Amazon Athena

https://aws.amazon.com/athena

Install

Install Ibis and dependencies for the Athena backend:

Install with the athena extra:

pip install 'ibis-framework[athena]'

And connect:

import ibis

con = ibis.athena.connect(s3_staging_dir="s3://...")
NoteAt a minimum, the s3_staging_dir argument must be provided.

This argument tells the underlying driver library–pyathena–and ultimately Athena itself where to dump query results.

  1. Adjust other connection parameters as needed.

Install for Athena:

conda install -c conda-forge ibis-athena
import ibis

con = ibis.athena.connect(s3_staging_dir="s3://...")
NoteAt a minimum, the s3_staging_dir argument must be provided.

This argument tells the underlying driver library–pyathena–and ultimately Athena itself where to dump query results.

  1. Adjust other connection parameters as needed.

Install for Athena:

mamba install -c conda-forge ibis-athena
import ibis

con = ibis.athena.connect(s3_staging_dir="s3://my-bucket/")
NoteAt a minimum, the s3_staging_dir argument must be provided.

This argument tells the underlying driver library–pyathena–and ultimately Athena itself where to dump query results.

  1. Adjust other connection parameters as needed.

Connect

ibis.athena.connect

con = ibis.athena.connect(
    s3_staging_dir="s3://my-bucket/",
)
NoteAt a minimum, the s3_staging_dir argument must be provided.

This argument tells the underlying driver library–pyathena–and ultimately Athena itself where to dump query results.

Connection Parameters

do_connect

do_connect(self, *, s3_staging_dir, cursor_class=ArrowCursor, memtable_volume=None, schema_name='default', catalog_name='awsdatacatalog', **config)

Create an Ibis client connected to an Amazon Athena instance.

athena.Backend

begin

begin(self)

compile

compile(self, expr, /, *, limit=None, params=None, pretty=False)

Compile an expression to a SQL string.

Parameters

Name Type Description Default
expr ir.Expr An ibis expression to compile. required
limit str | int | None An integer to effect a specific row limit. A value of None means no limit. None
params Mapping[ir.Expr, Any] | None Mapping of scalar parameter expressions to value. None
pretty bool Pretty print the SQL query during compilation. False

Returns

Name Type Description
str Compiled expression

connect

connect(self, *args, **kwargs)

Connect to the database.

Parameters

Name Type Description Default
*args Mandatory connection parameters, see the docstring of do_connect for details. ()
**kwargs Extra connection parameters, see the docstring of do_connect for details. {}

Notes

This creates a new backend instance with saved args and kwargs, then calls reconnect and finally returns the newly created and connected backend instance.

Returns

Name Type Description
BaseBackend An instance of the backend

create_database

create_database(self, name, /, *, location=None, catalog=None, force=False)

Create a database named name in catalog.

NoteIbis does not use the word schema to refer to database hierarchy.

A collection of table is referred to as a database. A collection of database is referred to as a catalog.

These terms are mapped onto the corresponding features in each backend (where available), regardless of the terminology the backend uses.

See the Table Hierarchy Concepts Guide for more info.

Parameters

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

create_table

create_table(self, name, /, obj=None, *, schema=None, database=None, temp=False, overwrite=None, comment=None, properties=None, location=None, stored_as='PARQUET', partitioned_by=())

Create a table in Amazon Athena.

Parameters

Name Type Description Default
name str Name of the table to create required
obj ir.Table | pd.DataFrame | pa.Table | pl.DataFrame | pl.LazyFrame | None The data with which to populate the table; optional, but one of obj or schema must be specified None
schema sch.SchemaLike | None The schema of the table to create; optional, but one of obj or schema must be specified None
database str | None The database to insert the table into. If not provided, the current database is used. You can provide a single database name, like "mydb". For multi-level hierarchies, you can pass in a dotted string path like "catalog.database" or a tuple of strings like ("catalog", "database"). None
temp bool This parameter is not yet supported in the Amazon Athena backend, because Amazon Athena doesn’t implement temporary tables False
overwrite bool | None If True, replace the table if it already exists, otherwise fail if the table exists None
comment str | None Add a comment to the table None
properties Mapping[str, Any] | None Table properties to set on creation None
location str | None s3 location to store table data. Defaults to the s3_staging_dir bucket with the table name as the bucket key. None
stored_as str The file format in which to store table data. Defaults to parquet. 'PARQUET'
partitioned_by sch.SchemaLike Iterable of column name and type pairs/mapping/schema by which to partition the table. ()

create_view

create_view(self, name, /, obj, *, database=None, overwrite=False)

Create a view from an Ibis expression.

Parameters

Name Type Description Default
name str The name of the view to create. required
obj ir.Table The Ibis expression to create the view from. required
database str | None The database that the view should be created in. None
overwrite bool If True, replace an existing view with the same name. False

Returns

Name Type Description
ir.Table A table expression representing the view.

disconnect

disconnect(self)

Disconnect from the backend.

drop_database

drop_database(self, name, /, *, catalog=None, force=False)

Drop the database with name in catalog.

NoteIbis does not use the word schema to refer to database hierarchy.

A collection of table is referred to as a database. A collection of database is referred to as a catalog.

These terms are mapped onto the corresponding features in each backend (where available), regardless of the terminology the backend uses.

See the Table Hierarchy Concepts Guide for more info.

Parameters

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

drop_table

drop_table(self, name, /, *, database=None, force=False)

Drop a table from the backend.

Parameters

Name Type Description Default
name str The name of the table to drop required
database tuple[str, str] | str | None The database that the table is located in. None
force bool If True, do not raise an error if the table does not exist. False

drop_view

drop_view(self, name, /, *, database=None, force=False)

Drop a view from the backend.

Parameters

Name Type Description Default
name str The name of the view to drop. required
database str | None The database that the view is located in. None
force bool If True, do not raise an error if the view does not exist. False

execute

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

Execute an Ibis expression and return a pandas DataFrame, Series, or scalar.

Parameters

Name Type Description Default
expr ir.Expr Ibis expression to execute. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
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
kwargs Any Keyword arguments {}

Returns

Name Type Description
DataFrame | Series | scalar The result of the expression execution.

from_connection

from_connection(cls, con, /, *, memtable_volume=None)

Create an Ibis client from an existing connection to an Amazon Athena instance.

Parameters

Name Type Description Default
con An existing connection to an Amazon Athena instance. required
memtable_volume str | None The volume to use for Ibis memtables. None

get_schema

get_schema(self, table_name, *, catalog=None, database=None)

Compute the schema of a table.

Parameters

Name Type Description Default
table_name str May not be fully qualified. Use database if you want to qualify the identifier. required
catalog str | None Catalog name None
database str | None Database name None

Returns

Name Type Description
sch.Schema Ibis schema

has_operation

has_operation(cls, operation, /)

Return whether the backend supports the given operation.

Parameters

Name Type Description Default
operation type[ops.Value] Operation type, a Python class object. required

insert

insert(self, name, /, obj, *, database=None, overwrite=False)

Insert data into a table.

NoteIbis does not use the word schema to refer to database hierarchy.

A collection of table is referred to as a database. A collection of database is referred to as a catalog.

These terms are mapped onto the corresponding features in each backend (where available), regardless of whether the backend itself uses the same terminology.

Parameters

Name Type Description Default
name str The name of the table to which data needs will be inserted required
obj pd.DataFrame | ir.Table | list | dict The source data or expression to insert required
database str | None Name of the attached database that the table is located in. For backends that support multi-level table hierarchies, you can pass in a dotted string path like "catalog.database" or a tuple of strings like ("catalog", "database"). None
overwrite bool If True then replace existing contents of table False

list_catalogs

list_catalogs(self, *, like=None)

list_databases

list_databases(self, like=None, catalog=None)

List existing databases in the current connection.

NoteIbis does not use the word schema to refer to database hierarchy.

A collection of table is referred to as a database. A collection of database is referred to as a catalog.

These terms are mapped onto the corresponding features in each backend (where available), regardless of the terminology the backend uses.

See the Table Hierarchy Concepts Guide for more info.

Parameters

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

Returns

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

list_tables

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

The table names that match like in the given database.

For some backends, the tables may be files in a directory, or other equivalent entities in a SQL database.

Parameters

Name Type Description Default
like str | None A pattern in Python’s regex format. None
database tuple[str, str] | str | None The database, or (catalog, database) from which to list tables. For backends that support a single-level table hierarchy, you can pass in a string like "bar". For backends that support multi-level table hierarchies, you can pass in a dotted string path like "catalog.database" or a tuple of strings like ("catalog", "database"). If not provided, the current database (and catalog, if applicable for this backend) is used. See the Table Hierarchy Concepts Guide for more info. None

Returns

Name Type Description
list[str] The list of the table names that match the pattern like.

Examples

This example uses the DuckDB backend, but the list_tables API works the same for other backends.

>>> import ibis
>>> con = ibis.duckdb.connect()
>>> foo = con.create_table("foo", schema=ibis.schema(dict(a="int")))
>>> con.list_tables()
['foo']
>>> bar = con.create_view("bar", foo)
>>> con.list_tables()
['bar', 'foo']
>>> con.create_database("my_database")
>>> con.list_tables(database="my_database")
[]
>>> con.raw_sql("CREATE TABLE my_database.baz (a INTEGER)")
<duckdb.duckdb.DuckDBPyConnection object at 0x...>
>>> con.list_tables(database="my_database")
['baz']

raw_sql

raw_sql(self, query, **kwargs)

read_csv

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

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

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 the backend loading function. {}

Returns

Name Type Description
ir.Table The just-registered table

read_delta

read_delta(self, path, /, *, table_name=None, **kwargs)

Register a Delta Lake table in the current database.

Parameters

Name Type Description Default
path 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 the underlying backend or library. {}

Returns

Name Type Description
ir.Table The just-registered table.

read_json

read_json(self, path, /, *, table_name=None, **kwargs)

Register a JSON file as a table in the current backend.

Parameters

Name Type Description Default
path str | Path The data source. A string or Path to the JSON 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 the backend loading function. {}

Returns

Name 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 backend.

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 the backend loading function. {}

Returns

Name Type Description
ir.Table The just-registered table

reconnect

reconnect(self)

Reconnect to the database already configured with connect.

register_options

register_options(cls)

Register custom backend options.

rename_table

rename_table(self, old_name, new_name)

Rename an existing table.

Parameters

Name Type Description Default
old_name str The old name of the table. required
new_name str The new name of the table. required

sql

sql(self, query, /, *, schema=None, dialect=None)

Create an Ibis table expression from a SQL query.

Parameters

Name Type Description Default
query str A SQL query string required
schema SchemaLike | None The schema of the query. If not provided, Ibis will try to infer the schema of the query. None
dialect str | None The SQL dialect of the query. If not provided, the backend’s dialect is assumed. This argument can be useful when the query is written in a different dialect from the backend. None

Returns

Name Type Description
ir.Table The table expression representing the query

table

table(self, name, /, *, database=None)

Construct a table expression from the corresponding table in the backend.

Parameters

Name Type Description Default
name str Table name required
database tuple[str, str] | str | None The database, or (catalog, database) from which to get the table. For backends that support a single-level table hierarchy, you can pass in a string like "bar". For backends that support multi-level table hierarchies, you can pass in a dotted string path like "catalog.database" or a tuple of strings like ("catalog", "database"). If not provided, the current database (and catalog, if applicable for this backend) is used. See the Table Hierarchy Concepts Guide for more info. None

Returns

Name Type Description
Table Table expression

Examples

>>> import ibis
>>> backend = ibis.duckdb.connect()

Get the “foo” table from the current database (and catalog, if applicable for this backend):

>>> backend.table("foo")

Get the “foo” table from the “bar” database (in DuckDB’s language they would say the “bar” schema, in SQL this would be "bar"."foo")

>>> backend.table("foo", database="bar")

Get the “foo” table from the “bar” database, within the “baz” catalog (in DuckDB’s language they would say the “bar” schema, and “baz” database, in SQL this would be "baz"."bar"."foo")

>>> backend.table("foo", database=("baz", "bar"))

to_csv

to_csv(self, expr, /, path, *, params=None, **kwargs)

Write the results of executing the given expression to a CSV file.

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

Parameters

Name Type Description Default
expr ir.Table The ibis expression to execute and persist to CSV. required
path str | Path The data source. A string or Path to the CSV file. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
kwargs Any Additional keyword arguments passed to pyarrow.csv.CSVWriter {}
https required

to_delta

to_delta(self, expr, /, path, *, params=None, **kwargs)

Write the results of executing the given expression to a Delta Lake table.

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

Parameters

Name Type Description Default
expr ir.Table The ibis expression to execute and persist to Delta Lake table. required
path str | Path The data source. A string or Path to the Delta Lake table. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
kwargs Any Additional keyword arguments passed to deltalake.writer.write_deltalake method {}

to_json

to_json(self, expr, /, path, **kwargs)

Write the results of expr to a json file of [{column -> value}, …] objects.

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

Parameters

Name Type Description Default
expr ir.Table The ibis expression to execute and persist to Delta Lake table. required
path str | Path The data source. A string or Path to the Delta Lake table. required
kwargs Any Additional, backend-specifc keyword arguments. {}

to_pandas

to_pandas(self, expr, /, *, params=None, limit=None, **kwargs)

Execute an Ibis expression and return a pandas DataFrame, Series, or scalar.

Note

This method is a wrapper around execute.

Parameters

Name Type Description Default
expr ir.Expr Ibis expression to execute. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
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
kwargs Any Keyword arguments {}

to_pandas_batches

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

Execute an Ibis expression and return an iterator of pandas DataFrames.

Parameters

Name Type Description Default
expr ir.Expr Ibis expression to execute. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
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
chunk_size int Maximum number of rows in each returned DataFrame batch. This may have no effect depending on the backend. 1000000
kwargs Any Keyword arguments {}

Returns

Name Type Description
Iterator[pd.DataFrame] An iterator of pandas DataFrames.

to_parquet

to_parquet(self, expr, /, path, *, params=None, **kwargs)

Write the results of executing the given expression to a parquet file.

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

Parameters

Name Type Description Default
expr ir.Table The ibis expression to execute and persist to parquet. required
path str | Path The data source. A string or Path to the parquet file. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
**kwargs Any Additional keyword arguments passed to pyarrow.parquet.ParquetWriter {}
https required

to_parquet_dir

to_parquet_dir(self, expr, /, directory, *, params=None, **kwargs)

Write the results of executing the given expression to a parquet file in a directory.

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

Parameters

Name Type Description Default
expr ir.Table The ibis expression to execute and persist to parquet. required
directory str | Path The data source. A string or Path to the directory where the parquet file will be written. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
**kwargs Any Additional keyword arguments passed to pyarrow.dataset.write_dataset {}
https required

to_polars

to_polars(self, expr, /, *, params=None, limit=None, **kwargs)

Execute expression and return results in as a polars DataFrame.

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

Parameters

Name Type Description Default
expr ir.Expr Ibis expression to export to polars. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
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
kwargs Any Keyword arguments {}

Returns

Name Type Description
dataframe A polars DataFrame holding the results of the executed expression.

to_pyarrow

to_pyarrow(self, expr, /, *, params=None, limit=None, **kwargs)

Execute expression to a pyarrow object.

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
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
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
kwargs Any Keyword arguments {}

Returns

Name Type Description
result If the passed expression is a Table, a pyarrow table is returned. If the passed expression is a Column, a pyarrow array is returned. If the passed expression is a Scalar, a pyarrow scalar is returned.

to_pyarrow_batches

to_pyarrow_batches(self, expr, /, *, params=None, limit=None, **_)

Return a stream of record batches.

The returned RecordBatchReader contains a cursor with an unbounded lifetime.

For analytics use cases this is usually nothing to fret about. In some cases you may need to explicit release the cursor.

Parameters

Name Type Description Default
expr ir.Expr Ibis expression required
params Mapping[ir.Scalar, Any] | None Bound parameters None
limit int | str | None Limit the result to this number of rows None

to_torch

to_torch(self, expr, /, *, params=None, limit=None, **kwargs)

Execute an expression and return results as a dictionary of torch tensors.

Parameters

Name Type Description Default
expr ir.Expr Ibis expression to execute. required
params Mapping[ir.Scalar, Any] | None Parameters to substitute into the expression. None
limit int | str | None An integer to effect a specific row limit. A value of None means no limit. None
kwargs Any Keyword arguments passed into the backend’s to_torch implementation. {}

Returns

Name Type Description
dict[str, torch.Tensor] A dictionary of torch tensors, keyed by column name.

truncate_table

truncate_table(self, name, /, *, database=None)

Delete all rows from a table.

NoteIbis does not use the word schema to refer to database hierarchy.

A collection of tables is referred to as a database. A collection of database is referred to as a catalog. These terms are mapped onto the corresponding features in each backend (where available), regardless of whether the backend itself uses the same terminology.

Parameters

Name Type Description Default
name str Table name required
database str | None Name of the attached database that the table is located in. For backends that support multi-level table hierarchies, you can pass in a dotted string path like "catalog.database" or a tuple of strings like ("catalog", "database"). None
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