+1 On 24 Dec 2015 22:01, "Vinay Shukla" <vinayshu...@gmail.com> wrote:
> +1 > Tested on HDP 2.3, YARN cluster mode, spark-shell > > On Wed, Dec 23, 2015 at 6:14 AM, Allen Zhang <allenzhang...@126.com> > wrote: > >> >> +1 (non-binding) >> >> I have just tarball a new binary and tested am.nodelabelexpression and >> executor.nodelabelexpression manully, result is expected. >> >> >> >> >> At 2015-12-23 21:44:08, "Iulian Dragoș" <iulian.dra...@typesafe.com> >> wrote: >> >> +1 (non-binding) >> >> Tested Mesos deployments (client and cluster-mode, fine-grained and >> coarse-grained). Things look good >> <https://ci.typesafe.com/view/Spark/job/mit-docker-test-ref/8/console>. >> >> iulian >> >> On Wed, Dec 23, 2015 at 2:35 PM, Sean Owen <so...@cloudera.com> wrote: >> >>> Docker integration tests still fail for Mark and I, and should >>> probably be disabled: >>> https://issues.apache.org/jira/browse/SPARK-12426 >>> >>> ... but if anyone else successfully runs these (and I assume Jenkins >>> does) then not a blocker. >>> >>> I'm having intermittent trouble with other tests passing, but nothing >>> unusual. >>> Sigs and hashes are OK. >>> >>> We have 30 issues fixed for 1.6.1. All but those resolved in the last >>> 24 hours or so should be fixed for 1.6.0 right? I can touch that up. >>> >>> >>> >>> >>> >>> On Tue, Dec 22, 2015 at 8:10 PM, Michael Armbrust >>> <mich...@databricks.com> 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) >>> > >>> > 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 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). >>> >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org >>> For additional commands, e-mail: dev-h...@spark.apache.org >>> >>> >> >> >> -- >> >> -- >> Iulian Dragos >> >> ------ >> Reactive Apps on the JVM >> www.typesafe.com >> >> >
