My quick take: no blockers at this point, except for one potential issue. Still some 'critical' bugs worth a look. The release seems to pass tests but i get a lot of spurious failures; it took about 16 hours of running tests to get everything to pass at least once.
Current score: 56 issues targeted at 1.5.0, of which 14 bugs, of which no blockers and 8 critical. This one might be a blocker as it seems to mean that SBT + Scala 2.11 does not compile: https://issues.apache.org/jira/browse/SPARK-10227 pretty simple issue, but weigh in on the PR: https://github.com/apache/spark/pull/8433 For reference here are the Critical ones: Key Component Summary Assignee SPARK-6484 Spark Core Ganglia metrics xml reporter doesn't escape correctly Josh Rosen SPARK-6701 Tests, YARN Flaky test: o.a.s.deploy.yarn.YarnClusterSuite Python application SPARK-7420 Tests Flaky test: o.a.s.streaming.JobGeneratorSuite "Do not clear received block data too soon" Tathagata Das SPARK-8119 Spark Core HeartbeatReceiver should not adjust application executor resources Andrew Or SPARK-8414 Spark Core Ensure ContextCleaner actually triggers clean ups Andrew Or SPARK-8447 Shuffle Test external shuffle service with all shuffle managers SPARK-10224 Streaming BlockGenerator may lost data in the last block SPARK-10287 SQL After processing a query using JSON data, Spark SQL continuously refreshes metadata of the table Total: 8 issues I'm seeing the following tests fail intermittently, with "-Phive -Phive-thriftserver -Phadoop-2.6" on Ubuntu 15 / Java 7: - security mismatch password *** FAILED *** Expected exception java.io.IOException to be thrown, but java.nio.channels.CancelledKeyException was thrown. (ConnectionManagerSuite.scala:123) DAGSchedulerSuite: ... - misbehaved resultHandler should not crash DAGScheduler and SparkContext *** FAILED *** java.lang.UnsupportedOperationException: taskSucceeded() called on a finished JobWaiter was not instance of org.apache.spark.scheduler.DAGSchedulerSuiteDummyException (DAGSchedulerSuite.scala:861) HeartbeatReceiverSuite: ... - normal heartbeat *** FAILED *** 3 did not equal 2 (HeartbeatReceiverSuite.scala:104) - Unpersisting HttpBroadcast on executors only in distributed mode *** FAILED *** ... - Unpersisting HttpBroadcast on executors and driver in distributed mode *** FAILED *** ... - Unpersisting TorrentBroadcast on executors only in distributed mode *** FAILED *** ... - Unpersisting TorrentBroadcast on executors and driver in distributed mode *** FAILED *** StreamingContextSuite: ... - stop gracefully *** FAILED *** 1749735 did not equal 1190429 Received records = 1749735, processed records = 1190428 (StreamingContextSuite.scala:279) DirectKafkaStreamSuite: - offset recovery *** FAILED *** The code passed to eventually never returned normally. Attempted 193 times over 10.010808486 seconds. Last failure message: strings.forall({ ((elem: Any) => DirectKafkaStreamSuite.collectedData.contains(elem)) }) was false. (DirectKafkaStreamSuite.scala:249) On Wed, Aug 26, 2015 at 5:28 AM, Reynold Xin <r...@databricks.com> wrote: > Please vote on releasing the following candidate as Apache Spark version > 1.5.0. The vote is open until Friday, Aug 29, 2015 at 5:00 UTC and passes if > a majority of at least 3 +1 PMC votes are cast. > > [ ] +1 Release this package as Apache Spark 1.5.0 > [ ] -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 v1.5.0-rc2: > https://github.com/apache/spark/tree/727771352855dbb780008c449a877f5aaa5fc27a > > The release files, including signatures, digests, etc. can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-bin/ > > Release artifacts are signed with the following key: > https://people.apache.org/keys/committer/pwendell.asc > > The staging repository for this release (published as 1.5.0-rc2) can be > found at: > https://repository.apache.org/content/repositories/orgapachespark-1141/ > > The staging repository for this release (published as 1.5.0) can be found > at: > https://repository.apache.org/content/repositories/orgapachespark-1140/ > > The documentation corresponding to this release can be found at: > http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc2-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 1.5 QA period, so -1 votes > should only occur for significant regressions from 1.4. Bugs already present > in 1.4, minor regressions, or bugs related to new features will not block > this release. > > > =============================================================== > What should happen to JIRA tickets still targeting 1.5.0? > =============================================================== > 1. It is OK for documentation patches to target 1.5.0 and still go into > branch-1.5, since documentations will be packaged separately from the > release. > 2. New features for non-alpha-modules should target 1.6+. > 3. Non-blocker bug fixes should target 1.5.1 or 1.6.0, or drop the target > version. > > > ================================================== > Major changes to help you focus your testing > ================================================== > > As of today, Spark 1.5 contains more than 1000 commits from 220+ > contributors. I've curated a list of important changes for 1.5. For the > complete list, please refer to Apache JIRA changelog. > > RDD/DataFrame/SQL APIs > > - New UDAF interface > - DataFrame hints for broadcast join > - expr function for turning a SQL expression into DataFrame column > - Improved support for NaN values > - StructType now supports ordering > - TimestampType precision is reduced to 1us > - 100 new built-in expressions, including date/time, string, math > - memory and local disk only checkpointing > > DataFrame/SQL Backend Execution > > - Code generation on by default > - Improved join, aggregation, shuffle, sorting with cache friendly > algorithms and external algorithms > - Improved window function performance > - Better metrics instrumentation and reporting for DF/SQL execution plans > > Data Sources, Hive, Hadoop, Mesos and Cluster Management > > - Dynamic allocation support in all resource managers (Mesos, YARN, > Standalone) > - Improved Mesos support (framework authentication, roles, dynamic > allocation, constraints) > - Improved YARN support (dynamic allocation with preferred locations) > - Improved Hive support (metastore partition pruning, metastore connectivity > to 0.13 to 1.2, internal Hive upgrade to 1.2) > - Support persisting data in Hive compatible format in metastore > - Support data partitioning for JSON data sources > - Parquet improvements (upgrade to 1.7, predicate pushdown, faster metadata > discovery and schema merging, support reading non-standard legacy Parquet > files generated by other libraries) > - Faster and more robust dynamic partition insert > - DataSourceRegister interface for external data sources to specify short > names > > SparkR > > - YARN cluster mode in R > - GLMs with R formula, binomial/Gaussian families, and elastic-net > regularization > - Improved error messages > - Aliases to make DataFrame functions more R-like > > Streaming > > - Backpressure for handling bursty input streams. > - Improved Python support for streaming sources (Kafka offsets, Kinesis, > MQTT, Flume) > - Improved Python streaming machine learning algorithms (K-Means, linear > regression, logistic regression) > - Native reliable Kinesis stream support > - Input metadata like Kafka offsets made visible in the batch details UI > - Better load balancing and scheduling of receivers across cluster > - Include streaming storage in web UI > > Machine Learning and Advanced Analytics > > - Feature transformers: CountVectorizer, Discrete Cosine transformation, > MinMaxScaler, NGram, PCA, RFormula, StopWordsRemover, and VectorSlicer. > - Estimators under pipeline APIs: naive Bayes, k-means, and isotonic > regression. > - Algorithms: multilayer perceptron classifier, PrefixSpan for sequential > pattern mining, association rule generation, 1-sample Kolmogorov-Smirnov > test. > - Improvements to existing algorithms: LDA, trees/ensembles, GMMs > - More efficient Pregel API implementation for GraphX > - Model summary for linear and logistic regression. > - Python API: distributed matrices, streaming k-means and linear models, > LDA, power iteration clustering, etc. > - Tuning and evaluation: train-validation split and multiclass > classification evaluator. > - Documentation: document the release version of public API methods > --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
