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From "Aki Tanaka (JIRA)" <j...@apache.org>
Subject [jira] [Created] (HADOOP-15206) BZip2 drops and duplicates records when input split size is small
Date Thu, 01 Feb 2018 20:44:00 GMT
Aki Tanaka created HADOOP-15206:
-----------------------------------

             Summary: BZip2 drops and duplicates records when input split size is small
                 Key: HADOOP-15206
                 URL: https://issues.apache.org/jira/browse/HADOOP-15206
             Project: Hadoop Common
          Issue Type: Bug
    Affects Versions: 3.0.0, 2.8.3
            Reporter: Aki Tanaka


BZip2 can drop and duplicate record when input split file is small. I confirmed that this
issue happens when the input split size is between 1byte and 4bytes.

I am seeing the following 2 problem behaviors.

 

1. Drop record:

BZip2 skips the first record in the input file when the input split size is small

 

Set the split size to 3 and tested to load 100 records (0, 1, 2..99)
{code:java}
2018-02-01 10:52:33,502 INFO  [Thread-17] mapred.TestTextInputFormat (TestTextInputFormat.java:verifyPartitions(317))
- splits[1]=file:/work/count-mismatch2/hadoop/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-jobclient/target/test-dir/TestTextInputFormat/test.bz2:3+3
count=99{code}
> The input format read only 99 records but not 100 records

 

2. Duplicate Record:

2 input splits has same BZip2 records when the input split size is small

 

Set the split size to 1 and tested to load 100 records (0, 1, 2..99)

 
{code:java}
2018-02-01 11:18:49,309 INFO [Thread-17] mapred.TestTextInputFormat (TestTextInputFormat.java:verifyPartitions(318))
- splits[3]=file /work/count-mismatch2/hadoop/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-jobclient/target/test-dir/TestTextInputFormat/test.bz2:3+1
count=99
2018-02-01 11:18:49,310 WARN [Thread-17] mapred.TestTextInputFormat (TestTextInputFormat.java:verifyPartitions(308))
- conflict with 1 in split 4 at position 8
{code}
 

I experienced this error when I execute Spark (SparkSQL) job under the following conditions:

* The file size of the input files are small (around 1KB)

* Hadoop cluster has many slave nodes (able to launch many executor tasks)

 



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