Java Usage

Bunsen allows any org.hl7.fhir.dstu3.model.* Java object to be encoded as an Apache Spark Dataset, transformed and queried, and then optionally decoded back to the original Java forms.

Creating Encoders

Here is a simple example of converting a local list of FHIR Condition objects into a Dataset and some simple queries of that dataset:

FhirEncoders encoders = FhirEncoders.forStu3().getOrCreate();

List<Condition> conditionList = // A list of org.hl7.fhir.dstu3.model.Condition objects.

Dataset<Condition> conditions = spark.createDataset(conditionList,

// Query for conditions based on arbitrary Spark SQL expressions
Dataset<Condition> activeConditions = conditions
    .where("clinicalStatus == 'active' and verificationStatus == 'confirmed'");

// Count the query results
long activeConditionCount = activeConditions.count();

// Convert the results back into a list of org.hl7.fhir.dstu3.model.Condition objects.
List<Condition> retrievedConditions = activeConditions.collectAsList();

Converting Existing Datasets

In other cases, users may have an existing Spark Dataset of FHIR resources in their JSON or XML forms. In this case, the HAPI FHIR APIs in Spark map functions to convert to the data model, and then use Bunsen to encode the produced data mdoel as FHIR resources.

// Created as a static field to avoid creation costs on each invocation.
private static final FhirContext ctx = FhirContext.forDstu3();

// <snip>

FhirEncoders encoders = FhirEncoders.forStu3().getOrCreate();

Dataset<String> conditionJsons = // A Dataset of FHIR conditions in JSON form.

Dataset<Condition> conditions =
    (MapFunction<String,Condition>) conditionString -> {
      return (Condition) ctx.newJsonParser().parseResource(conditionString);

// Arbitrary queries or further transformations the the conditions Dataset goes here.