Skip to main content

Spark - java.util.NoSuchElementException: next on empty iterator [SPARK-27514]

By Dinesh

 

Recently, we did upgrade from HDP 3 to CDP 7, which involved upgrading Spark from 2.3 to 2.4.

  • We did compile and build our Jar with new dependencies. But, code started failing with below error - 
23/02/09 16:47:44 INFO yarn.ApplicationMaster: Final app status: FAILED, exitCode: 15, (reason: User class threw exception: jav
a.util.NoSuchElementException: next on empty iterator
        at scala.collection.Iterator$$anon$2.next(Iterator.scala:39)
        at scala.collection.Iterator$$anon$2.next(Iterator.scala:37)
        at scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:63)
        at scala.collection.IterableLike$class.head(IterableLike.scala:107)
        at scala.collection.mutable.ArrayBuffer.scala$collection$IndexedSeqOptimized$$super$head(ArrayBuffer.scala:48)
        at scala.collection.IndexedSeqOptimized$class.head(IndexedSeqOptimized.scala:126)
        at scala.collection.mutable.ArrayBuffer.head(ArrayBuffer.scala:48)
        at org.apache.spark.sql.catalyst.optimizer.CollapseWindow$$anonfun$apply$13.applyOrElse(Optimizer.scala:736)
        at org.apache.spark.sql.catalyst.optimizer.CollapseWindow$$anonfun$apply$13.applyOrElse(Optimizer.scala:731)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:282)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:282)
        at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:71)
        at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:281)
        at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformUp(LogicalPlan.scala:29)
        at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$class.transformUp(AnalysisHelper.scala:158)
        at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformUp(LogicalPlan.scala:29)
        at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformUp(LogicalPlan.scala:29)


We were not able to debug root cause of the error. We tried setting multiple properties as described in https://spark.apache.org/docs/2.4.0/sql-migration-guide-upgrade.html#upgrading-from-spark-sql-23-to-24 

But, we did not receive any success. Finally, we realized that Spark was not able to build the plan, as it was dropping certain columns (, may be due to predicate pushdown) . Thus, leading to Empty ArrayBuffer. And, when we call head on Empty ArrayBuffer then it leads to error as above. For ex - 

scala> import scala.collection.mutable.ArrayBuffer
import scala.collection.mutable.ArrayBuffer

scala> val buff = new ArrayBuffer[String]()
buff: scala.collection.mutable.ArrayBuffer[String] = ArrayBuffer()

scala> buff.head
java.util.NoSuchElementException: next on empty iterator
  at scala.collection.Iterator$$anon$2.next(Iterator.scala:39)
  at scala.collection.Iterator$$anon$2.next(Iterator.scala:37)
  at scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:63)
  at scala.collection.IterableLike$class.head(IterableLike.scala:107)
  at scala.collection.mutable.ArrayBuffer.scala$collection$IndexedSeqOptimized$$super$head(ArrayBuffer.scala:48)
  at scala.collection.IndexedSeqOptimized$class.head(IndexedSeqOptimized.scala:126)
  at scala.collection.mutable.ArrayBuffer.head(ArrayBuffer.scala:48)
  ... 49 elided


Solution Anyways to solve above problem, we did persist on dataframe before calling further action on transformation, which solved this error for us. But, creating a possible performance impact of calling unnecessary persist.


One further debugging, we were able to find code piece and bug relating to same, and we requested Cloudera to provide fix for same - 


Comments

Post a Comment

Popular posts

Hive Parse JSON with Array Columns and Explode it in to Multiple rows.

By Dinesh
 Say we have a JSON String like below -  { "billingCountry":"US" "orderItems":[       {          "itemId":1,          "product":"D1"       },   {          "itemId":2,          "product":"D2"       }    ] } And, our aim is to get output parsed like below -  itemId product 1 D1 2 D2   First, We can parse JSON as follows to get JSON String get_json_object(value, '$.orderItems.itemId') as itemId get_json_object(value, '$.orderItems.product') as product Second, Above will result String value like "[1,2]". We want to convert it to Array as follows - split(regexp_extract(get_json_object(value, '$.orderItems.itemId'),'^\\["(.*)\\"]$',1),'","') as itemId split(regexp_extract(get_json_object(value, '$.orderItems.product'),'^\\["(.*)\\"]$',1),&
Post a Comment
Read more




Read from a hive table and write back to it using spark sql

By Dinesh
In context to Spark 2.2 - if we read from an hive table and write to same, we get following exception- scala > dy . write . mode ( "overwrite" ). insertInto ( "incremental.test2" ) org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; 1. This error means that our process is reading from same table and writing to same table. 2. Normally, this should work as process writes to directory .hiveStaging... 3. This error occurs in case of saveAsTable method, as it overwrites entire table instead of individual partitions. 4. This error should not occur with insertInto method, as it overwrites partitions not the table. 5. A reason why this happening is because Hive table has following Spark TBLProperties in its definition. This problem will solve for insertInto met
Post a Comment
Read more




Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary

By Dinesh
Exception -  Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary at org.apache.parquet.column.Dictionary.decodeToBinary(Dictionary.java:44) at org.apache.spark.sql.execution.vectorized.ColumnVector.getUTF8String(ColumnVector.java:645) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) Analysis - This might occur because of data type mismatch between Hive Table & written Parquet file. Solution - Correct the data type to match between Hive Table & Parquet
Post a Comment
Read more




Hadoop Distcp Error Duplicate files in input path

By Dinesh
  One may face following error while copying data from one cluster to other, using Distcp  Command: hadoop distcp -i {src} {tgt} Error: org.apache.hadoop.toolsCopyListing$DulicateFileException: File would cause duplicates. Ideally there can't be same file names. So, what might be happening in your case is you trying to copy partitioned table from one cluster to other. And, 2 different named partitions have same file name. Your solution is to correct Source path  {src}  in your command, such that you provide path uptil partitioned sub directory, not the file. For ex - Refer below : /a/partcol=1/file1.txt /a/partcol=2/file1.txt If you use  {src}  as  "/a/*/*"  then you will get the error  "File would cause duplicates." But, if you use  {src}  as  "/a"  then you will not get error in copying.
Post a Comment
Read more




org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.;

By Dinesh
  Caused by: org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.; at org.apache.spark.sql.execution.command.DDLUtils$.verifyNotReadPath(ddl.scala:906) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:192) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:134) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis.apply(DataSourceStrategy.scala:134) at org.apache.spark.sql.execution.datasource
Post a Comment
Read more