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From Kousuke Saruta <saru...@oss.nttdata.co.jp>
Subject Re: [VOTE] Release Apache Spark 1.6.0 (RC4)
Date Wed, 23 Dec 2015 08:33:41 GMT
+1

On 2015/12/23 16:14, Jean-Baptiste Onofré wrote:
> +1 (non binding)
>
> Tested with samples on standalone and yarn.
>
> Regards
> JB
>
> On 12/22/2015 09:10 PM, Michael Armbrust wrote:
>> Please vote on releasing the following candidate as Apache Spark version
>> 1.6.0!
>>
>> The vote is open until Friday, December 25, 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-rc4
>> (4062cda3087ae42c6c3cb24508fc1d3a931accdf)
>> <https://github.com/apache/spark/tree/v1.6.0-rc4>_
>>
>> The release files, including signatures, digests, etc. can be found at:
>> http://people.apache.org/~pwendell/spark-releases/spark-1.6.0-rc4-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-1176/
>>
>> The test repository (versioned as v1.6.0-rc4) for this release can be
>> found at:
>> https://repository.apache.org/content/repositories/orgapachespark-1175/
>>
>> The documentation corresponding to this release can be found at:
>> http://people.apache.org/~pwendell/spark-releases/spark-1.6.0-rc4-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 RC3
>>
>>
>>    - SPARK-12404 - Fix serialization error for Datasets with
>> Timestamps/Arrays/Decimal
>>    - SPARK-12218 - Fix incorrect pushdown of filters to parquet
>>    - SPARK-12395 - Fix join columns of outer join for DataFrame using
>>    - SPARK-12413 - Fix mesos HA
>>
>>
>>
>>   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 <https://issues.apache.org/jira/browse/SPARK-2629>
>>     |trackStateByKey| has been renamed to |mapWithState|
>>
>>
>>       Spark SQL
>>
>>   * SPARK-12165 <https://issues.apache.org/jira/browse/SPARK-12165>
>>     SPARK-12189 <https://issues.apache.org/jira/browse/SPARK-12189> Fix
>>     bugs in eviction of storage memory by execution.
>>   * SPARK-12258
>>     <https://issues.apache.org/jira/browse/SPARK-12258> correct passing
>>     null into ScalaUDF
>>
>>
>>     Notable Features Since 1.5
>>
>>
>>       Spark SQL
>>
>>   * SPARK-11787 <https://issues.apache.org/jira/browse/SPARK-11787>
>>     Parquet Performance - Improve Parquet scan performance when using
>>     flat schemas.
>>   * SPARK-10810
>> <https://issues.apache.org/jira/browse/SPARK-10810>Session
>>     Management - Isolated devault database (i.e |USE mydb|) even on
>>     shared clusters.
>>   * SPARK-9999 <https://issues.apache.org/jira/browse/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 <https://issues.apache.org/jira/browse/SPARK-10000>
>>     Unified Memory Management - Shared memory for execution and caching
>>     instead of exclusive division of the regions.
>>   * SPARK-11197 <https://issues.apache.org/jira/browse/SPARK-11197> SQL
>>     Queries on Files - Concise syntax for running SQL queries over files
>>     of any supported format without registering a table.
>>   * SPARK-11745 <https://issues.apache.org/jira/browse/SPARK-11745>
>>     Reading non-standard JSON files - Added options to read non-standard
>>     JSON files (e.g. single-quotes, unquoted attributes)
>>   * SPARK-10412 <https://issues.apache.org/jira/browse/SPARK-10412>
>>     Per-operator Metrics for SQL Execution - Display statistics on a
>>     peroperator basis for memory usage and spilled data size.
>>   * SPARK-11329 <https://issues.apache.org/jira/browse/SPARK-11329> Star
>>     (*) expansion for StructTypes - Makes it easier to nest and unest
>>     arbitrary numbers of columns
>>   * SPARK-10917 <https://issues.apache.org/jira/browse/SPARK-10917>,
>>     SPARK-11149 <https://issues.apache.org/jira/browse/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 <https://issues.apache.org/jira/browse/SPARK-11111> Fast
>>     null-safe joins - Joins using null-safe equality (|<=>|) will now
>>     execute using SortMergeJoin instead of computing a cartisian 
>> product.
>>   * SPARK-11389 <https://issues.apache.org/jira/browse/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 <https://issues.apache.org/jira/browse/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 <https://issues.apache.org/jira/browse/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 <https://issues.apache.org/jira/browse/SPARK-9858>
>>     Adaptive query execution - Intial support for automatically
>>     selecting the number of reducers for joins and aggregations.
>>   * SPARK-9241 <https://issues.apache.org/jira/browse/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
>>       o SPARK-2629 <https://issues.apache.org/jira/browse/SPARK-2629>
>>         New improved state management - |mapWithState| - a DStream
>>         transformation for stateful stream processing, supercedes
>>         |updateStateByKey| in functionality and performance.
>>       o SPARK-11198 <https://issues.apache.org/jira/browse/SPARK-11198>
>>         Kinesis record deaggregation - Kinesis streams have been
>>         upgraded to use KCL 1.4.0 and supports transparent deaggregation
>>         of KPL-aggregated records.
>>       o SPARK-10891 <https://issues.apache.org/jira/browse/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.
>>       o SPARK-6328 <https://issues.apache.org/jira/browse/SPARK-6328>
>>         Python Streamng Listener API - Get streaming statistics
>>         (scheduling delays, batch processing times, etc.) in streaming.
>>
>>   * UI Improvements
>>       o Made failures visible in the streaming tab, in the timelines,
>>         batch list, and batch details page.
>>       o Made output operations visible in the streaming tab as progress
>>         bars.
>>
>>
>>       MLlib
>>
>>
>>         New algorithms/models
>>
>>   * SPARK-8518 <https://issues.apache.org/jira/browse/SPARK-8518>
>>     Survival analysis - Log-linear model for survival analysis
>>   * SPARK-9834 <https://issues.apache.org/jira/browse/SPARK-9834> Normal
>>     equation for least squares - Normal equation solver, providing
>>     R-like model summary statistics
>>   * SPARK-3147 <https://issues.apache.org/jira/browse/SPARK-3147> Online
>>     hypothesis testing - A/B testing in the Spark Streaming framework
>>   * SPARK-9930 <https://issues.apache.org/jira/browse/SPARK-9930> New
>>     feature transformers - ChiSqSelector, QuantileDiscretizer, SQL
>>     transformer
>>   * SPARK-6517 <https://issues.apache.org/jira/browse/SPARK-6517>
>>     Bisecting K-Means clustering - Fast top-down clustering variant of
>>     K-Means
>>
>>
>>         API improvements
>>
>>   * ML Pipelines
>>       o SPARK-6725 <https://issues.apache.org/jira/browse/SPARK-6725>
>>         Pipeline persistence - Save/load for ML Pipelines, with partial
>>         coverage of spark.ml <http://spark.ml/>algorithms
>>       o SPARK-5565 <https://issues.apache.org/jira/browse/SPARK-5565>
>>         LDA in ML Pipelines - API for Latent Dirichlet Allocation in ML
>>         Pipelines
>>   * R API
>>       o SPARK-9836 <https://issues.apache.org/jira/browse/SPARK-9836>
>>         R-like statistics for GLMs - (Partial) R-like stats for ordinary
>>         least squares via summary(model)
>>       o SPARK-9681 <https://issues.apache.org/jira/browse/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 <https://issues.apache.org/jira/browse/SPARK-7685>,
>>     SPARK-9642 <https://issues.apache.org/jira/browse/SPARK-9642>
>>     Instance weights for GLMs - Logistic and Linear Regression can take
>>     instance weights
>>   * SPARK-10384 <https://issues.apache.org/jira/browse/SPARK-10384>,
>>     SPARK-10385 <https://issues.apache.org/jira/browse/SPARK-10385>
>>     Univariate and bivariate statistics in DataFrames - Variance,
>>     stddev, correlations, etc.
>>   * SPARK-10117 <https://issues.apache.org/jira/browse/SPARK-10117>
>>     LIBSVM data source - LIBSVM as a SQL data source
>>
>>
>>             Documentation improvements
>>
>>   * SPARK-7751 <https://issues.apache.org/jira/browse/SPARK-7751> @since
>>     versions - Documentation includes initial version when classes and
>>     methods were added
>>   * SPARK-11337 <https://issues.apache.org/jira/browse/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
>>     <https://issues.apache.org/jira/browse/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
>>     <https://issues.apache.org/jira/browse/SPARK-11724>).
>>   * With the improved query planner for queries having distinct
>>     aggregations (SPARK-9241
>>     <https://issues.apache.org/jira/browse/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
>>     <https://issues.apache.org/jira/browse/SPARK-12077>).
>>
>


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