+1 Tested on Mac.
Matei > On Jul 22, 2016, at 11:18 AM, Joseph Bradley <jos...@databricks.com> wrote: > > +1 > > Mainly tested ML/Graph/R. Perf tests from Tim Hunter showed minor speedups > from 1.6 for common ML algorithms. > > On Thu, Jul 21, 2016 at 9:41 AM, Ricardo Almeida > <ricardo.alme...@actnowib.com <mailto:ricardo.alme...@actnowib.com>> wrote: > +1 (non binding) > > Tested PySpark Core, DataFrame/SQL, MLlib and Streaming on a standalone > cluster > > On 21 July 2016 at 05:24, Reynold Xin <r...@databricks.com > <mailto:r...@databricks.com>> wrote: > +1 > > > On Wednesday, July 20, 2016, Krishna Sankar <ksanka...@gmail.com > <mailto:ksanka...@gmail.com>> wrote: > +1 (non-binding, of course) > > 1. Compiled OS X 10.11.5 (El Capitan) OK Total time: 24:07 min > mvn clean package -Pyarn -Phadoop-2.7 -DskipTests > 2. Tested pyspark, mllib (iPython 4.0) > 2.0 Spark version is 2.0.0 > 2.1. statistics (min,max,mean,Pearson,Spearman) OK > 2.2. Linear/Ridge/Lasso Regression OK > 2.3. Classification : Decision Tree, Naive Bayes OK > 2.4. Clustering : KMeans OK > Center And Scale OK > 2.5. RDD operations OK > State of the Union Texts - MapReduce, Filter,sortByKey (word count) > 2.6. Recommendation (Movielens medium dataset ~1 M ratings) OK > Model evaluation/optimization (rank, numIter, lambda) with itertools OK > 3. Scala - MLlib > 3.1. statistics (min,max,mean,Pearson,Spearman) OK > 3.2. LinearRegressionWithSGD OK > 3.3. Decision Tree OK > 3.4. KMeans OK > 3.5. Recommendation (Movielens medium dataset ~1 M ratings) OK > 3.6. saveAsParquetFile OK > 3.7. Read and verify the 3.6 save(above) - sqlContext.parquetFile, > registerTempTable, sql OK > 3.8. result = sqlContext.sql("SELECT > OrderDetails.OrderID,ShipCountry,UnitPrice,Qty,Discount FROM Orders INNER > JOIN OrderDetails ON Orders.OrderID = OrderDetails.OrderID") OK > 4.0. Spark SQL from Python OK > 4.1. result = sqlContext.sql("SELECT * from people WHERE State = 'WA'") OK > 5.0. Packages > 5.1. com.databricks.spark.csv - read/write OK (--packages > com.databricks:spark-csv_2.10:1.4.0) > 6.0. DataFrames > 6.1. cast,dtypes OK > 6.2. groupBy,avg,crosstab,corr,isNull,na.drop OK > 6.3. All joins,sql,set operations,udf OK > [Dataframe Operations very fast from 11 secs to 3 secs, to 1.8 secs, to 1.5 > secs! Good work !!!] > 7.0. GraphX/Scala > 7.1. Create Graph (small and bigger dataset) OK > 7.2. Structure APIs - OK > 7.3. Social Network/Community APIs - OK > 7.4. Algorithms : PageRank of 2 datasets, aggregateMessages() - OK > > Cheers > <k/> > > On Tue, Jul 19, 2016 at 7:35 PM, Reynold Xin <r...@databricks.com <>> wrote: > Please vote on releasing the following candidate as Apache Spark version > 2.0.0. The vote is open until Friday, July 22, 2016 at 20:00 PDT and passes > if a majority of at least 3 +1 PMC votes are cast. > > [ ] +1 Release this package as Apache Spark 2.0.0 > [ ] -1 Do not release this package because ... > > > The tag to be voted on is v2.0.0-rc5 > (13650fc58e1fcf2cf2a26ba11c819185ae1acc1f). > > This release candidate resolves ~2500 issues: > https://s.apache.org/spark-2.0.0-jira <https://s.apache.org/spark-2.0.0-jira> > > The release files, including signatures, digests, etc. can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-2.0.0-rc5-bin/ > <http://people.apache.org/~pwendell/spark-releases/spark-2.0.0-rc5-bin/> > > Release artifacts are signed with the following key: > https://people.apache.org/keys/committer/pwendell.asc > <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-1195/ > <https://repository.apache.org/content/repositories/orgapachespark-1195/> > > The documentation corresponding to this release can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-2.0.0-rc5-docs/ > <http://people.apache.org/~pwendell/spark-releases/spark-2.0.0-rc5-docs/> > > > ================================= > How can I help test this release? > ================================= > If you are a Spark user, you can help us test this release by taking an > existing Spark workload and running on this release candidate, then reporting > any regressions from 1.x. > > ========================================== > What justifies a -1 vote for this release? > ========================================== > Critical bugs impacting major functionalities. > > Bugs already present in 1.x, missing features, or bugs related to new > features will not necessarily block this release. Note that historically > Spark documentation has been published on the website separately from the > main release so we do not need to block the release due to documentation > errors either. > > > >
