+1
built from sha w/ make-distribution.sh
tested basic examples (0 data) w/ local on fedora 20 (openjdk 1.7,
python 2.7.5)
tested detection and log processing (25GB data) w/ mesos (0.19.0) & nfs
on rhel 7 (openjdk 1.7, python 2.7.5)
On 09/03/2014 03:24 AM, Patrick Wendell 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.
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