-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 >> >> >
