Hi Michael, What about the memory leak bug? https://issues.apache.org/jira/browse/SPARK-11293 Even after the memory rewrite in 1.6.0, it still happens in some cases. Will it be fixed for 1.6.1? Thanks,
*Romi Kuntsman*, *Big Data Engineer* http://www.totango.com On Mon, Feb 1, 2016 at 9:59 PM, Michael Armbrust <mich...@databricks.com> wrote: > We typically do not allow changes to the classpath in maintenance releases. > > On Mon, Feb 1, 2016 at 8:16 AM, Hamel Kothari <hamelkoth...@gmail.com> > wrote: > >> I noticed that the Jackson dependency was bumped to 2.5 in master for >> something spark-streaming related. Is there any reason that this upgrade >> can't be included with 1.6.1? >> >> According to later comments on this thread: >> https://issues.apache.org/jira/browse/SPARK-8332 and my personal >> experience using with Spark with Jackson 2.5 hasn't caused any issues but >> it does have some useful new features. It should be fully backwards >> compatible according to the Jackson folks. >> >> On Mon, Feb 1, 2016 at 10:29 AM Ted Yu <yuzhih...@gmail.com> wrote: >> >>> SPARK-12624 has been resolved. >>> According to Wenchen, SPARK-12783 is fixed in 1.6.0 release. >>> >>> Are there other blockers for Spark 1.6.1 ? >>> >>> Thanks >>> >>> On Wed, Jan 13, 2016 at 5:39 PM, Michael Armbrust < >>> mich...@databricks.com> wrote: >>> >>>> Hey All, >>>> >>>> While I'm not aware of any critical issues with 1.6.0, there are >>>> several corner cases that users are hitting with the Dataset API that are >>>> fixed in branch-1.6. As such I'm considering a 1.6.1 release. >>>> >>>> At the moment there are only two critical issues targeted for 1.6.1: >>>> - SPARK-12624 - When schema is specified, we should treat undeclared >>>> fields as null (in Python) >>>> - SPARK-12783 - Dataset map serialization error >>>> >>>> When these are resolved I'll likely begin the release process. If >>>> there are any other issues that we should wait for please contact me. >>>> >>>> Michael >>>> >>> >>> >
