Databricks SQL Connector for Python

The Databricks SQL Connector for Python is a Python library that allows you to use Python code to run SQL commands on Databricks clusters and Databricks SQL warehouses. The Databricks SQL Connector for Python is easier to set up and use than similar Python libraries such as pyodbc. This library follows PEP 249 – Python Database API Specification v2.0.

Note

The Databricks SQL Connector for Python also includes a SQLAlchemy dialect for Databricks. See Use SQLAlchemy with Databricks.

Requirements

A development machine running Python >=3.8 and <=3.11.
Databricks recommends that you use Python virtual environments, such as those provided by venv that are included with Python. Virtual environments help to ensure that you are using the correct versions of Python and the Databricks SQL Connector for Python together. Setting up and using virtual environments is outside of the scope of this article. For more information, see Creating virtual environments.
An existing cluster or SQL warehouse.

Get started

Install the Databricks SQL Connector for Python library on your development machine by running pip install databricks-sql-connector or python -m pip install databricks-sql-connector.
Gather the following information for the cluster or SQL warehouse that you want to use:
- The server hostname of the cluster. You can get this from the Server Hostname value in the Advanced Options > JDBC/ODBC tab for your cluster.
- The HTTP path of the cluster. You can get this from the HTTP Path value in the Advanced Options > JDBC/ODBC tab for your cluster.
- The server hostname of the SQL warehouse. You can get this from the Server Hostname value in the Connection Details tab for your SQL warehouse.
- The HTTP path of the SQL warehouse. You can get this from the HTTP Path value in the Connection Details tab for your SQL warehouse.

Authentication

The Databricks SQL Connector for Python supports the following Databricks authentication types:

Databricks personal access token authentication
OAuth machine-to-machine (M2M) authentication
OAuth user-to-machine (U2M) authentication

Note

Basic authentication using a Databricks username and password reached end of life on July 10, 2024. See End of life for Databricks-managed passwords.

Databricks personal access token authentication

To use the Databricks SQL Connector for Python with Databricks personal access token authentication, you must first create a Databricks personal access token. To do so, follow the steps in Databricks personal access tokens for workspace users.

To authenticate the Databricks SQL Connector for Python, use the following code snippet. This snippet assumes that you have set the following environment variables:

DATABRICKS_SERVER_HOSTNAMEset to the Server Hostname value for your cluster or SQL warehouse.
DATABRICKS_HTTP_PATH, set to HTTP Path value for your cluster or SQL warehouse.
DATABRICKS_TOKEN, set to the Databricks personal access token.

To set environment variables, see your operating system’s documentation.

from databricks import sql
import os

with sql.connect(server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME"),
                 http_path       = os.getenv("DATABRICKS_HTTP_PATH"),
                 access_token    = os.getenv("DATABRICKS_TOKEN")) as connection:
# ...

OAuth machine-to-machine (M2M) authentication

Databricks SQL Connector for Python versions 2.5.0 and above support OAuth machine-to-machine (M2M) authentication. You must also install the Databricks SDK for Python (for example by running pip install databricks-sdk or python -m pip install databricks-sdk).

To use the Databricks SQL Connector for Python with OAuth M2M authentication, you must do the following:

Create a Databricks service principal in your Databricks workspace, and create an OAuth secret for that service principal.

To create the service principal and its OAuth secret, see Authenticate access to Databricks with a service principal using OAuth (OAuth M2M). Make a note of the service principal’s UUID or Application ID value, and the Secret value for the service principal’s OAuth secret.
Give that service principal access to your cluster or warehouse.

To give the service principal access to your cluster or warehouse, see Compute permissions or Manage a SQL warehouse.

To authenticate the Databricks SQL Connector for Python, use the following code snippet. This snippet assumes that you have set the following environment variables:

DATABRICKS_SERVER_HOSTNAME set to the Server Hostname value for your cluster or SQL warehouse.
DATABRICKS_HTTP_PATH, set to HTTP Path value for your cluster or SQL warehouse.
DATABRICKS_CLIENT_ID, set to the service principal’s UUID or Application ID value.
DATABRICKS_CLIENT_SECRET, set to the Secret value for the service principal’s OAuth secret.

To set environment variables, see your operating system’s documentation.

from databricks.sdk.core import Config, oauth_service_principal
from databricks import sql
import os

server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME")

def credential_provider():
  config = Config(
    host          = f"https://{server_hostname}",
    client_id     = os.getenv("DATABRICKS_CLIENT_ID"),
    client_secret = os.getenv("DATABRICKS_CLIENT_SECRET"))
  return oauth_service_principal(config)

with sql.connect(server_hostname      = server_hostname,
                 http_path            = os.getenv("DATABRICKS_HTTP_PATH"),
                 credentials_provider = credential_provider) as connection:
# ...

OAuth user-to-machine (U2M) authentication

Databricks SQL Connector for Python versions 2.1.0 and above support OAuth user-to-machine (U2M) authentication.

To authenticate the Databricks SQL Connector for Python with OAuth U2M authentication, use the following code snippet. OAuth U2M authentication uses real-time human sign-in and consent to authenticate the target Databricks user account. This snippet assumes that you have set the following environment variables:

Set DATABRICKS_SERVER_HOSTNAME to the Server Hostname value for your cluster or SQL warehouse.
Set DATABRICKS_HTTP_PATH to HTTP Path value for your cluster or SQL warehouse.

To set environment variables, see your operating system’s documentation.

from databricks import sql
import os

with sql.connect(server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME"),
                 http_path       = os.getenv("DATABRICKS_HTTP_PATH"),
                 auth_type       = "databricks-oauth") as connection:
# ...

Examples

The following code examples demonstrate how to use the Databricks SQL Connector for Python to query and insert data, query metadata, manage cursors and connections, and configure logging.

Note

The following code examples demonstrate how to use a Databricks personal access token for authentication. To use other available Databricks authentication types instead, see Authentication.

These code example retrieve their server_hostname, http_path, and access_token connection variable values from these environment variables:

DATABRICKS_SERVER_HOSTNAME, which represents the Server Hostname value from the requirements.
DATABRICKS_HTTP_PATH, which represents the HTTP Path value from the requirements.
DATABRICKS_TOKEN, which represents your access token from the requirements.

You can use other approaches to retrieving these connection variable values. Using environment variables is just one approach among many.

Query data
Insert data
Query metadata
Manage cursors and connections
Manage files in Unity Catalog volumes
Configure logging

Query data

The following code example demonstrates how to call the Databricks SQL Connector for Python to run a basic SQL command on a cluster or SQL warehouse. This command returns the first two rows from the trips table in the samples catalog’s nyctaxi schema.

from databricks import sql
import os

with sql.connect(server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME"),
                 http_path       = os.getenv("DATABRICKS_HTTP_PATH"),
                 access_token    = os.getenv("DATABRICKS_TOKEN")) as connection:

  with connection.cursor() as cursor:
    cursor.execute("SELECT * FROM samples.nyctaxi.trips LIMIT 2")
    result = cursor.fetchall()

    for row in result:
      print(row)

Insert data

The following example demonstrate how to insert small amounts of data (thousands of rows):

from databricks import sql
import os

with sql.connect(server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME"),
                 http_path       = os.getenv("DATABRICKS_HTTP_PATH"),
                 access_token    = os.getenv("DATABRICKS_TOKEN")) as connection:

  with connection.cursor() as cursor:
    cursor.execute("CREATE TABLE IF NOT EXISTS squares (x int, x_squared int)")

    squares = [(i, i * i) for i in range(100)]
    values = ",".join([f"({x}, {y})" for (x, y) in squares])

    cursor.execute(f"INSERT INTO squares VALUES {values}")

    cursor.execute("SELECT * FROM squares LIMIT 10")

    result = cursor.fetchall()

    for row in result:
      print(row)

For large amounts of data, you should first upload the data to cloud storage and then execute the COPY INTO command.

Query metadata

There are dedicated methods for retrieving metadata. The following example retrieves metadata about columns in a sample table:

from databricks import sql
import os

with sql.connect(server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME"),
                 http_path       = os.getenv("DATABRICKS_HTTP_PATH"),
                 access_token    = os.getenv("DATABRICKS_TOKEN")) as connection:

  with connection.cursor() as cursor:
    cursor.columns(schema_name="default", table_name="squares")
    print(cursor.fetchall())

Manage cursors and connections

It is a best practice to close any connections and cursors that are no longer in use. This frees resources on Databricks clusters and Databricks SQL warehouses.

You can use a context manager (the with syntax used in previous examples) to manage the resources, or explicitly call close:

from databricks import sql
import os

connection = sql.connect(server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME"),
                         http_path       = os.getenv("DATABRICKS_HTTP_PATH"),
                         access_token    = os.getenv("DATABRICKS_TOKEN"))

cursor = connection.cursor()

cursor.execute("SELECT * from range(10)")
print(cursor.fetchall())

cursor.close()
connection.close()

Manage files in Unity Catalog volumes

The Databricks SQL Connector enables you to write local files to Unity Catalog volumes, download files from volumes, and delete files from volumes, as shown in the following example:

from databricks import sql
import os

# For writing local files to volumes and downloading files from volumes,
# you must set the staging_allows_local_path argument to the path to the
# local folder that contains the files to be written or downloaded.
# For deleting files in volumes, you must also specify the
# staging_allows_local_path argument, but its value is ignored,
# so in that case its value can be set for example to an empty string.
with sql.connect(server_hostname            = os.getenv("DATABRICKS_SERVER_HOSTNAME"),
                 http_path                  = os.getenv("DATABRICKS_HTTP_PATH"),
                 access_token               = os.getenv("DATABRICKS_TOKEN"),
                 staging_allowed_local_path = "/tmp/") as connection:

  with connection.cursor() as cursor:

    # Write a local file to the specified path in a volume.
    # Specify OVERWRITE to overwrite any existing file in that path.
    cursor.execute(
      "PUT '/temp/my-data.csv' INTO '/Volumes/main/default/my-volume/my-data.csv' OVERWRITE"
    )

    # Download a file from the specified path in a volume.
    cursor.execute(
      "GET '/Volumes/main/default/my-volume/my-data.csv' TO '/tmp/my-downloaded-data.csv'"
    )

    # Delete a file from the specified path in a volume.
    cursor.execute(
      "REMOVE '/Volumes/main/default/my-volume/my-data.csv'"
    )

Configure logging

The Databricks SQL Connector uses Python’s standard logging module. You can configure the logging level similar to the following:

from databricks import sql
import os, logging

logging.getLogger("databricks.sql").setLevel(logging.DEBUG)
logging.basicConfig(filename = "results.log",
                    level    = logging.DEBUG)

connection = sql.connect(server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME"),
                         http_path       = os.getenv("DATABRICKS_HTTP_PATH"),
                         access_token    = os.getenv("DATABRICKS_TOKEN"))

cursor = connection.cursor()

cursor.execute("SELECT * from range(10)")

result = cursor.fetchall()

for row in result:
   logging.debug(row)

cursor.close()
connection.close()

Testing

To test your code, use Python test frameworks such as pytest. To test your code under simulated conditions without calling Databricks REST API endpoints or changing the state of your Databricks accounts or workspaces, you can use Python mocking libraries such as unittest.mock.

For example, given the following file named helpers.py containing a get_connection_personal_access_token function that uses a Databricks personal access token to return a connection to a Databricks workspace, and a select_nyctaxi_trips function that uses the connection to get the specified number of data rows from the trips table in the samples catalog’s nyctaxi schema:

# helpers.py

from databricks import sql
from databricks.sql.client import Connection, List, Row, Cursor

def get_connection_personal_access_token(
  server_hostname: str,
  http_path: str,
  access_token: str
) -> Connection:
  return sql.connect(
    server_hostname = server_hostname,
    http_path = http_path,
    access_token = access_token
  )

def select_nyctaxi_trips(
  connection: Connection,
  num_rows: int
) -> List[Row]:
  cursor: Cursor = connection.cursor()
  cursor.execute(f"SELECT * FROM samples.nyctaxi.trips LIMIT {num_rows}")
  result: List[Row] = cursor.fetchall()
  return result

And given the following file named main.py that calls the get_connection_personal_access_token and select_nyctaxi_trips functions:

# main.py

from databricks.sql.client import Connection, List, Row
import os
from helpers import get_connection_personal_access_token, select_nyctaxi_trips

connection: Connection = get_connection_personal_access_token(
  server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME"),
  http_path = os.getenv("DATABRICKS_HTTP_PATH"),
  access_token = os.getenv("DATABRICKS_TOKEN")
)

rows: List[Row] = select_nyctaxi_trips(
  connection = connection,
  num_rows = 2
)

for row in rows:
  print(row)

The following file named test_helpers.py tests whether the select_nyctaxi_trips function returns the expected response. Rather than creating a real connection to the target workspace, this test mocks a Connection object. The test also mocks some data that conforms to the schema and values that are in the real data. The test returns the mocked data through the mocked connection and then checks whether one of the mocked data rows’ values matches the expected value.

# test_helpers.py

import pytest
from databricks.sql.client import Connection, List, Row
from datetime import datetime
from helpers import select_nyctaxi_trips
from unittest.mock import create_autospec

@pytest.fixture
def mock_data() -> List[Row]:
  return [
    Row(
      tpep_pickup_datetime = datetime(2016, 2, 14, 16, 52, 13),
      tpep_dropoff_datetime = datetime(2016, 2, 14, 17, 16, 4),
      trip_distance = 4.94,
      fare_amount = 19.0,
      pickup_zip = 10282,
      dropoff_zip = 10171
    ),
    Row(
      tpep_pickup_datetime = datetime(2016, 2, 4, 18, 44, 19),
      tpep_dropoff_datetime = datetime(2016, 2, 4, 18, 46),
      trip_distance = 0.28,
      fare_amount = 3.5,
      pickup_zip = 10110,
      dropoff_zip = 10110
    )
  ]

def test_select_nyctaxi_trips(mock_data: List[Row]):
  # Create a mock Connection.
  mock_connection = create_autospec(Connection)

  # Set the mock Connection's cursor().fetchall() to the mock data.
  mock_connection.cursor().fetchall.return_value = mock_data

  # Call the real function with the mock Connection.
  response: List[Row] = select_nyctaxi_trips(
    connection = mock_connection,
    num_rows = 2)

  # Check the value of one of the mocked data row's columns.
  assert response[1].fare_amount == 3.5

Because the select_nyctaxi_trips function contains a SELECT statement and therefore does not change the state of the trips table, mocking is not absolutely required in this example. However, mocking enables you to quickly run your tests without waiting for an actual connection to be made with the workspace. Also, mocking enables you to run simulated tests multiple times for functions that might change a table’s state, such as INSERT INTO, UPDATE, and DELETE FROM.

Package

databricks-sql-connector

Usage: pip install databricks-sql-connector

See also databricks-sql-connector in the Python Package Index (PyPI).

Module

databricks.sql

Usage: from databricks import sql

Classes

Selected classes include the following:

Classes

Connection

A session on a Databricks compute resource.

Cursor

A mechanism for traversing over data records.

Row

A row of data in a SQL query result.

`Connection` class

To create a Connection object, call the databricks.sql.connect method with the following parameters:

Parameters

server_hostname

Type: str

The server hostname for the cluster or SQL warehouse. To get the server hostname, see the instructions earlier in this article.

This parameter is required.

Example: dbc-a1b2345c-d6e7.cloud.databricks.com

http_path

Type: str

The HTTP path of the cluster or SQL warehouse. To get the HTTP path, see the instructions earlier in this article.

This parameter is required.

Example: sql/protocolv1/o/1234567890123456/1234-567890-test123 for a cluster. /sql/1.0/warehouses/a1b234c567d8e9fa for a SQL warehouse.

access_token, auth_type, credentials_provider, password, username

Type: str

Information about Databricks authentication settings. For details see Authentication.

session_configuration

Type: dict[str, Any]

A dictionary of Spark session configuration parameters. Setting a configuration is equivalent to using the SET key=val SQL command. Run the SQL command SET -v to get a full list of available configurations.

Defaults to None.

This parameter is optional.

Example: {"spark.sql.variable.substitute": True}

http_headers

Type: List[Tuple[str, str]]]

Additional (key, value) pairs to set in HTTP headers on every RPC request the client makes. Typical usage will not set any extra HTTP headers. Defaults to None.

This parameter is optional.

Since version 2.0

catalog

Type: str

Initial catalog to use for the connection. Defaults to None (in which case the default catalog, typically hive_metastore will be used).

This parameter is optional.

Since version 2.0

schema

Type: str

Initial schema to use for the connection. Defaults to None (in which case the default schema default will be used).

This parameter is optional.

Since version 2.0

use_cloud_fetch

Type: bool

True to send fetch requests directly to the cloud object store to download chunks of data. False (the default) to send fetch requests directly to Databricks.

If use_cloud_fetch is set to True but network access is blocked, then the fetch requests will fail.

Since version 2.8

Selected Connection methods include the following:

Methods

close

Closes the connection to the database and releases all associated resources on the server. Any additional calls to this connection will throw an Error.

No parameters.

No return value.

cursor

Returns a new Cursor object that enables traversal over the records in a database.

No parameters.

`Cursor` class

To create a Cursor object, call the Connection class’s cursor method.

Selected Cursor attributes include the following:

Attributes

arraysize

Used with the fetchmany method, specifies the internal buffer size, which is also how many rows are actually fetched from the server at a time. The default value is 10000. For narrow results (results in which each row does not contain a lot of data), you should increase this value for better performance.

Read-write access.

description

Contains a Python list of tuple objects. Each of these tuple objects contains 7 values, with the first 2 items of each tuple object containing information describing a single result column as follows:

name: The name of the column.
type_code: A string representing the type of the column. For example, an integer column will have a type code of int.

The remaining 5 items of each 7-item tuple object are not implemented, and their values are not defined. They will typically be returned as 4 None values followed by a single True value.

Read-only access.

Selected Cursor methods include the following:

Methods

cancel

Interrupts the running of any database query or command that the cursor has started. To release the associated resources on the server, call the close method after calling the cancel method.

No parameters.

No return value.

close

Closes the cursor and releases the associated resources on the server. Closing an already closed cursor might throw an error.

No parameters.

No return value.

execute

Prepares and then runs a database query or command.

No return value.

Parameters:

operation

Type: str

The query or command to prepare and then run.

This parameter is required.

Example without the parameters parameter:

cursor.execute(
 'SELECT * FROM samples.nyctaxi.trips WHERE pickup_zip="10019" LIMIT 2'
)

Example with the parameters parameter:

cursor.execute(
 'SELECT * FROM samples.nyctaxi.trips WHERE zip=%(pickup_zip)s LIMIT 2',
 { 'pickup_zip': '10019' }
)

parameters

Type: dictionary

A sequence of parameters to use with the operation parameter.

This parameter is optional. The default is None.

executemany

Prepares and then runs a database query or command using all parameter sequences in the seq_of_parameters argument. Only the final result set is retained.

No return value.

Parameters:

operation

Type: str

The query or command to prepare and then run.

This parameter is required.

seq_of_parameters

Type: list of dict

A sequence of many sets of parameter values to use with the operation parameter.

This parameter is required.

catalogs

Execute a metadata query about the catalogs. Actual results should then be fetched using fetchmany or fetchall.

Important fields in the result set include:

Field name: TABLE_CAT. Type: str. The name of the catalog.

No parameters.

No return value.

Since version 1.0

schemas

Execute a metadata query about the schemas. Actual results should then be fetched using fetchmany or fetchall.

Important fields in the result set include:

Field name: TABLE_SCHEM. Type: str. The name of the schema.
Field name: TABLE_CATALOG. Type: str. The catalog to which the schema belongs.

No return value.

Since version 1.0

Parameters:

catalog_name

Type: str

A catalog name to retrieve information about. The % character is interpreted as a wildcard.

This parameter is optional.

schema_name

Type: str

A schema name to retrieve information about. The % character is interpreted as a wildcard.

This parameter is optional.

tables

Execute a metadata query about tables and views. Actual results should then be fetched using fetchmany or fetchall.

Important fields in the result set include:

Field name: TABLE_CAT. Type: str. The catalog to which the table belongs.
Field name: TABLE_SCHEM. Type: str. The schema to which the table belongs.
Field name: TABLE_NAME. Type: str. The name of the table.
Field name: TABLE_TYPE. Type: str. The kind of relation, for example VIEW or TABLE (applies to Databricks Runtime 10.4 LTS and above as well as to Databricks SQL; prior versions of the Databricks Runtime return an empty string).

No return value.

Since version 1.0

Parameters

catalog_name

Type: str

A catalog name to retrieve information about. The % character is interpreted as a wildcard.

This parameter is optional.

schema_name

Type: str

A schema name to retrieve information about. The % character is interpreted as a wildcard.

This parameter is optional.

table_name

Type: str

A table name to retrieve information about. The % character is interpreted as a wildcard.

This parameter is optional.

table_types

Type: List[str]

A list of table types to match, for example TABLE or VIEW.

This parameter is optional.

columns

Execute a metadata query about the columns. Actual results should then be fetched using fetchmany or fetchall.

Important fields in the result set include:

Field name: TABLE_CAT. Type: str. The catalog to which the column belongs.
Field name: TABLE_SCHEM. Type: str. The schema to which the column belongs.
Field name: TABLE_NAME. Type: str. The name of the table to which the column belongs.
Field name: COLUMN_NAME. Type: str. The name of the column.

No return value.

Since version 1.0

Parameters:

catalog_name

Type: str

A catalog name to retrieve information about. The % character is interpreted as a wildcard.

This parameter is optional.

schema_name

Type: str

A schema name to retrieve information about. The % character is interpreted as a wildcard.

This parameter is optional.

table_name

Type: str

A table name to retrieve information about. The % character is interpreted as a wildcard.

This parameter is optional.

column_name

Type: str

A column name to retrieve information about. The % character is interpreted as a wildcard.

This parameter is optional.

fetchall

Gets all (or all remaining) rows of a query.

No parameters.

Returns all (or all remaining) rows of the query as a Python list of Row objects.

Throws an Error if the previous call to the execute method did not return any data or no execute call has yet been made.

fetchmany

Gets the next rows of a query.

Returns up to size (or the arraysize attribute if size is not specified) of the next rows of a query as a Python list of Row objects.

If there are fewer than size rows left to be fetched, all remaining rows will be returned.

Throws an Error if the previous call to the execute method did not return any data or no execute call has yet been made.

Parameters:

size

Type: int

The number of next rows to get.

This parameter is optional. If not specified, the value of the arraysize attribute is used.

Example: cursor.fetchmany(10)

fetchone

Gets the next row of the dataset.

No parameters.

Returns the next row of the dataset as a single sequence as a Python tuple object, or returns None if there is no more available data.

Throws an Error if the previous call to the execute method did not return any data or no execute call has yet been made.

fetchall_arrow

Gets all (or all remaining) rows of a query, as a PyArrow Table object. Queries returning very large amounts of data should use fetchmany_arrow instead to reduce memory consumption.

No parameters.

Returns all (or all remaining) rows of the query as a PyArrow table.

Throws an Error if the previous call to the execute method did not return any data or no execute call has yet been made.

Since version 2.0

fetchmany_arrow

Gets the next rows of a query as a PyArrow Table object.

Returns up to the size argument (or the arraysize attribute if size is not specified) of the next rows of a query as a Python PyArrow Table object.

Throws an Error if the previous call to the execute method did not return any data or no execute call has yet been made.

Since version 2.0

Parameters:

size

Type: int

The number of next rows to get.

This parameter is optional. If not specified, the value of the arraysize attribute is used.

Example: cursor.fetchmany_arrow(10)

`Row` class

The row class is a tuple-like data structure that represents an individual result row. If the row contains a column with the name "my_column", you can access the "my_column" field of row via row.my_column. You can also use numeric indicies to access fields, for example row[0]. If the column name is not allowed as an attribute method name (for example, it begins with a digit), then you can access the field as row["1_my_column"].

Since version 1.0

Selected Row methods include:

asDict

Returns a dictionary representation of the row, which is indexed by field names. If there are duplicate field names, one of the duplicate fields (but only one) will be returned in the dictionary. Which duplicate field is returned is not defined.

No parameters.

Returns a dict of fields.

Type conversions

The following table maps Apache Spark SQL data types to their Python data type equivalents.

Apache Spark SQL data type	Python data type
`array`	`numpy.ndarray`
`bigint`	`int`
`binary`	`bytearray`
`boolean`	`bool`
`date`	`datetime.date`
`decimal`	`decimal.Decimal`
`double`	`float`
`int`	`int`
`map`	`str`
`null`	`NoneType`
`smallint`	`int`
`string`	`str`
`struct`	`str`
`timestamp`	`datetime.datetime`
`tinyint`	`int`

Troubleshooting

`tokenAuthWrapperInvalidAccessToken: Invalid access token` message

Issue: When you run your code, you see a message similar to Error during request to server: tokenAuthWrapperInvalidAccessToken: Invalid access token.

Possible cause: The value passed to access_token is not a valid Databricks personal access token.

Recommended fix: Check that the value passed to access_token is correct and try again.

`gaierror(8, 'nodename nor servname provided, or not known')` message

Issue: When you run your code, you see a message similar to Error during request to server: gaierror(8, 'nodename nor servname provided, or not known').

Possible cause: The value passed to server_hostname is not the correct host name.

Recommended fix: Check that the value passed to server_hostname is correct and try again.

For more information on finding the server hostname, see Get connection details for a Databricks compute resource.

`IpAclError` message

Issue: When you run your code, you see the message Error during request to server: IpAclValidation when you try to use the connector on a Databricks notebook.

Possible cause: You may have IP allow listing enabled for the Databricks workspace. With IP allow listing, connections from Spark clusters back to the control plane are not allowed by default.

Recommended fix: Ask your administrator to add the compute plane subnet to the IP allow list.

Additional resources

For more information, see:

The Databricks SQL Connector for Python repository on GitHub
Data types
Built-in Types (for bool, bytearray, float, int, and str) on the Python website
datetime (for datetime.date and datatime.datetime) on the Python website
decimal (for decimal.Decimal) on the Python website
Built-in Constants (for NoneType) on the Python website

Databricks SQL Connector for Python

Requirements

Get started

Authentication

Databricks personal access token authentication

OAuth machine-to-machine (M2M) authentication

OAuth user-to-machine (U2M) authentication

Examples

Testing

API reference

Troubleshooting

tokenAuthWrapperInvalidAccessToken: Invalid access token message

gaierror(8, 'nodename nor servname provided, or not known') message

IpAclError message

Additional resources

`tokenAuthWrapperInvalidAccessToken: Invalid access token` message

`gaierror(8, 'nodename nor servname provided, or not known')` message

`IpAclError` message