A mistake in the original email. The vote closes at 20:00 UTC, Aug 24, rather than Aug 17.
On Thursday, August 20, 2015, Shivaram Venkataraman < shiva...@eecs.berkeley.edu> wrote: > FYI > > The staging repository published as version 1.5.0 is at > https://repository.apache.org/content/repositories/orgapachespark-1136 > while the staging repository published as version 1.5.0-rc1 is at > https://repository.apache.org/content/repositories/orgapachespark-1137 > > Thanks > Shivaram > > On Thu, Aug 20, 2015 at 9:37 PM, Reynold Xin <r...@databricks.com > <javascript:;>> wrote: > > Please vote on releasing the following candidate as Apache Spark version > > 1.5.0! > > > > The vote is open until Monday, Aug 17, 2015 at 20:00 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-rc1: > > > https://github.com/apache/spark/tree/4c56ad772637615cc1f4f88d619fac6c372c8552 > > > > The release files, including signatures, digests, etc. can be found at: > > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc1-bin/ > > > > 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-1137/ > > > > The documentation corresponding to this release can be found at: > > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc1-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 > > >
