We are getting close to merging patches for SPARK-12155 <https://issues.apache.org/jira/browse/SPARK-12155> and SPARK-12253 <https://issues.apache.org/jira/browse/SPARK-12253>. I'll be cutting RC2 shortly after that.
Michael On Tue, Dec 8, 2015 at 10:31 AM, Michael Armbrust <mich...@databricks.com> wrote: > An update: the vote fails due to the -1. I'll post another RC as soon as > we've resolved these issues. In the mean time I encourage people to > continue testing and post any problems they encounter here. > > On Sun, Dec 6, 2015 at 6:24 PM, Yin Huai <yh...@databricks.com> wrote: > >> -1 >> >> Tow blocker bugs have been found after this RC. >> https://issues.apache.org/jira/browse/SPARK-12089 can cause data >> corruption when an external sorter spills data. >> https://issues.apache.org/jira/browse/SPARK-12155 can prevent tasks from >> acquiring memory even when the executor indeed can allocate memory by >> evicting storage memory. >> >> https://issues.apache.org/jira/browse/SPARK-12089 has been fixed. We are >> still working on https://issues.apache.org/jira/browse/SPARK-12155. >> >> On Fri, Dec 4, 2015 at 3:04 PM, Mark Hamstra <m...@clearstorydata.com> >> wrote: >> >>> 0 >>> >>> Currently figuring out who is responsible for the regression that I am >>> seeing in some user code ScalaUDFs that make use of Timestamps and where >>> NULL from a CSV file read in via a TestHive#registerTestTable is now >>> producing 1969-12-31 23:59:59.999999 instead of null. >>> >>> On Thu, Dec 3, 2015 at 1:57 PM, Sean Owen <so...@cloudera.com> wrote: >>> >>>> Licenses and signature are all fine. >>>> >>>> Docker integration tests consistently fail for me with Java 7 / Ubuntu >>>> and "-Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver" >>>> >>>> *** RUN ABORTED *** >>>> java.lang.NoSuchMethodError: >>>> >>>> org.apache.http.impl.client.HttpClientBuilder.setConnectionManagerShared(Z)Lorg/apache/http/impl/client/HttpClientBuilder; >>>> at >>>> org.glassfish.jersey.apache.connector.ApacheConnector.<init>(ApacheConnector.java:240) >>>> at >>>> org.glassfish.jersey.apache.connector.ApacheConnectorProvider.getConnector(ApacheConnectorProvider.java:115) >>>> at >>>> org.glassfish.jersey.client.ClientConfig$State.initRuntime(ClientConfig.java:418) >>>> at >>>> org.glassfish.jersey.client.ClientConfig$State.access$000(ClientConfig.java:88) >>>> at >>>> org.glassfish.jersey.client.ClientConfig$State$3.get(ClientConfig.java:120) >>>> at >>>> org.glassfish.jersey.client.ClientConfig$State$3.get(ClientConfig.java:117) >>>> at >>>> org.glassfish.jersey.internal.util.collection.Values$LazyValueImpl.get(Values.java:340) >>>> at >>>> org.glassfish.jersey.client.ClientConfig.getRuntime(ClientConfig.java:726) >>>> at >>>> org.glassfish.jersey.client.ClientRequest.getConfiguration(ClientRequest.java:285) >>>> at >>>> org.glassfish.jersey.client.JerseyInvocation.validateHttpMethodAndEntity(JerseyInvocation.java:126) >>>> >>>> I also get this failure consistently: >>>> >>>> DirectKafkaStreamSuite >>>> - offset recovery *** FAILED *** >>>> recoveredOffsetRanges.forall(((or: (org.apache.spark.streaming.Time, >>>> Array[org.apache.spark.streaming.kafka.OffsetRange])) => >>>> >>>> earlierOffsetRangesAsSets.contains(scala.Tuple2.apply[org.apache.spark.streaming.Time, >>>> >>>> scala.collection.immutable.Set[org.apache.spark.streaming.kafka.OffsetRange]](or._1, >>>> >>>> scala.this.Predef.refArrayOps[org.apache.spark.streaming.kafka.OffsetRange](or._2).toSet[org.apache.spark.streaming.kafka.OffsetRange])))) >>>> was false Recovered ranges are not the same as the ones generated >>>> (DirectKafkaStreamSuite.scala:301) >>>> >>>> On Wed, Dec 2, 2015 at 8:26 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 Saturday, December 5, 2015 at 21:00 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-rc1 >>>> > (bf525845cef159d2d4c9f4d64e158f037179b5c4) >>>> > >>>> > The release files, including signatures, digests, etc. can be found >>>> at: >>>> > >>>> http://people.apache.org/~pwendell/spark-releases/spark-v1.6.0-rc1-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-1165/ >>>> > >>>> > The test repository (versioned as v1.6.0-rc1) for this release can be >>>> found >>>> > at: >>>> > >>>> https://repository.apache.org/content/repositories/orgapachespark-1164/ >>>> > >>>> > The documentation corresponding to this release can be found at: >>>> > >>>> http://people.apache.org/~pwendell/spark-releases/spark-1.6.0-rc1-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 == >>>> > ================================================== >>>> > >>>> > Spark SQL >>>> > >>>> > SPARK-10810 Session Management - The ability to create multiple >>>> isolated SQL >>>> > Contexts that have their own configuration and default database. >>>> This is >>>> > turned on by default in the thrift server. >>>> > 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 Metics for SQL Execution - Display >>>> statistics on a >>>> > per-operator 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 implementing 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 - Initial support for >>>> automatically >>>> > selecting the number of reducers for joins and aggregations. >>>> > >>>> > Spark Streaming >>>> > >>>> > API Updates >>>> > >>>> > SPARK-2629 New improved state management - trackStateByKey - a >>>> DStream >>>> > transformation for stateful stream processing, supersedes >>>> 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 arbitrary >>>> 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 Streaming 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.ml algorithms >>>> > 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. >>>> >>>> --------------------------------------------------------------------- >>>> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org >>>> For additional commands, e-mail: dev-h...@spark.apache.org >>>> >>>> >>> >> >
