Returns:
            DataFrame representation of the SQL query.
        """
        return self.sql(
            query, options=options, param_values=param_values, **named_params
        )

    def create_dataframe(
        self,
        partitions: list[list[pa.RecordBatch]],
        name: str | None = None,
        schema: pa.Schema | None = None,
    ) -> DataFrame:
        """Create and return a dataframe using the provided partitions.

        Args:
            partitions: :py:class:`pa.RecordBatch` partitions to register.
            name: Resultant dataframe name.
            schema: Schema for the partitions.

        Returns:
            DataFrame representation of the SQL query.
        """
        return DataFrame(self.ctx.create_dataframe(partitions, name, schema))

    def create_dataframe_from_logical_plan(self, plan: LogicalPlan) -> DataFrame:
        """Create a :py:class:`~datafusion.dataframe.DataFrame` from an existing plan.

        Args:
            plan: Logical plan.

        Returns:
            DataFrame representation of the logical plan.
        """
        return DataFrame(self.ctx.create_dataframe_from_logical_plan(plan._raw_plan))

    def from_pylist(
        self, data: list[dict[str, Any]], name: str | None = None
    ) -> DataFrame:
        """Create a :py:class:`~datafusion.dataframe.DataFrame` from a list.

        Args:
            data: List of dictionaries.
            name: Name of the DataFrame.

        Returns:
            DataFrame representation of the list of dictionaries.
        """
        return DataFrame(self.ctx.from_pylist(data, name))

    def from_pydict(
        self, data: dict[str, list[Any]], name: str | None = None
    ) -> DataFrame:
        """Create a :py:class:`~datafusion.dataframe.DataFrame` from a dictionary.

        Args:
            data: Dictionary of lists.
            name: Name of the DataFrame.

        Returns:
            DataFrame representation of the dictionary of lists.
        """
        return DataFrame(self.ctx.from_pydict(data, name))

    def from_arrow(
        self,
        data: ArrowStreamExportable | ArrowArrayExportable,
        name: str | None = None,
    ) -> DataFrame:
        """Create a :py:class:`~datafusion.dataframe.DataFrame` from an Arrow source.

        The Arrow data source can be any object that implements either
        ``__arrow_c_stream__`` or ``__arrow_c_array__``. For the latter, it must return
        a struct array.

        Arrow data can be Polars, Pandas, Pyarrow etc.

        Args:
            data: Arrow data source.
            name: Name of the DataFrame.

        Returns:
            DataFrame representation of the Arrow table.
        """
        return DataFrame(self.ctx.from_arrow(data, name))

    def from_pandas(self, data: pd.DataFrame, name: str | None = None) -> DataFrame:
        """Create a :py:class:`~datafusion.dataframe.DataFrame` from a Pandas DataFrame.

        Args:
            data: Pandas DataFrame.
            name: Name of the DataFrame.

        Returns:
            DataFrame representation of the Pandas DataFrame.
        """
        return DataFrame(self.ctx.from_pandas(data, name))

    def from_polars(self, data: pl.DataFrame, name: str | None = None) -> DataFrame:
        """Create a :py:class:`~datafusion.dataframe.DataFrame` from a Polars DataFrame.

        Args:
            data: Polars DataFrame.
            name: Name of the DataFrame.

        Returns:
            DataFrame representation of the Polars DataFrame.
        """
        return DataFrame(self.ctx.from_polars(data, name))

    # https://github.com/apache/datafusion-python/pull/1016#discussion_r1983239116
    # is the discussion on how we arrived at adding register_view
    def register_view(self, name: str, df: DataFrame) -> None:
        """Register a :py:class:`~datafusion.dataframe.DataFrame` as a view.

        Args:
            name (str): The name to register the view under.
            df (DataFrame): The DataFrame to be converted into a view and registered.
        """
        view = df.into_view()
        self.ctx.register_table(name, view)

    def register_table(
        self,
        name: str,
        table: Table | TableProviderExportable | DataFrame | pa.dataset.Dataset,
    ) -> None:
        """Register a :py:class:`~datafusion.Table` with this context.

        The registered table can be referenced from SQL statements executed against
        this context.

        Args:
            name: Name of the resultant table.
            table: Any object that can be converted into a :class:`Table`.
        """
        self.ctx.register_table(name, table)

    def deregister_table(self, name: str) -> None:
        """Remove a table from the session."""
        self.ctx.deregister_table(name)

    def register_table_factory(
        self,
        format: str,
        factory: TableProviderFactory | TableProviderFactoryExportable,
    ) -> None:
        """Register a :py:class:`~datafusion.TableProviderFactoryExportable`.

        The registered factory can be referenced from SQL DDL statements executed
        against this context.

        Args:
            format: The value to be used in `STORED AS ${format}` clause.
            factory: A PyCapsule that implements :class:`TableProviderFactoryExportable`
        """
        self.ctx.register_table_factory(format, factory)

    def catalog_names(self) -> set[str]:
        """Returns the list of catalogs in this context."""
        return self.ctx.catalog_names()

    def register_catalog_provider_list(
        self,
        provider: CatalogProviderListExportable | CatalogProviderList | CatalogList,
    ) -> None:
        """Register a catalog provider list."""
        if isinstance(provider, CatalogList):
            self.ctx.register_catalog_provider_list(provider.catalog)
        else:
            self.ctx.register_catalog_provider_list(provider)

    def register_catalog_provider(
        self, name: str, provider: CatalogProviderExportable | CatalogProvider | Catalog
    ) -> None:
        """Register a catalog provider."""
        if isinstance(provider, Catalog):
            self.ctx.register_catalog_provider(name, provider.catalog)
        else:
            self.ctx.register_catalog_provider(name, provider)

    @deprecated("Use register_table() instead.")
    def register_table_provider(
        self,
        name: str,
        provider: Table | TableProviderExportable | DataFrame | pa.dataset.Dataset,
    ) -> None:
        """Register a table provider.

        Deprecated: use :meth:`register_table` instead.
        """
        self.register_table(name, provider)

    def register_udtf(self, func: TableFunction) -> None:
        """Register a user defined table function."""
        self.ctx.register_udtf(func._udtf)

    def register_batch(self, name: str, batch: pa.RecordBatch) -> None:
        """Register a single :py:class:`pa.RecordBatch` as a table.

        Args:
            name: Name of the resultant table.
            batch: Record batch to register as a table.

        Examples:
            >>> ctx = dfn.SessionContext()
            >>> batch = pa.RecordBatch.from_pydict({"a": [1, 2, 3]})
            >>> ctx.register_batch("batch_tbl", batch)
            >>> ctx.sql("SELECT * FROM batch_tbl").collect()[0].column(0)
            <pyarrow.lib.Int64Array object at ...>
            [
              1,
              2,
              3
            ]
        """
        self.ctx.register_batch(name, batch)

    def deregister_udtf(self, name: str) -> None:
        """Remove a user-defined table function from the session.

        Args:
            name: Name of the UDTF to deregister.
        """
        self.ctx.deregister_udtf(name)

    def register_record_batches(
        self, name: str, partitions: list[list[pa.RecordBatch]]
    ) -> None:
        """Register record batches as a table.

        This function will convert the provided partitions into a table and
        register it into the session using the given name.

        Args:
            name: Name of the resultant table.
            partitions: Record batches to register as a table.
        """
        self.ctx.register_record_batches(name, partitions)

    def read_batch(self, batch: pa.RecordBatch) -> DataFrame:
        """Return a :py:class:`~datafusion.DataFrame` reading a single batch.

        Convenience wrapper around :py:meth:`read_batches` for the single-batch
        case. Unlike :py:meth:`register_batch`, this does not register the
        batch as a named table; it returns an anonymous
        :py:class:`~datafusion.DataFrame` directly.

        Args:
            batch: Record batch to wrap as a DataFrame.

        Examples:
            >>> ctx = dfn.SessionContext()
            >>> batch = pa.RecordBatch.from_pydict({"a": [1, 2, 3]})
            >>> ctx.read_batch(batch).to_pydict()
            {'a': [1, 2, 3]}
        """
        return self.read_batches([batch])

    def read_batches(self, batches: Iterable[pa.RecordBatch]) -> DataFrame:
        """Return a :py:class:`~datafusion.DataFrame` reading the given batches.

        All batches must share the same schema. Any iterable of
        :py:class:`pa.RecordBatch` is accepted (list, tuple, generator);
        it is materialized into a list before being handed to the
        underlying Rust binding. Unlike :py:meth:`register_record_batches`,
        this does not register the batches as a named table; it returns
        an anonymous :py:class:`~datafusion.DataFrame` directly.

        Args:
            batches: Record batches to wrap as a DataFrame.

        Examples:
            >>> ctx = dfn.SessionContext()
            >>> b1 = pa.RecordBatch.from_pydict({"a": [1, 2]})
            >>> b2 = pa.RecordBatch.from_pydict({"a": [3, 4]})
            >>> ctx.read_batches([b1, b2]).to_pydict()
            {'a': [1, 2, 3, 4]}

            A generator works too:

            >>> ctx.read_batches(b for b in [b1, b2]).to_pydict()
            {'a': [1, 2, 3, 4]}
        """
        return DataFrame(self.ctx.read_batches(list(batches)))

    def register_parquet(
        self,
        name: str,
        path: str | pathlib.Path,
        table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
        parquet_pruning: bool = True,
        file_extension: str = ".parquet",
        skip_metadata: bool = True,
        schema: pa.Schema | None = None,
        file_sort_order: Sequence[Sequence[SortKey]] | None = None,
        object_store: Any | None = None,
    ) -> None:
        """Register a Parquet file as a table.

        The registered table can be referenced from SQL statement executed
        against this context.

        Args:
            name: Name of the table to register.
            path: Path to the Parquet file.
            table_partition_cols: Partition columns.
            parquet_pruning: Whether the parquet reader should use the
                predicate to prune row groups.
            file_extension: File extension; only files with this extension are
                selected for data input.
            skip_metadata: Whether the parquet reader should skip any metadata
                that may be in the file schema. This can help avoid schema
                conflicts due to metadata.
            schema: The data source schema.
            file_sort_order: Sort order for the file. Each sort key can be
                specified as a column name (``str``), an expression
                (``Expr``), or a ``SortExpr``.
            object_store: A pre-configured object store instance (e.g.
                :py:class:`~datafusion.object_store.AmazonS3`,
                :py:class:`~datafusion.object_store.GoogleCloud`,
                :py:class:`~datafusion.object_store.MicrosoftAzure`) to use
                for accessing the file. When provided, the store is
                automatically registered for the URL scheme and host parsed
                from ``path``, removing the need to call
                :py:meth:`register_object_store` separately. This is
                especially useful in multi-threaded environments where
                setting credentials via ``os.environ`` is not thread-safe.

        Examples:
            Register a local Parquet file:

            >>> import datafusion
            >>> ctx = datafusion.SessionContext()
            >>> ctx.register_parquet("my_table", "data.parquet")  # doctest: +SKIP

            Register from S3 with inline credentials (thread-safe):

            >>> from datafusion.object_store import AmazonS3  # doctest: +SKIP
            >>> store = AmazonS3(
            ...     bucket_name="my-bucket",
            ...     region="us-east-1",
            ...     access_key_id="...",
            ...     secret_access_key="...",
            ... )  # doctest: +SKIP
            >>> ctx.register_parquet(
            ...     "my_table",
            ...     "s3://my-bucket/data.parquet",
            ...     object_store=store,
            ... )  # doctest: +SKIP
        """
        if object_store is not None:
            self._register_object_store_for_path(path, object_store)
        if table_partition_cols is None:
            table_partition_cols = []
        table_partition_cols = _convert_table_partition_cols(table_partition_cols)
        self.ctx.register_parquet(
            name,
            path,
            table_partition_cols,
            parquet_pruning,
            file_extension,
            skip_metadata,
            schema,
            self._convert_file_sort_order(file_sort_order),
        )

    def register_csv(
        self,
        name: str,
        path: str | pathlib.Path | list[str | pathlib.Path],
        schema: pa.Schema | None = None,
        has_header: bool = True,
        delimiter: str = ",",
        schema_infer_max_records: int = DEFAULT_MAX_INFER_SCHEMA,
        file_extension: str = ".csv",
        file_compression_type: str | None = None,
        options: CsvReadOptions | None = None,
        object_store: Any | None = None,
    ) -> None:
        """Register a CSV file as a table.

        The registered table can be referenced from SQL statement executed against.

        Args:
            name: Name of the table to register.
            path: Path to the CSV file. It also accepts a list of Paths.
            schema: An optional schema representing the CSV file. If None, the
                CSV reader will try to infer it based on data in file.
            has_header: Whether the CSV file have a header. If schema inference
                is run on a file with no headers, default column names are
                created.
            delimiter: An optional column delimiter.
            schema_infer_max_records: Maximum number of rows to read from CSV
                files for schema inference if needed.
            file_extension: File extension; only files with this extension are
                selected for data input.
            file_compression_type: File compression type.
            options: Set advanced options for CSV reading. This cannot be
                combined with any of the other options in this method.
            object_store: A pre-configured object store instance to use for
                accessing the file. When provided, the store is automatically
                registered for the URL scheme and host parsed from ``path``.
        """
        if object_store is not None:
            # For list paths, register from the first entry
            register_path = path[0] if isinstance(path, list) else path
            self._register_object_store_for_path(register_path, object_store)
        if options is not None and (
            schema is not None
            or not has_header
            or delimiter != ","
            or schema_infer_max_records != DEFAULT_MAX_INFER_SCHEMA
            or file_extension != ".csv"
            or file_compression_type is not None
        ):
            message = (
                "Combining CsvReadOptions parameter with additional options "
                "is not supported. Use CsvReadOptions to set parameters."
            )
            warnings.warn(
                message,
                category=UserWarning,
                stacklevel=2,
            )

        options = (
            options
            if options is not None
            else CsvReadOptions(
                schema=schema,
                has_header=has_header,
                delimiter=delimiter,
                schema_infer_max_records=schema_infer_max_records,
                file_extension=file_extension,
                file_compression_type=file_compression_type,
            )
        )

        self.ctx.register_csv(
            name,
            path,
            options.to_inner(),
        )

    def register_json(
        self,
        name: str,
        path: str | pathlib.Path,
        schema: pa.Schema | None = None,
        schema_infer_max_records: int = 1000,
        file_extension: str = ".json",
        table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
        file_compression_type: str | None = None,
        object_store: Any | None = None,
    ) -> None:
        """Register a JSON file as a table.

        The registered table can be referenced from SQL statement executed
        against this context.

        Args:
            name: Name of the table to register.
            path: Path to the JSON file.
            schema: The data source schema.
            schema_infer_max_records: Maximum number of rows to read from JSON
                files for schema inference if needed.
            file_extension: File extension; only files with this extension are
                selected for data input.
            table_partition_cols: Partition columns.
            file_compression_type: File compression type.
            object_store: A pre-configured object store instance to use for
                accessing the file. When provided, the store is automatically
                registered for the URL scheme and host parsed from ``path``.
        """
        if object_store is not None:
            self._register_object_store_for_path(path, object_store)
        if table_partition_cols is None:
            table_partition_cols = []
        table_partition_cols = _convert_table_partition_cols(table_partition_cols)
        self.ctx.register_json(
            name,
            path,
            schema,
            schema_infer_max_records,
            file_extension,
            table_partition_cols,
            file_compression_type,
        )

    def register_avro(
        self,
        name: str,
        path: str | pathlib.Path,
        schema: pa.Schema | None = None,
        file_extension: str = ".avro",
        table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
        object_store: Any | None = None,
    ) -> None:
        """Register an Avro file as a table.

        The registered table can be referenced from SQL statement executed against
        this context.

        Args:
            name: Name of the table to register.
            path: Path to the Avro file.
            schema: The data source schema.
            file_extension: File extension to select.
            table_partition_cols:  Partition columns.
            object_store: A pre-configured object store instance to use for
                accessing the file. When provided, the store is automatically
                registered for the URL scheme and host parsed from ``path``.
        """
        if object_store is not None:
            self._register_object_store_for_path(path, object_store)
        if table_partition_cols is None:
            table_partition_cols = []
        table_partition_cols = _convert_table_partition_cols(table_partition_cols)
        self.ctx.register_avro(name, path, schema, file_extension, table_partition_cols)

    def register_arrow(
        self,
        name: str,
        path: str | pathlib.Path,
        schema: pa.Schema | None = None,
        file_extension: str = ".arrow",
        table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
        object_store: Any | None = None,
    ) -> None:
        """Register an Arrow IPC file as a table.

        The registered table can be referenced from SQL statements executed
        against this context.

        Args:
            name: Name of the table to register.
            path: Path to the Arrow IPC file.
            schema: The data source schema.
            file_extension: File extension to select.
            table_partition_cols: Partition columns.
            object_store: A pre-configured object store instance to use for
                accessing the file. When provided, the store is automatically
                registered for the URL scheme and host parsed from ``path``.

        Examples:
            >>> import tempfile, os
            >>> ctx = dfn.SessionContext()
            >>> table = pa.table({"x": [10, 20, 30]})
            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = os.path.join(tmpdir, "data.arrow")
            ...     with pa.ipc.new_file(path, table.schema) as writer:
            ...         writer.write_table(table)
            ...     ctx.register_arrow("arrow_tbl", path)
            ...     ctx.sql("SELECT * FROM arrow_tbl").collect()[0].column(0)
            <pyarrow.lib.Int64Array object at ...>
            [
              10,
              20,
              30
            ]

            Provide an explicit ``schema`` to override schema inference:

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = os.path.join(tmpdir, "data.arrow")
            ...     with pa.ipc.new_file(path, table.schema) as writer:
            ...         writer.write_table(table)
            ...     ctx.register_arrow(
            ...         "arrow_schema",
            ...         path,
            ...         schema=pa.schema([("x", pa.int64())]),
            ...     )
            ...     ctx.sql("SELECT * FROM arrow_schema").collect()[0].column(0)
            <pyarrow.lib.Int64Array object at ...>
            [
              10,
              20,
              30
            ]

            Use ``file_extension`` to read files with a non-default extension:

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = os.path.join(tmpdir, "data.ipc")
            ...     with pa.ipc.new_file(path, table.schema) as writer:
            ...         writer.write_table(table)
            ...     ctx.register_arrow(
            ...         "arrow_ipc", path, file_extension=".ipc"
            ...     )
            ...     ctx.sql("SELECT * FROM arrow_ipc").collect()[0].column(0)
            <pyarrow.lib.Int64Array object at ...>
            [
              10,
              20,
              30
            ]
        """
        if object_store is not None:
            self._register_object_store_for_path(path, object_store)
        if table_partition_cols is None:
            table_partition_cols = []
        table_partition_cols = _convert_table_partition_cols(table_partition_cols)
        self.ctx.register_arrow(
            name, path, schema, file_extension, table_partition_cols
        )

    def register_dataset(self, name: str, dataset: pa.dataset.Dataset) -> None:
        """Register a :py:class:`pa.dataset.Dataset` as a table.

        Args:
            name: Name of the table to register.
            dataset: PyArrow dataset.
        """
        self.ctx.register_dataset(name, dataset)

    def register_udf(self, udf: ScalarUDF) -> None:
        """Register a user-defined function (UDF) with the context."""
        self.ctx.register_udf(udf._udf)

    def deregister_udf(self, name: str) -> None:
        """Remove a user-defined scalar function from the session.

        Args:
            name: Name of the UDF to deregister.
        """
        self.ctx.deregister_udf(name)

    def register_udaf(self, udaf: AggregateUDF) -> None:
        """Register a user-defined aggregation function (UDAF) with the context."""
        self.ctx.register_udaf(udaf._udaf)

    def enable_spark_functions(self) -> None:
        """Register all Spark-compatible functions for SQL access.

        Registers every UDF/UDAF/UDWF from the ``datafusion-spark`` crate,
        overriding any DataFusion built-ins of the same name with their
        Spark-semantics version (e.g. ``substring`` becomes 1-indexed,
        ``concat`` propagates NULL, ``round`` uses HALF_UP rounding).

        For DataFrame use, import the typed wrappers from
        :py:mod:`datafusion.functions.spark` directly; this method is only
        needed for SQL queries.

        Examples:
            >>> ctx = dfn.SessionContext()
            >>> ctx.enable_spark_functions()
            >>> ctx.sql(
            ...     "SELECT sha2('hello', 256) AS h"
            ... ).collect_column("h")[0].as_py()
            '2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824'
        """
        self.ctx.enable_spark_functions()

    def deregister_udaf(self, name: str) -> None:
        """Remove a user-defined aggregate function from the session.

        Args:
            name: Name of the UDAF to deregister.
        """
        self.ctx.deregister_udaf(name)

    def register_udwf(self, udwf: WindowUDF) -> None:
        """Register a user-defined window function (UDWF) with the context."""
        self.ctx.register_udwf(udwf._udwf)

    def deregister_udwf(self, name: str) -> None:
        """Remove a user-defined window function from the session.

        Args:
            name: Name of the UDWF to deregister.
        """
        self.ctx.deregister_udwf(name)

    def udf(self, name: str) -> ScalarUDF:
        """Look up a registered scalar UDF by name.

        Returns the same ``ScalarUDF`` wrapper that :py:meth:`register_udf`
        accepts, so it can be invoked as an expression in the DataFrame API
        or re-registered into a different :py:class:`SessionContext`.
        Built-in scalar functions from the session's function registry are
        also looked up.

        Args:
            name: Name of the registered scalar UDF.

        Raises:
            KeyError: If no scalar UDF is registered under ``name``.

        Examples:
            Register a UDF, then look it up by name and use it in the
            DataFrame API:

            >>> ctx = dfn.SessionContext()
            >>> nullcheck = dfn.udf(
            ...     lambda x: x.is_null(),
            ...     [pa.int64()],
            ...     pa.bool_(),
            ...     volatility="immutable",
            ...     name="nullcheck",
            ... )
            >>> ctx.register_udf(nullcheck)
            >>> fn = ctx.udf("nullcheck")
            >>> df = ctx.from_pydict({"a": [1, None, 3]})
            >>> df.select(fn(col("a")).alias("is_null")).to_pydict()
            {'is_null': [False, True, False]}

            Late-binding: the function name can come from configuration
            rather than an imported symbol, which is useful when the set
            of UDFs is plugin-driven or chosen at runtime:

            >>> config = {"null_check": "nullcheck"}
            >>> fn = ctx.udf(config["null_check"])
            >>> df.select(fn(col("a")).alias("is_null")).to_pydict()
            {'is_null': [False, True, False]}
        """
        from datafusion.user_defined import ScalarUDF as _ScalarUDF  # noqa: PLC0415

        return _ScalarUDF._from_internal(self.ctx.udf(name))

    def udaf(self, name: str) -> AggregateUDF:
        """Look up a registered aggregate UDF by name.

        Returns the same ``AggregateUDF`` wrapper that :py:meth:`register_udaf`
        accepts. Built-in aggregate functions such as ``sum`` or ``avg`` are
        also discoverable through this lookup. See :py:meth:`udf` for a worked
        late-binding example; the pattern is identical for aggregates.

        Args:
            name: Name of the registered aggregate UDF.

        Raises:
            KeyError: If no aggregate UDF is registered under ``name``.

        Examples:
            Look up a built-in aggregate by name and use it in
            :py:meth:`~datafusion.DataFrame.aggregate`:

            >>> ctx = dfn.SessionContext()
            >>> sum_fn = ctx.udaf("sum")
            >>> df = ctx.from_pydict({"a": [1, 2, 3]})
            >>> df.aggregate([], [sum_fn(col("a")).alias("total")]).to_pydict()
            {'total': [6]}
        """
        from datafusion.user_defined import (  # noqa: PLC0415
            AggregateUDF as _AggregateUDF,
        )

        return _AggregateUDF._from_internal(self.ctx.udaf(name))

    def udwf(self, name: str) -> WindowUDF:
        """Look up a registered window UDF by name.

        Returns the same ``WindowUDF`` wrapper that :py:meth:`register_udwf`
        accepts. Built-in window functions such as ``row_number`` or ``rank``
        are also discoverable through this lookup. See :py:meth:`udf` for a
        worked late-binding example; the pattern is identical for window
        functions.

        Args:
            name: Name of the registered window UDF.

        Raises:
            KeyError: If no window UDF is registered under ``name``.

        Examples:
            Look up a built-in window function by name and use it in
            ``select``:

            >>> ctx = dfn.SessionContext()
            >>> rn = ctx.udwf("row_number")
            >>> df = ctx.from_pydict({"a": [10, 20, 30]})
            >>> df.select(col("a"), rn().alias("rn")).to_pydict()
            {'a': [10, 20, 30], 'rn': [1, 2, 3]}
        """
        from datafusion.user_defined import WindowUDF as _WindowUDF  # noqa: PLC0415

        return _WindowUDF._from_internal(self.ctx.udwf(name))

    def udfs(self) -> list[str]:
        """Return the sorted names of all registered scalar UDFs.

        Includes both user-registered and built-in scalar functions. Pair
        with :py:meth:`udf` to drive discovery, validation, or config-based
        dispatch.

        Examples:
            >>> ctx = dfn.SessionContext()
            >>> "abs" in ctx.udfs()
            True
        """
        return self.ctx.udfs()

    def udafs(self) -> list[str]:
        """Return the sorted names of all registered aggregate UDFs.

        Examples:
            >>> ctx = dfn.SessionContext()
            >>> "sum" in ctx.udafs()
            True
        """
        return self.ctx.udafs()

    def udwfs(self) -> list[str]:
        """Return the sorted names of all registered window UDFs.

        Examples:
            >>> ctx = dfn.SessionContext()
            >>> "row_number" in ctx.udwfs()
            True
        """
        return self.ctx.udwfs()

    def catalog(self, name: str = "datafusion") -> Catalog:
        """Retrieve a catalog by name."""
        return Catalog(self.ctx.catalog(name))

    def table(self, name: str) -> DataFrame:
        """Retrieve a previously registered table by name."""
        return DataFrame(self.ctx.table(name))

    def table_exist(self, name: str) -> bool:
        """Return whether a table with the given name exists."""
        return self.ctx.table_exist(name)

    def empty_table(self) -> DataFrame:
        """Create an empty :py:class:`~datafusion.dataframe.DataFrame`."""
        return DataFrame(self.ctx.empty_table())

    def session_id(self) -> str:
        """Return an id that uniquely identifies this :py:class:`SessionContext`."""
        return self.ctx.session_id()

    def session_start_time(self) -> str:
        """Return the session start time as an RFC 3339 formatted string.

        Examples:
            >>> ctx = SessionContext()
            >>> ctx.session_start_time()  # doctest: +SKIP
            '2026-01-01T12:34:56.123456789+00:00'
        """
        return self.ctx.session_start_time()

    def enable_ident_normalization(self) -> bool:
        """Return whether identifier normalization (lowercasing) is enabled.

        Examples:
            >>> ctx = SessionContext()
            >>> ctx.enable_ident_normalization()
            True
        """
        return self.ctx.enable_ident_normalization()

    def copied_config(self) -> SessionConfig:
        """Return a copy of the active :py:class:`SessionConfig`.

        Mutating the returned config does not affect this context; use
        the result when you need a starting point for a new context or
        want to inspect the current settings independent of further
        changes here.

        Examples:
            >>> ctx = SessionContext(SessionConfig().with_batch_size(1024))
            >>> isinstance(ctx.copied_config(), SessionConfig)
            True
        """
        config = SessionConfig()
        config.config_internal = self.ctx.copied_config()
        return config

    @staticmethod
    def parse_capacity_limit(config_name: str, limit: str) -> int:
        """Parse a size string into a byte count.

        Accepts strings like ``"100M"``, ``"1.5G"``, or ``"512K"``.
        ``"0"`` is accepted and returns 0. ``config_name`` is used purely
        for error messages and identifies which configuration setting the
        limit belongs to. Use this helper when constructing a
        :py:class:`RuntimeEnvBuilder` from a human-friendly size string.

        Examples:
            >>> SessionContext.parse_capacity_limit(
            ...     "datafusion.runtime.memory_limit", "1M"
            ... )
            1048576
            >>> SessionContext.parse_capacity_limit(
            ...     "datafusion.runtime.memory_limit", "0"
            ... )
            0
        """
        return SessionContextInternal.parse_capacity_limit(config_name, limit)

    def parse_sql_expr(self, sql: str, schema: DFSchema) -> Expr:
        """Parse a SQL expression string into a logical expression.

        Args:
            sql: SQL expression string.
            schema: Schema to use for resolving column references.

        Returns:
            Parsed expression.

        Examples:
            >>> from datafusion.common import DFSchema
            >>> ctx = SessionContext()
            >>> schema = DFSchema.empty()
            >>> ctx.parse_sql_expr("1 + 2", schema=schema)
            Expr(Int64(1) + Int64(2))
        """
        from datafusion.expr import Expr  # noqa: PLC0415

        return Expr(self.ctx.parse_sql_expr(sql, schema))

    def execute_logical_plan(self, plan: LogicalPlan) -> DataFrame:
        """Execute a :py:class:`~datafusion.plan.LogicalPlan` and return a DataFrame.

        Args:
            plan: Logical plan to execute.

        Returns:
            DataFrame resulting from the execution.

        Examples:
            >>> ctx = SessionContext()
            >>> df = ctx.from_pydict({"a": [1, 2, 3]})
            >>> plan = df.logical_plan()
            >>> df2 = ctx.execute_logical_plan(plan)
            >>> df2.collect()[0].column(0)
            <pyarrow.lib.Int64Array object at ...>
            [
              1,
              2,
              3
            ]
        """
        return DataFrame(self.ctx.execute_logical_plan(plan._raw_plan))

    def refresh_catalogs(self) -> None:
        """Refresh catalog metadata.

        Examples:
            >>> ctx = SessionContext()
            >>> ctx.refresh_catalogs()
        """
        self.ctx.refresh_catalogs()

    def remove_optimizer_rule(self, name: str) -> bool:
        """Remove an optimizer rule by name.

        Args:
            name: Name of the optimizer rule to remove.

        Returns:
            True if a rule with the given name was found and removed.

        Examples:
            >>> ctx = SessionContext()
            >>> ctx.remove_optimizer_rule("nonexistent_rule")
            False
        """
        return self.ctx.remove_optimizer_rule(name)

    def add_physical_optimizer_rule(
        self, rule: PhysicalOptimizerRuleExportable
    ) -> None:
        """Append a user-defined physical optimizer rule to the session.

        The rule is imported via its ``__datafusion_physical_optimizer_rule__``
        PyCapsule, typically produced by a separate compiled extension. The
        underlying :class:`SessionState` is rebuilt from its current state
        with the new rule appended, so previously registered tables, UDFs,
        and catalogs are preserved.

        Args:
            rule: Object exposing ``__datafusion_physical_optimizer_rule__``,
                a :class:`PhysicalOptimizerRuleExportable`.

        Examples:
            >>> from datafusion import SessionContext
            >>> ctx = SessionContext()
            >>> from my_extension import MyPhysicalOptimizerRule  # doctest: +SKIP
            >>> rule = MyPhysicalOptimizerRule()  # doctest: +SKIP
            >>> ctx.add_physical_optimizer_rule(rule)  # doctest: +SKIP
        """
        self.ctx.add_physical_optimizer_rule(rule)

    def set_query_planner(self, planner: QueryPlannerExportable | _PyCapsule) -> None:
        """Install a custom query planner on this session.

        The planner is imported through its ``__datafusion_query_planner__``
        PyCapsule and installed on this context, in the same way
        :meth:`~SessionContext.add_physical_optimizer_rule` installs a rule.
        The query planner is part of the session state, so it applies to this
        context and to every context sharing its session — including ones
        already returned by
        :meth:`~SessionContext.with_logical_extension_codec` and friends.

        A session holds exactly one planner, so calling this again replaces the
        previous one rather than layering. To chain planners, have the new
        planner wrap the capsule from
        :meth:`~SessionContext.__datafusion_query_planner__`, captured
        *before* the new planner is installed.

        Install any extension codecs before a layered planner. Installing a
        codec afterwards rebuilds the installed planner against it, but not the
        fallback inside it, which keeps the codecs it was imported with. Note
        also that the planner is built against the codecs of the context this
        method is called on, so installing the same planner again on a different
        handle rebinds the session's planner to *that* handle's codecs. See the
        FFI extensions guide for the full multi-library registration recipe.

        Args:
            planner: Object exposing ``__datafusion_query_planner__`` (see
                :class:`QueryPlannerExportable`) or a raw
                ``datafusion_query_planner`` PyCapsule.

        Examples:
            >>> from my_extension import DistributedQueryPlanner  # doctest: +SKIP
            >>> ctx = SessionContext()
            >>> ctx.set_query_planner(DistributedQueryPlanner())  # doctest: +SKIP
            >>> ctx.sql("SELECT * FROM remote_table").collect()  # doctest: +SKIP

            Layer a planner on top of the one already installed by capturing
            the existing planner first:

            >>> fallback = ctx.__datafusion_query_planner__()  # doctest: +SKIP
            >>> ctx.set_query_planner(
            ...     DistributedQueryPlanner(fallback=fallback)
            ... )  # doctest: +SKIP
        """
        self.ctx.set_query_planner(planner)

    def table_provider(self, name: str) -> Table:
        """Return the :py:class:`~datafusion.catalog.Table` for the given table name.

        Args:
            name: Name of the table.

        Returns:
            The table provider.

        Raises:
            KeyError: If the table is not found.

        Examples:
            >>> import pyarrow as pa
            >>> ctx = SessionContext()
            >>> batch = pa.RecordBatch.from_pydict({"x": [1, 2]})
            >>> ctx.register_record_batches("my_table", [[batch]])
            >>> tbl = ctx.table_provider("my_table")
            >>> tbl.schema
            x: int64
        """
        from datafusion.catalog import Table  # noqa: PLC0415

        return Table(self.ctx.table_provider(name))

    def read_json(
        self,
        path: str | pathlib.Path,
        schema: pa.Schema | None = None,
        schema_infer_max_records: int = 1000,
        file_extension: str = ".json",
        table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
        file_compression_type: str | None = None,
        object_store: Any | None = None,
    ) -> DataFrame:
        """Read a line-delimited JSON data source.

        Args:
            path: Path to the JSON file.
            schema: The data source schema.
            schema_infer_max_records: Maximum number of rows to read from JSON
                files for schema inference if needed.
            file_extension: File extension; only files with this extension are
                selected for data input.
            table_partition_cols: Partition columns.
            file_compression_type: File compression type.
            object_store: A pre-configured object store instance to use for
                accessing the file. When provided, the store is automatically
                registered for the URL scheme and host parsed from ``path``.

        Returns:
            DataFrame representation of the read JSON files.
        """
        if object_store is not None:
            self._register_object_store_for_path(path, object_store)
        if table_partition_cols is None:
            table_partition_cols = []
        table_partition_cols = _convert_table_partition_cols(table_partition_cols)
        return DataFrame(
            self.ctx.read_json(
                path,
                schema,
                schema_infer_max_records,
                file_extension,
                table_partition_cols,
                file_compression_type,
            )
        )

    def read_csv(
        self,
        path: str | pathlib.Path | list[str] | list[pathlib.Path],
        schema: pa.Schema | None = None,
        has_header: bool = True,
        delimiter: str = ",",
        schema_infer_max_records: int = DEFAULT_MAX_INFER_SCHEMA,
        file_extension: str = ".csv",
        table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
        file_compression_type: str | None = None,
        options: CsvReadOptions | None = None,
        object_store: Any | None = None,
    ) -> DataFrame:
        """Read a CSV data source.

        Args:
            path: Path to the CSV file
            schema: An optional schema representing the CSV files. If None, the
                CSV reader will try to infer it based on data in file.
            has_header: Whether the CSV file have a header. If schema inference
                is run on a file with no headers, default column names are
                created.
            delimiter: An optional column delimiter.
            schema_infer_max_records: Maximum number of rows to read from CSV
                files for schema inference if needed.
            file_extension:  File extension; only files with this extension are
                selected for data input.
            table_partition_cols:  Partition columns.
            file_compression_type:  File compression type.
            options: Set advanced options for CSV reading. This cannot be
                combined with any of the other options in this method.
            object_store: A pre-configured object store instance to use for
                accessing the file. When provided, the store is automatically
                registered for the URL scheme and host parsed from ``path``.

        Returns:
            DataFrame representation of the read CSV files
        """
        if object_store is not None:
            register_path = path[0] if isinstance(path, list) else path
            self._register_object_store_for_path(register_path, object_store)
        if options is not None and (
            schema is not None
            or not has_header
            or delimiter != ","
            or schema_infer_max_records != DEFAULT_MAX_INFER_SCHEMA
            or file_extension != ".csv"
            or table_partition_cols is not None
            or file_compression_type is not None
        ):
            message = (
                "Combining CsvReadOptions parameter with additional options "
                "is not supported. Use CsvReadOptions to set parameters."
            )
            warnings.warn(
                message,
                category=UserWarning,
                stacklevel=2,
            )

        options = (
            options
            if options is not None
            else CsvReadOptions(
                schema=schema,
                has_header=has_header,
                delimiter=delimiter,
                schema_infer_max_records=schema_infer_max_records,
                file_extension=file_extension,
                table_partition_cols=table_partition_cols,
                file_compression_type=file_compression_type,
            )
        )

        return DataFrame(
            self.ctx.read_csv(
                path,
                options.to_inner(),
            )
        )

    def read_parquet(
        self,
        path: str | pathlib.Path,
        table_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
        parquet_pruning: bool = True,
        file_extension: str = ".parquet",
        skip_metadata: bool = True,
        schema: pa.Schema | None = None,
        file_sort_order: Sequence[Sequence[SortKey]] | None = None,
        object_store: Any | None = None,
    ) -> DataFrame:
        """Read a Parquet source into a :py:class:`~datafusion.dataframe.Dataframe`.

        Args:
            path: Path to the Parquet file.
            table_partition_cols: Partition columns.
            parquet_pruning: Whether the parquet reader should use the predicate
                to prune row groups.
            file_extension: File extension; only files with this extension are
                selected for data input.
            skip_metadata: Whether the parquet reader should skip any metadata
                that may be in the file schema. This can help avoid schema
                conflicts due to metadata.
            schema: An optional schema representing the parquet files. If None,
                the parquet reader will try to infer it based on data in the
                file.
            file_sort_order: Sort order for the file. Each sort key can be
                specified as a column name (``str``), an expression
                (``Expr``), or a ``SortExpr``.
            object_store: A pre-configured object store instance (e.g.
                :py:class:`~datafusion.object_store.AmazonS3`,
                :py:class:`~datafusion.object_store.GoogleCloud`,
                :py:class:`~datafusion.object_store.MicrosoftAzure`) to use
                for accessing the file. When provided, the store is
                automatically registered for the URL scheme and host parsed
                from ``path``, removing the need to call
                :py:meth:`register_object_store` separately. This is
                especially useful in multi-threaded environments where
                setting credentials via ``os.environ`` is not thread-safe.

        Returns:
            DataFrame representation of the read Parquet files

        Examples:
            Read a local Parquet file:

            >>> import datafusion
            >>> ctx = datafusion.SessionContext()
            >>> df = ctx.read_parquet("data.parquet")  # doctest: +SKIP

            Read from S3 with inline credentials (thread-safe):

            >>> from datafusion.object_store import AmazonS3  # doctest: +SKIP
            >>> store = AmazonS3(
            ...     bucket_name="my-bucket",
            ...     region="us-east-1",
            ...     access_key_id="...",
            ...     secret_access_key="...",
            ... )  # doctest: +SKIP
            >>> df = ctx.read_parquet(
            ...     "s3://my-bucket/data.parquet",
            ...     object_store=store,
            ... )  # doctest: +SKIP
        """
        if object_store is not None:
            self._register_object_store_for_path(path, object_store)
        if table_partition_cols is None:
            table_partition_cols = []
        table_partition_cols = _convert_table_partition_cols(table_partition_cols)
        file_sort_order = self._convert_file_sort_order(file_sort_order)
        return DataFrame(
            self.ctx.read_parquet(
                path,
                table_partition_cols,
                parquet_pruning,
                file_extension,
                skip_metadata,
                schema,
                file_sort_order,
            )
        )

    def read_avro(
        self,
        path: str | pathlib.Path,
        schema: pa.Schema | None = None,
        file_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
        file_extension: str = ".avro",
        object_store: Any | None = None,
    ) -> DataFrame:
        """Create a :py:class:`DataFrame` for reading Avro data source.

        Args:
            path: Path to the Avro file.
            schema: The data source schema.
            file_partition_cols: Partition columns.
            file_extension: File extension to select.
            object_store: A pre-configured object store instance to use for
                accessing the file. When provided, the store is automatically
                registered for the URL scheme and host parsed from ``path``.

        Returns:
            DataFrame representation of the read Avro file
        """
        if object_store is not None:
            self._register_object_store_for_path(path, object_store)
        if file_partition_cols is None:
            file_partition_cols = []
        file_partition_cols = _convert_table_partition_cols(file_partition_cols)
        return DataFrame(
            self.ctx.read_avro(path, schema, file_partition_cols, file_extension)
        )

    def read_arrow(
        self,
        path: str | pathlib.Path,
        schema: pa.Schema | None = None,
        file_extension: str = ".arrow",
        file_partition_cols: list[tuple[str, str | pa.DataType]] | None = None,
        object_store: Any | None = None,
    ) -> DataFrame:
        """Create a :py:class:`DataFrame` for reading an Arrow IPC data source.

        Args:
            path: Path to the Arrow IPC file.
            schema: The data source schema.
            file_extension: File extension to select.
            file_partition_cols: Partition columns.
            object_store: A pre-configured object store instance to use for
                accessing the file. When provided, the store is automatically
                registered for the URL scheme and host parsed from ``path``.

        Returns:
            DataFrame representation of the read Arrow IPC file.

        Examples:
            >>> import tempfile, os
            >>> ctx = dfn.SessionContext()
            >>> table = pa.table({"a": [1, 2, 3]})
            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = os.path.join(tmpdir, "data.arrow")
            ...     with pa.ipc.new_file(path, table.schema) as writer:
            ...         writer.write_table(table)
            ...     df = ctx.read_arrow(path)
            ...     df.collect()[0].column(0)
            <pyarrow.lib.Int64Array object at ...>
            [
              1,
              2,
              3
            ]

            Provide an explicit ``schema`` to override schema inference:

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = os.path.join(tmpdir, "data.arrow")
            ...     with pa.ipc.new_file(path, table.schema) as writer:
            ...         writer.write_table(table)
            ...     df = ctx.read_arrow(path, schema=pa.schema([("a", pa.int64())]))
            ...     df.collect()[0].column(0)
            <pyarrow.lib.Int64Array object at ...>
            [
              1,
              2,
              3
            ]

            Use ``file_extension`` to read files with a non-default extension:

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = os.path.join(tmpdir, "data.ipc")
            ...     with pa.ipc.new_file(path, table.schema) as writer:
            ...         writer.write_table(table)
            ...     df = ctx.read_arrow(path, file_extension=".ipc")
            ...     df.collect()[0].column(0)
            <pyarrow.lib.Int64Array object at ...>
            [
              1,
              2,
              3
            ]
        """
        if object_store is not None:
            self._register_object_store_for_path(path, object_store)
        if file_partition_cols is None:
            file_partition_cols = []
        file_partition_cols = _convert_table_partition_cols(file_partition_cols)
        return DataFrame(
            self.ctx.read_arrow(path, schema, file_extension, file_partition_cols)
        )

    def read_empty(self) -> DataFrame:
        """Create an empty :py:class:`DataFrame` with no columns or rows.

        See Also:
            This is an alias for :meth:`empty_table`.
        """
        return self.empty_table()

    def read_table(
        self, table: Table | TableProviderExportable | DataFrame | pa.dataset.Dataset
    ) -> DataFrame:
        """Creates a :py:class:`~datafusion.dataframe.DataFrame` from a table."""
        return DataFrame(self.ctx.read_table(table))

    def execute(self, plan: ExecutionPlan, partitions: int) -> RecordBatchStream:
        """Execute the ``plan`` and return the results."""
        return RecordBatchStream(self.ctx.execute(plan._raw_plan, partitions))

    @staticmethod
    def _convert_file_sort_order(
        file_sort_order: Sequence[Sequence[SortKey]] | None,
    ) -> list[list[expr_internal.SortExpr]] | None:
        """Convert nested ``SortKey`` sequences into raw sort expressions.

        Each ``SortKey`` can be a column name string, an ``Expr``, or a
        ``SortExpr`` and will be converted using
        :func:`datafusion.expr.sort_list_to_raw_sort_list`.
        """
        # Convert each ``SortKey`` in the provided sort order to the low-level
        # representation expected by the Rust bindings.
        return (
            [sort_list_to_raw_sort_list(f) for f in file_sort_order]
            if file_sort_order is not None
            else None
        )

    @staticmethod
    def _convert_table_partition_cols(
        table_partition_cols: list[tuple[str, str | pa.DataType]],
    ) -> list[tuple[str, pa.DataType]]:
        warn = False
        converted_table_partition_cols = []

        for col, data_type in table_partition_cols:
            if isinstance(data_type, str):
                warn = True
                if data_type == "string":
                    converted_data_type = pa.string()
                elif data_type == "int":
                    converted_data_type = pa.int32()
                else:
                    message = (
                        f"Unsupported literal data type '{data_type}' for partition "
                        "column. Supported types are 'string' and 'int'"
                    )
                    raise ValueError(message)
            else:
                converted_data_type = data_type

            converted_table_partition_cols.append((col, converted_data_type))

        if warn:
            message = (
                "using literals for table_partition_cols data types is deprecated,"
                "use pyarrow types instead"
            )
            warnings.warn(
                message,
                category=DeprecationWarning,
                stacklevel=2,
            )

        return converted_table_partition_cols

    def __datafusion_task_context_provider__(self) -> Any:
        """Access the PyCapsule FFI_TaskContextProvider."""
        return self.ctx.__datafusion_task_context_provider__()

    @property
    def __datafusion_codec_id__(self) -> str:
        """Identity this context carries when installed as an extension codec.

        A context can be installed on another session as an extension codec,
        which tags the payloads it writes with this string. It is unique per
        session, so two contexts can be installed on one session and a plan
        written through one will not be decoded by the other.

        Contexts derived from the same session — including the ones returned by
        :py:meth:`with_logical_extension_codec` and
        :py:meth:`with_python_udf_inlining` — report the same id, so only one of
        them can be installed on a given session.

        Examples:
            >>> from datafusion import SessionContext
            >>> ctx = SessionContext()
            >>> ctx.__datafusion_codec_id__.startswith("session:")
            True
            >>> ctx.__datafusion_codec_id__ == SessionContext().__datafusion_codec_id__
            False
        """
        return self.ctx.__datafusion_codec_id__

    def __datafusion_logical_extension_codec__(self, session: Any = None) -> Any:
        """Access the PyCapsule FFI_LogicalExtensionCodec.

        ``session`` is accepted so a context satisfies the same protocol an
        extension library implements, where the argument is how the library
        reaches the session it is being installed on. A context already is one,
        so the argument is ignored.
        """
        return self.ctx.__datafusion_logical_extension_codec__(session)

    def __datafusion_query_planner__(self, session: Any = None) -> Any:
        """Access the ``FFI_QueryPlanner`` PyCapsule for the current planner.

        See :meth:`__datafusion_logical_extension_codec__` for ``session``.
        """
        return self.ctx.__datafusion_query_planner__(session)

    def with_logical_extension_codec(
        self,
        codec: LogicalExtensionCodecExportable | _PyCapsule,
        codec_id: str | None = None,
    ) -> SessionContext:
        """Create a new session context with an additional logical codec.

        Only FFI codecs are supported. Pass any object implementing
        ``__datafusion_logical_extension_codec__`` (see
        :py:class:`~datafusion.user_defined.LogicalExtensionCodecExportable`).

        Codecs compose: each call appends the codec rather than replacing
        codecs installed earlier, so one session can carry codecs from several
        independent libraries and the order they are installed in does not
        affect decoding.

        A serialized plan records which codec wrote each payload, as a short id
        taken from the codec's class. ``codec_id`` overrides that id and is
        normally unnecessary. Pass it when installing from a bare ``PyCapsule``,
        which has no class to take an id from, or when installing two instances
        of one class, which otherwise claim the same id and raise ``ValueError``.

        The returned context shares its session state with the original, so a
        later registration on either is visible to both, and an installed query
        planner is rebound on the shared session even if the returned context is
        discarded.

        See :ref:`ffi` in the online documentation for how ids are assigned,
        what an extension codec has to implement, and a worked multi-library
        registration recipe.

        Examples:
            >>> from datafusion import SessionContext
            >>> ctx = SessionContext()
            >>> ctx = ctx.with_logical_extension_codec(
            ...     my_library.Codec()
            ... )  # doctest: +SKIP

            Installing from a bare capsule, pinning the id so encoded
            plans remain decodable on another session:

            >>> ctx = ctx.with_logical_extension_codec(
            ...     capsule, codec_id="my_library.Codec"
            ... )  # doctest: +SKIP
        """
        new_internal = self.ctx.with_logical_extension_codec(codec, codec_id)
        new = SessionContext.__new__(SessionContext)
        new.ctx = new_internal
        return new

    def logical_extension_codec_ids(self) -> list[str]:
        """List the logical extension codecs installed on this session.

        Returns the identity of each installed codec, in install order. Those
        identities are what encoding stamps onto a payload and what decoding
        dispatches on, so this is how to check which library owns a plan and
        whether a session is able to decode one.

        DataFusion's own default codec is not listed. It handles whatever no
        installed codec claims, and it carries no identity to list.

        Examples:
            >>> from datafusion import SessionContext
            >>> ctx = SessionContext()
            >>> ctx.logical_extension_codec_ids()
            []
            >>> ctx = ctx.with_logical_extension_codec(
            ...     my_library.Codec()
            ... )  # doctest: +SKIP
            >>> ctx.logical_extension_codec_ids()  # doctest: +SKIP
            ['my_library.Codec']
        """
        return self.ctx.logical_extension_codec_ids()

    def __datafusion_physical_extension_codec__(self, session: Any = None) -> Any:
        """Access the PyCapsule FFI_PhysicalExtensionCodec.

        See :meth:`__datafusion_logical_extension_codec__` for ``session``.
        """
        return self.ctx.__datafusion_physical_extension_codec__(session)

    def physical_extension_codec_ids(self) -> list[str]:
        """List the physical extension codecs installed on this session.

        See :py:meth:`logical_extension_codec_ids`.

        Examples:
            >>> from datafusion import SessionContext
            >>> ctx = SessionContext()
            >>> ctx.physical_extension_codec_ids()
            []
        """
        return self.ctx.physical_extension_codec_ids()

    def with_physical_extension_codec(
        self,
        codec: PhysicalExtensionCodecExportable | _PyCapsule,
        codec_id: str | None = None,
    ) -> SessionContext:
        """Create a new session context with an additional physical codec.

        Only FFI codecs are supported. Pass any object implementing
        ``__datafusion_physical_extension_codec__`` (see
        :py:class:`~datafusion.user_defined.PhysicalExtensionCodecExportable`).

        Composes and assigns an id exactly as
        :py:meth:`with_logical_extension_codec` does, including when to pass
        ``codec_id`` and what the returned context shares. See that method.

        Examples:
            >>> from datafusion import SessionContext
            >>> ctx = SessionContext()
            >>> ctx = ctx.with_physical_extension_codec(
            ...     my_library.PhysicalCodec()
            ... )  # doctest: +SKIP

            >>> ctx = ctx.with_physical_extension_codec(
            ...     capsule, codec_id="my_library.PhysicalCodec"
            ... )  # doctest: +SKIP
        """
        new_internal = self.ctx.with_physical_extension_codec(codec, codec_id)
        new = SessionContext.__new__(SessionContext)
        new.ctx = new_internal
        return new

    def with_python_udf_inlining(self, *, enabled: bool) -> SessionContext:
        """Control whether Python UDFs are embedded in serialized expressions.

        ``enabled`` is keyword-only and required: callers must pick a
        mode explicitly. Fresh sessions inline UDFs (``enabled=True``
        behavior) until this method overrides the toggle.

        With ``enabled=True``, serialized expressions carry the Python
        code for any scalar, aggregate, or window UDFs they reference.
        The receiver rebuilds the UDFs from those bytes and does not
        need to register them first.

        With ``enabled=False``, serialized expressions store only the
        UDF names. This has two uses:

        * **Cross-language portability.** The bytes can be decoded by a
          non-Python receiver, which must already have UDFs registered
          under matching names.
        * **Safer deserialization.** :meth:`Expr.from_bytes` will refuse
          to rebuild Python UDFs rather than call ``cloudpickle.loads``
          on untrusted input.

        The setting affects :meth:`Expr.to_bytes` and
        :meth:`Expr.from_bytes` whenever this session is passed as the
        ``ctx`` argument. :func:`pickle.dumps` and :func:`pickle.loads`
        do not pass a context, so to apply the setting through pickle,
        register this session with
        :func:`datafusion.ipc.set_sender_ctx` on the sender and
        :func:`datafusion.ipc.set_worker_ctx` on the receiver.

        .. warning:: Security
            This setting narrows only :meth:`Expr.from_bytes`. Calling
            :func:`pickle.loads` on untrusted bytes remains unsafe
            regardless of the toggle.

        Returns a new :class:`SessionContext` with the toggle applied;
        the original context's own codec settings are unchanged. The
        returned context shares its session state with the original, so
        a later registration on either is visible to both. If a custom
        query planner is installed, it is rebuilt against the new codecs
        on the shared session, so the original context plans with them
        too. This happens on the shared session, so it takes effect even
        if the returned context is discarded.

        Examples:
            >>> import pyarrow as pa
            >>> from datafusion import SessionContext, Expr, col, udf
            >>> ctx = SessionContext()
            >>> identity = udf(lambda a: a, [pa.int64()], pa.int64(),
            ...                volatility="immutable", name="identity_demo")
            >>> ctx.register_udf(identity)
            >>> blob = identity(col("x")).to_bytes(ctx)
            >>> strict = SessionContext().with_python_udf_inlining(enabled=False)
            >>> try:
            ...     Expr.from_bytes(blob, strict)
            ... except Exception as e:
            ...     print("Refusing to deserialize" in str(e))
            True
        """
        new_internal = self.ctx.with_python_udf_inlining(enabled)
        new = SessionContext.__new__(SessionContext)
        new.ctx = new_internal
        return new