![]() Initial schema to use for the connection. ![]() Defaults to None (in which case the default catalog, typically hive_metastore, will be used). Initial catalog to use for the connection. Typical usage will not set any extra HTTP headers. Values = ",".join([f"(Īdditional (key, value) pairs to set in HTTP headers on every RPC request the client makes. The following example demonstrate how to insert small amounts of data (thousands of rows): from databricks import sqlĬursor.execute("CREATE TABLE IF NOT EXISTS squares (x int, x_squared int)") Http_path = os.getenv("DATABRICKS_HTTP_PATH"),Īccess_token = os.getenv("DATABRICKS_TOKEN")) as connection:Ĭursor.execute("SELECT * FROM default.diamonds LIMIT 2") With sql.connect(server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME"), The diamonds table is included in the Sample datasets. This command returns the first two rows from the diamonds table. 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. Using environment variables is just one approach among many. You can use other approaches to retrieving these connection variable values. DATABRICKS_TOKEN, which represents your access token from the requirements.DATABRICKS_HTTP_PATH, which represents the HTTP Path value from the requirements.DATABRICKS_SERVER_HOSTNAME, which represents the Server Hostname value from the requirements.These code example retrieve their server_hostname, http_path, and access_token connection variable values from these environment variables: 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. Install the Databricks SQL Connector for Python library on your development machine by running pip install databricks-sql-connector. For example, the code examples later in this article use environment variables. ![]() Instead, you should retrieve this information from a secure location. You can also use an Azure Active Directory access token.Īs a security best practice, you should not hard-code this information into your code. You can use an Azure Databricks personal access token for the workspace. You can get this from the HTTP Path value in the Advanced Options > JDBC/ODBC tab for your cluster. A development machine running Python >=3.7 and JDBC/ODBC tab for your cluster.This library follows PEP 249 – Python Database API Specification v2.0. The Databricks SQL Connector for Python is easier to set up and use than similar Python libraries such as pyodbc. The Databricks SQL Connector for Python is a Python library that allows you to use Python code to run SQL commands on Azure Databricks clusters and Databricks SQL warehouses.
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