You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/01/21 07:50:00 UTC
[jira] [Resolved] (SPARK-25587) NPE in Dataset when reading from
Parquet as Product
[ https://issues.apache.org/jira/browse/SPARK-25587?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-25587.
----------------------------------
Resolution: Duplicate
> NPE in Dataset when reading from Parquet as Product
> ---------------------------------------------------
>
> Key: SPARK-25587
> URL: https://issues.apache.org/jira/browse/SPARK-25587
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.0
> Environment: Spark version 2.4.0 (RC2).
> {noformat}
> $ spark-submit --version
> Welcome to
> ____ __
> / __/__ ___ _____/ /__
> _\ \/ _ \/ _ `/ __/ '_/
> /___/ .__/\_,_/_/ /_/\_\ version 2.4.0
> /_/
> Using Scala version 2.11.12, Java HotSpot(TM) 64-Bit Server VM, 1.8.0_181
> Branch
> Compiled by user on 2018-09-27T14:50:10Z
> Revision
> Url
> Type --help for more information.
> {noformat}
> Reporter: Michael Heuer
> Priority: Major
>
> In an attempt to replicate the following issue in ADAM, a library downstream of Spark
> https://github.com/bigdatagenomics/adam/issues/2058
> also reported as
> https://issues.apache.org/jira/browse/SPARK-25588
> the following Spark Shell script throws NPE when attempting to read from Parquet.
> {code:scala}
> sc.setLogLevel("INFO")
> import spark.implicits._
> case class Inner(
> names: Seq[String] = Seq()
> )
> case class Outer(
> inners: Seq[Inner] = Seq()
> )
> val inner = Inner(Seq("name0", "name1"))
> val outer = Outer(Seq(inner))
> val dataset = sc.parallelize(Seq(outer)).toDS()
> val path = "outers.parquet"
> dataset.toDF().write.format("parquet").save(path)
> val roundtrip = spark.read.parquet(path).as[Outer]
> roundtrip.first
> {code}
> Stack trace
> {noformat}
> $ spark-shell -i failure.scala
> ...
> 2018-10-01 16:57:48 INFO ParquetWriteSupport:54 - Initialized Parquet WriteSupport with Catalyst schema:
> {
> "type" : "struct",
> "fields" : [ {
> "name" : "inners",
> "type" : {
> "type" : "array",
> "elementType" : {
> "type" : "struct",
> "fields" : [ {
> "name" : "names",
> "type" : {
> "type" : "array",
> "elementType" : "string",
> "containsNull" : true
> },
> "nullable" : true,
> "metadata" : { }
> } ]
> },
> "containsNull" : true
> },
> "nullable" : true,
> "metadata" : { }
> } ]
> }
> and corresponding Parquet message type:
> message spark_schema {
> optional group inners (LIST) {
> repeated group list {
> optional group element {
> optional group names (LIST) {
> repeated group list {
> optional binary element (UTF8);
> }
> }
> }
> }
> }
> }
> 16:57:48 INFO InternalParquetRecordWriter:165 - Flushing mem columnStore to file. allocated memory: 0
> 2018-10-01 16:57:48 INFO InternalParquetRecordWriter:165 - Flushing mem columnStore to file. allocated memory: 26
> ...
> 2018-10-01 16:57:49 INFO FileSourceStrategy:54 - Output Data Schema: struct<inners: array<struct<names:array<string>>>>
> 2018-10-01 16:57:49 INFO FileSourceScanExec:54 - Pushed Filters:
> java.lang.NullPointerException
> 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.sql.catalyst.encoders.OuterScopes$$anonfun$getOuterScope$1.apply(OuterScopes.scala:70)
> at org.apache.spark.sql.catalyst.expressions.objects.NewInstance$$anonfun$10.apply(objects.scala:485)
> at org.apache.spark.sql.catalyst.expressions.objects.NewInstance$$anonfun$10.apply(objects.scala:485)
> at scala.Option.map(Option.scala:146)
> at org.apache.spark.sql.catalyst.expressions.objects.NewInstance.doGenCode(objects.scala:485)
> at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:108)
> at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:105)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:105)
> at org.apache.spark.sql.catalyst.expressions.If.doGenCode(conditionalExpressions.scala:70)
> at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:108)
> at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:105)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:105)
> at org.apache.spark.sql.catalyst.expressions.objects.MapObjects.doGenCode(objects.scala:796)
> at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:108)
> at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:105)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:105)
> at org.apache.spark.sql.catalyst.expressions.objects.InvokeLike$$anonfun$3.apply(objects.scala:99)
> at org.apache.spark.sql.catalyst.expressions.objects.InvokeLike$$anonfun$3.apply(objects.scala:98)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.immutable.List.foreach(List.scala:392)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.immutable.List.map(List.scala:296)
> at org.apache.spark.sql.catalyst.expressions.objects.InvokeLike$class.prepareArguments(objects.scala:98)
> at org.apache.spark.sql.catalyst.expressions.objects.NewInstance.prepareArguments(objects.scala:431)
> at org.apache.spark.sql.catalyst.expressions.objects.NewInstance.doGenCode(objects.scala:483)
> at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:108)
> at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:105)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:105)
> at org.apache.spark.sql.catalyst.expressions.codegen.GenerateSafeProjection$$anonfun$3.apply(GenerateSafeProjection.scala:155)
> at org.apache.spark.sql.catalyst.expressions.codegen.GenerateSafeProjection$$anonfun$3.apply(GenerateSafeProjection.scala:152)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.immutable.List.foreach(List.scala:392)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.immutable.List.map(List.scala:296)
> at org.apache.spark.sql.catalyst.expressions.codegen.GenerateSafeProjection$.create(GenerateSafeProjection.scala:152)
> at org.apache.spark.sql.catalyst.expressions.codegen.GenerateSafeProjection$.create(GenerateSafeProjection.scala:38)
> at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.generate(CodeGenerator.scala:1193)
> at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3383)
> at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
> at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
> at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3365)
> at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
> at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
> at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3364)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:2545)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:2552)
> at org.apache.spark.sql.Dataset.first(Dataset.scala:2559)
> ... 59 elided
> {noformat}
> Regression from Spark version 2.3.1, which runs the above Spark Shell script correctly.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org