[SOLVED] Importing Manually Declared Nested Schema from Package Causes NullPointerException

I'm trying to parse XML files into DataFrames using Databricks' spark-xml with this line of code:

val xmlDF = spark
    .read
    .option("rowTag", "MeterReadingDocument")
    .option("valueTag", "foo") // meaningless, used to parse tags with no child elements
    .option("inferSchema", "false")
    .schema(schema)
    .xml(connectionString)

As you can see, I have provided a schema in order to avoid the costly operation of schema inference. This schema is defined as

 val schema = MyProjectUtils.Schemas.meterReadingDocumentSchema

Where MyProjectUtils is a package containing an object Schemas with the schema definition:

object Schemas {
...
// nested schemas 
...

val meterReadingDocumentSchema = StructType(
    Array(
      StructField("ReadingStatusRefTable", readingStatusRefTableSchema, nullable = true),
      StructField("Header", headerSchema, nullable = true),
      StructField("ImportExportParameters", importExportParametersSchema, nullable = true),
      StructField("Channels", channelsSchema, nullable = true),
      StructField("_xmlns:xsd", StringType, nullable = true),
      StructField("_xmlns:xsi", StringType, nullable = true)
    )
  )
}

You will notice the readingStatusRefTableSchema, headerSchema and other custom Schemas which are StructTypes corresponding to nested elements within the XML. These are in turn, nested as well, for example:

val headerSchema = StructType(
    Array(
      StructField("Creation_Datetime", creationDatetimeSchema, nullable = true),
      StructField("Export_Template", exportTemplateSchema, nullable = true),
      StructField("System", SystemSchema, nullable = true),
      StructField("Path", pathSchema, nullable = true),
      StructField("Timezone", timezoneSchema, nullable = true)
    )
  )

with

val creationDatetimeSchema = StructType(
    Array(
      StructField("_Datetime", TimestampType, nullable = true),
      StructField("foo", StringType, nullable = true)
    )
  )

(I can provide more details on the nested nature of the schema if relevant)

If I declare these nested schemas on a notebook or as an object within the notebook I use to read data, this works and loads the data. But when I create a jar from this project and execute it, I get the following stack trace:


INFO ApplicationMaster [shutdown-hook-0]: Unregistering ApplicationMaster with FAILED (diag message: User class threw exception: java.lang.NullPointerException
    at org.apache.spark.sql.types.ArrayType.existsRecursively(ArrayType.scala:102)
    at org.apache.spark.sql.types.StructType.$anonfun$existsRecursively$1(StructType.scala:508)
    at org.apache.spark.sql.types.StructType.$anonfun$existsRecursively$1$adapted(StructType.scala:508)
    at scala.collection.IndexedSeqOptimized.prefixLengthImpl(IndexedSeqOptimized.scala:41)
    at scala.collection.IndexedSeqOptimized.exists(IndexedSeqOptimized.scala:49)
    at scala.collection.IndexedSeqOptimized.exists$(IndexedSeqOptimized.scala:49)
    at scala.collection.mutable.ArrayOps$ofRef.exists(ArrayOps.scala:198)
    at org.apache.spark.sql.types.StructType.existsRecursively(StructType.scala:508)
    at org.apache.spark.sql.types.StructType.$anonfun$existsRecursively$1(StructType.scala:508)
    at org.apache.spark.sql.types.StructType.$anonfun$existsRecursively$1$adapted(StructType.scala:508)
    at scala.collection.IndexedSeqOptimized.prefixLengthImpl(IndexedSeqOptimized.scala:41)
    at scala.collection.IndexedSeqOptimized.exists(IndexedSeqOptimized.scala:49)
    at scala.collection.IndexedSeqOptimized.exists$(IndexedSeqOptimized.scala:49)
    at scala.collection.mutable.ArrayOps$ofRef.exists(ArrayOps.scala:198)
    at org.apache.spark.sql.types.StructType.existsRecursively(StructType.scala:508)
    at org.apache.spark.sql.catalyst.util.CharVarcharUtils$.hasCharVarchar(CharVarcharUtils.scala:56)
    at org.apache.spark.sql.catalyst.util.CharVarcharUtils$.failIfHasCharVarchar(CharVarcharUtils.scala:63)
    at org.apache.spark.sql.DataFrameReader.schema(DataFrameReader.scala:76)
    at com.mycompany.DataIngestion$.delayedEndpoint$com$mycompany$DataIngestion$1(DataIngestion.scala:44)
    at com.mycompany.DataIngestion$delayedInit$body.apply(DataIngestion.scala:10)
    at scala.Function0.apply$mcV$sp(Function0.scala:39)
    at scala.Function0.apply$mcV$sp$(Function0.scala:39)
    at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:17)
    at scala.App.$anonfun$main$1$adapted(App.scala:80)
    at scala.collection.immutable.List.foreach(List.scala:431)
    at scala.App.main(App.scala:80)
    at scala.App.main$(App.scala:78)
    at com.mycompany.DataIngestion$.main(DataIngestion.scala:10)
    at com.mycompany.DataIngestion.main(DataIngestion.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:739)
)

I added another simpler csv file and created a schema for it in the Schemas object. This schema has no nested structs from the same Schemas object and writing.

 val simplerDocSchema = MyProjectUtils.Schemas.anotherDocSchema

spark
      .read
      .csv(path)
      .schema(simplerDocSchema)
      .load(connectionString)

Schemas {
 ...
val anotherDocSchema: StructType = StructType(
    Array(
      StructField("ID", StringType, nullable = true),
      StructField("DATE", StringType, nullable = true),
      StructField("CODE", StringType, nullable = true),
      StructField("AD", StringType, nullable = true),
      StructField("ACCOUNT", StringType, nullable = true)
    )
  )
}

I expected this to fail as well but ran OK in a compiled project as well as in a notebook