Getting Started

Bunsen offers first-class integration with Apache Spark for Python, Java, and Scala users. This page describes the steps to get started with them.

Initial Setup

Scala or Java users of Spark can simply add the bunsen-shaded JAR to the Spark job. PySpark users wanting to use the provided wrapper functions will also need to include the Python files in the PYTHONPATH, all of which can be found in the assembly:

>>> unzip bunsen-assembly-0.1.0-dist.zip
>>> export PYTHONPATH=$PWD/bunsen-assembly-0.1.0/python:$PYTHONPATH
>>> pyspark --jars bunsen-assembly-0.1.0/jars/bunsen-shaded-0.1.0.jar

Bunsen currently uses FHIR STU3 and Spark 2.1. The assembly itself can be downloaded from the releases in Maven Central.

Simple Queries

A production deployment would typically bulk load large volumes of data, but the easiest way to experiment with Bunsen is simply to pull in some existing FHIR bundles. Bunsen offers a convenience method to load a collection of bundles from a given directory, or users can provide their own data via Python or Java APIs.

Here’s a simple example loading test bundles from a directory:

>>> from bunsen.bundles import load_from_directory, extract_entry
>>>
>>> bundles = load_from_directory(spark, 'path/to/test/bundles')
>>>
>>> # The extract_entry method returns a Spark dataframe containing all observations
>>> # that were in the bundle. We can then perform arbitrary Spark operations on them.
>>> observations = extract_entry(spark, bundles, 'observation').cache()
>>>
>>> observations.select('subject.reference', 'code.text').limit(5).show(truncate=False)
+---------------+-------------------------------------+
|reference      |text                                 |
+---------------+-------------------------------------+
|Patient/1032702|Tobacco smoking status               |
|Patient/9995679|Blood pressure systolic and diastolic|
|Patient/9995679|Systolic blood pressure              |
|Patient/9995679|Diastolic blood pressure             |
|Patient/9995679|Blood pressure systolic and diastolic|
+---------------+-------------------------------------+

See the bundles module for details on use.

Spark SQL Integration

Once the FHIR data is available in Spark, it can be registered as a table or saved to a Hive database, and then queried with the full power of SQL.

Bunsen also supports basic use of simple value sets to simplify queries. In the example below we will register our observations dataframe, and we declare some value sets based on standard terminologies. We use the set of all values with a transitive is-a relationship in the given termoniology.

>>> observations.registerTempTable('observations')
>>> from bunsen.valuesets import push_valuesets, isa_loinc, isa_snomed
>>> push_valuesets(spark,
>>>                {'body_weight'          : isa_loinc('29463-7'),
>>>                 'bmi'                  : isa_loinc('39156-5'),
>>>                 'heart_rate'           : isa_loinc('8867-4'),
>>>                 'abnormal_weight_loss' : isa_snomed('267024001'),
>>>                 'stroke'               : isa_snomed('230690007')})

Now we can query our data with standard Spark SQL using the in_valueset user-defined function to reference the valuesets used above. See the valuesets module for details on use.

>>> spark.sql("""
>>> select subject.reference,
>>>        effectiveDateTime,
>>>        valueQuantity.value
>>> from observations
>>> where in_valueset(code, "heart_rate")
>>> limit 5
>>> """).show()
+---------------+-----------------+-------+
|      reference|effectiveDateTime|  value|
+---------------+-----------------+-------+
|Patient/9995679|       2006-12-27|54.0000|
|Patient/9995679|       2007-04-18|60.0000|
|Patient/9995679|       2007-07-18|80.0000|
|Patient/9995679|       2008-01-16|47.0000|
|Patient/9995679|       2008-06-25|47.0000|
+---------------+-----------------+-------+

Bring Your Own Value Sets

The above examples show is-a relationships in standard ontologies, but users can also bring their own datasets or import them from sources like the Value Set Authority Center.

To do so, import the value set into a list of (code system, code value) tuples, then use the push push_valuesets() function to broadcast them to the cluster. Here’s an example:

>>> hypertension_meds = \
>>>  [('http://snomed.info/sct', '68180051503'),
>>>   ('http://snomed.info/sct', '68180048003')]
>>>
>>> # Push a combination of is-a relationships and our own value sets.
>>> push_valuesets(spark,
>>>                {'hypertension'  : isa_snomed('59621000'),
>>>                 'glucose_level' : isa_loinc('2345-7'),
>>>                 'hypertension_meds' : hypertension_meds})

Once those valuesets are pushed to the cluster, we can use them in our queries like any other:

>>> spark.sql("""
>>> select subject.reference
>>> from medicationstatements
>>> where in_valueset(medicationCodeableConcept, 'hypertension_meds')
>>> """).show()
+---------------+
|      reference|
+---------------+
|Patient/9995467|
|Patient/9995467|
|Patient/9995467|
|Patient/9995467|
|Patient/9995467|
+---------------+