How to work with FHIR Data in Apache Spark using SQL

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

The CData JDBC Driver offers unmatched performance for interacting with live FHIR data due to optimized data processing built into the driver. When you issue complex SQL queries to FHIR, the driver pushes supported SQL operations, like filters and aggregations, directly to FHIR 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 FHIR data using native data types.

Install the CData JDBC Driver for FHIR

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

Start a Spark Shell and Connect to FHIR Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for FHIR JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for FHIR/lib/cdata.jdbc.fhir.jar
   

2. With the shell running, you can connect to FHIR with a JDBC URL and use the SQL Context load() function to read a table.

   Set URL to the Service Base URL of the FHIR server. This is the address where the resources are defined in the FHIR server you would like to connect to. Set ConnectionType to a supported connection type. Set ContentType to the format of your documents. Set AuthScheme based on the authentication requirements for your FHIR server.

   Generic, Azure-based, AWS-based, and Google-based FHIR server implementations are supported.

   Sample Service Base URLs

   • Generic: http://my_fhir_server/r4b/
   • Azure: https://MY_AZURE_FHIR.azurehealthcareapis.com/
   • AWS: https://healthlake.REGION.amazonaws.com/datastore/DATASTORE_ID/r4/
   • Google: https://healthcare.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/datasets/DATASET_ID/fhirStores/FHIR_STORE_ID/fhir/

   Generic FHIR Instances

   The product supports connections to custom instances of FHIR. Authentication to custom FHIR servers is handled via OAuth (read more about OAuth in the Help documentation. Before you can connect to custom FHIR instances, you must set ConnectionType to Generic.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.fhir.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 FHIR, using the connection string generated above.

   scala> val fhir_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:fhir:URL=http://test.fhir.org/r4b/;ConnectionType=Generic;ContentType=JSON;AuthScheme=None;").option("dbtable","Patient").option("driver","cdata.jdbc.fhir.FHIRDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the FHIR data as a temporary table:

   scala> fhir_df.registerTable("patient")

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

   scala> fhir_df.sqlContext.sql("SELECT Id, [name-use] FROM Patient WHERE [address-city] = New York").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 FHIR in Apache Spark, you are able to perform fast and complex analytics on FHIR 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.