我正在尝试使用在 3rd 方库中定义的现有域对象,即 HAPI-FHIR 的Patient
对象来创建一个强类型的 Spark DataSet[Patient]
,如下所示:
scala> val patients = sc.loadFromMongoDB(ReadConfig(Map("uri" -> "mongodb://mongodb/fhir.patients")))
patients: com.mongodb.spark.rdd.MongoRDD[org.bson.Document] = MongoRDD[0] at RDD at MongoRDD.scala:47
scala> val patientsDataSet = patients.toDS[Patient](classOf[Patient])
但是,当我在上面进行 RDD#toDS 调用时,我得到了一个很长的StackOverflowError
.
完整的堆栈跟踪在这里:https ://gist.github.com/vratnagiri-veriskhealth/6dcec9dbc6f74308019ab16c8d278a9b
鉴于我上面提到的域对象的复杂性,我意识到这可能是一个愚蠢的差事,但是,鉴于我是一个 scala 新手,我确实想确保我不会错过任何可能得到这个的简单调整在我放弃这个追求之前工作。
这是堆栈跟踪的一部分:
java.lang.StackOverflowError
at org.spark-project.guava.collect.ImmutableCollection.<init>(ImmutableCollection.java:48)
at org.spark-project.guava.collect.ImmutableSet.<init>(ImmutableSet.java:396)
at org.spark-project.guava.collect.ImmutableMapEntrySet.<init>(ImmutableMapEntrySet.java:35)
at org.spark-project.guava.collect.RegularImmutableMap$EntrySet.<init>(RegularImmutableMap.java:174)
at org.spark-project.guava.collect.RegularImmutableMap$EntrySet.<init>(RegularImmutableMap.java:174)
at org.spark-project.guava.collect.RegularImmutableMap.createEntrySet(RegularImmutableMap.java:170)
at org.spark-project.guava.collect.ImmutableMap.entrySet(ImmutableMap.java:385)
at org.spark-project.guava.collect.ImmutableMap.entrySet(ImmutableMap.java:61)
at org.spark-project.guava.reflect.TypeResolver.where(TypeResolver.java:97)
at org.spark-project.guava.reflect.TypeResolver.accordingTo(TypeResolver.java:65)
at org.spark-project.guava.reflect.TypeToken.resolveType(TypeToken.java:266)
at org.spark-project.guava.reflect.TypeToken$1.getGenericReturnType(TypeToken.java:469)
at org.spark-project.guava.reflect.Invokable.getReturnType(Invokable.java:109)
at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$2.apply(JavaTypeInference.scala:110)
at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$2.apply(JavaTypeInference.scala:109)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$inferDataType(JavaTypeInference.scala:109)
at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$inferDataType(JavaTypeInference.scala:95)
at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$2.apply(JavaTypeInference.scala:111)
at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$2.apply(JavaTypeInference.scala:109)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
谢谢!