[jira] [Resolved] (SPARK-10914) UnsafeRow serialization breaks when two machines have different Oops size

["Reynold Xin (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-10914?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Reynold Xin resolved SPARK-10914.
---------------------------------
       Resolution: Fixed
         Assignee: Reynold Xin
    Fix Version/s: 1.6.0
                   1.5.2

> UnsafeRow serialization breaks when two machines have different Oops size
> -------------------------------------------------------------------------
>
>                 Key: SPARK-10914
>                 URL: https://issues.apache.org/jira/browse/SPARK-10914
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.5.0, 1.5.1
>         Environment: Ubuntu 14.04 (spark-slave), 12.04 (master)
>            Reporter: Ben Moran
>            Assignee: Reynold Xin
>             Fix For: 1.5.2, 1.6.0
>
>
> *Updated description (by rxin on Oct 8, 2015)*
> To reproduce, launch Spark using
> {code}
> MASTER=local-cluster[2,1,1024] bin/spark-shell --conf "spark.executor.extraJavaOptions=-XX:-UseCompressedOops"
> {code}
> And then run the following
> {code}
> scala> sql("select 1 xx").collect()
> {code}
> The problem is that UnsafeRow contains 3 pieces of information when pointing to some data in memory (an object, a base offset, and length). When the row is serialized with Java/Kryo serialization, the object layout in memory can change if two machines have different pointer width (Oops in JVM).
> *Original bug report description*:
> Using an inner join, to match together two integer columns, I generally get no results when there should be matches.  But the results vary and depend on whether the dataframes are coming from SQL, JSON, or cached, as well as the order in which I cache things and query them.
> This minimal example reproduces it consistently for me in the spark-shell, on new installs of both 1.5.0 and 1.5.1 (pre-built against Hadoop 2.6 from http://spark.apache.org/downloads.html.)
> {code}
> /* x is {"xx":1}{"xx":2} and y is just {"yy":1}{"yy:2} */
> val x = sql("select 1 xx union all select 2") 
> val y = sql("select 1 yy union all select 2")
> x.join(y, $"xx" === $"yy").count() /* expect 2, get 0 */
> /* If I cache both tables it works: */
> x.cache()
> y.cache()
> x.join(y, $"xx" === $"yy").count() /* expect 2, get 2 */
> /* but this still doesn't work: */
> x.join(y, $"xx" === $"yy").filter("yy=1").count() /* expect 1, get 0 */
> {code}



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