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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
""":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
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 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_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`,
You can’t perform that action at this time.