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A DBAPI for interactive Databricks clusters.
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databricks_dbapi
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README.rst

databricks-dbapi

A thin wrapper around pyhive for creating a DBAPI connection to an interactive Databricks cluster.

Installation

Install using pip install databricks-dbapi

Usage

The connect() function returns a pyhive Hive connection object, which internally wraps a thrift connection.

Using a Databricks API token (recommended):

import os

from databricks_dbapi import databricks


token = os.environ["DATABRICKS_TOKEN"]
host = os.environ["DATABRICKS_HOST"]
cluster = os.environ["DATABRICKS_CLUSTER"]


connection = databricks.connect(
    host=host,
    cluster=cluster,
    token=token,
)
cursor = connection.cursor()

cursor.execute("SELECT * FROM some_table LIMIT 100")

print(cursor.fetchone())
print(cursor.fetchall())

Using your username and password (not recommended):

import os

from databricks_dbapi import databricks


user = os.environ["DATABRICKS_USER"]
password = os.environ["DATABRICKS_PASSWORD"]
host = os.environ["DATABRICKS_HOST"]
cluster = os.environ["DATABRICKS_CLUSTER"]


connection = databricks.connect(
    host=host,
    cluster=cluster,
    user=user,
    password=password
)
cursor = connection.cursor()

cursor.execute("SELECT * FROM some_table LIMIT 100")

print(cursor.fetchone())
print(cursor.fetchall())

Connecting on Azure platform, or with http_path:

import os

from databricks_dbapi import databricks


token = os.environ["DATABRICKS_TOKEN"]
host = os.environ["DATABRICKS_HOST"]
http_path = os.environ["DATABRICKS_HTTP_PATH"]


connection = databricks.connect(
    host=host,
    http_path=http_path,
    token=token,
)
cursor = connection.cursor()

cursor.execute("SELECT * FROM some_table LIMIT 100")

print(cursor.fetchone())
print(cursor.fetchall())

The pyhive connection also provides async functionality:

import os

from databricks_dbapi import databricks
from TCLIService.ttypes import TOperationState


token = os.environ["DATABRICKS_TOKEN"]
host = os.environ["DATABRICKS_HOST"]
cluster = os.environ["DATABRICKS_CLUSTER"]


connection = databricks.connect(
    host=host,
    cluster=cluster,
    token=token,
)
cursor = connection.cursor()

cursor.execute("SELECT * FROM some_table LIMIT 100", async_=True)

status = cursor.poll().operationState
while status in (TOperationState.INITIALIZED_STATE, TOperationState.RUNNING_STATE):
    logs = cursor.fetch_logs()
    for message in logs:
        print(message)

    # If needed, an asynchronous query can be cancelled at any time with:
    # cursor.cancel()

    status = cursor.poll().operationState

print(cursor.fetchall())

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