com.univocity.parsers.common.TextParsingException: Error processing input: org.apache.spark.TaskKilledException - null Parser Configuration: CsvParserSettings: Column reordering enabled=true Empty value=null Header extraction enabled=false Headers=[C0, C1, C2, C3, C4, C5, C6, C7, C8, C9, C10] Ignore leading whitespaces=false Ignore trailing whitespaces=false Input buffer size=128 Input reading on separate thread=false Line separator detection enabled=false Maximum number of characters per column=1000 Maximum number of columns=20 Null value= Number of records to read=all Parse unescaped quotes=true Row processor=none Selected fields=none Skip empty lines=trueFormat configuration: CsvFormat: Comment character=\0 Field delimiter=\t Line separator (normalized)=\n Line separator sequence=\n Quote character=" Quote escape character=quote escape Quote escape escape character=\0, line=706, char=197760. Content parsed: [mexic]

Apache's JIRA Issue Tracker | Shubhanshu Mishra | 9 months ago
  1. 0

    I am using the spark from the master branch and when I run the following command on a large tab separated file then I get the contents of the file being written to the stderr {code} df = sqlContext.read.load("temp.txt", format="csv", header="false", inferSchema="true", delimiter="\t") {code} Here is a sample of output: {code} ^M[Stage 1:> (0 + 2) / 2]16/03/23 14:01:02 ERROR Executor: Exception in task 1.0 in stage 1.0 (TID 2) com.univocity.parsers.common.TextParsingException: Error processing input: Length of parsed input (1000001) exceeds the maximum number of characters defined in your parser settings (1000000). Identified line separator characters in the parsed content. This may be the cause of the error. The line separator in your parser settings is set to '\n'. Parsed content: Privacy-shake",: a haptic interface for managing privacy settings in mobile location sharing applications privacy shake a haptic interface for managing privacy settings in mobile location sharing applications 2010 2010/09/07 international conference on human computer interaction interact 43331058 19371[\n] 3D4F6CA1 Between the Profiles: Another such Bias. Technology Acceptance Studies on Social Network Services between the profiles another such bias technology acceptance studies on social network services 2015 2015/08/02 10.1007/978-3-319-21383-5_12 international conference on human-computer interaction interact 43331058 19502[\n] ....... ......... web snippets 2008 2008/05/04 10.1007/978-3-642-01344-7_13 international conference on web information systems and technologies webist 44F29802 19489 06FA3FFA Interactive 3D User Interfaces for Neuroanatomy Exploration interactive 3d user interfaces for neuroanatomy exploration 2009 internationa] at com.univocity.parsers.common.AbstractParser.handleException(AbstractParser.java:241) at com.univocity.parsers.common.AbstractParser.parseNext(AbstractParser.java:356) at org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.next(CSVParser.scala:137) at org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.next(CSVParser.scala:120) at scala.collection.Iterator$class.foreach(Iterator.scala:742) at org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.foreach(CSVParser.scala:120) at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:155) at org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.foldLeft(CSVParser.scala:120) at scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:212) at org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.aggregate(CSVParser.scala:120) at org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1058) at org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1058) at org.apache.spark.SparkContext$$anonfun$35.apply(SparkContext.scala:1827) at org.apache.spark.SparkContext$$anonfun$35.apply(SparkContext.scala:1827) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:69) at org.apache.spark.scheduler.Task.run(Task.scala:82) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:231) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) Caused by: java.lang.ArrayIndexOutOfBoundsException 16/03/23 14:01:03 ERROR TaskSetManager: Task 0 in stage 1.0 failed 1 times; aborting job ^M[Stage 1:> (0 + 1) / 2] {code} For a small sample (<10,000 lines) of the data, I am not getting any error. But as soon as I go above more than 100,000 samples, I start getting the error. I don't think the spark platform should output the actual data to stderr ever as it decreases the readability.

    Apache's JIRA Issue Tracker | 9 months ago | Shubhanshu Mishra
    com.univocity.parsers.common.TextParsingException: Error processing input: org.apache.spark.TaskKilledException - null Parser Configuration: CsvParserSettings: Column reordering enabled=true Empty value=null Header extraction enabled=false Headers=[C0, C1, C2, C3, C4, C5, C6, C7, C8, C9, C10] Ignore leading whitespaces=false Ignore trailing whitespaces=false Input buffer size=128 Input reading on separate thread=false Line separator detection enabled=false Maximum number of characters per column=1000 Maximum number of columns=20 Null value= Number of records to read=all Parse unescaped quotes=true Row processor=none Selected fields=none Skip empty lines=trueFormat configuration: CsvFormat: Comment character=\0 Field delimiter=\t Line separator (normalized)=\n Line separator sequence=\n Quote character=" Quote escape character=quote escape Quote escape escape character=\0, line=706, char=197760. Content parsed: [mexic]
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    Root Cause Analysis

    1. org.apache.spark.TaskKilledException

      No message provided

      at org.apache.spark.InterruptibleIterator.hasNext()
    2. Spark
      InterruptibleIterator.hasNext
      1. org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
      1 frame
    3. Scala
      Iterator$$anon$11.hasNext
      1. scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:369)
      1 frame
    4. org.apache.spark
      StringIteratorReader.read
      1. org.apache.spark.sql.execution.datasources.csv.StringIteratorReader.refill(CSVParser.scala:167)
      2. org.apache.spark.sql.execution.datasources.csv.StringIteratorReader.read(CSVParser.scala:195)
      3. org.apache.spark.sql.execution.datasources.csv.StringIteratorReader.read(CSVParser.scala:215)
      3 frames
    5. com.univocity.parsers
      AbstractParser.parseNext
      1. com.univocity.parsers.common.input.DefaultCharInputReader.reloadBuffer(DefaultCharInputReader.java:81)
      2. com.univocity.parsers.common.input.AbstractCharInputReader.updateBuffer(AbstractCharInputReader.java:118)
      3. com.univocity.parsers.common.input.AbstractCharInputReader.nextChar(AbstractCharInputReader.java:180)
      4. com.univocity.parsers.csv.CsvParser.parseValue(CsvParser.java:94)
      5. com.univocity.parsers.csv.CsvParser.parseField(CsvParser.java:179)
      6. com.univocity.parsers.csv.CsvParser.parseRecord(CsvParser.java:75)
      7. com.univocity.parsers.common.AbstractParser.parseNext(AbstractParser.java:328)
      7 frames
    6. org.apache.spark
      BulkCsvReader.next
      1. org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.next(CSVParser.scala:137)
      2. org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.next(CSVParser.scala:120)
      2 frames
    7. Scala
      Iterator$class.foreach
      1. scala.collection.Iterator$class.foreach(Iterator.scala:742)
      1 frame
    8. org.apache.spark
      BulkCsvReader.foreach
      1. org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.foreach(CSVParser.scala:120)
      1 frame
    9. Scala
      TraversableOnce$class.foldLeft
      1. scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:155)
      1 frame
    10. org.apache.spark
      BulkCsvReader.foldLeft
      1. org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.foldLeft(CSVParser.scala:120)
      1 frame
    11. Scala
      TraversableOnce$class.aggregate
      1. scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:212)
      1 frame
    12. org.apache.spark
      BulkCsvReader.aggregate
      1. org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.aggregate(CSVParser.scala:120)
      1 frame
    13. Spark
      Executor$TaskRunner.run
      1. org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1058)
      2. org.apache.spark.rdd.RDD$$anonfun$aggregate$1$$anonfun$22.apply(RDD.scala:1058)
      3. org.apache.spark.SparkContext$$anonfun$35.apply(SparkContext.scala:1827)
      4. org.apache.spark.SparkContext$$anonfun$35.apply(SparkContext.scala:1827)
      5. org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:69)
      6. org.apache.spark.scheduler.Task.run(Task.scala:82)
      7. org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:231)
      7 frames
    14. Java RT
      Thread.run
      1. java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
      2. java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
      3. java.lang.Thread.run(Thread.java:745)
      3 frames