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…on is false/null ## What changes were proposed in this pull request? CheckCartesianProduct raises an AnalysisException also when the join condition is always false/null. In this case, we shouldn't raise it, since the result will not be a cartesian product. ## How was this patch tested? added UT Author: Marco Gaido <marcogaido91@gmail.com> Closes #20333 from mgaido91/SPARK-23087.
## What changes were proposed in this pull request? Currently, KMeans assumes the only possible distance measure to be used is the Euclidean. This PR aims to add the cosine distance support to the KMeans algorithm. ## How was this patch tested? existing and added UTs. Author: Marco Gaido <marcogaido91@gmail.com> Author: Marco Gaido <mgaido@hortonworks.com> Closes #19340 from mgaido91/SPARK-22119.
## What changes were proposed in this pull request? streaming programming guide changes ## How was this patch tested? manually Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #20340 from felixcheung/rstreamdoc.
… .lintr configuration ## What changes were proposed in this pull request? When running the `run-tests` script, seems we don't run lintr on the changes of `lint-r` script and `.lintr` configuration. ## How was this patch tested? Jenkins builds Author: hyukjinkwon <gurwls223@gmail.com> Closes #20339 from HyukjinKwon/check-r-changed.
## What changes were proposed in this pull request? Pipe action convert objects into strings using a way that was affected by the default encoding setting of Python environment. This patch fixed the problem. The detailed description is added here: https://issues.apache.org/jira/browse/SPARK-20947 ## How was this patch tested? Run the following statement in pyspark-shell, and it will NOT raise exception if this patch is applied: ```python sc.parallelize([u'\u6d4b\u8bd5']).pipe('cat').collect() ``` Author: 王晓哲 <wxz@linkdoc.com> Closes #18277 from chaoslawful/fix_pipe_encoding_error.
## What changes were proposed in this pull request? The clean up logic on the worker perviously determined the liveness of a particular applicaiton based on whether or not it had running executors. This would fail in the case that a directory was made for a driver running in cluster mode if that driver had no running executors on the same machine. To preserve driver directories we consider both executors and running drivers when checking directory liveness. ## How was this patch tested? Manually started up two node cluster with a single core on each node. Turned on worker directory cleanup and set the interval to 1 second and liveness to one second. Without the patch the driver directory is removed immediately after the app is launched. With the patch it is not ### Without Patch ``` INFO 2018-01-05 23:48:24,693 Logging.scala:54 - Asked to launch driver driver-20180105234824-0000 INFO 2018-01-05 23:48:25,293 Logging.scala:54 - Changing view acls to: cassandra INFO 2018-01-05 23:48:25,293 Logging.scala:54 - Changing modify acls to: cassandra INFO 2018-01-05 23:48:25,294 Logging.scala:54 - Changing view acls groups to: INFO 2018-01-05 23:48:25,294 Logging.scala:54 - Changing modify acls groups to: INFO 2018-01-05 23:48:25,294 Logging.scala:54 - SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(cassandra); groups with view permissions: Set(); users with modify permissions: Set(cassandra); groups with modify permissions: Set() INFO 2018-01-05 23:48:25,330 Logging.scala:54 - Copying user jar file:/home/automaton/writeRead-0.1.jar to /var/lib/spark/worker/driver-20180105234824-0000/writeRead-0.1.jar INFO 2018-01-05 23:48:25,332 Logging.scala:54 - Copying /home/automaton/writeRead-0.1.jar to /var/lib/spark/worker/driver-20180105234824-0000/writeRead-0.1.jar INFO 2018-01-05 23:48:25,361 Logging.scala:54 - Launch Command: "/usr/lib/jvm/jdk1.8.0_40//bin/java" .... **** INFO 2018-01-05 23:48:56,577 Logging.scala:54 - Removing directory: /var/lib/spark/worker/driver-20180105234824-0000 ### << Cleaned up **** -- One minute passes while app runs (app has 1 minute sleep built in) -- WARN 2018-01-05 23:49:58,080 ShuffleSecretManager.java:73 - Attempted to unregister application app-20180105234831-0000 when it is not registered INFO 2018-01-05 23:49:58,081 ExternalShuffleBlockResolver.java:163 - Application app-20180105234831-0000 removed, cleanupLocalDirs = false INFO 2018-01-05 23:49:58,081 ExternalShuffleBlockResolver.java:163 - Application app-20180105234831-0000 removed, cleanupLocalDirs = false INFO 2018-01-05 23:49:58,082 ExternalShuffleBlockResolver.java:163 - Application app-20180105234831-0000 removed, cleanupLocalDirs = true INFO 2018-01-05 23:50:00,999 Logging.scala:54 - Driver driver-20180105234824-0000 exited successfully ``` With Patch ``` INFO 2018-01-08 23:19:54,603 Logging.scala:54 - Asked to launch driver driver-20180108231954-0002 INFO 2018-01-08 23:19:54,975 Logging.scala:54 - Changing view acls to: automaton INFO 2018-01-08 23:19:54,976 Logging.scala:54 - Changing modify acls to: automaton INFO 2018-01-08 23:19:54,976 Logging.scala:54 - Changing view acls groups to: INFO 2018-01-08 23:19:54,976 Logging.scala:54 - Changing modify acls groups to: INFO 2018-01-08 23:19:54,976 Logging.scala:54 - SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(automaton); groups with view permissions: Set(); users with modify permissions: Set(automaton); groups with modify permissions: Set() INFO 2018-01-08 23:19:55,029 Logging.scala:54 - Copying user jar file:/home/automaton/writeRead-0.1.jar to /var/lib/spark/worker/driver-20180108231954-0002/writeRead-0.1.jar INFO 2018-01-08 23:19:55,031 Logging.scala:54 - Copying /home/automaton/writeRead-0.1.jar to /var/lib/spark/worker/driver-20180108231954-0002/writeRead-0.1.jar INFO 2018-01-08 23:19:55,038 Logging.scala:54 - Launch Command: ...... INFO 2018-01-08 23:21:28,674 ShuffleSecretManager.java:69 - Unregistered shuffle secret for application app-20180108232000-0000 INFO 2018-01-08 23:21:28,675 ExternalShuffleBlockResolver.java:163 - Application app-20180108232000-0000 removed, cleanupLocalDirs = false INFO 2018-01-08 23:21:28,675 ExternalShuffleBlockResolver.java:163 - Application app-20180108232000-0000 removed, cleanupLocalDirs = false INFO 2018-01-08 23:21:28,681 ExternalShuffleBlockResolver.java:163 - Application app-20180108232000-0000 removed, cleanupLocalDirs = true INFO 2018-01-08 23:21:31,703 Logging.scala:54 - Driver driver-20180108231954-0002 exited successfully ***** INFO 2018-01-08 23:21:32,346 Logging.scala:54 - Removing directory: /var/lib/spark/worker/driver-20180108231954-0002 ### < Happening AFTER the Run completes rather than during it ***** ``` Author: Russell Spitzer <Russell.Spitzer@gmail.com> Closes #20298 from RussellSpitzer/SPARK-22976-master.
…ttributes ## What changes were proposed in this pull request? This PR fixes the wrong comment on `org.apache.spark.sql.parquet.row.attributes` which is useful for UDTs like Vector/Matrix. Please see [SPARK-22320](https://issues.apache.org/jira/browse/SPARK-22320) for the usage. Originally, [SPARK-19411](bf49368#diff-ee26d4c4be21e92e92a02e9f16dbc285L314) left this behind during removing optional column metadatas. In the same PR, the same comment was removed at line 310-311. ## How was this patch tested? N/A (This is about comments). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #20346 from dongjoon-hyun/minor_comment_parquet.
The race in the code is because the handle might update its state to the wrong state if the connection handling thread is still processing incoming data; so the handle needs to wait for the connection to finish up before checking the final state. The race in the test is because when waiting for a handle to reach a final state, the waitFor() method needs to wait until all handle state is updated (which also includes waiting for the connection thread above to finish). Otherwise, waitFor() may return too early, which would cause a bunch of different races (like the listener not being yet notified of the state change, or being in the middle of being notified, or the handle not being properly disposed and causing postChecks() to assert). On top of that I found, by code inspection, a couple of potential races that could make a handle end up in the wrong state when being killed. The original version of this fix introduced the flipped version of the first race described above; the connection closing might override the handle state before the handle might have a chance to do cleanup. The fix there is to only dispose of the handle from the connection when there is an error, and let the handle dispose itself in the normal case. The fix also caused a bug in YarnClusterSuite to be surfaced; the code was checking for a file in the classpath that was not expected to be there in client mode. Because of the above issues, the error was not propagating correctly and the (buggy) test was incorrectly passing. Tested by running the existing unit tests a lot (and not seeing the errors I was seeing before). Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #20297 from vanzin/SPARK-23020.
## What changes were proposed in this pull request? The example jar file is now in ./examples/jars directory of Spark distribution. Author: Arseniy Tashoyan <tashoyan@users.noreply.github.com> Closes #20349 from tashoyan/patch-1.
## What changes were proposed in this pull request? This PR is to update the docs for UDF registration ## How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #20348 from gatorsmile/testUpdateDoc.
…and optimizer rules ## What changes were proposed in this pull request? Dump the statistics of effective runs of analyzer and optimizer rules. ## How was this patch tested? Do a manual run of TPCDSQuerySuite ``` === Metrics of Analyzer/Optimizer Rules === Total number of runs: 175899 Total time: 25.486559948 seconds Rule Effective Time / Total Time Effective Runs / Total Runs org.apache.spark.sql.catalyst.optimizer.ColumnPruning 1603280450 / 2868461549 761 / 1877 org.apache.spark.sql.catalyst.analysis.Analyzer$CTESubstitution 2045860009 / 2056602674 37 / 788 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveAggregateFunctions 440719059 / 1693110949 38 / 1982 org.apache.spark.sql.catalyst.optimizer.Optimizer$OptimizeSubqueries 1429834919 / 1446016225 39 / 285 org.apache.spark.sql.catalyst.optimizer.PruneFilters 33273083 / 1389586938 3 / 1592 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences 821183615 / 1266668754 616 / 1982 org.apache.spark.sql.catalyst.optimizer.ReorderJoin 775837028 / 866238225 132 / 1592 org.apache.spark.sql.catalyst.analysis.DecimalPrecision 550683593 / 748854507 211 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveSubquery 513075345 / 634370596 49 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$FixNullability 33475731 / 606406532 12 / 742 org.apache.spark.sql.catalyst.analysis.TypeCoercion$ImplicitTypeCasts 193144298 / 545403925 86 / 1982 org.apache.spark.sql.catalyst.optimizer.BooleanSimplification 18651497 / 495725004 7 / 1592 org.apache.spark.sql.catalyst.optimizer.PushPredicateThroughJoin 369257217 / 489934378 709 / 1592 org.apache.spark.sql.catalyst.optimizer.RemoveRedundantAliases 3707000 / 468291609 9 / 1592 org.apache.spark.sql.catalyst.optimizer.InferFiltersFromConstraints 410155900 / 435254175 192 / 285 org.apache.spark.sql.execution.datasources.FindDataSourceTable 348885539 / 371855866 233 / 1982 org.apache.spark.sql.catalyst.optimizer.NullPropagation 11307645 / 307531225 26 / 1592 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveFunctions 120324545 / 304948785 294 / 1982 org.apache.spark.sql.catalyst.analysis.TypeCoercion$FunctionArgumentConversion 92323199 / 286695007 38 / 1982 org.apache.spark.sql.catalyst.optimizer.PushDownPredicate 230084193 / 265845972 785 / 1592 org.apache.spark.sql.catalyst.analysis.TypeCoercion$PromoteStrings 45938401 / 265144009 40 / 1982 org.apache.spark.sql.catalyst.analysis.TypeCoercion$InConversion 14888776 / 261499450 1 / 1982 org.apache.spark.sql.catalyst.analysis.TypeCoercion$CaseWhenCoercion 113796384 / 244913861 29 / 1982 org.apache.spark.sql.catalyst.optimizer.ConstantFolding 65008069 / 236548480 126 / 1592 org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractGenerator 0 / 226338929 0 / 1982 org.apache.spark.sql.catalyst.analysis.ResolveTimeZone 98134906 / 221323770 417 / 1982 org.apache.spark.sql.catalyst.optimizer.ReorderAssociativeOperator 0 / 208421703 0 / 1592 org.apache.spark.sql.catalyst.optimizer.OptimizeIn 8762534 / 199351958 16 / 1592 org.apache.spark.sql.catalyst.analysis.TypeCoercion$DateTimeOperations 11980016 / 190779046 27 / 1982 org.apache.spark.sql.catalyst.optimizer.SimplifyBinaryComparison 0 / 188887385 0 / 1592 org.apache.spark.sql.catalyst.optimizer.SimplifyConditionals 0 / 186812106 0 / 1592 org.apache.spark.sql.catalyst.optimizer.SimplifyCaseConversionExpressions 0 / 183885230 0 / 1592 org.apache.spark.sql.catalyst.optimizer.SimplifyCasts 17128295 / 182901910 69 / 1592 org.apache.spark.sql.catalyst.analysis.TypeCoercion$Division 14579110 / 180309340 8 / 1982 org.apache.spark.sql.catalyst.analysis.TypeCoercion$BooleanEquality 0 / 176740516 0 / 1982 org.apache.spark.sql.catalyst.analysis.TypeCoercion$IfCoercion 0 / 170781986 0 / 1982 org.apache.spark.sql.catalyst.optimizer.LikeSimplification 771605 / 164136736 1 / 1592 org.apache.spark.sql.catalyst.optimizer.RemoveDispensableExpressions 0 / 155958962 0 / 1592 org.apache.spark.sql.catalyst.analysis.ResolveCreateNamedStruct 0 / 151222943 0 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveWindowOrder 7534632 / 146596355 14 / 1982 org.apache.spark.sql.catalyst.analysis.TypeCoercion$EltCoercion 0 / 144488654 0 / 1982 org.apache.spark.sql.catalyst.analysis.TypeCoercion$ConcatCoercion 0 / 142403338 0 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveWindowFrame 12067635 / 141500665 21 / 1982 org.apache.spark.sql.catalyst.analysis.TimeWindowing 0 / 140431958 0 / 1982 org.apache.spark.sql.catalyst.analysis.TypeCoercion$WindowFrameCoercion 0 / 125471960 0 / 1982 org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin 14226972 / 124922019 11 / 1592 org.apache.spark.sql.catalyst.analysis.TypeCoercion$StackCoercion 0 / 123613887 0 / 1982 org.apache.spark.sql.catalyst.optimizer.RewriteCorrelatedScalarSubquery 8491071 / 121179056 7 / 1592 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics 55526073 / 120290529 11 / 1982 org.apache.spark.sql.catalyst.optimizer.ConstantPropagation 0 / 113886790 0 / 1592 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveDeserializer 52383759 / 107160222 148 / 1982 org.apache.spark.sql.catalyst.analysis.CleanupAliases 52543524 / 102091518 344 / 1086 org.apache.spark.sql.catalyst.optimizer.RemoveRedundantProject 40682895 / 94403652 342 / 1877 org.apache.spark.sql.catalyst.analysis.Analyzer$ExtractWindowExpressions 38473816 / 89740578 23 / 1982 org.apache.spark.sql.catalyst.optimizer.CollapseProject 46806090 / 83315506 281 / 1877 org.apache.spark.sql.catalyst.optimizer.FoldablePropagation 0 / 78750087 0 / 1592 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveAliases 13742765 / 77227258 47 / 1982 org.apache.spark.sql.catalyst.optimizer.CombineFilters 53386729 / 76960344 448 / 1592 org.apache.spark.sql.execution.datasources.DataSourceAnalysis 68034341 / 75724186 24 / 742 org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions 0 / 71151084 0 / 750 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveMissingReferences 12139848 / 67599140 8 / 1982 org.apache.spark.sql.catalyst.optimizer.PullupCorrelatedPredicates 45017938 / 65968777 23 / 285 org.apache.spark.sql.execution.datasources.v2.PushDownOperatorsToDataSource 0 / 60937767 0 / 285 org.apache.spark.sql.catalyst.optimizer.CollapseRepartition 0 / 59897237 0 / 1592 org.apache.spark.sql.catalyst.optimizer.PushProjectionThroughUnion 8547262 / 53941370 10 / 1592 org.apache.spark.sql.catalyst.analysis.Analyzer$HandleNullInputsForUDF 0 / 52735976 0 / 742 org.apache.spark.sql.catalyst.analysis.TypeCoercion$WidenSetOperationTypes 9797713 / 52401665 9 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$PullOutNondeterministic 0 / 51741500 0 / 742 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations 28614911 / 51061186 233 / 1990 org.apache.spark.sql.execution.datasources.PruneFileSourcePartitions 0 / 50621510 0 / 285 org.apache.spark.sql.catalyst.optimizer.CombineUnions 2777800 / 50262112 17 / 1877 org.apache.spark.sql.catalyst.analysis.Analyzer$GlobalAggregates 1640641 / 49633909 46 / 1982 org.apache.spark.sql.catalyst.optimizer.DecimalAggregates 20198374 / 48488419 100 / 385 org.apache.spark.sql.catalyst.optimizer.LimitPushDown 0 / 45052523 0 / 1592 org.apache.spark.sql.catalyst.optimizer.CombineLimits 0 / 44719443 0 / 1592 org.apache.spark.sql.catalyst.optimizer.EliminateSorts 0 / 44216930 0 / 1592 org.apache.spark.sql.catalyst.optimizer.RewritePredicateSubquery 36235699 / 44165786 148 / 285 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveNewInstance 0 / 42750307 0 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast 0 / 41811748 0 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveOrdinalInOrderByAndGroupBy 3819476 / 41776562 4 / 1982 org.apache.spark.sql.catalyst.optimizer.ComputeCurrentTime 0 / 40527808 0 / 285 org.apache.spark.sql.catalyst.optimizer.CollapseWindow 0 / 36832538 0 / 1592 org.apache.spark.sql.catalyst.optimizer.EliminateSerialization 0 / 36120667 0 / 1592 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveAggAliasInGroupBy 0 / 32435826 0 / 1982 org.apache.spark.sql.execution.datasources.PreprocessTableCreation 0 / 32145218 0 / 742 org.apache.spark.sql.execution.datasources.ResolveSQLOnFile 0 / 30295614 0 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolvePivot 0 / 30111655 0 / 1982 org.apache.spark.sql.catalyst.expressions.codegen.package$ExpressionCanonicalizer$CleanExpressions 59930 / 28038201 26 / 8280 org.apache.spark.sql.catalyst.analysis.ResolveInlineTables 0 / 27808108 0 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveSubqueryColumnAliases 0 / 27066690 0 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGenerate 0 / 26660210 0 / 1982 org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveNaturalAndUsingJoin 0 / 25255184 0 / 1982 org.apache.spark.sql.catalyst.analysis.ResolveTableValuedFunctions 0 / 24663088 0 / 1990 org.apache.spark.sql.catalyst.analysis.SubstituteUnresolvedOrdinals 9709079 / 24450670 4 / 788 org.apache.spark.sql.catalyst.analysis.ResolveHints$ResolveBroadcastHints 0 / 23776535 0 / 750 org.apache.spark.sql.catalyst.optimizer.ReplaceExpressions 0 / 22697895 0 / 285 org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts 0 / 22523798 0 / 285 org.apache.spark.sql.catalyst.optimizer.ReplaceDistinctWithAggregate 988593 / 21535410 15 / 300 org.apache.spark.sql.catalyst.optimizer.EliminateMapObjects 0 / 20269996 0 / 285 org.apache.spark.sql.catalyst.optimizer.RewriteDistinctAggregates 0 / 19388592 0 / 285 org.apache.spark.sql.catalyst.analysis.EliminateSubqueryAliases 17675532 / 18971185 215 / 285 org.apache.spark.sql.catalyst.optimizer.GetCurrentDatabase 0 / 18271152 0 / 285 org.apache.spark.sql.catalyst.optimizer.PropagateEmptyRelation 2077097 / 17190855 3 / 288 org.apache.spark.sql.catalyst.analysis.EliminateBarriers 0 / 16736359 0 / 1086 org.apache.spark.sql.execution.OptimizeMetadataOnlyQuery 0 / 16669341 0 / 285 org.apache.spark.sql.catalyst.analysis.UpdateOuterReferences 0 / 14470235 0 / 742 org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithAntiJoin 6715625 / 12190561 1 / 300 org.apache.spark.sql.catalyst.optimizer.ReplaceIntersectWithSemiJoin 3451793 / 11431432 7 / 300 org.apache.spark.sql.execution.python.ExtractPythonUDFFromAggregate 0 / 10810568 0 / 285 org.apache.spark.sql.catalyst.optimizer.RemoveRepetitionFromGroupExpressions 344198 / 10475276 1 / 286 org.apache.spark.sql.catalyst.analysis.Analyzer$WindowsSubstitution 0 / 10386630 0 / 788 org.apache.spark.sql.catalyst.analysis.EliminateUnions 0 / 10096526 0 / 788 org.apache.spark.sql.catalyst.analysis.AliasViewChild 0 / 9991706 0 / 742 org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation 0 / 9649334 0 / 288 org.apache.spark.sql.catalyst.analysis.ResolveHints$RemoveAllHints 0 / 8739109 0 / 750 org.apache.spark.sql.execution.datasources.PreprocessTableInsertion 0 / 8420889 0 / 742 org.apache.spark.sql.catalyst.analysis.EliminateView 0 / 8319134 0 / 285 org.apache.spark.sql.catalyst.optimizer.RemoveLiteralFromGroupExpressions 0 / 7392627 0 / 286 org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter 0 / 7170516 0 / 300 org.apache.spark.sql.catalyst.optimizer.SimplifyCreateArrayOps 0 / 7109643 0 / 1592 org.apache.spark.sql.catalyst.optimizer.SimplifyCreateStructOps 0 / 6837590 0 / 1592 org.apache.spark.sql.catalyst.optimizer.SimplifyCreateMapOps 0 / 6617848 0 / 1592 org.apache.spark.sql.catalyst.optimizer.CombineConcats 0 / 5768406 0 / 1592 org.apache.spark.sql.catalyst.optimizer.ReplaceDeduplicateWithAggregate 0 / 5349831 0 / 285 org.apache.spark.sql.catalyst.optimizer.CombineTypedFilters 0 / 5186642 0 / 285 org.apache.spark.sql.catalyst.optimizer.EliminateDistinct 0 / 2427686 0 / 285 org.apache.spark.sql.catalyst.optimizer.CostBasedJoinReorder 0 / 2420436 0 / 285 ``` Author: gatorsmile <gatorsmile@gmail.com> Closes #20342 from gatorsmile/reportExecution.
## What changes were proposed in this pull request? Revert the unneeded test case changes we made in SPARK-23000 Also fixes the test suites that do not call `super.afterAll()` in the local `afterAll`. The `afterAll()` of `TestHiveSingleton` actually reset the environments. ## How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #20341 from gatorsmile/testRelated.
## What changes were proposed in this pull request? Several improvements: * provide a default implementation for the batch get methods * rename `getChildColumn` to `getChild`, which is more concise * remove `getStruct(int, int)`, it's only used to simplify the codegen, which is an internal thing, we should not add a public API for this purpose. ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #20277 from cloud-fan/column-vector.
## What changes were proposed in this pull request? Note that this PR was made based on the top of #20151. So, it almost leaves the main codes intact. This PR proposes to add a script for the preparation of automatic PySpark coverage generation. Now, it's difficult to check the actual coverage in case of PySpark. With this script, it allows to run tests by the way we did via `run-tests` script before. The usage is exactly the same with `run-tests` script as this basically wraps it. This script and PR alone should also be useful. I was asked about how to run this before, and seems some reviewers (including me) need this. It would be also useful to run it manually. It usually requires a small diff in normal Python projects but PySpark cases are a bit different because apparently we are unable to track the coverage after it's forked. So, here, I made a custom worker that forces the coverage, based on the top of #20151. I made a simple demo. Please take a look - https://spark-test.github.io/pyspark-coverage-site. To show up the structure, this PR adds the files as below: ``` python ├── .coveragerc # Runtime configuration when we run the script. ├── run-tests-with-coverage # The script that has coverage support and wraps run-tests script. └── test_coverage # Directories that have files required when running coverage. ├── conf │ └── spark-defaults.conf # Having the configuration 'spark.python.daemon.module'. ├── coverage_daemon.py # A daemon having custom fix and wrapping our daemon.py └── sitecustomize.py # Initiate coverage with COVERAGE_PROCESS_START ``` Note that this PR has a minor nit: [This scope](https://github.com/apache/spark/blob/04e44b37cc04f62fbf9e08c7076349e0a4d12ea8/python/pyspark/daemon.py#L148-L169) in `daemon.py` is not in the coverage results as basically I am producing the coverage results in `worker.py` separately and then merging it. I believe it's not a big deal. In a followup, I might have a site that has a single up-to-date PySpark coverage from the master branch as the fallback / default, or have a site that has multiple PySpark coverages and the site link will be left to each pull request. ## How was this patch tested? Manually tested. Usage is the same with the existing Python test script - `./python/run-tests`. For example, ``` sh run-tests-with-coverage --python-executables=python3 --modules=pyspark-sql ``` Running this will generate HTMLs under `./python/test_coverage/htmlcov`. Console output example: ``` sh run-tests-with-coverage --python-executables=python3,python --modules=pyspark-core Running PySpark tests. Output is in /.../spark/python/unit-tests.log Will test against the following Python executables: ['python3', 'python'] Will test the following Python modules: ['pyspark-core'] Starting test(python): pyspark.tests Starting test(python3): pyspark.tests ... Tests passed in 231 seconds Combining collected coverage data under /.../spark/python/test_coverage/coverage_data Reporting the coverage data at /...spark/python/test_coverage/coverage_data/coverage Name Stmts Miss Branch BrPart Cover -------------------------------------------------------------- pyspark/__init__.py 41 0 8 2 96% ... pyspark/profiler.py 74 11 22 5 83% pyspark/rdd.py 871 40 303 32 93% pyspark/rddsampler.py 68 10 32 2 82% ... -------------------------------------------------------------- TOTAL 8521 3077 2748 191 59% Generating HTML files for PySpark coverage under /.../spark/python/test_coverage/htmlcov ``` Author: hyukjinkwon <gurwls223@gmail.com> Closes #20204 from HyukjinKwon/python-coverage.
## What changes were proposed in this pull request? ClosureCleaner moved from warning to debug ## How was this patch tested? Existing tests Author: Rekha Joshi <rekhajoshm@gmail.com> Author: rjoshi2 <rekhajoshm@gmail.com> Closes #20337 from rekhajoshm/SPARK-11630-1.
… streaming apps ## What changes were proposed in this pull request? The allJobs and the job pages attempt to use stage attempt and DAG visualization from the store, but for long running jobs they are not guaranteed to be retained, leading to exceptions when these pages are rendered. To fix it `store.lastStageAttempt(stageId)` and `store.operationGraphForJob(jobId)` are wrapped in `store.asOption` and default values are used if the info is missing. ## How was this patch tested? Manual testing of the UI, also using the test command reported in SPARK-23121: ./bin/spark-submit --class org.apache.spark.examples.streaming.HdfsWordCount ./examples/jars/spark-examples_2.11-2.4.0-SNAPSHOT.jar /spark Closes #20287 Author: Sandor Murakozi <smurakozi@gmail.com> Closes #20330 from smurakozi/SPARK-23121.
## What changes were proposed in this pull request? Typo fixes ## How was this patch tested? Local build / Doc-only changes Author: Jacek Laskowski <jacek@japila.pl> Closes #20344 from jaceklaskowski/typo-fixes.
## What changes were proposed in this pull request? a new interface which allows data source to report partitioning and avoid shuffle at Spark side. The design is pretty like the internal distribution/partitioing framework. Spark defines a `Distribution` interfaces and several concrete implementations, and ask the data source to report a `Partitioning`, the `Partitioning` should tell Spark if it can satisfy a `Distribution` or not. ## How was this patch tested? new test Author: Wenchen Fan <wenchen@databricks.com> Closes #20201 from cloud-fan/partition-reporting.
…ndas udf (full shuffle) ## What changes were proposed in this pull request? Add support for using pandas UDFs with groupby().agg(). This PR introduces a new type of pandas UDF - group aggregate pandas UDF. This type of UDF defines a transformation of multiple pandas Series -> a scalar value. Group aggregate pandas UDFs can be used with groupby().agg(). Note group aggregate pandas UDF doesn't support partial aggregation, i.e., a full shuffle is required. This PR doesn't support group aggregate pandas UDFs that return ArrayType, StructType or MapType. Support for these types is left for future PR. ## How was this patch tested? GroupbyAggPandasUDFTests Author: Li Jin <ice.xelloss@gmail.com> Closes #19872 from icexelloss/SPARK-22274-groupby-agg.
…rtitioner when defaultParallelism is set ## What changes were proposed in this pull request? #20002 purposed a way to safe check the default partitioner, however, if `spark.default.parallelism` is set, the defaultParallelism still could be smaller than the proper number of partitions for upstreams RDDs. This PR tries to extend the approach to address the condition when `spark.default.parallelism` is set. The requirements where the PR helps with are : - Max partitioner is not eligible since it is atleast an order smaller, and - User has explicitly set 'spark.default.parallelism', and - Value of 'spark.default.parallelism' is lower than max partitioner - Since max partitioner was discarded due to being at least an order smaller, default parallelism is worse - even though user specified. Under the rest cases, the changes should be no-op. ## How was this patch tested? Add corresponding test cases in `PairRDDFunctionsSuite` and `PartitioningSuite`. Author: Xingbo Jiang <xingbo.jiang@databricks.com> Closes #20091 from jiangxb1987/partitioner.
…octet_length ## What changes were proposed in this pull request? We need to override the prettyName for bit_length and octet_length for getting the expected auto-generated alias name. ## How was this patch tested? The existing tests Author: gatorsmile <gatorsmile@gmail.com> Closes #20358 from gatorsmile/test2.3More.
Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #20353 from vanzin/SPARK-20664.
…ient. Because the call to the constructor of HiveClientImpl crosses class loader boundaries, different versions of the same class (Configuration in this case) were loaded, and that caused a runtime error when instantiating the client. By using a safer type in the signature of the constructor, it's possible to avoid the problem. I considered removing 'sharesHadoopClasses', but it may still be desired (even though there are 0 users of it since it was not working). When Spark starts to support Hadoop 3, it may be necessary to use that option to load clients for older Hive metastore versions that don't know about Hadoop 3. Tested with added unit test. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #20169 from vanzin/SPARK-17088.
## What changes were proposed in this pull request? Added documentation for new transformer. Author: Bago Amirbekian <bago@databricks.com> Closes #20285 from MrBago/sizeHintDocs.
## What changes were proposed in this pull request?
The hint of the plan segment is lost, if the plan segment is replaced by the cached data.
```Scala
val df1 = spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
val df2 = spark.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
df2.cache()
val df3 = df1.join(broadcast(df2), Seq("key"), "inner")
```
This PR is to fix it.
## How was this patch tested?
Added a test
Author: gatorsmile <gatorsmile@gmail.com>
Closes #20365 from gatorsmile/fixBroadcastHintloss.
## What changes were proposed in this pull request?
The broadcast hint of the cached plan is lost if we cache the plan. This PR is to correct it.
```Scala
val df1 = spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
val df2 = spark.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
broadcast(df2).cache()
df2.collect()
val df3 = df1.join(df2, Seq("key"), "inner")
```
## How was this patch tested?
Added a test.
Author: gatorsmile <gatorsmile@gmail.com>
Closes #20368 from gatorsmile/cachedBroadcastHint.
## What changes were proposed in this pull request? Increased timeout from 50 ms to 300 ms (50 ms was really too low). ## How was this patch tested? Multiple rounds of tests. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #20371 from tdas/SPARK-23197.
## What changes were proposed in this pull request?
We extract Python UDFs in logical aggregate which depends on aggregate expression or grouping key in ExtractPythonUDFFromAggregate rule. But Python UDFs which don't depend on above expressions should also be extracted to avoid the issue reported in the JIRA.
A small code snippet to reproduce that issue looks like:
```python
import pyspark.sql.functions as f
df = spark.createDataFrame([(1,2), (3,4)])
f_udf = f.udf(lambda: str("const_str"))
df2 = df.distinct().withColumn("a", f_udf())
df2.show()
```
Error exception is raised as:
```
: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF0#50
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:91)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:90)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:90)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$38.apply(HashAggregateExec.scala:514)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$38.apply(HashAggregateExec.scala:513)
```
This exception raises because `HashAggregateExec` tries to bind the aliased Python UDF expression (e.g., `pythonUDF0#50 AS a#44`) to grouping key.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes #20360 from viirya/SPARK-23177.
…SparkR DataFrame ## What changes were proposed in this pull request? A fix to https://issues.apache.org/jira/browse/SPARK-21727, "Operating on an ArrayType in a SparkR DataFrame throws error" ## How was this patch tested? - Ran tests at R\pkg\tests\run-all.R (see below attached results) - Tested the following lines in SparkR, which now seem to execute without error: ``` indices <- 1:4 myDf <- data.frame(indices) myDf$data <- list(rep(0, 20)) mySparkDf <- as.DataFrame(myDf) collect(mySparkDf) ``` [2018-01-22 SPARK-21727 Test Results.txt](https://github.com/apache/spark/files/1653535/2018-01-22.SPARK-21727.Test.Results.txt) felixcheung yanboliang sun-rui shivaram _The contribution is my original work and I license the work to the project under the project’s open source license_ Author: neilalex <neil@neilalex.com> Closes #20352 from neilalex/neilalex-sparkr-arraytype.
This reverts commit 44cc4da.
## What changes were proposed in this pull request? In the document of `ContinuousReader.setOffset`, we say this method is used to specify the start offset. We also have a `ContinuousReader.getStartOffset` to get the value back. I think it makes more sense to rename `ContinuousReader.setOffset` to `setStartOffset`. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #20486 from cloud-fan/rename.
…eTypedFilters ## What changes were proposed in this pull request? In the current test case for CombineTypedFilters, we lack the test of FilterFunction, so let's add it. In addition, in TypedFilterOptimizationSuite's existing test cases, Let's extract a common LocalRelation. ## How was this patch tested? add new test cases. Author: caoxuewen <cao.xuewen@zte.com.cn> Closes #20482 from heary-cao/TypedFilterOptimizationSuite.
…r all file-based data sources ## What changes were proposed in this pull request? Like Parquet, all file-based data source handles `spark.sql.files.ignoreMissingFiles` correctly. We had better have a test coverage for feature parity and in order to prevent future accidental regression for all data sources. ## How was this patch tested? Pass Jenkins with a newly added test case. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #20479 from dongjoon-hyun/SPARK-23305.
…re are occurrences of backslashes. From [PEP 257](https://www.python.org/dev/peps/pep-0257/): > For consistency, always use """triple double quotes""" around docstrings. Use r"""raw triple double quotes""" if you use any backslashes in your docstrings. For Unicode docstrings, use u"""Unicode triple-quoted strings""". For example, this is what help (kafka_wordcount) shows: ``` DESCRIPTION Counts words in UTF8 encoded, ' ' delimited text received from the network every second. Usage: kafka_wordcount.py <zk> <topic> To run this on your local machine, you need to setup Kafka and create a producer first, see http://kafka.apache.org/documentation.html#quickstart and then run the example `$ bin/spark-submit --jars external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar examples/src/main/python/streaming/kafka_wordcount.py localhost:2181 test` ``` This is what it shows, after the fix: ``` DESCRIPTION Counts words in UTF8 encoded, '\n' delimited text received from the network every second. Usage: kafka_wordcount.py <zk> <topic> To run this on your local machine, you need to setup Kafka and create a producer first, see http://kafka.apache.org/documentation.html#quickstart and then run the example `$ bin/spark-submit --jars \ external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar \ examples/src/main/python/streaming/kafka_wordcount.py \ localhost:2181 test` ``` The thing worth noticing is no linebreak here in the help. ## What changes were proposed in this pull request? Change triple double quotes to raw triple double quotes when there are occurrences of backslashes in docstrings. ## How was this patch tested? Manually as this is a doc fix. Author: Shashwat Anand <me@shashwat.me> Closes #20497 from ashashwat/docstring-fixes.
…na.replace in PySpark" This reverts commit 0fcde87. See the discussion in [SPARK-21658](https://issues.apache.org/jira/browse/SPARK-21658), [SPARK-19454](https://issues.apache.org/jira/browse/SPARK-19454) and #16793 Author: hyukjinkwon <gurwls223@gmail.com> Closes #20496 from HyukjinKwon/revert-SPARK-21658.
…ader ## What changes were proposed in this pull request? This PR proposes to add `columnSchema` in Python side too. ```python >>> from pyspark.ml.image import ImageSchema >>> ImageSchema.columnSchema.simpleString() 'struct<origin:string,height:int,width:int,nChannels:int,mode:int,data:binary>' ``` ## How was this patch tested? Manually tested and unittest was added in `python/pyspark/ml/tests.py`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #20475 from HyukjinKwon/SPARK-23256.
## What changes were proposed in this pull request? Fix decimalArithmeticOperations.sql test ## How was this patch tested? N/A Author: Yuming Wang <wgyumg@gmail.com> Author: wangyum <wgyumg@gmail.com> Author: Yuming Wang <yumwang@ebay.com> Closes #20498 from wangyum/SPARK-22036.
…d timestamp before cleaning up them
## What changes were proposed in this pull request?
Sort jobs/stages/tasks/queries with the completed timestamp before cleaning up them to make the behavior consistent with 2.2.
## How was this patch tested?
- Jenkins.
- Manually ran the following codes and checked the UI for jobs/stages/tasks/queries.
```
spark.ui.retainedJobs 10
spark.ui.retainedStages 10
spark.sql.ui.retainedExecutions 10
spark.ui.retainedTasks 10
```
```
new Thread() {
override def run() {
spark.range(1, 2).foreach { i =>
Thread.sleep(10000)
}
}
}.start()
Thread.sleep(5000)
for (_ <- 1 to 20) {
new Thread() {
override def run() {
spark.range(1, 2).foreach { i =>
}
}
}.start()
}
Thread.sleep(15000)
spark.range(1, 2).foreach { i =>
}
sc.makeRDD(1 to 100, 100).foreach { i =>
}
```
Author: Shixiong Zhu <zsxwing@gmail.com>
Closes #20481 from zsxwing/SPARK-23307.
…fle reader To fix regression for TPC-DS queries Author: Sital Kedia <skedia@fb.com> Closes #20492 from sitalkedia/turn_off_async_inputstream.
## What changes were proposed in this pull request? Spark SQL executions page throws the following error and the page crashes: ``` HTTP ERROR 500 Problem accessing /SQL/. Reason: Server Error Caused by: java.lang.NullPointerException at scala.collection.immutable.StringOps$.length$extension(StringOps.scala:47) at scala.collection.immutable.StringOps.length(StringOps.scala:47) at scala.collection.IndexedSeqOptimized$class.isEmpty(IndexedSeqOptimized.scala:27) at scala.collection.immutable.StringOps.isEmpty(StringOps.scala:29) at scala.collection.TraversableOnce$class.nonEmpty(TraversableOnce.scala:111) at scala.collection.immutable.StringOps.nonEmpty(StringOps.scala:29) at org.apache.spark.sql.execution.ui.ExecutionTable.descriptionCell(AllExecutionsPage.scala:182) at org.apache.spark.sql.execution.ui.ExecutionTable.row(AllExecutionsPage.scala:155) at org.apache.spark.sql.execution.ui.ExecutionTable$$anonfun$8.apply(AllExecutionsPage.scala:204) at org.apache.spark.sql.execution.ui.ExecutionTable$$anonfun$8.apply(AllExecutionsPage.scala:204) at org.apache.spark.ui.UIUtils$$anonfun$listingTable$2.apply(UIUtils.scala:339) at org.apache.spark.ui.UIUtils$$anonfun$listingTable$2.apply(UIUtils.scala:339) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.AbstractTraversable.map(Traversable.scala:104) at org.apache.spark.ui.UIUtils$.listingTable(UIUtils.scala:339) at org.apache.spark.sql.execution.ui.ExecutionTable.toNodeSeq(AllExecutionsPage.scala:203) at org.apache.spark.sql.execution.ui.AllExecutionsPage.render(AllExecutionsPage.scala:67) at org.apache.spark.ui.WebUI$$anonfun$2.apply(WebUI.scala:82) at org.apache.spark.ui.WebUI$$anonfun$2.apply(WebUI.scala:82) at org.apache.spark.ui.JettyUtils$$anon$3.doGet(JettyUtils.scala:90) at javax.servlet.http.HttpServlet.service(HttpServlet.java:687) at javax.servlet.http.HttpServlet.service(HttpServlet.java:790) at org.eclipse.jetty.servlet.ServletHolder.handle(ServletHolder.java:848) at org.eclipse.jetty.servlet.ServletHandler.doHandle(ServletHandler.java:584) at org.eclipse.jetty.server.handler.ContextHandler.doHandle(ContextHandler.java:1180) at org.eclipse.jetty.servlet.ServletHandler.doScope(ServletHandler.java:512) at org.eclipse.jetty.server.handler.ContextHandler.doScope(ContextHandler.java:1112) at org.eclipse.jetty.server.handler.ScopedHandler.handle(ScopedHandler.java:141) at org.eclipse.jetty.server.handler.ContextHandlerCollection.handle(ContextHandlerCollection.java:213) at org.eclipse.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:134) at org.eclipse.jetty.server.Server.handle(Server.java:534) at org.eclipse.jetty.server.HttpChannel.handle(HttpChannel.java:320) at org.eclipse.jetty.server.HttpConnection.onFillable(HttpConnection.java:251) at org.eclipse.jetty.io.AbstractConnection$ReadCallback.succeeded(AbstractConnection.java:283) at org.eclipse.jetty.io.FillInterest.fillable(FillInterest.java:108) at org.eclipse.jetty.io.SelectChannelEndPoint$2.run(SelectChannelEndPoint.java:93) at org.eclipse.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:303) at org.eclipse.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148) at org.eclipse.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136) at org.eclipse.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671) at org.eclipse.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589) at java.lang.Thread.run(Thread.java:748) ``` One of the possible reason that this page fails may be the `SparkListenerSQLExecutionStart` event get dropped before processed, so the execution description and details don't get updated. This was not a issue in 2.2 because it would ignore any job start event that arrives before the corresponding execution start event, which doesn't sound like a good decision. We shall try to handle the null values in the front page side, that is, try to give a default value when `execution.details` or `execution.description` is null. Another possible approach is not to spill the `LiveExecutionData` in `SQLAppStatusListener.update(exec: LiveExecutionData)` if `exec.details` is null. This is not ideal because this way you will not see the execution if `SparkListenerSQLExecutionStart` event is lost, because `AllExecutionsPage` only read executions from KVStore. ## How was this patch tested? After the change, the page shows the following:  Author: Xingbo Jiang <xingbo.jiang@databricks.com> Closes #20502 from jiangxb1987/executionPage.
…running ## What changes were proposed in this pull request? When a task is still running, metrics like executorRunTime are not available. Then `schedulerDelay` will be almost the same as `duration` and that's confusing. This PR makes `schedulerDelay` return 0 when the task is running which is the same behavior as 2.2. ## How was this patch tested? `AppStatusUtilsSuite.schedulerDelay` Author: Shixiong Zhu <zsxwing@gmail.com> Closes #20493 from zsxwing/SPARK-23326.
…rting Spark DataFrame to Pandas DataFrame. ## What changes were proposed in this pull request? In #18664, there was a change in how `DateType` is being returned to users ([line 1968 in dataframe.py](https://github.com/apache/spark/pull/18664/files#diff-6fc344560230bf0ef711bb9b5573f1faR1968)). This can cause client code which works in Spark 2.2 to fail. See [SPARK-23290](https://issues.apache.org/jira/browse/SPARK-23290?focusedCommentId=16350917&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-16350917) for an example. This pr modifies to use `datetime.date` for date type as Spark 2.2 does. ## How was this patch tested? Tests modified to fit the new behavior and existing tests. Author: Takuya UESHIN <ueshin@databricks.com> Closes #20506 from ueshin/issues/SPARK-23290.
…r not in PySpark SQL tests ## What changes were proposed in this pull request? This PR proposes to log if PyArrow and Pandas are installed or not so we can check if related tests are going to be skipped or not. ## How was this patch tested? Manually tested: I don't have PyArrow installed in PyPy. ```bash $ ./run-tests --python-executables=python3 ``` ``` ... Will test against the following Python executables: ['python3'] Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming'] Will test PyArrow related features against Python executable 'python3' in 'pyspark-sql' module. Will test Pandas related features against Python executable 'python3' in 'pyspark-sql' module. Starting test(python3): pyspark.mllib.tests Starting test(python3): pyspark.sql.tests Starting test(python3): pyspark.streaming.tests Starting test(python3): pyspark.tests ``` ```bash $ ./run-tests --modules=pyspark-streaming ``` ``` ... Will test against the following Python executables: ['python2.7', 'pypy'] Will test the following Python modules: ['pyspark-streaming'] Starting test(pypy): pyspark.streaming.tests Starting test(pypy): pyspark.streaming.util Starting test(python2.7): pyspark.streaming.tests Starting test(python2.7): pyspark.streaming.util ``` ```bash $ ./run-tests ``` ``` ... Will test against the following Python executables: ['python2.7', 'pypy'] Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming'] Will test PyArrow related features against Python executable 'python2.7' in 'pyspark-sql' module. Will test Pandas related features against Python executable 'python2.7' in 'pyspark-sql' module. Will skip PyArrow related features against Python executable 'pypy' in 'pyspark-sql' module. PyArrow >= 0.8.0 is required; however, PyArrow was not found. Will test Pandas related features against Python executable 'pypy' in 'pyspark-sql' module. Starting test(pypy): pyspark.streaming.tests Starting test(pypy): pyspark.sql.tests Starting test(pypy): pyspark.tests Starting test(python2.7): pyspark.mllib.tests ``` ```bash $ ./run-tests --modules=pyspark-sql --python-executables=pypy ``` ``` ... Will test against the following Python executables: ['pypy'] Will test the following Python modules: ['pyspark-sql'] Will skip PyArrow related features against Python executable 'pypy' in 'pyspark-sql' module. PyArrow >= 0.8.0 is required; however, PyArrow was not found. Will test Pandas related features against Python executable 'pypy' in 'pyspark-sql' module. Starting test(pypy): pyspark.sql.tests Starting test(pypy): pyspark.sql.catalog Starting test(pypy): pyspark.sql.column Starting test(pypy): pyspark.sql.conf ``` After some modification to produce other cases: ```bash $ ./run-tests ``` ``` ... Will test against the following Python executables: ['python2.7', 'pypy'] Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming'] Will skip PyArrow related features against Python executable 'python2.7' in 'pyspark-sql' module. PyArrow >= 20.0.0 is required; however, PyArrow 0.8.0 was found. Will skip Pandas related features against Python executable 'python2.7' in 'pyspark-sql' module. Pandas >= 20.0.0 is required; however, Pandas 0.20.2 was found. Will skip PyArrow related features against Python executable 'pypy' in 'pyspark-sql' module. PyArrow >= 20.0.0 is required; however, PyArrow was not found. Will skip Pandas related features against Python executable 'pypy' in 'pyspark-sql' module. Pandas >= 20.0.0 is required; however, Pandas 0.22.0 was found. Starting test(pypy): pyspark.sql.tests Starting test(pypy): pyspark.streaming.tests Starting test(pypy): pyspark.tests Starting test(python2.7): pyspark.mllib.tests ``` ```bash ./run-tests-with-coverage ``` ``` ... Will test against the following Python executables: ['python2.7', 'pypy'] Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming'] Will test PyArrow related features against Python executable 'python2.7' in 'pyspark-sql' module. Will test Pandas related features against Python executable 'python2.7' in 'pyspark-sql' module. Coverage is not installed in Python executable 'pypy' but 'COVERAGE_PROCESS_START' environment variable is set, exiting. ``` Author: hyukjinkwon <gurwls223@gmail.com> Closes #20473 from HyukjinKwon/SPARK-23300.
…() to handle str type properly in Python 2.
## What changes were proposed in this pull request?
In Python 2, when `pandas_udf` tries to return string type value created in the udf with `".."`, the execution fails. E.g.,
```python
from pyspark.sql.functions import pandas_udf, col
import pandas as pd
df = spark.range(10)
str_f = pandas_udf(lambda x: pd.Series(["%s" % i for i in x]), "string")
df.select(str_f(col('id'))).show()
```
raises the following exception:
```
...
java.lang.AssertionError: assertion failed: Invalid schema from pandas_udf: expected StringType, got BinaryType
at scala.Predef$.assert(Predef.scala:170)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:93)
...
```
Seems like pyarrow ignores `type` parameter for `pa.Array.from_pandas()` and consider it as binary type when the type is string type and the string values are `str` instead of `unicode` in Python 2.
This pr adds a workaround for the case.
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes #20507 from ueshin/issues/SPARK-23334.
## What changes were proposed in this pull request? This is a follow-up of #20492 which broke lint-java checks. This pr fixes the lint-java issues. ``` [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java:[79] (sizes) LineLength: Line is longer than 100 characters (found 114). ``` ## How was this patch tested? Checked manually in my local environment. Author: Takuya UESHIN <ueshin@databricks.com> Closes #20514 from ueshin/issues/SPARK-23310/fup1.
…e reader ## What changes were proposed in this pull request? #20483 tried to provide a way to turn off the new columnar cache reader, to restore the behavior in 2.2. However even we turn off that config, the behavior is still different than 2.2. If the output data are rows, we still enable whole stage codegen for the scan node, which is different with 2.2, we should also fix it. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #20513 from cloud-fan/cache.
…e v2 related plans
## What changes were proposed in this pull request?
`DataSourceV2Relation` keeps a `fullOutput` and resolves the real output on demand by column name lookup. i.e.
```
lazy val output: Seq[Attribute] = reader.readSchema().map(_.name).map { name =>
fullOutput.find(_.name == name).get
}
```
This will be broken after we canonicalize the plan, because all attribute names become "None", see https://github.com/apache/spark/blob/v2.3.0-rc1/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Canonicalize.scala#L42
To fix this, `DataSourceV2Relation` should just keep `output`, and update the `output` when doing column pruning.
## How was this patch tested?
a new test case
Author: Wenchen Fan <wenchen@databricks.com>
Closes #20485 from cloud-fan/canonicalize.
… API or functions ## What changes were proposed in this pull request? Update the description and tests of three external API or functions `createFunction `, `length` and `repartitionByRange ` ## How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #20495 from gatorsmile/updateFunc.
…rReplaceTempView ## What changes were proposed in this pull request? Replace `registerTempTable` by `createOrReplaceTempView`. ## How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #20523 from gatorsmile/updateExamples.
…n PySpark tests (to skip or test) ## What changes were proposed in this pull request? This PR proposes to explicitly specify Pandas and PyArrow versions in PySpark tests to skip or test. We declared the extra dependencies: https://github.com/apache/spark/blob/b8bfce51abf28c66ba1fc67b0f25fe1617c81025/python/setup.py#L204 In case of PyArrow: Currently we only check if pyarrow is installed or not without checking the version. It already fails to run tests. For example, if PyArrow 0.7.0 is installed: ``` ====================================================================== ERROR: test_vectorized_udf_wrong_return_type (pyspark.sql.tests.ScalarPandasUDF) ---------------------------------------------------------------------- Traceback (most recent call last): File "/.../spark/python/pyspark/sql/tests.py", line 4019, in test_vectorized_udf_wrong_return_type f = pandas_udf(lambda x: x * 1.0, MapType(LongType(), LongType())) File "/.../spark/python/pyspark/sql/functions.py", line 2309, in pandas_udf return _create_udf(f=f, returnType=return_type, evalType=eval_type) File "/.../spark/python/pyspark/sql/udf.py", line 47, in _create_udf require_minimum_pyarrow_version() File "/.../spark/python/pyspark/sql/utils.py", line 132, in require_minimum_pyarrow_version "however, your version was %s." % pyarrow.__version__) ImportError: pyarrow >= 0.8.0 must be installed on calling Python process; however, your version was 0.7.0. ---------------------------------------------------------------------- Ran 33 tests in 8.098s FAILED (errors=33) ``` In case of Pandas: There are few tests for old Pandas which were tested only when Pandas version was lower, and I rewrote them to be tested when both Pandas version is lower and missing. ## How was this patch tested? Manually tested by modifying the condition: ``` test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.' test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.' test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.' ``` ``` test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.' test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.' test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.' ``` ``` test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.' test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.' test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.' ``` ``` test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.' test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.' test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.' ``` Author: hyukjinkwon <gurwls223@gmail.com> Closes #20487 from HyukjinKwon/pyarrow-pandas-skip.
## What changes were proposed in this pull request? When `DebugFilesystem` closes opened stream, if any exception occurs, we still need to remove the open stream record from `DebugFilesystem`. Otherwise, it goes to report leaked filesystem connection. ## How was this patch tested? Existing tests. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #20524 from viirya/SPARK-23345.
## What changes were proposed in this pull request? This PR migrates the MemoryStream to DataSourceV2 APIs. One additional change is in the reported keys in StreamingQueryProgress.durationMs. "getOffset" and "getBatch" replaced with "setOffsetRange" and "getEndOffset" as tracking these make more sense. Unit tests changed accordingly. ## How was this patch tested? Existing unit tests, few updated unit tests. Author: Tathagata Das <tathagata.das1565@gmail.com> Author: Burak Yavuz <brkyvz@gmail.com> Closes #20445 from tdas/SPARK-23092.
## What changes were proposed in this pull request? This is a followup pr of #20487. When importing module but it doesn't exists, the error message is slightly different between Python 2 and 3. E.g., in Python 2: ``` No module named pandas ``` in Python 3: ``` No module named 'pandas' ``` So, one test to check an import error fails in Python 3 without pandas. This pr fixes it. ## How was this patch tested? Tested manually in my local environment. Author: Takuya UESHIN <ueshin@databricks.com> Closes #20538 from ueshin/issues/SPARK-23319/fup1.
…by default ## What changes were proposed in this pull request? This is to revert the changes made in #19499 , because this causes a regression. We should not ignore the table-specific compression conf when the Hive serde tables are converted to the data source tables. ## How was this patch tested? The existing tests. Author: gatorsmile <gatorsmile@gmail.com> Closes #20536 from gatorsmile/revert22279.
…ta types ## What changes were proposed in this pull request? For inserting/appending data to an existing table, Spark should adjust the data types of the input query according to the table schema, or fail fast if it's uncastable. There are several ways to insert/append data: SQL API, `DataFrameWriter.insertInto`, `DataFrameWriter.saveAsTable`. The first 2 ways create `InsertIntoTable` plan, and the last way creates `CreateTable` plan. However, we only adjust input query data types for `InsertIntoTable`, and users may hit weird errors when appending data using `saveAsTable`. See the JIRA for the error case. This PR fixes this bug by adjusting data types for `CreateTable` too. ## How was this patch tested? new test. Author: Wenchen Fan <wenchen@databricks.com> Closes #20527 from cloud-fan/saveAsTable.
## What changes were proposed in this pull request? This is a followup of #20435. While reorganizing the packages for streaming data source v2, the top level stream read/write support interfaces should not be in the reader/writer package, but should be in the `sources.v2` package, to follow the `ReadSupport`, `WriteSupport`, etc. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #20509 from cloud-fan/followup.
…ryAllocator` ## What changes were proposed in this pull request? In `HeapMemoryAllocator`, when allocating memory from pool, and the key of pool is memory size. Actually some size of memory ,such as 1025bytes,1026bytes,......1032bytes, we can think they are the same，because we allocate memory in multiples of 8 bytes. In this case, we can improve memory reuse. ## How was this patch tested? Existing tests and added unit tests Author: liuxian <liu.xian3@zte.com.cn> Closes #19077 from 10110346/headmemoptimize.
## What changes were proposed in this pull request? This PR upgrade snappy-java from 1.1.2.6 to 1.1.7.1. 1.1.7.1 release notes: - Improved performance for big-endian architecture - The other performance improvement in [snappy-1.1.5](https://github.com/google/snappy/releases/tag/1.1.5) 1.1.4 release notes: - Fix a 1% performance regression when snappy is used in PIE executables. - Improve compression performance by 5%. - Improve decompression performance by 20%. More details: https://github.com/xerial/snappy-java/blob/master/Milestone.md ## How was this patch tested? manual tests Author: Yuming Wang <wgyumg@gmail.com> Closes #20510 from wangyum/SPARK-23336.
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What changes were proposed in this pull request?
Apache spark latest pull
How was this patch tested?
Existing