DataFusion

https://arrow.apache.org/datafusion

Warning

This backend is experimental and is subject to backwards incompatible changes.

Install

Install Ibis and dependencies for the Apache DataFusion backend:

pip

Install with the Apache datafusion extra:

pip install 'ibis-framework[datafusion]'

And connect:

import ibis

con = ibis.datafusion.connect()

1

Adjust connection parameters as needed.

conda

Install for Apache DataFusion:

conda install -c conda-forge ibis-datafusion

And connect:

import ibis

con = ibis.datafusion.connect()

1

Adjust connection parameters as needed.

mamba

Install for Apache DataFusion:

mamba install -c conda-forge ibis-datafusion

And connect:

import ibis

con = ibis.datafusion.connect()

1

Adjust connection parameters as needed.

Connect

ibis.datafusion.connect

con = ibis.datafusion.connect()

con = ibis.datafusion.connect(
    config={"table1": "path/to/file.parquet", "table2": "path/to/file.csv"}
)

Note

ibis.datafusion.connect is a thin wrapper around ibis.backends.datafusion.Backend.do_connect.

Connection Parameters

do_connect

do_connect(self, config=None)

Create a Datafusion backend for use with Ibis.

Parameters

Name	Type	Description	Default
config	Mapping[str, str | Path] | SessionContext | None	Mapping of table names to files.	None

Examples

>>> import ibis
>>> config = {"t": "path/to/file.parquet", "s": "path/to/file.csv"}
>>> ibis.datafusion.connect(config)

datafusion.Backend

compile

compile(self, expr, params=None, **kwargs)

Compile an expression.

create_database

create_database(self, name, force=False)

Create a new database.

Parameters

Name	Type	Description	Default
name	str	Name of the new database.	required
force	bool	If False, an exception is raised if the database already exists.	False

create_schema

create_schema(self, name, database=None, force=False)

Create a schema named name in database.

Parameters

Name	Type	Description	Default
name	str	Name of the schema to create.	required
database	str | None	Name of the database in which to create the schema. If None, the current database is used.	None
force	bool	If False, an exception is raised if the schema exists.	False

create_table

create_table(self, *_, **__)

Create a new table.

Parameters

Name	Type	Description	Default
name	str	Name of the new table.	required
obj	pd.DataFrame | pa.Table | ir.Table | None	An Ibis table expression or pandas table that will be used to extract the schema and the data of the new table. If not provided, schema must be given.	None
schema	ibis.Schema | None	The schema for the new table. Only one of schema or obj can be provided.	None
database	str | None	Name of the database where the table will be created, if not the default.	None
temp	bool	Whether a table is temporary or not	False
overwrite	bool	Whether to clobber existing data	False

Returns

Type	Description
Table	The table that was created.

create_view

create_view(self, *_, **__)

Create a new view from an expression.

Parameters

Name	Type	Description	Default
name	str	Name of the new view.	required
obj	ir.Table	An Ibis table expression that will be used to create the view.	required
database	str | None	Name of the database where the view will be created, if not provided the database’s default is used.	None
overwrite	bool	Whether to clobber an existing view with the same name	False

Returns

Type	Description
Table	The view that was created.

drop_database

drop_database(self, name, force=False)

Drop a database with name name.

Parameters

Name	Type	Description	Default
name	str	Database to drop.	required
force	bool	If False, an exception is raised if the database does not exist.	False

drop_schema

drop_schema(self, name, database=None, force=False)

Drop the schema with name in database.

Parameters

Name	Type	Description	Default
name	str	Name of the schema to drop.	required
database	str | None	Name of the database to drop the schema from. If None, the current database is used.	None
force	bool	If False, an exception is raised if the schema does not exist.	False

drop_table

drop_table(self, *_, **__)

Drop a table.

Parameters

Name	Type	Description	Default
name	str	Name of the table to drop.	required
database	str | None	Name of the database where the table exists, if not the default.	None
force	bool	If False, an exception is raised if the table does not exist.	False

drop_view

drop_view(self, *_, **__)

Drop a view.

Parameters

Name	Type	Description	Default
name	str	Name of the view to drop.	required
database	str | None	Name of the database where the view exists, if not the default.	None
force	bool	If False, an exception is raised if the view does not exist.	False

execute

execute(self, expr, params=None, limit='default', **kwargs)

Execute an expression.

has_operation

has_operation(cls, operation)

Return whether the backend implements support for operation.

Parameters

Name	Type	Description	Default
operation	type[ops.Value]	A class corresponding to an operation.	required

Returns

Type	Description
bool	Whether the backend implements the operation.

Examples

>>> import ibis
>>> import ibis.expr.operations as ops
>>> ibis.sqlite.has_operation(ops.ArrayIndex)
False
>>> ibis.postgres.has_operation(ops.ArrayIndex)
True

list_databases

list_databases(self, like=None)

List existing databases in the current connection.

Parameters

Name	Type	Description	Default
like	str | None	A pattern in Python’s regex format to filter returned database names.	None

Returns

Type	Description
list[str]	The database names that exist in the current connection, that match the like pattern if provided.

list_schemas

list_schemas(self, like=None, database=None)

List existing schemas in the current connection.

Parameters

Name	Type	Description	Default
like	str | None	A pattern in Python’s regex format to filter returned schema names.	None
database	str | None	The database to list schemas from. If None, the current database is searched.	None

Returns

Type	Description
list[str]	The schema names that exist in the current connection, that match the like pattern if provided.

list_tables

list_tables(self, like=None, database=None)

List the available tables.

read_csv

read_csv(self, path, table_name=None, **kwargs)

Register a CSV file as a table in the current database.

Parameters

Name	Type	Description	Default
path	str | Path	The data source. A string or Path to the CSV file.	required
table_name	str | None	An optional name to use for the created table. This defaults to a sequentially generated name.	None
**kwargs	Any	Additional keyword arguments passed to Datafusion loading function.	{}

Returns

Type	Description
ir.Table	The just-registered table

read_delta

read_delta(self, source_table, table_name=None, **kwargs)

Register a Delta Lake table as a table in the current database.

Parameters

Name	Type	Description	Default
source_table	str | Path	The data source. Must be a directory containing a Delta Lake table.	required
table_name	str | None	An optional name to use for the created table. This defaults to a sequentially generated name.	None
**kwargs	Any	Additional keyword arguments passed to deltalake.DeltaTable.	{}

Returns

Type	Description
ir.Table	The just-registered table

read_parquet

read_parquet(self, path, table_name=None, **kwargs)

Register a parquet file as a table in the current database.

Parameters

Name	Type	Description	Default
path	str | Path	The data source.	required
table_name	str | None	An optional name to use for the created table. This defaults to a sequentially generated name.	None
**kwargs	Any	Additional keyword arguments passed to Datafusion loading function.	{}

Returns

Type	Description
ir.Table	The just-registered table

register

register(self, source, table_name=None, **kwargs)

Register a data set with table_name located at source.

Parameters

Name	Type	Description	Default
source	str | Path | pa.Table | pa.RecordBatch | pa.Dataset | pd.DataFrame	The data source(s). May be a path to a file or directory of parquet/csv files, a pandas dataframe, or a pyarrow table, dataset or record batch.	required
table_name	str | None	The name of the table	None
kwargs	Any	Datafusion-specific keyword arguments	{}

Examples

Register a csv:

>>> import ibis
>>> conn = ibis.datafusion.connect(config)
>>> conn.register("path/to/data.csv", "my_table")
>>> conn.table("my_table")

Register a PyArrow table:

>>> import pyarrow as pa
>>> tab = pa.table({"x": [1, 2, 3]})
>>> conn.register(tab, "my_table")
>>> conn.table("my_table")

Register a PyArrow dataset:

>>> import pyarrow.dataset as ds
>>> dataset = ds.dataset("path/to/table")
>>> conn.register(dataset, "my_table")
>>> conn.table("my_table")

table

table(self, name, schema=None)

Get an ibis expression representing a DataFusion table.

Parameters

Name	Type	Description	Default
name	str	The name of the table to retrieve	required
schema	sch.Schema | None	An optional schema for the table	None

Returns

Type	Description
Table	A table expression

to_pyarrow_batches

to_pyarrow_batches(self, expr, *, params=None, limit=None, chunk_size=1000000, **kwargs)

Execute expression and return a RecordBatchReader.

This method is eager and will execute the associated expression immediately.

Parameters

Name	Type	Description	Default
expr	ir.Expr	Ibis expression to export to pyarrow	required
limit	int | str | None	An integer to effect a specific row limit. A value of None means “no limit”. The default is in ibis/config.py.	None
params	Mapping[ir.Scalar, Any] | None	Mapping of scalar parameter expressions to value.	None
chunk_size	int	Maximum number of rows in each returned record batch.	1000000
kwargs	Any	Keyword arguments	{}

Returns

Type	Description
results	RecordBatchReader