+1. Ran some regression tests on Spark on Yarn (hadoop 2.6 and 2.7). Tom
On Wednesday, December 16, 2015 3:32 PM, Michael Armbrust <mich...@databricks.com> wrote: Please vote on releasing the following candidate as Apache Spark version 1.6.0! The vote is open until Saturday, December 19, 2015 at 18:00 UTC and passes if a majority of at least 3 +1 PMC votes are cast. [ ] +1 Release this package as Apache Spark 1.6.0[ ] -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 v1.6.0-rc3 (168c89e07c51fa24b0bb88582c739cec0acb44d7) The release files, including signatures, digests, etc. can be found at:http://people.apache.org/~pwendell/spark-releases/spark-1.6.0-rc3-bin/ Release artifacts are signed with the following key:https://people.apache.org/keys/committer/pwendell.asc The staging repository for this release can be found at:https://repository.apache.org/content/repositories/orgapachespark-1174/ The test repository (versioned as v1.6.0-rc3) for this release can be found at:https://repository.apache.org/content/repositories/orgapachespark-1173/ The documentation corresponding to this release can be found at:http://people.apache.org/~pwendell/spark-releases/spark-1.6.0-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 1.6 QA period, so -1 votes should only occur for significant regressions from 1.5. Bugs already present in 1.5, minor regressions, or bugs related to new features will not block this release. ================================================================= What should happen to JIRA tickets still targeting 1.6.0? =================================================================1. It is OK for documentation patches to target 1.6.0 and still go into branch-1.6, since documentations will be published separately from the release.2. New features for non-alpha-modules should target 1.7+.3. Non-blocker bug fixes should target 1.6.1 or 1.7.0, or drop the target version. ==================================================== Major changes to help you focus your testing ==================================================== Notable changes since 1.6 RC2 - SPARK_VERSION has been set correctly - SPARK-12199 ML Docs are publishing correctly - SPARK-12345 Mesos cluster mode has been fixed Notable changes since 1.6 RC1 Spark Streaming - SPARK-2629 trackStateByKey has been renamed to mapWithState Spark SQL - SPARK-12165 SPARK-12189 Fix bugs in eviction of storage memory by execution. - SPARK-12258 correct passing null into ScalaUDF Notable Features Since 1.5 Spark SQL - SPARK-11787 Parquet Performance - Improve Parquet scan performance when using flat schemas. - SPARK-10810 Session Management - Isolated devault database (i.e USE mydb) even on shared clusters. - SPARK-9999 Dataset API - A type-safe API (similar to RDDs) that performs many operations on serialized binary data and code generation (i.e. Project Tungsten). - SPARK-10000 Unified Memory Management - Shared memory for execution and caching instead of exclusive division of the regions. - SPARK-11197 SQL Queries on Files - Concise syntax for running SQL queries over files of any supported format without registering a table. - SPARK-11745 Reading non-standard JSON files - Added options to read non-standard JSON files (e.g. single-quotes, unquoted attributes) - SPARK-10412 Per-operator Metrics for SQL Execution - Display statistics on a peroperator basis for memory usage and spilled data size. - SPARK-11329 Star (*) expansion for StructTypes - Makes it easier to nest and unest arbitrary numbers of columns - SPARK-10917, SPARK-11149 In-memory Columnar Cache Performance - Significant (up to 14x) speed up when caching data that contains complex types in DataFrames or SQL. - SPARK-11111 Fast null-safe joins - Joins using null-safe equality (<=>) will now execute using SortMergeJoin instead of computing a cartisian product. - SPARK-11389 SQL Execution Using Off-Heap Memory - Support for configuring query execution to occur using off-heap memory to avoid GC overhead - SPARK-10978 Datasource API Avoid Double Filter - When implemeting a datasource with filter pushdown, developers can now tell Spark SQL to avoid double evaluating a pushed-down filter. - SPARK-4849 Advanced Layout of Cached Data - storing partitioning and ordering schemes in In-memory table scan, and adding distributeBy and localSort to DF API - SPARK-9858 Adaptive query execution - Intial support for automatically selecting the number of reducers for joins and aggregations. - SPARK-9241 Improved query planner for queries having distinct aggregations - Query plans of distinct aggregations are more robust when distinct columns have high cardinality. Spark Streaming - API Updates - SPARK-2629 New improved state management - mapWithState - a DStream transformation for stateful stream processing, supercedes updateStateByKey in functionality and performance. - SPARK-11198 Kinesis record deaggregation - Kinesis streams have been upgraded to use KCL 1.4.0 and supports transparent deaggregation of KPL-aggregated records. - SPARK-10891 Kinesis message handler function - Allows arbitraray function to be applied to a Kinesis record in the Kinesis receiver before to customize what data is to be stored in memory. - SPARK-6328 Python Streamng Listener API - Get streaming statistics (scheduling delays, batch processing times, etc.) in streaming. - UI Improvements - Made failures visible in the streaming tab, in the timelines, batch list, and batch details page. - Made output operations visible in the streaming tab as progress bars. MLlib New algorithms/models - SPARK-8518 Survival analysis - Log-linear model for survival analysis - SPARK-9834 Normal equation for least squares - Normal equation solver, providing R-like model summary statistics - SPARK-3147 Online hypothesis testing - A/B testing in the Spark Streaming framework - SPARK-9930 New feature transformers - ChiSqSelector, QuantileDiscretizer, SQL transformer - SPARK-6517 Bisecting K-Means clustering - Fast top-down clustering variant of K-Means API improvements - ML Pipelines - SPARK-6725 Pipeline persistence - Save/load for ML Pipelines, with partial coverage of spark.mlalgorithms - SPARK-5565 LDA in ML Pipelines - API for Latent Dirichlet Allocation in ML Pipelines - R API - SPARK-9836 R-like statistics for GLMs - (Partial) R-like stats for ordinary least squares via summary(model) - SPARK-9681 Feature interactions in R formula - Interaction operator ":" in R formula - Python API - Many improvements to Python API to approach feature parity Misc improvements - SPARK-7685 , SPARK-9642 Instance weights for GLMs - Logistic and Linear Regression can take instance weights - SPARK-10384, SPARK-10385 Univariate and bivariate statistics in DataFrames - Variance, stddev, correlations, etc. - SPARK-10117 LIBSVM data source - LIBSVM as a SQL data source Documentation improvements - SPARK-7751 @since versions - Documentation includes initial version when classes and methods were added - SPARK-11337 Testable example code - Automated testing for code in user guide examples Deprecations - In spark.mllib.clustering.KMeans, the "runs" parameter has been deprecated. - In spark.ml.classification.LogisticRegressionModel and spark.ml.regression.LinearRegressionModel, the "weights" field has been deprecated, in favor of the new name "coefficients." This helps disambiguate from instance (row) weights given to algorithms. Changes of behavior - spark.mllib.tree.GradientBoostedTrees validationTol has changed semantics in 1.6. Previously, it was a threshold for absolute change in error. Now, it resembles the behavior of GradientDescent convergenceTol: For large errors, it uses relative error (relative to the previous error); for small errors (< 0.01), it uses absolute error. - spark.ml.feature.RegexTokenizer: Previously, it did not convert strings to lowercase before tokenizing. Now, it converts to lowercase by default, with an option not to. This matches the behavior of the simpler Tokenizer transformer. - Spark SQL's partition discovery has been changed to only discover partition directories that are children of the given path. (i.e. if path="/my/data/x=1" then x=1 will no longer be considered a partition but only children of x=1.) This behavior can be overridden by manually specifying the basePath that partitioning discovery should start with (SPARK-11678). - When casting a value of an integral type to timestamp (e.g. casting a long value to timestamp), the value is treated as being in seconds instead of milliseconds (SPARK-11724). - With the improved query planner for queries having distinct aggregations (SPARK-9241), the plan of a query having a single distinct aggregation has been changed to a more robust version. To switch back to the plan generated by Spark 1.5's planner, please set spark.sql.specializeSingleDistinctAggPlanning to true (SPARK-12077).