org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 3.0 failed 1 times, most recent failure: Lost task 1.0 in stage 3.0 (TID 5, localhost): com.univocity.parsers.common.TextParsingException: Error processing input: Length of parsed input (1001) exceeds the maximum number of characters defined in your parser settings (1000). 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: I did it my way": moving away from the tyranny of turn-by-turn pedestrian navigation i did it my way moving away from the tyranny of turn by turn pedestrian navigation 2010 2010/09/07 10.1145/1851600.1851660 international conference on human computer interaction interact 43331058 18871[\n] 770CA612 Fixed in time and "time in motion": mobility of vision through a SenseCam lens fixed in time and time in motion mobility of vision through a sensecam lens 2009 2009/09/15 10.1145/1613858.1613861 international conference on human computer interaction interact 43331058 19370[\n] 7B5DE5DE Assistive Wearable Technology for Visually Impaired assistive wearable technology for visually impaired 2015 2015/08/24 international conference on human computer interaction interact 43331058 19555[\n] 085BEC09 HOUDINI: Introducing Object Tracking and Pen Recognition for LLP Tabletops houdini introducing object tracking and pen recognition for llp tabletops 2014 2014/06/22 10.1007/978-3-319-07230-2_23 international c 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=36, char=9828. Content parsed: [I did it my way": moving away from the tyranny of turn-by-turn pedestrian navigation i did it my way moving away from the tyranny of turn by turn pedestrian navigation 2010 2010/09/07 10.1145/1851600.1851660 international conference on human computer interaction interact 43331058 18871 770CA612 Fixed in time and "time in motion": mobility of vision through a SenseCam lens fixed in time and time in motion mobility of vision through a sensecam lens 2009 2009/09/15 10.1145/1613858.1613861 international conference on human computer interaction interact 43331058 19370 7B5DE5DE Assistive Wearable Technology for Visually Impaired assistive wearable technology for visually impaired 2015 2015/08/24 international conference on human computer interaction interact 43331058 19555 085BEC09 HOUDINI: Introducing Object Tracking and Pen Recognition for LLP Tabletops houdini introducing object tracking and pen recognition for llp tabletops 2014 2014/06/22 10.1007/978-3-319-07230-2_23 international c]

Apache's JIRA Issue Tracker | Shubhanshu Mishra | 8 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 | 8 months ago | Shubhanshu Mishra
    org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 3.0 failed 1 times, most recent failure: Lost task 1.0 in stage 3.0 (TID 5, localhost): com.univocity.parsers.common.TextParsingException: Error processing input: Length of parsed input (1001) exceeds the maximum number of characters defined in your parser settings (1000). 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: I did it my way": moving away from the tyranny of turn-by-turn pedestrian navigation i did it my way moving away from the tyranny of turn by turn pedestrian navigation 2010 2010/09/07 10.1145/1851600.1851660 international conference on human computer interaction interact 43331058 18871[\n] 770CA612 Fixed in time and "time in motion": mobility of vision through a SenseCam lens fixed in time and time in motion mobility of vision through a sensecam lens 2009 2009/09/15 10.1145/1613858.1613861 international conference on human computer interaction interact 43331058 19370[\n] 7B5DE5DE Assistive Wearable Technology for Visually Impaired assistive wearable technology for visually impaired 2015 2015/08/24 international conference on human computer interaction interact 43331058 19555[\n] 085BEC09 HOUDINI: Introducing Object Tracking and Pen Recognition for LLP Tabletops houdini introducing object tracking and pen recognition for llp tabletops 2014 2014/06/22 10.1007/978-3-319-07230-2_23 international c 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=36, char=9828. Content parsed: [I did it my way": moving away from the tyranny of turn-by-turn pedestrian navigation i did it my way moving away from the tyranny of turn by turn pedestrian navigation 2010 2010/09/07 10.1145/1851600.1851660 international conference on human computer interaction interact 43331058 18871 770CA612 Fixed in time and "time in motion": mobility of vision through a SenseCam lens fixed in time and time in motion mobility of vision through a sensecam lens 2009 2009/09/15 10.1145/1613858.1613861 international conference on human computer interaction interact 43331058 19370 7B5DE5DE Assistive Wearable Technology for Visually Impaired assistive wearable technology for visually impaired 2015 2015/08/24 international conference on human computer interaction interact 43331058 19555 085BEC09 HOUDINI: Introducing Object Tracking and Pen Recognition for LLP Tabletops houdini introducing object tracking and pen recognition for llp tabletops 2014 2014/06/22 10.1007/978-3-319-07230-2_23 international c]
  2. 0

    Error when copying dataframe to spark context

    GitHub | 1 month ago | aissaelouafi
    org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0 in stage 3.0 (TID 3, localhost): java.lang.NumberFormatException: For input string: "<p>It seems like a lot of the cruises don't run in this month due to Hurricane season so I'm looking for other good options.</p>"
  3. 0

    Slave lost error in pyspark

    Stack Overflow | 3 weeks ago | newleaf
    org.apache.spark.SparkException: Job aborted due to stage failure: Task 6 in stage 6.0 failed 4 times, most recent failure: Lost task 6.3 in stage 6.0 ExecutorLostFailure (executor 2 exited caused by one of the running tasks) Reason: Slave lost When I did persist, through spark UI I saw the shuffleWrite memory is very high and took a long time and still returned errors. Through some search, I found these might be the out of memory problem. Following this link out of memory error Java I did a repartition up to 1000, still not so helpful. I set up the SparkConf as conf = (SparkConf().set("spark.driver.maxResultSize", "150g").set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")) My server side memory could be up to 200GB Do yo have any good idea to do this or point me to related links. Pyspark will be most helpful Here is the error log from YARN: Application application_1477088172315_0118 failed 2 times due to AM Container for appattempt_1477088172315_0118_000006 exited with exitCode: 10 For more detailed output, check application tracking page: Then, click on links to logs of each attempt. Diagnostics: Exception from container-launch. Container id: container_1477088172315_0118_06_000001 Exit code: 10 Stack trace: ExitCodeException exitCode=10:
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  5. 0

    definig dataframe on existing hbase table

    GitHub | 8 months ago | kamelia78
    org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): java.lang.IllegalArgumentException: offset (0) + length (8) exceed the capacity of the array: 3
  6. 0

    BytesUtils 中 toDouble 和 toLong 错误

    GitHub | 1 year ago | secfree
    org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 8.0 failed 1 times, most recent failure: Lost task 0.0 in stage 8.0 (TID 7, localhost): java.lang.IllegalArgumentException: offset (70) + length (8) exceed the capacity of the array: 71

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    Root Cause Analysis

    1. org.apache.spark.SparkException

      Job aborted due to stage failure: Task 1 in stage 3.0 failed 1 times, most recent failure: Lost task 1.0 in stage 3.0 (TID 5, localhost): com.univocity.parsers.common.TextParsingException: Error processing input: Length of parsed input (1001) exceeds the maximum number of characters defined in your parser settings (1000). 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: I did it my way": moving away from the tyranny of turn-by-turn pedestrian navigation i did it my way moving away from the tyranny of turn by turn pedestrian navigation 2010 2010/09/07 10.1145/1851600.1851660 international conference on human computer interaction interact 43331058 18871[\n] 770CA612 Fixed in time and "time in motion": mobility of vision through a SenseCam lens fixed in time and time in motion mobility of vision through a sensecam lens 2009 2009/09/15 10.1145/1613858.1613861 international conference on human computer interaction interact 43331058 19370[\n] 7B5DE5DE Assistive Wearable Technology for Visually Impaired assistive wearable technology for visually impaired 2015 2015/08/24 international conference on human computer interaction interact 43331058 19555[\n] 085BEC09 HOUDINI: Introducing Object Tracking and Pen Recognition for LLP Tabletops houdini introducing object tracking and pen recognition for llp tabletops 2014 2014/06/22 10.1007/978-3-319-07230-2_23 international c 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=36, char=9828. Content parsed: [I did it my way": moving away from the tyranny of turn-by-turn pedestrian navigation i did it my way moving away from the tyranny of turn by turn pedestrian navigation 2010 2010/09/07 10.1145/1851600.1851660 international conference on human computer interaction interact 43331058 18871 770CA612 Fixed in time and "time in motion": mobility of vision through a SenseCam lens fixed in time and time in motion mobility of vision through a sensecam lens 2009 2009/09/15 10.1145/1613858.1613861 international conference on human computer interaction interact 43331058 19370 7B5DE5DE Assistive Wearable Technology for Visually Impaired assistive wearable technology for visually impaired 2015 2015/08/24 international conference on human computer interaction interact 43331058 19555 085BEC09 HOUDINI: Introducing Object Tracking and Pen Recognition for LLP Tabletops houdini introducing object tracking and pen recognition for llp tabletops 2014 2014/06/22 10.1007/978-3-319-07230-2_23 international c]

      at com.univocity.parsers.common.AbstractParser.handleException()
    2. com.univocity.parsers
      AbstractParser.parseNext
      1. com.univocity.parsers.common.AbstractParser.handleException(AbstractParser.java:241)
      2. com.univocity.parsers.common.AbstractParser.parseNext(AbstractParser.java:356)
      2 frames
    3. 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
    4. Scala
      Iterator$class.foreach
      1. scala.collection.Iterator$class.foreach(Iterator.scala:742)
      1 frame
    5. org.apache.spark
      BulkCsvReader.foreach
      1. org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.foreach(CSVParser.scala:120)
      1 frame
    6. Scala
      TraversableOnce$class.foldLeft
      1. scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:155)
      1 frame
    7. org.apache.spark
      BulkCsvReader.foldLeft
      1. org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.foldLeft(CSVParser.scala:120)
      1 frame
    8. Scala
      TraversableOnce$class.aggregate
      1. scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:212)
      1 frame
    9. org.apache.spark
      BulkCsvReader.aggregate
      1. org.apache.spark.sql.execution.datasources.csv.BulkCsvReader.aggregate(CSVParser.scala:120)
      1 frame
    10. 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
    11. 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