[
https://issues.apache.org/jira/browse/SPARK-14103?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15216660#comment-15216660
]
Shubhanshu Mishra commented on SPARK-14103:
-------------------------------------------
[~srowen] yes, you are right. The issue is not with "\r\n" but something else.
I convered the "\r\n" in the file to "\n" and the error continues to exist.
Another issue which should be addressed is the long debug message which is
printed to the console with the whole content of the file. This is annoying. It
should just print the stack trace and the error message and not the whole
string of file contents which were read by the parser.
> Python DataFrame CSV load on large file is writing to console in Ipython
> ------------------------------------------------------------------------
>
> Key: SPARK-14103
> URL: https://issues.apache.org/jira/browse/SPARK-14103
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Environment: Ubuntu, Python 2.7.11, Anaconda 2.5.0, Spark from Master
> branch
> Reporter: Shubhanshu Mishra
> Labels: csv, csvparser, dataframe, pyspark
>
> 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
