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From Timothy O <tod...@yahoo-inc.com.INVALID>
Subject Re: [VOTE] Release Apache Spark 1.6.0 (RC3)
Date Thu, 17 Dec 2015 15:39:47 GMT
+1 


    On Thursday, December 17, 2015 8:22 AM, Kousuke Saruta <sarutak@oss.nttdata.co.jp>
wrote:
 

  +1
 
 On 2015/12/17 6:32, Michael Armbrust 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).
   
 
 

  
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