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Posted to issues@spark.apache.org by "Chris Thistlethwaite (JIRA)" <ji...@apache.org> on 2019/04/02 13:04:00 UTC
[jira] [Issue Comment Deleted] (SPARK-27259) Processing Compressed
HDFS files with spark failing with error:
"java.lang.IllegalArgumentException: requirement failed: length (-1) cannot
be negative" from spark 2.2.X
[ https://issues.apache.org/jira/browse/SPARK-27259?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Chris Thistlethwaite updated SPARK-27259:
-----------------------------------------
Comment: was deleted
(was: Fix this please for my)
> Processing Compressed HDFS files with spark failing with error: "java.lang.IllegalArgumentException: requirement failed: length (-1) cannot be negative" from spark 2.2.X
> -------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-27259
> URL: https://issues.apache.org/jira/browse/SPARK-27259
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.4.0
> Reporter: Simon poortman
> Priority: Major
>
>
> From spark 2.2.x versions, when spark job processing any compressed HDFS files with custom input file format then spark jobs are failing with error "java.lang.IllegalArgumentException: requirement failed: length (-1) cannot be negative", the custom input file format will return the number of bytes length value as -1 for compressed file formats due to the compressed HDFS file are non splitable, so for compressed input file format the split will be offset as 0 and number of bytes length as -1, spark should consider the bytes length value -1 as valid split for the compressed file formats.
>
> We observed that earlier versions of spark doesn’t have this validation, and found that from spark 2.2.x new validation got introduced in the class InputFileBlockHolder, so spark should accept the number of bytes length value -1 as valid length for input splits from spark 2.2.x as well.
>
> +Below is the stack trace.+
> Caused by: java.lang.IllegalArgumentException: requirement failed: length (-1) cannot be negative
> at scala.Predef$.require(Predef.scala:224)
> at org.apache.spark.rdd.InputFileBlockHolder$.set(InputFileBlockHolder.scala:70)
> at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:226)
> at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:214)
> at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:94)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:109)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
>
> +Below is the code snippet which caused this issue.+
> ** {color:#ff0000}require(length >= 0, s"length ($length) cannot be negative"){color} // This validation caused the issue.
>
> {code:java}
> // code placeholder
> org.apache.spark.rdd.InputFileBlockHolder - spark-core
>
> def set(filePath: String, startOffset: Long, length: Long): Unit = {
> require(filePath != null, "filePath cannot be null")
> require(startOffset >= 0, s"startOffset ($startOffset) cannot be negative")
> require(length >= 0, s"length ($length) cannot be negative")
> inputBlock.set(new FileBlock(UTF8String.fromString(filePath), startOffset, length))
> }
> {code}
>
> +Steps to reproduce the issue.+
> Please refer the below code to reproduce the issue.
> {code:java}
> // code placeholder
> import org.apache.hadoop.mapred.JobConf
> val hadoopConf = new JobConf()
> import org.apache.hadoop.mapred.FileInputFormat
> import org.apache.hadoop.fs.Path
> FileInputFormat.setInputPaths(hadoopConf, new Path("/output656/part-r-00000.gz"))
> val records = sc.hadoopRDD(hadoopConf,classOf[com.platform.custom.storagehandler.INFAInputFormat], classOf[org.apache.hadoop.io.LongWritable], classOf[org.apache.hadoop.io.Writable])
> records.count()
> {code}
>
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