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https://issues.apache.org/jira/browse/SPARK-32122?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon resolved SPARK-32122.
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Resolution: Cannot Reproduce
Let's resolve it as cannot reproduce since it can't in 3.0. We could identify
which JIRA fixed and discuss about feasibility about porting back.
> Exception while writing dataframe with enum fields
> --------------------------------------------------
>
> Key: SPARK-32122
> URL: https://issues.apache.org/jira/browse/SPARK-32122
> Project: Spark
> Issue Type: Question
> Components: SQL
> Affects Versions: 2.4.3
> Reporter: Sai kiran Krishna murthy
> Priority: Minor
> Labels: avro, spark-sql
>
> I have an avro schema with one field which is an enum and I am trying to
> enforce this schema when I am writing my dataframe, the code looks something
> like this
> {code:java}
> case class Name1(id:String,count:Int,val_type:String)
> val schema = """{
> | "type" : "record",
> | "name" : "name1",
> | "namespace" : "com.data",
> | "fields" : [
> | {
> | "name" : "id",
> | "type" : "string"
> | },
> | {
> | "name" : "count",
> | "type" : "int"
> | },
> | {
> | "name" : "val_type",
> | "type" : {
> | "type" : "enum",
> | "name" : "ValType",
> | "symbols" : [ "s1", "s2" ]
> | }
> | }
> | ]
> |}""".stripMargin
> val df = Seq(
> Name1("1",2,"s1"),
> Name1("1",3,"s2"),
> Name1("1",4,"s2"),
> Name1("11",2,"s1")).toDF()
> df.write.format("avro").option("avroSchema",schema).save("data/tes2/")
> {code}
> This code fails with the following exception,
>
> {noformat}
> 2020-06-28 23:28:10 ERROR Utils:91 - Aborting task
> org.apache.avro.AvroRuntimeException: Not a union: "string"
> at org.apache.avro.Schema.getTypes(Schema.java:299)
> at
> org.apache.spark.sql.avro.AvroSerializer.org$apache$spark$sql$avro$AvroSerializer$$resolveNullableType(AvroSerializer.scala:229)
> at
> org.apache.spark.sql.avro.AvroSerializer$$anonfun$3.apply(AvroSerializer.scala:209)
> at
> org.apache.spark.sql.avro.AvroSerializer$$anonfun$3.apply(AvroSerializer.scala:208)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.immutable.List.foreach(List.scala:392)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.immutable.List.map(List.scala:296)
> at
> org.apache.spark.sql.avro.AvroSerializer.newStructConverter(AvroSerializer.scala:208)
> at
> org.apache.spark.sql.avro.AvroSerializer.<init>(AvroSerializer.scala:51)
> at
> org.apache.spark.sql.avro.AvroOutputWriter.serializer$lzycompute(AvroOutputWriter.scala:42)
> at
> org.apache.spark.sql.avro.AvroOutputWriter.serializer(AvroOutputWriter.scala:42)
> at
> org.apache.spark.sql.avro.AvroOutputWriter.write(AvroOutputWriter.scala:64)
> at
> org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.write(FileFormatDataWriter.scala:137)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:245)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:242)
> at
> org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:248)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> 2020-06-28 23:28:10 ERROR Utils:91 - Aborting task{noformat}
>
> I understand this is because of the type of val_type is `String` in the case
> class. Can you please advice how I can solve this problem without having to
> change the underlying avro schema?
> Thanks!
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