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Posted to issues@spark.apache.org by "Prashanth Sandela (JIRA)" <ji...@apache.org> on 2019/01/04 19:41:00 UTC
[jira] [Comment Edited] (SPARK-19809) NullPointerException on
zero-size ORC file
[ https://issues.apache.org/jira/browse/SPARK-19809?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16734510#comment-16734510 ]
Prashanth Sandela edited comment on SPARK-19809 at 1/4/19 7:40 PM:
-------------------------------------------------------------------
[~dongjoon] I'm encountering same similar issue with spark version 2.3.1
I'm trying to read from a table which was ingested by sqoop. There are few 0 byte files for this table. The file sizes looks like below:
{noformat}
-rw-rw-r-- 3 cloud-user root 17.3 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00000
-rw-rw-r-- 3 cloud-user root 10.3 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00001
-rw-rw-r-- 3 cloud-user root 19.9 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00002
-rw-rw-r-- 3 cloud-user root 13.0 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00003
-rw-rw-r-- 3 cloud-user root 0 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00004
-rw-rw-r-- 3 cloud-user root 3.4 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00005
-rw-rw-r-- 3 cloud-user root 13.8 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00006
-rw-rw-r-- 3 cloud-user root 0 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00007
-rw-rw-r-- 3 cloud-user root 0 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00008
-rw-rw-r-- 3 cloud-user root 6.9 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00009
-rw-rw-r-- 3 cloud-user root 9.0 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00010
-rw-rw-r-- 3 cloud-user root 11.4 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00011
-rw-rw-r-- 3 cloud-user root 14.7 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00012
-rw-rw-r-- 3 cloud-user root 17.4 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00013
-rw-rw-r-- 3 cloud-user root 17.1 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00014{noformat}
Spark throws exception while reading this table.
{noformat}
scala> spark.read.table("table_with_few_zero_byte_files").show()
java.lang.RuntimeException: serious problem at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1021) at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getSplits(OrcInputFormat.java:1048) at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:200) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:340) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3273) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484) at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253) at org.apache.spark.sql.Dataset.head(Dataset.scala:2484) at org.apache.spark.sql.Dataset.take(Dataset.scala:2698) at org.apache.spark.sql.Dataset.showString(Dataset.scala:254) at org.apache.spark.sql.Dataset.show(Dataset.scala:723) at org.apache.spark.sql.Dataset.show(Dataset.scala:682) at org.apache.spark.sql.Dataset.show(Dataset.scala:691) ... 49 elided Caused by: java.lang.NullPointerException at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$BISplitStrategy.getSplits(OrcInputFormat.java:560) at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1010) ... 99 more
scala> sql("set spark.sql.hive.convertMetastoreOrc=true")
res22: org.apache.spark.sql.DataFrame = [key: string, value: string]
scala> spark.read.table("table_with_few_zero_byte_files").show()
java.lang.IndexOutOfBoundsException at java.nio.Buffer.checkIndex(Buffer.java:540) at java.nio.HeapByteBuffer.get(HeapByteBuffer.java:139) at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.extractMetaInfoFromFooter(ReaderImpl.java:377) at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.<init>(ReaderImpl.java:319) at org.apache.hadoop.hive.ql.io.orc.OrcFile.createReader(OrcFile.java:187) at org.apache.spark.sql.hive.orc.OrcFileOperator$$anonfun$getFileReader$2.apply(OrcFileOperator.scala:75) at org.apache.spark.sql.hive.orc.OrcFileOperator$$anonfun$getFileReader$2.apply(OrcFileOperator.scala:73) at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) at scala.collection.TraversableOnce$class.collectFirst(TraversableOnce.scala:145) at scala.collection.AbstractIterator.collectFirst(Iterator.scala:1336) at org.apache.spark.sql.hive.orc.OrcFileOperator$.getFileReader(OrcFileOperator.scala:86) at org.apache.spark.sql.hive.orc.OrcFileOperator$$anonfun$readSchema$1.apply(OrcFileOperator.scala:95) at org.apache.spark.sql.hive.orc.OrcFileOperator$$anonfun$readSchema$1.apply(OrcFileOperator.scala:95) at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241) at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241) at scala.collection.immutable.List.foreach(List.scala:381) at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241) at scala.collection.immutable.List.flatMap(List.scala:344) at org.apache.spark.sql.hive.orc.OrcFileOperator$.readSchema(OrcFileOperator.scala:95) at org.apache.spark.sql.hive.orc.OrcFileFormat.inferSchema(OrcFileFormat.scala:63) at org.apache.spark.sql.hive.HiveMetastoreCatalog.org$apache$spark$sql$hive$HiveMetastoreCatalog$$inferIfNeeded(HiveMetastoreCatalog.scala:239) at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$6$$anonfun$7.apply(HiveMetastoreCatalog.scala:193) at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$6$$anonfun$7.apply(HiveMetastoreCatalog.scala:192) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$6.apply(HiveMetastoreCatalog.scala:192) at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$6.apply(HiveMetastoreCatalog.scala:185) at org.apache.spark.sql.hive.HiveMetastoreCatalog.withTableCreationLock(HiveMetastoreCatalog.scala:54) at org.apache.spark.sql.hive.HiveMetastoreCatalog.convertToLogicalRelation(HiveMetastoreCatalog.scala:185) at org.apache.spark.sql.hive.RelationConversions.org$apache$spark$sql$hive$RelationConversions$$convert(HiveStrategies.scala:205) at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:226) at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:215) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:288) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187) at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304) at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286) at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:215) at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:180) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:87) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:84) at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57) at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66) at scala.collection.mutable.ArrayBuffer.foldLeft(ArrayBuffer.scala:48) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:84) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:76) at scala.collection.immutable.List.foreach(List.scala:381) at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:76) at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:124) at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:118) at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:103) at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57) at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55) at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47) at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:74) at org.apache.spark.sql.SparkSession.table(SparkSession.scala:627) at org.apache.spark.sql.SparkSession.table(SparkSession.scala:623) at org.apache.spark.sql.DataFrameReader.table(DataFrameReader.scala:654) ... 49 elided
{noformat}
Unfortunately, we don't control the creation of table. What would be a config that could help me read this table?
was (Author: prashanthsandela):
[~dongjoon] I'm encountering same similar issue with spark version 2.3.1
I'm trying to read from a table which was ingested by sqoop. There are few 0 byte files for this table. The file sizes looks like below:
{noformat}
-rw-rw-r-- 3 cloud-user root 17.3 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00000
-rw-rw-r-- 3 cloud-user root 10.3 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00001
-rw-rw-r-- 3 cloud-user root 19.9 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00002
-rw-rw-r-- 3 cloud-user root 13.0 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00003
-rw-rw-r-- 3 cloud-user root 0 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00004
-rw-rw-r-- 3 cloud-user root 3.4 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00005
-rw-rw-r-- 3 cloud-user root 13.8 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00006
-rw-rw-r-- 3 cloud-user root 0 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00007
-rw-rw-r-- 3 cloud-user root 0 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00008
-rw-rw-r-- 3 cloud-user root 6.9 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00009
-rw-rw-r-- 3 cloud-user root 9.0 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00010
-rw-rw-r-- 3 cloud-user root 11.4 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00011
-rw-rw-r-- 3 cloud-user root 14.7 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00012
-rw-rw-r-- 3 cloud-user root 17.4 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00013
-rw-rw-r-- 3 cloud-user root 17.1 M 2019-01-03 22:20 /apps/hive/warehouse/default.db/table_with_few_zero_byte_files/part-m-00014{noformat}
Spark throws exception while doing count
{noformat}
scala> spark.read.table("table_with_few_zero_byte_files").show()
java.lang.RuntimeException: serious problem at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1021) at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getSplits(OrcInputFormat.java:1048) at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:200) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.rdd.RDD.partitions(RDD.scala:251) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:340) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3273) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484) at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253) at org.apache.spark.sql.Dataset.head(Dataset.scala:2484) at org.apache.spark.sql.Dataset.take(Dataset.scala:2698) at org.apache.spark.sql.Dataset.showString(Dataset.scala:254) at org.apache.spark.sql.Dataset.show(Dataset.scala:723) at org.apache.spark.sql.Dataset.show(Dataset.scala:682) at org.apache.spark.sql.Dataset.show(Dataset.scala:691) ... 49 elided Caused by: java.lang.NullPointerException at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$BISplitStrategy.getSplits(OrcInputFormat.java:560) at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1010) ... 99 more
scala> sql("set spark.sql.hive.convertMetastoreOrc=true")
res22: org.apache.spark.sql.DataFrame = [key: string, value: string]
scala> spark.read.table("table_with_few_zero_byte_files").show()
java.lang.IndexOutOfBoundsException at java.nio.Buffer.checkIndex(Buffer.java:540) at java.nio.HeapByteBuffer.get(HeapByteBuffer.java:139) at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.extractMetaInfoFromFooter(ReaderImpl.java:377) at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.<init>(ReaderImpl.java:319) at org.apache.hadoop.hive.ql.io.orc.OrcFile.createReader(OrcFile.java:187) at org.apache.spark.sql.hive.orc.OrcFileOperator$$anonfun$getFileReader$2.apply(OrcFileOperator.scala:75) at org.apache.spark.sql.hive.orc.OrcFileOperator$$anonfun$getFileReader$2.apply(OrcFileOperator.scala:73) at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) at scala.collection.TraversableOnce$class.collectFirst(TraversableOnce.scala:145) at scala.collection.AbstractIterator.collectFirst(Iterator.scala:1336) at org.apache.spark.sql.hive.orc.OrcFileOperator$.getFileReader(OrcFileOperator.scala:86) at org.apache.spark.sql.hive.orc.OrcFileOperator$$anonfun$readSchema$1.apply(OrcFileOperator.scala:95) at org.apache.spark.sql.hive.orc.OrcFileOperator$$anonfun$readSchema$1.apply(OrcFileOperator.scala:95) at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241) at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241) at scala.collection.immutable.List.foreach(List.scala:381) at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241) at scala.collection.immutable.List.flatMap(List.scala:344) at org.apache.spark.sql.hive.orc.OrcFileOperator$.readSchema(OrcFileOperator.scala:95) at org.apache.spark.sql.hive.orc.OrcFileFormat.inferSchema(OrcFileFormat.scala:63) at org.apache.spark.sql.hive.HiveMetastoreCatalog.org$apache$spark$sql$hive$HiveMetastoreCatalog$$inferIfNeeded(HiveMetastoreCatalog.scala:239) at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$6$$anonfun$7.apply(HiveMetastoreCatalog.scala:193) at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$6$$anonfun$7.apply(HiveMetastoreCatalog.scala:192) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$6.apply(HiveMetastoreCatalog.scala:192) at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$6.apply(HiveMetastoreCatalog.scala:185) at org.apache.spark.sql.hive.HiveMetastoreCatalog.withTableCreationLock(HiveMetastoreCatalog.scala:54) at org.apache.spark.sql.hive.HiveMetastoreCatalog.convertToLogicalRelation(HiveMetastoreCatalog.scala:185) at org.apache.spark.sql.hive.RelationConversions.org$apache$spark$sql$hive$RelationConversions$$convert(HiveStrategies.scala:205) at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:226) at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:215) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:288) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187) at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304) at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286) at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:215) at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:180) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:87) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:84) at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57) at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66) at scala.collection.mutable.ArrayBuffer.foldLeft(ArrayBuffer.scala:48) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:84) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:76) at scala.collection.immutable.List.foreach(List.scala:381) at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:76) at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:124) at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:118) at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:103) at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57) at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55) at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47) at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:74) at org.apache.spark.sql.SparkSession.table(SparkSession.scala:627) at org.apache.spark.sql.SparkSession.table(SparkSession.scala:623) at org.apache.spark.sql.DataFrameReader.table(DataFrameReader.scala:654) ... 49 elided
{noformat}
Unfortunately, we don't control the creation of table. What would be a config that could help me read this table?
> NullPointerException on zero-size ORC file
> ------------------------------------------
>
> Key: SPARK-19809
> URL: https://issues.apache.org/jira/browse/SPARK-19809
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.3, 2.0.2, 2.1.1, 2.2.1
> Reporter: Michał Dawid
> Assignee: Dongjoon Hyun
> Priority: Major
> Fix For: 2.3.0
>
> Attachments: image-2018-02-26-20-29-49-410.png, spark.sql.hive.convertMetastoreOrc.txt
>
>
> When reading from hive ORC table if there are some 0 byte files we get NullPointerException:
> {code}java.lang.NullPointerException
> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$BISplitStrategy.getSplits(OrcInputFormat.java:560)
> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1010)
> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getSplits(OrcInputFormat.java:1048)
> at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
> at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
> at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
> at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
> at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> at scala.collection.AbstractTraversable.map(Traversable.scala:105)
> at org.apache.spark.rdd.UnionRDD.getPartitions(UnionRDD.scala:66)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
> at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:242)
> at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:240)
> at scala.Option.getOrElse(Option.scala:120)
> at org.apache.spark.rdd.RDD.partitions(RDD.scala:240)
> at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:190)
> at org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:165)
> at org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174)
> at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
> at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
> at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
> at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086)
> at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$execute$1(DataFrame.scala:1498)
> at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$collect(DataFrame.scala:1505)
> at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1375)
> at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1374)
> at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2099)
> at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1374)
> at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1456)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:497)
> at org.apache.zeppelin.spark.ZeppelinContext.showDF(ZeppelinContext.java:209)
> at org.apache.zeppelin.spark.SparkSqlInterpreter.interpret(SparkSqlInterpreter.java:129)
> at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:94)
> at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:341)
> at org.apache.zeppelin.scheduler.Job.run(Job.java:176)
> at org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:139)
> at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
> at java.util.concurrent.FutureTask.run(FutureTask.java:266)
> at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
> at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745){code}
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