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 >>> >>> >> >
