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From Luciano Resende <luckbr1...@gmail.com>
Subject Re: [VOTE] Release Apache Spark 1.6.0 (RC3)
Date Sun, 20 Dec 2015 02:43:12 GMT
+1 (non-binding)

Tested Standalone mode, SparkR and couple Stream Apps, all seem ok.

On Wed, Dec 16, 2015 at 1:32 PM, Michael Armbrust <michael@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)
> <https://github.com/apache/spark/tree/v1.6.0-rc3>*
>
> 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  <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 1.5Spark 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
>       - 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.
>       - 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.
>       - 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.
>       - 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
>       - 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.
>
> MLlibNew 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
>       - SPARK-6725  <https://issues.apache.org/jira/browse/SPARK-6725> Pipeline
>       persistence - Save/load for ML Pipelines, with partial coverage of
>       spark.mlalgorithms
>       - SPARK-5565  <https://issues.apache.org/jira/browse/SPARK-5565> LDA
>       in ML Pipelines - API for Latent Dirichlet Allocation in ML
>       Pipelines
>    - R API
>       - 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)
>       - 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 sourceDocumentation 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>).
>
>


-- 
Luciano Resende
http://people.apache.org/~lresende
http://twitter.com/lresende1975
http://lresende.blogspot.com/

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