+1 (non binding) (Pyspark K-Means still shows the numeric diff, of course.)
2015-12-23 9:33 GMT+01:00 Kousuke Saruta <saru...@oss.nttdata.co.jp>: > +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>). >>> >>> >> > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > For additional commands, e-mail: dev-h...@spark.apache.org > >
