context.py

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# to you under the Apache License, Version 2.0 (the
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#   http://www.apache.org/licenses/LICENSE-2.0
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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 sys
import uuid
import warnings
from typing import TYPE_CHECKING, Any, Protocol

if sys.version_info >= (3, 13):
    from warnings import deprecated
else:
    from typing_extensions import deprecated


from urllib.parse import urlparse

import pyarrow as pa

from datafusion import extensions as _extensions
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]

    if sys.version_info >= (3, 13):
        from types import CapsuleType as _PyCapsule
    else:
        from typing_extensions import CapsuleType as _PyCapsule

    from datafusion.catalog import CatalogProvider, Table
    from datafusion.common import DFSchema
    from datafusion.expr import Expr, SortKey

    # Type-only on purpose. `datafusion.extensions` is the one home for the
    # capsule-getter protocols; importing these at runtime would make them
    # reachable as `datafusion.context.*`, and for
    # `PhysicalOptimizerRuleExportable` would restore the 54.0.0 path that
    # 55.0.0 drops. Runtime checks go through the private `_extensions` alias.
    from datafusion.extensions import (
        PhysicalOptimizerRuleExportable,
        QueryPlannerExportable,
        SessionComponentsExportable,
        SessionPlannerExportable,
    )
    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.

    See :ref:`io_custom_table_provider` for registering one, and
    :ref:`extension_providers` for writing one.

    Args:
        session: See
            :py:class:`~datafusion.user_defined.LogicalExtensionCodecExportable`.
            For this getter it is a session when the provider is registered
            through :py:meth:`SessionContext.register_table` and the host's
            logical codec when it goes through
            :py:meth:`datafusion.catalog.Schema.register_table`, so
            duck-typing it is not optional.
    """

    def __datafusion_table_provider__(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.

        Each entry is applied as though passed to :py:meth:`set`, so the same
        keys are rejected. See :ref:`configuration`.

        Args:
            config_options: Options to apply, keyed by fully qualified name.

        Raises:
            ValueError: If a key names no known option, or a value does not
                parse as that option's declared type. Which of several bad
                entries is reported is not defined.

        Example usage:

        >>> from datafusion import SessionConfig, SessionContext
        >>> ctx = SessionContext(SessionConfig())
        >>> config = SessionConfig(
        ...     config_options={"datafusion.execution.batch_size": "1024"}
        ... )
        >>> ctx = SessionContext(config.with_information_schema(True))
        >>> ctx.sql(
        ...     "select value from information_schema.df_settings"
        ...     " where name = 'datafusion.execution.batch_size'"
        ... ).collect()[0]["value"][0]
        <pyarrow.StringScalar: '1024'>
        """
        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 by its fully qualified key.

        Not every key that ``information_schema.df_settings`` lists can be set
        here: the ``datafusion.runtime.*`` entries come from the runtime
        environment rather than from the session config. See
        :ref:`configuration`.

        Args:
            key: Option key including its namespace, such as
                ``datafusion.execution.batch_size``.
            value: Option value as a string, parsed according to the type the
                option declares.

        Returns:
            This :py:class:`SessionConfig`, modified in place, so that calls
            chain.

        Raises:
            ValueError: If ``key`` names no known option, or if ``value`` does
                not parse as that option's declared type.

        Example usage:

        >>> from datafusion import SessionConfig, SessionContext
        >>> config = SessionConfig().set("datafusion.execution.batch_size", "1024")
        >>> ctx = SessionContext(config.with_information_schema(True))
        >>> ctx.sql(
        ...     "select value from information_schema.df_settings"
        ...     " where name = 'datafusion.execution.batch_size'"
        ... ).collect()[0]["value"][0]
        <pyarrow.StringScalar: '1024'>
        """
        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.

    **A context is a handle on a session, not the session itself.** The
    ``with_*`` methods — :py:meth:`with_logical_extension_codec`,
    :py:meth:`with_physical_extension_codec`,
    :py:meth:`with_python_udf_inlining`, and :py:meth:`with_extensions` —
    return a new context wrapping the *same* underlying session. Only the
    Python-side codec settings differ; catalogs, tables, registered functions,
    and configuration are the one shared session, so a registration through
    either handle is visible to both.

    A few things therefore belong to the session rather than to a handle, and
    take effect even if the handle that set them is discarded: the query
    planner (see :py:meth:`set_query_planner`), and the rebuild of an installed
    foreign planner that follows installing a codec. :ref:`extension_sessions`
    in the online documentation works through when that matters.

    **Keep a context alive for as long as anything derived from it is in use.**
    A :py:class:`~datafusion.DataFrame`, logical plan, or exported capsule does
    not extend the session's lifetime. Once the last context on a session is
    collected, any operation that reaches an extension codec fails with
    ``TaskContextProvider went out of scope over FFI boundary``.
    """

    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)