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
