Process & Analyze Phoenix Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Phoenix data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live Phoenix data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live Phoenix data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.apachephoenix.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

👁 Loading the JDBC JAR File into AWS

Access Phoenix Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Phoenix data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Phoenix, and create a basic report.

Configure the Connection to Phoenix

Connect to Phoenix by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.apachephoenix.ApachePhoenixDriver"
url = "jdbc:apachephoenix:RTK=5246...;Server=localhost;Port=8765;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.apachephoenix.jar

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

Connect to Apache Phoenix via the Phoenix Query Server. Set the Server and Port (if different from the default port) properties to connect to Apache Phoenix. The Server property will typically be the host name or IP address of the server hosting Apache Phoenix.

Authenticating to Apache Phoenix

By default, no authentication will be used (plain). If authentication is configured for your server, set AuthScheme to NEGOTIATE and set the User and Password properties (if necessary) to authenticate through Kerberos. 👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

Load Phoenix Data

Once you configure the connection, you can load Phoenix data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "MyTable") \
	.load ()

Display Phoenix Data

Check the loaded Phoenix data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

👁 Displaying Phoenix Data

Analyze Phoenix Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Phoenix data for reporting, visualization, and analysis.

% sql

SELECT Id, Column1 FROM SAMPLE_VIEW ORDER BY Column1 DESC LIMIT 5

👁 Displaying Phoenix Data

The data from Phoenix is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Phoenix and start working with your live Phoenix data in Databricks. Reach out to our Support Team if you have any questions.