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URL: https://www.cdata.com/kb/tech/databricks-jdbc-apache-spark.rst

⇱ How to work with Databricks Data in Apache Spark using SQL

How to work with Databricks Data in Apache Spark using SQL

👁 Jerod Johnson
Jerod Johnson
Director, Technology Evangelism
Access and process Databricks Data in Apache Spark using the CData JDBC Driver.

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Databricks, Spark can work with live Databricks data. This article describes how to connect to and query Databricks data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Databricks data due to optimized data processing built into the driver. When you issue complex SQL queries to Databricks, the driver pushes supported SQL operations, like filters and aggregations, directly to Databricks and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Databricks data using native data types.

About Databricks Data Integration

Accessing and integrating live data from Databricks has never been easier with CData. Customers rely on CData connectivity to:

  • Access all versions of Databricks from Runtime Versions 9.1 - 13.X to both the Pro and Classic Databricks SQL versions.
  • Leave Databricks in their preferred environment thanks to compatibility with any hosting solution.
  • Secure authenticate in a variety of ways, including personal access token, Azure Service Principal, and Azure AD.
  • Upload data to Databricks using Databricks File System, Azure Blog Storage, and AWS S3 Storage.

While many customers are using CData's solutions to migrate data from different systems into their Databricks data lakehouse, several customers use our live connectivity solutions to federate connectivity between their databases and Databricks. These customers are using SQL Server Linked Servers or Polybase to get live access to Databricks from within their existing RDBMs.

Read more about common Databricks use-cases and how CData's solutions help solve data problems in our blog: What is Databricks Used For? 6 Use Cases.


Getting Started


Install the CData JDBC Driver for Databricks

Download the CData JDBC Driver for Databricks installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Databricks Data

  1. Open a terminal and start the Spark shell with the CData JDBC Driver for Databricks JAR file as the jars parameter: 
    $ spark-shell --jars /CData/CData JDBC Driver for Databricks/lib/cdata.jdbc.databricks.jar
  2. With the shell running, you can connect to Databricks with a JDBC URL and use the SQL Context load() function to read a table.

    To connect to a Databricks cluster, set the properties as described below.

    Note: The needed values can be found in your Databricks instance by navigating to Clusters, and selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.

    • Server: Set to the Server Hostname of your Databricks cluster.
    • HTTPPath: Set to the HTTP Path of your Databricks cluster.
    • Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).

    Built-in Connection String Designer

    For assistance in constructing the JDBC URL, use the connection string designer built into the Databricks JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

    java -jar cdata.jdbc.databricks.jar

    Fill in the connection properties and copy the connection string to the clipboard.

    👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

    Configure the connection to Databricks, using the connection string generated above. 

    scala> val databricks_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:databricks:Server=127.0.0.1;Port=443;TransportMode=HTTP;HTTPPath=MyHTTPPath;UseSSL=True;User=MyUser;Password=MyPassword;").option("dbtable","Customers").option("driver","cdata.jdbc.databricks.DatabricksDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.

  4. Register the Databricks data as a temporary table:

    scala> databricks_df.registerTable("customers")

  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> databricks_df.sqlContext.sql("SELECT City, CompanyName FROM Customers WHERE Country = US").collect.foreach(println)

    You will see the results displayed in the console, similar to the following:

    👁 Data in Apache Spark (Salesforce is shown)

Using the CData JDBC Driver for Databricks in Apache Spark, you are able to perform fast and complex analytics on Databricks data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.