BigQuery

https://cloud.google.com/bigquery

Install

Install Ibis and dependencies for the BigQuery backend:

Install with the bigquery extra:

pip install 'ibis-framework[bigquery]'

And connect:

import ibis

con = ibis.bigquery.connect()
1
Adjust connection parameters as needed.

Install for BigQuery:

conda install -c conda-forge ibis-bigquery

And connect:

import ibis

con = ibis.bigquery.connect()
1
Adjust connection parameters as needed.

Install for BigQuery:

mamba install -c conda-forge ibis-bigquery

And connect:

import ibis

con = ibis.bigquery.connect()
1
Adjust connection parameters as needed.

Connect

ibis.bigquery.connect

con = ibis.bigquery.connect(
    project_id="ibis-bq-project",
    dataset_id="testing",
)
Note

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

Connection Parameters

do_connect

do_connect(['self', 'project_id=None', "dataset_id=''", 'credentials=None', 'application_name=None', 'auth_local_webserver=True', 'auth_external_data=False', "auth_cache='default'", "partition_column='PARTITIONTIME'", 'client=None', 'storage_client=None', 'location=None'])

Create a Backend for use with Ibis.

Parameters
Name Type Description Default
project_id str | None A BigQuery project id. None
dataset_id str A dataset id that lives inside of the project indicated by project_id. ''
credentials google.auth.credentials.Credentials | None Optional credentials. None
application_name str | None A string identifying your application to Google API endpoints. None
auth_local_webserver bool Use a local webserver for the user authentication. Binds a webserver to an open port on localhost between 8080 and 8089, inclusive, to receive authentication token. If not set, defaults to False, which requests a token via the console. True
auth_external_data bool Authenticate using additional scopes required to query external data sources <https://cloud.google.com/bigquery/external-data-sources>_, such as Google Sheets, files in Google Cloud Storage, or files in Google Drive. If not set, defaults to False, which requests the default BigQuery scopes. False
auth_cache str Selects the behavior of the credentials cache. 'default'`` Reads credentials from disk if available, otherwise authenticates and caches credentials to disk.‘reauth’Authenticates and caches credentials to disk. `'none' Authenticates and does not cache credentials. Defaults to 'default'. 'default'
partition_column str | None Identifier to use instead of default _PARTITIONTIME partition column. Defaults to 'PARTITIONTIME'. 'PARTITIONTIME'
client bq.Client | None A Client from the google.cloud.bigquery package. If not set, one is created using the project_id and credentials. None
storage_client bqstorage.BigQueryReadClient | None A BigQueryReadClient from the google.cloud.bigquery_storage_v1 package. If not set, one is created using the project_id and credentials. None
location str | None Default location for BigQuery objects. None
Returns
Name Type Description
Backend An instance of the BigQuery backend.

ibis.connect URL format

In addition to ibis.bigquery.connect, you can also connect to BigQuery by passing a properly-formatted BigQuery connection URL to ibis.connect:

con = ibis.connect(f"bigquery://{project_id}/{dataset_id}")
Note

This assumes you have already authenticated via the gcloud CLI.

Finding your project_id and dataset_id

Log in to the Google Cloud Console to see which project_ids and dataset_ids are available to use.

bigquery_ids

BigQuery Authentication

The simplest way to authenticate with the BigQuery backend is to use Google’s gcloud CLI tool.

Once you have gcloud installed, you can authenticate to BigQuery (and other Google Cloud services) by running

gcloud auth login --update-adc

You will also likely want to configure a default project:

gcloud config set core/project <project_id>

For any authentication problems, or information on other ways of authenticating, see the gcloud CLI authorization guide.

bigquery.Backend

compile

compile(['self', 'expr', 'limit=None', 'params=None', 'pretty=True', '**kwargs'])

Compile an Ibis expression to a SQL string.

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', 'catalog=None', 'force=False', 'collate=None', '**options'])

Create a database named name in catalog.

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=False', 'default_collate=None', 'partition_by=None', 'cluster_by=None', 'options=None'])

Create a table in BigQuery.

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 BigQuery dataset in which to create the table; optional None
temp bool Whether the table is temporary False
overwrite bool If True, replace the table if it already exists, otherwise fail if the table exists False
default_collate str | None Default collation for string columns. See BigQuery’s documentation for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/collation-concepts None
partition_by str | None Partition the table by the given expression. See BigQuery’s documentation for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#partition_expression None
cluster_by Iterable[str] | None List of columns to cluster the table by. See BigQuery’s documentation for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#clustering_column_list None
options Mapping[str, Any] | None BigQuery-specific table options; see the BigQuery documentation for details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#table_option_list None

Returns

Name Type Description
Table The table that was just created

create_view

create_view(['self', 'name', 'obj', '*', 'database=None', 'overwrite=False'])

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

Name Type Description
Table The view that was created.

disconnect

disconnect(['self'])

Close the connection to the backend.

drop_database

drop_database(['self', 'name', 'catalog=None', 'force=False', 'cascade=False'])

Drop a BigQuery dataset.

drop_table

drop_table(['self', 'name', '*', 'database=None', 'force=False'])

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', 'name', '*', 'database=None', 'force=False'])

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'])

Compile and execute the given Ibis expression.

Compile and execute Ibis expression using this backend client interface, returning results in-memory in the appropriate object type

Parameters

Name Type Description Default
expr Ibis expression to execute required
limit Retrieve at most this number of values/rows. Overrides any limit already set on the expression. 'default'
params Query parameters None
kwargs Extra arguments specific to the backend {}

Returns

Name Type Description
pd.DataFrame | pd.Series | scalar Output from execution

from_connection

from_connection(['cls', 'client', "partition_column='PARTITIONTIME'", 'storage_client=None', "dataset_id=''"])

Create a BigQuery Backend from an existing Client.

Parameters

Name Type Description Default
client bq.Client A Client from the google.cloud.bigquery package. required
partition_column str | None Identifier to use instead of default _PARTITIONTIME partition column. Defaults to 'PARTITIONTIME'. 'PARTITIONTIME'
storage_client bqstorage.BigQueryReadClient | None A BigQueryReadClient from the google.cloud.bigquery_storage_v1 package. None
dataset_id str A dataset id that lives inside of the project attached to client. ''

get_schema

get_schema(['self', 'name', '*', 'catalog=None', 'database=None'])

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

Name 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

insert

insert(['self', 'table_name', 'obj', 'database=None', 'overwrite=False'])

Insert data into a table.

Parameters

Name Type Description Default
table_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. None
overwrite bool If True then replace existing contents of table False

list_databases

list_databases(['self', 'like=None', 'catalog=None'])

List existing databases in the current connection.

Ibis 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
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'])

List the tables in the database.

Ibis 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
like str | None A pattern to use for listing tables. None
database tuple[str, str] | str | None The database location to perform the list against. By default uses the current dataset (self.current_database) and project (self.current_catalog). To specify a table in a separate BigQuery dataset, you can pass in the dataset and project as a string "dataset.project", or as a tuple of strings (dataset, project). None

raw_sql

raw_sql(['self', 'query', 'params=None', 'page_size=None'])

read_csv

read_csv(['self', 'path', 'table_name=None', '**kwargs'])

Read CSV data into a BigQuery table.

Parameters

Name Type Description Default
path str | Path Path to a CSV file on GCS or the local filesystem. Globs are supported. required
table_name str | None Optional table name None
kwargs Any Additional keyword arguments passed to google.cloud.bigquery.LoadJobConfig. {}

Returns

Name Type Description
Table An Ibis table expression

read_delta

read_delta(['self', 'source', 'table_name=None', '**kwargs'])

Register a Delta Lake table in the current database.

Parameters

Name Type Description Default
source 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'])

Read newline-delimited JSON data into a BigQuery table.

Parameters

Name Type Description Default
path str | Path Path to a newline-delimited JSON file on GCS or the local filesystem. Globs are supported. required
table_name str | None Optional table name None
kwargs Any Additional keyword arguments passed to google.cloud.bigquery.LoadJobConfig. {}

Returns

Name Type Description
Table An Ibis table expression

read_parquet

read_parquet(['self', 'path', 'table_name=None', '**kwargs'])

Read Parquet data into a BigQuery table.

Parameters

Name Type Description Default
path str | Path Path to a Parquet file on GCS or the local filesystem. Globs are supported. required
table_name str | None Optional table name None
kwargs Any Additional keyword arguments passed to google.cloud.bigquery.LoadJobConfig. {}

Returns

Name Type Description
Table An Ibis table expression

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

set_database

set_database(['self', 'name'])

sql

sql(['self', 'query', 'schema=None', 'dialect=None'])

table

table(['self', 'name', 'database=None'])

Construct a table expression.

Parameters

Name Type Description Default
name str Table name required
database tuple[str, str] | str | None Database name None

Returns

Name Type Description
Table Table expression

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_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 and return results in as a pyarrow table.

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
Table A pyarrow table holding the results of the executed expression.

to_pyarrow_batches

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

Execute expression and return an iterator of pyarrow record batches.

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

Returns

Name Type Description
RecordBatchReader Collection of pyarrow RecordBatchs.

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.

Ibis 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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