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From Michael Armbrust <mich...@databricks.com>
Subject Re: [VOTE] Release Apache Spark 1.6.0 (RC3)
Date Tue, 22 Dec 2015 01:48:37 GMT
It's come to my attention that there have been several bug fixes merged
since 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

Normally, these would probably not be sufficient to hold the release,
however with the holidays going on in the US this week, we don't have the
resources to finalize 1.6 until next Monday.  Given this delay anyway, I
propose that we cut one final RC with the above fixes and plan for the
actual release first thing next week.

I'll post RC4 shortly and cancel this vote if there are no objections.
Since this vote nearly passed with no major issues, I don't anticipate any
problems with RC4.

Michael

On Sat, Dec 19, 2015 at 11:44 PM, Jeff Zhang <zjffdu@gmail.com> wrote:

> +1 (non-binding)
>
> All the test passed, and run it on HDP 2.3.2 sandbox successfully.
>
> On Sun, Dec 20, 2015 at 10:43 AM, Luciano Resende <luckbr1975@gmail.com>
> wrote:
>
>> +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/
>>
>
>
>
> --
> Best Regards
>
> Jeff Zhang
>

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