Thanks Tom. Interestingly it happened between RC2 and RC3. Now my vote is +1/2 unless the memory error is known and has a workaround.
Cheers <k/> On Fri, Sep 4, 2015 at 7:30 AM, Tom Graves <tgraves...@yahoo.com> wrote: > The upper/lower case thing is known. > https://issues.apache.org/jira/browse/SPARK-9550 > I assume it was decided to be ok and its going to be in the release notes > but Reynold or Josh can probably speak to it more. > > Tom > > > > On Thursday, September 3, 2015 10:21 PM, Krishna Sankar < > ksanka...@gmail.com> wrote: > > > +? > > 1. Compiled OSX 10.10 (Yosemite) OK Total time: 26:09 min > mvn clean package -Pyarn -Phadoop-2.6 -DskipTests > 2. Tested pyspark, mllib > 2.1. statistics (min,max,mean,Pearson,Spearman) OK > 2.2. Linear/Ridge/Laso Regression OK > 2.3. Decision Tree, Naive Bayes OK > 2.4. 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 4.3 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_2.11:1.2.0-s_2.11 didn’t work. But > com.databricks:spark-csv_2.11:1.2.0 worked) > 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 > > Two Problems: > > 1. The synthetic column names are lowercase ( i.e. now ‘sum(OrderPrice)’; > previously ‘SUM(OrderPrice)’, now ‘avg(Total)’; previously 'AVG(Total)'). > So programs that depend on the case of the synthetic column names would > fail. > 2. orders_3.groupBy("Year","Month").sum('Total').show() > fails with the error ‘java.io.IOException: Unable to acquire 4194304 > bytes of memory’ > orders_3.groupBy("CustomerID","Year").sum('Total').show() - fails with > the same error > Is this a known bug ? > Cheers > <k/> > P.S: Sorry for the spam, forgot Reply All > > On Tue, Sep 1, 2015 at 1:41 PM, Reynold Xin <r...@databricks.com> wrote: > > Please vote on releasing the following candidate as Apache Spark version > 1.5.0. The vote is open until Friday, Sep 4, 2015 at 21:00 UTC and passes > if a majority of at least 3 +1 PMC votes are cast. > > [ ] +1 Release this package as Apache Spark 1.5.0 > [ ] -1 Do not release this package because ... > > To learn more about Apache Spark, please see http://spark.apache.org/ > > > The tag to be voted on is v1.5.0-rc3: > > https://github.com/apache/spark/commit/908e37bcc10132bb2aa7f80ae694a9df6e40f31a > > The release files, including signatures, digests, etc. can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-bin/ > > Release artifacts are signed with the following key: > https://people.apache.org/keys/committer/pwendell.asc > > The staging repository for this release (published as 1.5.0-rc3) can be > found at: > https://repository.apache.org/content/repositories/orgapachespark-1143/ > > The staging repository for this release (published as 1.5.0) can be found > at: > https://repository.apache.org/content/repositories/orgapachespark-1142/ > > The documentation corresponding to this release can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-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. > > > ================================================ > What justifies a -1 vote for this release? > ================================================ > This vote is happening towards the end of the 1.5 QA period, so -1 votes > should only occur for significant regressions from 1.4. Bugs already > present in 1.4, minor regressions, or bugs related to new features will not > block this release. > > > =============================================================== > What should happen to JIRA tickets still targeting 1.5.0? > =============================================================== > 1. It is OK for documentation patches to target 1.5.0 and still go into > branch-1.5, since documentations will be packaged separately from the > release. > 2. New features for non-alpha-modules should target 1.6+. > 3. Non-blocker bug fixes should target 1.5.1 or 1.6.0, or drop the target > version. > > > ================================================== > Major changes to help you focus your testing > ================================================== > > As of today, Spark 1.5 contains more than 1000 commits from 220+ > contributors. I've curated a list of important changes for 1.5. For the > complete list, please refer to Apache JIRA changelog. > > RDD/DataFrame/SQL APIs > > - New UDAF interface > - DataFrame hints for broadcast join > - expr function for turning a SQL expression into DataFrame column > - Improved support for NaN values > - StructType now supports ordering > - TimestampType precision is reduced to 1us > - 100 new built-in expressions, including date/time, string, math > - memory and local disk only checkpointing > > DataFrame/SQL Backend Execution > > - Code generation on by default > - Improved join, aggregation, shuffle, sorting with cache friendly > algorithms and external algorithms > - Improved window function performance > - Better metrics instrumentation and reporting for DF/SQL execution plans > > Data Sources, Hive, Hadoop, Mesos and Cluster Management > > - Dynamic allocation support in all resource managers (Mesos, YARN, > Standalone) > - Improved Mesos support (framework authentication, roles, dynamic > allocation, constraints) > - Improved YARN support (dynamic allocation with preferred locations) > - Improved Hive support (metastore partition pruning, metastore > connectivity to 0.13 to 1.2, internal Hive upgrade to 1.2) > - Support persisting data in Hive compatible format in metastore > - Support data partitioning for JSON data sources > - Parquet improvements (upgrade to 1.7, predicate pushdown, faster > metadata discovery and schema merging, support reading non-standard legacy > Parquet files generated by other libraries) > - Faster and more robust dynamic partition insert > - DataSourceRegister interface for external data sources to specify short > names > > SparkR > > - YARN cluster mode in R > - GLMs with R formula, binomial/Gaussian families, and elastic-net > regularization > - Improved error messages > - Aliases to make DataFrame functions more R-like > > Streaming > > - Backpressure for handling bursty input streams. > - Improved Python support for streaming sources (Kafka offsets, Kinesis, > MQTT, Flume) > - Improved Python streaming machine learning algorithms (K-Means, linear > regression, logistic regression) > - Native reliable Kinesis stream support > - Input metadata like Kafka offsets made visible in the batch details UI > - Better load balancing and scheduling of receivers across cluster > - Include streaming storage in web UI > > Machine Learning and Advanced Analytics > > - Feature transformers: CountVectorizer, Discrete Cosine transformation, > MinMaxScaler, NGram, PCA, RFormula, StopWordsRemover, and VectorSlicer. > - Estimators under pipeline APIs: naive Bayes, k-means, and isotonic > regression. > - Algorithms: multilayer perceptron classifier, PrefixSpan for sequential > pattern mining, association rule generation, 1-sample Kolmogorov-Smirnov > test. > - Improvements to existing algorithms: LDA, trees/ensembles, GMMs > - More efficient Pregel API implementation for GraphX > - Model summary for linear and logistic regression. > - Python API: distributed matrices, streaming k-means and linear models, > LDA, power iteration clustering, etc. > - Tuning and evaluation: train-validation split and multiclass > classification evaluator. > - Documentation: document the release version of public API methods > > > > > >