You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Takeshi Yamamuro (JIRA)" <ji...@apache.org> on 2016/05/31 12:37:12 UTC
[jira] [Comment Edited] (SPARK-15654) Reading gzipped files results
in duplicate rows
[ https://issues.apache.org/jira/browse/SPARK-15654?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15307043#comment-15307043 ]
Takeshi Yamamuro edited comment on SPARK-15654 at 5/31/16 12:36 PM:
--------------------------------------------------------------------
Seems a root cause is that LineRecordReader cannot split files compressed by some codecs.
hadoop-v2.8+ throws an exception if these kinds of files are passed into LineRecordReader (https://github.com/apache/hadoop/blame/trunk/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/lib/input/LineRecordReader.java#L103)
This is fixed in MAPREDUCE-2094.
One solution is to set Long.MaxValue at defaultMaxSplitBytes in FileSourceStrategy if splittable files detected there.
https://github.com/apache/spark/compare/master...maropu:SPARK-15654
was (Author: maropu):
Seems a root cause is that LineRecordReader cannot split files compressed by some codecs.
hadoop-v2.8+ throws an exception if these kinds of files are passed into LineRecordReader (https://github.com/apache/hadoop/blame/trunk/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/lib/input/LineRecordReader.java#L103)
This is fixed in MAPREDUCE-2094.
One solution is to set Long.MaxValue at defaultMaxSplitBytes in FileSourceStrategy if splittable files detected there.
https://github.com/apache/spark/compare/master...maropu:SPARK-15654?expand=1
> Reading gzipped files results in duplicate rows
> -----------------------------------------------
>
> Key: SPARK-15654
> URL: https://issues.apache.org/jira/browse/SPARK-15654
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Jurriaan Pruis
> Priority: Blocker
>
> When gzipped files are larger then {{spark.sql.files.maxPartitionBytes}} reading the file will result in duplicate rows in the dataframe.
> Given an example gzipped wordlist (of 740K bytes):
> {code}
> $ gzcat words.gz |wc -l
> 235886
> {code}
> Reading it using spark results in the following output:
> {code}
> >>> sqlContext.setConf('spark.sql.files.maxPartitionBytes', '1000')
> >>> sqlContext.read.text("/Users/jurriaanpruis/spark/words.gz").count()
> 81244093
> >>> sqlContext.setConf('spark.sql.files.maxPartitionBytes', '10000')
> >>> sqlContext.read.text("/Users/jurriaanpruis/spark/words.gz").count()
> 8348566
> >>> sqlContext.setConf('spark.sql.files.maxPartitionBytes', '100000')
> >>> sqlContext.read.text("/Users/jurriaanpruis/spark/words.gz").count()
> 1051469
> >>> sqlContext.setConf('spark.sql.files.maxPartitionBytes', '1000000')
> >>> sqlContext.read.text("/Users/jurriaanpruis/spark/words.gz").count()
> 235886
> {code}
> You can clearly see how the number of rows scales with the number of partitions.
> Somehow the data is duplicated when the number of partitions exceeds one (which as seen above approximately scales with the partition size).
> When using distinct you'll get the correct answer:
> {code}
> >>> sqlContext.setConf('spark.sql.files.maxPartitionBytes', '10000')
> >>> sqlContext.read.text("/Users/jurriaanpruis/spark/words.gz").distinct().count()
> 235886
> {code}
> This looks like a pretty serious bug.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org