+1 (non-binding) - checked checksums of a few packages - ran few jobs against yarn client/cluster using hadoop2.3 package - played with spark-shell in yarn-client mode
On Wed, Sep 3, 2014 at 12:24 AM, Patrick Wendell <pwend...@gmail.com> wrote: > Please vote on releasing the following candidate as Apache Spark version > 1.1.0! > > The tag to be voted on is v1.1.0-rc4 (commit 2f9b2bd): > https://git-wip-us.apache.org/repos/asf?p=spark.git;a=commit;h=2f9b2bd7844ee8393dc9c319f4fefedf95f5e460 > > The release files, including signatures, digests, etc. can be found at: > http://people.apache.org/~pwendell/spark-1.1.0-rc4/ > > Release artifacts are signed with the following key: > https://people.apache.org/keys/committer/pwendell.asc > > The staging repository for this release can be found at: > https://repository.apache.org/content/repositories/orgapachespark-1031/ > > The documentation corresponding to this release can be found at: > http://people.apache.org/~pwendell/spark-1.1.0-rc4-docs/ > > Please vote on releasing this package as Apache Spark 1.1.0! > > The vote is open until Saturday, September 06, at 08:30 UTC and passes if > a majority of at least 3 +1 PMC votes are cast. > > [ ] +1 Release this package as Apache Spark 1.1.0 > [ ] -1 Do not release this package because ... > > To learn more about Apache Spark, please see > http://spark.apache.org/ > > == Regressions fixed since RC3 == > SPARK-3332 - Issue with tagging in EC2 scripts > SPARK-3358 - Issue with regression for m3.XX instances > > == What justifies a -1 vote for this release? == > This vote is happening very late into the QA period compared with > previous votes, so -1 votes should only occur for significant > regressions from 1.0.2. Bugs already present in 1.0.X will not block > this release. > > == What default changes should I be aware of? == > 1. The default value of "spark.io.compression.codec" is now "snappy" > --> Old behavior can be restored by switching to "lzf" > > 2. PySpark now performs external spilling during aggregations. > --> Old behavior can be restored by setting "spark.shuffle.spill" to "false". > > 3. PySpark uses a new heuristic for determining the parallelism of > shuffle operations. > --> Old behavior can be restored by setting > "spark.default.parallelism" to the number of cores in the cluster. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > For additional commands, e-mail: dev-h...@spark.apache.org > -- Marcelo --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
