## What changes were proposed in this pull request?
Easy fix in the link.
## How was this patch tested?
Tested manually
Author: Mahmut CAVDAR <mahmutcvdr@gmail.com>
Closes#19996 from mcavdar/master.
## What changes were proposed in this pull request?
`testthat` 2.0.0 is released and AppVeyor now started to use it instead of 1.0.2. And then, we started to have R tests failed in AppVeyor. See - https://ci.appveyor.com/project/ApacheSoftwareFoundation/spark/build/1967-master
```
Error in get(name, envir = asNamespace(pkg), inherits = FALSE) :
object 'run_tests' not found
Calls: ::: -> get
```
This seems because we rely on internal `testthat:::run_tests` here:
https://github.com/r-lib/testthat/blob/v1.0.2/R/test-package.R#L62-L75dc4c351837/R/pkg/tests/run-all.R (L49-L52)
However, seems it was removed out from 2.0.0. I tried few other exposed APIs like `test_dir` but I failed to make a good compatible fix.
Seems we better fix the `testthat` version first to make the build passed.
## How was this patch tested?
Manually tested and AppVeyor tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20003 from HyukjinKwon/SPARK-22817.
## What changes were proposed in this pull request?
pyspark.ml.tests is missing a py4j import. I've added the import and fixed the test that uses it. This test was only failing when testing without Hive.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#19997 from MrBago/fix-ImageReaderTest2.
## What changes were proposed in this pull request?
Basic tests for IfCoercion and CaseWhenCoercion
## How was this patch tested?
N/A
Author: Yuming Wang <wgyumg@gmail.com>
Closes#19949 from wangyum/SPARK-22762.
## What changes were proposed in this pull request?
Add a test suite to ensure all the [SSB (Star Schema Benchmark)](https://www.cs.umb.edu/~poneil/StarSchemaB.PDF) queries can be successfully analyzed, optimized and compiled without hitting the max iteration threshold.
## How was this patch tested?
Added `SSBQuerySuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19990 from maropu/SPARK-22800.
## What changes were proposed in this pull request?
As the discussion in https://github.com/apache/spark/pull/16481 and https://github.com/apache/spark/pull/18975#discussion_r155454606
Currently the BaseRelation returned by `dataSource.writeAndRead` only used in `CreateDataSourceTableAsSelect`, planForWriting and writeAndRead has some common code paths.
In this patch I removed the writeAndRead function and added the getRelation function which only use in `CreateDataSourceTableAsSelectCommand` while saving data to non-existing table.
## How was this patch tested?
Existing UT
Author: Yuanjian Li <xyliyuanjian@gmail.com>
Closes#19941 from xuanyuanking/SPARK-22753.
## What changes were proposed in this pull request?
Add a test suite to ensure all the TPC-H queries can be successfully analyzed, optimized and compiled without hitting the max iteration threshold.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19982 from gatorsmile/testTPCH.
## What changes were proposed in this pull request?
since hive 2.0+ upgrades log4j to log4j2，a lot of [changes](https://issues.apache.org/jira/browse/HIVE-11304) are made working on it.
as spark is not to ready to update its inner hive version(1.2.1) , so I manage to make little changes.
the function registerCurrentOperationLog is moved from SQLOperstion to its parent class ExecuteStatementOperation so spark can use it.
## How was this patch tested?
manual test
Closes#19721 from ChenjunZou/operation-log.
Author: zouchenjun <zouchenjun@youzan.com>
Closes#19961 from ChenjunZou/spark-22496.
## What changes were proposed in this pull request?
StreamExecution is now an abstract base class, which MicroBatchExecution (the current StreamExecution) inherits. When continuous processing is implemented, we'll have a new ContinuousExecution implementation of StreamExecution.
A few fields are also renamed to make them less microbatch-specific.
## How was this patch tested?
refactoring only
Author: Jose Torres <jose@databricks.com>
Closes#19926 from joseph-torres/continuous-refactor.
## What changes were proposed in this pull request?
This PR added the missing service metadata for `KubernetesClusterManager`. Without the metadata, the service loader couldn't load `KubernetesClusterManager`, and caused the driver to fail to create a `ExternalClusterManager`, as being reported in SPARK-22778. The PR also changed the `k8s:` prefix used to `k8s://`, which is what existing Spark on k8s users are familiar and used to.
## How was this patch tested?
Manual testing verified that the fix resolved the issue in SPARK-22778.
/cc vanzin felixcheung jiangxb1987
Author: Yinan Li <liyinan926@gmail.com>
Closes#19972 from liyinan926/fix-22778.
## What changes were proposed in this pull request?
Missing some changes about limit in TaskSetManager.scala
## How was this patch tested?
running tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: kellyzly <kellyzly@126.com>
Closes#19976 from kellyzly/SPARK-22660.2.
## What changes were proposed in this pull request?
In multiple text analysis problems, it is not often desirable for the rows to be split by "\n". There exists a wholeText reader for RDD API, and this JIRA just adds the same support for Dataset API.
## How was this patch tested?
Added relevant new tests for both scala and Java APIs
Author: Prashant Sharma <prashsh1@in.ibm.com>
Author: Prashant Sharma <prashant@apache.org>
Closes#14151 from ScrapCodes/SPARK-16496/wholetext.
## What changes were proposed in this pull request?
This PR adds check whether Java code generated by Catalyst can be compiled by `janino` correctly or not into `TPCDSQuerySuite`. Before this PR, this suite only checks whether analysis can be performed correctly or not.
This check will be able to avoid unexpected performance degrade by interpreter execution due to a Java compilation error.
## How was this patch tested?
Existing a test case, but updated it.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19971 from kiszk/SPARK-22774.
## What changes were proposed in this pull request?
`ColumnVector.anyNullsSet` is not called anywhere except tests, and we can easily replace it with `ColumnVector.numNulls > 0`
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19980 from cloud-fan/minor.
## What changes were proposed in this pull request?
These dictionary related APIs are special to `WritableColumnVector` and should not be in `ColumnVector`, which will be public soon.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19970 from cloud-fan/final.
SQLConf allows some callers to define a custom default value for
configs, and that complicates a little bit the handling of fallback
config entries, since most of the default value resolution is
hidden by the config code.
This change peaks into the internals of these fallback configs
to figure out the correct default value, and also returns the
current human-readable default when showing the default value
(e.g. through "set -v").
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19974 from vanzin/SPARK-22779.
## What changes were proposed in this pull request?
This PR provides DataSourceV2 API support for structured streaming, including new pieces needed to support continuous processing [SPARK-20928]. High level summary:
- DataSourceV2 includes new mixins to support micro-batch and continuous reads and writes. For reads, we accept an optional user specified schema rather than using the ReadSupportWithSchema model, because doing so would severely complicate the interface.
- DataSourceV2Reader includes new interfaces to read a specific microbatch or read continuously from a given offset. These follow the same setter pattern as the existing Supports* mixins so that they can work with SupportsScanUnsafeRow.
- DataReader (the per-partition reader) has a new subinterface ContinuousDataReader only for continuous processing. This reader has a special method to check progress, and next() blocks for new input rather than returning false.
- Offset, an abstract representation of position in a streaming query, is ported to the public API. (Each type of reader will define its own Offset implementation.)
- DataSourceV2Writer has a new subinterface ContinuousWriter only for continuous processing. Commits to this interface come tagged with an epoch number, as the execution engine will continue to produce new epoch commits as the task continues indefinitely.
Note that this PR does not propose to change the existing DataSourceV2 batch API, or deprecate the existing streaming source/sink internal APIs in spark.sql.execution.streaming.
## How was this patch tested?
Toy implementations of the new interfaces with unit tests.
Author: Jose Torres <jose@databricks.com>
Closes#19925 from joseph-torres/continuous-api.
## What changes were proposed in this pull request?
MLlib ```LinearRegression``` supports _huber_ loss addition to _leastSquares_ loss. The huber loss objective function is:
Refer Eq.(6) and Eq.(8) in [A robust hybrid of lasso and ridge regression](http://statweb.stanford.edu/~owen/reports/hhu.pdf). This objective is jointly convex as a function of (w, σ) ∈ R × (0,∞), we can use L-BFGS-B to solve it.
The current implementation is a straight forward porting for Python scikit-learn [```HuberRegressor```](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html). There are some differences:
* We use mean loss (```lossSum/weightSum```), but sklearn uses total loss (```lossSum```).
* We multiply the loss function and L2 regularization by 1/2. It does not affect the result if we multiply the whole formula by a factor, we just keep consistent with _leastSquares_ loss.
So if fitting w/o regularization, MLlib and sklearn produce the same output. If fitting w/ regularization, MLlib should set ```regParam``` divide by the number of instances to match the output of sklearn.
## How was this patch tested?
Unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19020 from yanboliang/spark-3181.
## What changes were proposed in this pull request?
This pr fixed a compilation error of TPCDS `q75`/`q77` caused by #19813;
```
java.util.concurrent.ExecutionException: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 371, Column 16: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 371, Column 16: Expression "bhj_matched" is not an rvalue
at com.google.common.util.concurrent.AbstractFuture$Sync.getValue(AbstractFuture.java:306)
at com.google.common.util.concurrent.AbstractFuture$Sync.get(AbstractFuture.java:293)
at com.google.common.util.concurrent.AbstractFuture.get(AbstractFuture.java:116)
at com.google.common.util.concurrent.Uninterruptibles.getUninterruptibly(Uninterruptibles.java:135)
```
## How was this patch tested?
Manually checked `q75`/`q77` can be properly compiled
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19969 from maropu/SPARK-22600-FOLLOWUP.
Use a semaphore to synchronize the tasks with the listener code
that is trying to cancel the job or stage, so that the listener
won't try to cancel a job or stage that has already finished.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19956 from vanzin/SPARK-22764.
## What changes were proposed in this pull request?
In SPARK-22550 which fixes 64KB JVM bytecode limit problem with elt, `buildCodeBlocks` is used to split codes. However, we should use `splitExpressionsWithCurrentInputs` because it considers both normal and wholestage codgen (it is not supported yet, so it simply doesn't split the codes).
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19964 from viirya/SPARK-22772.
## What changes were proposed in this pull request?
PR closed with all the comments -> https://github.com/apache/spark/pull/19793
It solves the problem when submitting a wrong CreateSubmissionRequest to Spark Dispatcher was causing a bad state of Dispatcher and making it inactive as a Mesos framework.
https://issues.apache.org/jira/browse/SPARK-22574
## How was this patch tested?
All spark test passed successfully.
It was tested sending a wrong request (without appArgs) before and after the change. The point is easy, check if the value is null before being accessed.
This was before the change, leaving the dispatcher inactive:
```
Exception in thread "Thread-22" java.lang.NullPointerException
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.getDriverCommandValue(MesosClusterScheduler.scala:444)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.buildDriverCommand(MesosClusterScheduler.scala:451)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.org$apache$spark$scheduler$cluster$mesos$MesosClusterScheduler$$createTaskInfo(MesosClusterScheduler.scala:538)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:570)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:555)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.scheduleTasks(MesosClusterScheduler.scala:555)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.resourceOffers(MesosClusterScheduler.scala:621)
```
And after:
```
"message" : "Malformed request: org.apache.spark.deploy.rest.SubmitRestProtocolException: Validation of message CreateSubmissionRequest failed!\n\torg.apache.spark.deploy.rest.SubmitRestProtocolMessage.validate(SubmitRestProtocolMessage.scala:70)\n\torg.apache.spark.deploy.rest.SubmitRequestServlet.doPost(RestSubmissionServer.scala:272)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:707)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:790)\n\torg.spark_project.jetty.servlet.ServletHolder.handle(ServletHolder.java:845)\n\torg.spark_project.jetty.servlet.ServletHandler.doHandle(ServletHandler.java:583)\n\torg.spark_project.jetty.server.handler.ContextHandler.doHandle(ContextHandler.java:1180)\n\torg.spark_project.jetty.servlet.ServletHandler.doScope(ServletHandler.java:511)\n\torg.spark_project.jetty.server.handler.ContextHandler.doScope(ContextHandler.java:1112)\n\torg.spark_project.jetty.server.handler.ScopedHandler.handle(ScopedHandler.java:141)\n\torg.spark_project.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:134)\n\torg.spark_project.jetty.server.Server.handle(Server.java:524)\n\torg.spark_project.jetty.server.HttpChannel.handle(HttpChannel.java:319)\n\torg.spark_project.jetty.server.HttpConnection.onFillable(HttpConnection.java:253)\n\torg.spark_project.jetty.io.AbstractConnection$ReadCallback.succeeded(AbstractConnection.java:273)\n\torg.spark_project.jetty.io.FillInterest.fillable(FillInterest.java:95)\n\torg.spark_project.jetty.io.SelectChannelEndPoint$2.run(SelectChannelEndPoint.java:93)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:303)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\n\tjava.lang.Thread.run(Thread.java:745)"
```
Author: German Schiavon <germanschiavon@gmail.com>
Closes#19966 from Gschiavon/fix-submission-request.
## What changes were proposed in this pull request?
While spark code changes, there are new events in event log: #19649
And we used to maintain a whitelist to avoid exceptions: #15663
Currently Spark history server will stop parsing on unknown events or unrecognized properties. We may still see part of the UI data.
For better compatibility, we can ignore unknown events and parse through the log file.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19953 from gengliangwang/ReplayListenerBus.
… than spark.network.timeout or not
## What changes were proposed in this pull request?
If spark.executor.heartbeatInterval bigger than spark.network.timeout,it will almost always cause exception below.
`Job aborted due to stage failure: Task 4763 in stage 3.0 failed 4 times, most recent failure: Lost task 4763.3 in stage 3.0 (TID 22383, executor id: 4761, host: xxx): ExecutorLostFailure (executor 4761 exited caused by one of the running tasks) Reason: Executor heartbeat timed out after 154022 ms`
Since many users do not get that point.He will set spark.executor.heartbeatInterval incorrectly.
This patch check this case when submit applications.
## How was this patch tested?
Test in cluster
Author: zhoukang <zhoukang199191@gmail.com>
Closes#19942 from caneGuy/zhoukang/check-heartbeat.
## What changes were proposed in this pull request?
We should not operate on `references` directly in `Expression.doGenCode`, instead we should use the high-level API `addReferenceObj`.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19962 from cloud-fan/codegen.
## What changes were proposed in this pull request?
Currently Spark can read table stats (e.g. `totalSize, numRows`) from Hive, we can also support to read partition stats from Hive using the same logic.
## How was this patch tested?
Added a new test case and modified an existing test case.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#19932 from wzhfy/read_hive_partition_stats.
## What changes were proposed in this pull request?
See jira description for the bug : https://issues.apache.org/jira/browse/SPARK-22042
Fix done in this PR is: In `EnsureRequirements`, apply `ReorderJoinPredicates` over the input tree before doing its core logic. Since the tree is transformed bottom-up, we can assure that the children are resolved before doing `ReorderJoinPredicates`.
Theoretically this will guarantee to cover all such cases while keeping the code simple. My small grudge is for cosmetic reasons. This PR will look weird given that we don't call rules from other rules (not to my knowledge). I could have moved all the logic for `ReorderJoinPredicates` into `EnsureRequirements` but that will make it a but crowded. I am happy to discuss if there are better options.
## How was this patch tested?
Added a new test case
Author: Tejas Patil <tejasp@fb.com>
Closes#19257 from tejasapatil/SPARK-22042_ReorderJoinPredicates.
## What changes were proposed in this pull request?
only drops the rows containing NaN in the input columns
## How was this patch tested?
existing tests and added tests
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#19894 from zhengruifeng/bucketizer_nan.
## What changes were proposed in this pull request?
some code cleanup/refactor and naming improvement.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19952 from cloud-fan/minor.
## What changes were proposed in this pull request?
The query execution/optimization does not guarantee the expressions are evaluated in order. We only can combine them if and only if both are deterministic. We need to update the optimizer rule: CombineFilters.
## How was this patch tested?
Updated the existing tests.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19947 from gatorsmile/combineFilters.
## What changes were proposed in this pull request?
As a follow-up of #19948 , this PR moves the test case and adds comments.
## How was this patch tested?
Pass the Jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19960 from dongjoon-hyun/SPARK-19809-2.
## What changes were proposed in this pull request?
We need to add some helper code to make testing ML transformers & models easier with streaming data. These tests might help us catch any remaining issues and we could encourage future PRs to use these tests to prevent new Models & Transformers from having issues.
I add a `MLTest` trait which extends `StreamTest` trait, and override `createSparkSession`. So ML testsuite can only extend `MLTest`, to use both ML & Stream test util functions.
I only modify one testcase in `LinearRegressionSuite`, for first pass review.
Link to #19746
## How was this patch tested?
`MLTestSuite` added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19843 from WeichenXu123/ml_stream_test_helper.
## What changes were proposed in this pull request?
SPARK-22543 fixes the 64kb compile error for deeply nested expression for non-wholestage codegen. This PR extends it to support wholestage codegen.
This patch brings some util methods in to extract necessary parameters for an expression if it is split to a function.
The util methods are put in object `ExpressionCodegen` under `codegen`. The main entry is `getExpressionInputParams` which returns all necessary parameters to evaluate the given expression in a split function.
This util methods can be used to split expressions too. This is a TODO item later.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19813 from viirya/reduce-expr-code-for-wholestage.
## What changes were proposed in this pull request?
We have two methods to reference an object `addReferenceMinorObj` and `addReferenceObj `. The latter creates a new global variable, which means new entries in the constant pool.
The PR unifies the two method in a single `addReferenceObj` which returns the code to access the object in the `references` array and doesn't add new mutable states.
## How was this patch tested?
added UTs.
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19916 from mgaido91/SPARK-22716.
## What changes were proposed in this pull request?
#19208 modified ```sharedParams.scala```, but didn't generated by ```SharedParamsCodeGen.scala```. This involves mismatch between them.
## How was this patch tested?
Existing test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19958 from yanboliang/spark-21087.
## What changes were proposed in this pull request?
Until 2.2.1, Spark raises `NullPointerException` on zero-size ORC files. Usually, these zero-size ORC files are generated by 3rd-party apps like Flume.
```scala
scala> sql("create table empty_orc(a int) stored as orc location '/tmp/empty_orc'")
$ touch /tmp/empty_orc/zero.orc
scala> sql("select * from empty_orc").show
java.lang.RuntimeException: serious problem at
org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1021)
...
Caused by: java.lang.NullPointerException at
org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$BISplitStrategy.getSplits(OrcInputFormat.java:560)
```
After [SPARK-22279](https://github.com/apache/spark/pull/19499), Apache Spark with the default configuration doesn't have this bug. Although Hive 1.2.1 library code path still has the problem, we had better have a test coverage on what we have now in order to prevent future regression on it.
## How was this patch tested?
Pass a newly added test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19948 from dongjoon-hyun/SPARK-19809-EMPTY-FILE.
## What changes were proposed in this pull request?
It solves the problem when submitting a wrong CreateSubmissionRequest to Spark Dispatcher was causing a bad state of Dispatcher and making it inactive as a Mesos framework.
https://issues.apache.org/jira/browse/SPARK-22574
## How was this patch tested?
All spark test passed successfully.
It was tested sending a wrong request (without appArgs) before and after the change. The point is easy, check if the value is null before being accessed.
This was before the change, leaving the dispatcher inactive:
```
Exception in thread "Thread-22" java.lang.NullPointerException
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.getDriverCommandValue(MesosClusterScheduler.scala:444)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.buildDriverCommand(MesosClusterScheduler.scala:451)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.org$apache$spark$scheduler$cluster$mesos$MesosClusterScheduler$$createTaskInfo(MesosClusterScheduler.scala:538)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:570)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:555)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.scheduleTasks(MesosClusterScheduler.scala:555)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.resourceOffers(MesosClusterScheduler.scala:621)
```
And after:
```
"message" : "Malformed request: org.apache.spark.deploy.rest.SubmitRestProtocolException: Validation of message CreateSubmissionRequest failed!\n\torg.apache.spark.deploy.rest.SubmitRestProtocolMessage.validate(SubmitRestProtocolMessage.scala:70)\n\torg.apache.spark.deploy.rest.SubmitRequestServlet.doPost(RestSubmissionServer.scala:272)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:707)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:790)\n\torg.spark_project.jetty.servlet.ServletHolder.handle(ServletHolder.java:845)\n\torg.spark_project.jetty.servlet.ServletHandler.doHandle(ServletHandler.java:583)\n\torg.spark_project.jetty.server.handler.ContextHandler.doHandle(ContextHandler.java:1180)\n\torg.spark_project.jetty.servlet.ServletHandler.doScope(ServletHandler.java:511)\n\torg.spark_project.jetty.server.handler.ContextHandler.doScope(ContextHandler.java:1112)\n\torg.spark_project.jetty.server.handler.ScopedHandler.handle(ScopedHandler.java:141)\n\torg.spark_project.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:134)\n\torg.spark_project.jetty.server.Server.handle(Server.java:524)\n\torg.spark_project.jetty.server.HttpChannel.handle(HttpChannel.java:319)\n\torg.spark_project.jetty.server.HttpConnection.onFillable(HttpConnection.java:253)\n\torg.spark_project.jetty.io.AbstractConnection$ReadCallback.succeeded(AbstractConnection.java:273)\n\torg.spark_project.jetty.io.FillInterest.fillable(FillInterest.java:95)\n\torg.spark_project.jetty.io.SelectChannelEndPoint$2.run(SelectChannelEndPoint.java:93)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:303)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\n\tjava.lang.Thread.run(Thread.java:745)"
```
Author: German Schiavon <germanschiavon@gmail.com>
Closes#19793 from Gschiavon/fix-submission-request.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-22289
add JSON encoding/decoding for Param[Matrix].
The issue was reported by Nic Eggert during saving LR model with LowerBoundsOnCoefficients.
There're two ways to resolve this as I see:
1. Support save/load on LogisticRegressionParams, and also adjust the save/load in LogisticRegression and LogisticRegressionModel.
2. Directly support Matrix in Param.jsonEncode, similar to what we have done for Vector.
After some discussion in jira, we prefer the fix to support Matrix as a valid Param type, for simplicity and convenience for other classes.
Note that in the implementation, I added a "class" field in the JSON object to match different JSON converters when loading, which is for preciseness and future extension.
## How was this patch tested?
new unit test to cover the LR case and JsonMatrixConverter
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#19525 from hhbyyh/lrsave.
In order to enable truncate for PostgreSQL databases in Spark JDBC, a change is needed to the query used for truncating a PostgreSQL table. By default, PostgreSQL will automatically truncate any descendant tables if a TRUNCATE query is executed. As this may result in (unwanted) side-effects, the query used for the truncate should be specified separately for PostgreSQL, specifying only to TRUNCATE a single table.
## What changes were proposed in this pull request?
Add `getTruncateQuery` function to `JdbcDialect.scala`, with default query. Overridden this function for PostgreSQL to only truncate a single table. Also sets `isCascadingTruncateTable` to false, as this will allow truncates for PostgreSQL.
## How was this patch tested?
Existing tests all pass. Added test for `getTruncateQuery`
Author: Daniel van der Ende <daniel.vanderende@gmail.com>
Closes#19911 from danielvdende/SPARK-22717.
## What changes were proposed in this pull request?
This PR proposes to print warnings before creating local by `java.io.File`.
I think we can't just simply disallow and throw an exception for such cases of `hdfs:/tmp/foo` case because it might break compatibility. Note that `hdfs:/tmp/foo` creates a directory called `hdfs:/`.
There were many discussion here about whether we should support this in other file systems or not; however, since the JIRA targets "Spark's local dir should accept only local paths", here I tried to resolve it by simply printing warnings.
I think we could open another JIRA and design doc if this is something we should support, separately. Another note, for your information, [SPARK-1529](https://issues.apache.org/jira/browse/SPARK-1529) is resolved as `Won't Fix`.
**Before**
```
./bin/spark-shell --conf spark.local.dir=file:/a/b/c
```
This creates a local directory as below:
```
file:/
└── a
└── b
└── c
...
```
**After**
```bash
./bin/spark-shell --conf spark.local.dir=file:/a/b/c
```
Now, it prints a warning as below:
```
...
17/12/09 21:58:49 WARN Utils: The configured local directories are not expected to be URIs; however, got suspicious values [file:/a/b/c]. Please check your configured local directories.
...
```
```bash
./bin/spark-shell --conf spark.local.dir=file:/a/b/c,/tmp/a/b/c,hdfs:/a/b/c
```
It also works with comma-separated ones:
```
...
17/12/09 22:05:01 WARN Utils: The configured local directories are not expected to be URIs; however, got suspicious values [file:/a/b/c, hdfs:/a/b/c]. Please check your configured local directories.
...
```
## How was this patch tested?
Manually tested:
```
./bin/spark-shell --conf spark.local.dir=C:\\a\\b\\c
./bin/spark-shell --conf spark.local.dir=/tmp/a/b/c
./bin/spark-shell --conf spark.local.dir=a/b/c
./bin/spark-shell --conf spark.local.dir=a/b/c,/tmp/a/b/c,C:\\a\\b\\c
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19934 from HyukjinKwon/SPARK-3685.
## What changes were proposed in this pull request?
Histogram is effective in dealing with skewed distribution. After we generate histogram information for column statistics, we need to adjust filter estimation based on histogram data structure.
## How was this patch tested?
We revised all the unit test cases by including histogram data structure.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Ron Hu <ron.hu@huawei.com>
Closes#19783 from ron8hu/supportHistogram.
## What changes were proposed in this pull request?
In the previous PRs, https://github.com/apache/spark/pull/17832 and https://github.com/apache/spark/pull/17835 , we convert `TIMESTAMP WITH TIME ZONE` and `TIME WITH TIME ZONE` to `TIMESTAMP` for all the JDBC sources. However, this conversion could be risky since it does not respect our SQL configuration `spark.sql.session.timeZone`.
In addition, each vendor might have different semantics for these two types. For example, Postgres simply returns `TIMESTAMP` types for `TIMESTAMP WITH TIME ZONE`. For such supports, we should do it case by case. This PR reverts the general support of `TIMESTAMP WITH TIME ZONE` and `TIME WITH TIME ZONE` for JDBC sources, except ORACLE Dialect.
When supporting the ORACLE's `TIMESTAMP WITH TIME ZONE`, we only support it when the JVM default timezone is the same as the user-specified configuration `spark.sql.session.timeZone` (whose default is the JVM default timezone). Now, we still treat `TIMESTAMP WITH TIME ZONE` as `TIMESTAMP` when fetching the values via the Oracle JDBC connector, whose client converts the timestamp values with time zone to the timestamp values using the local JVM default timezone (a test case is added to `OracleIntegrationSuite.scala` in this PR for showing the behavior). Thus, to avoid any future behavior change, we will not support it if JVM default timezone is different from `spark.sql.session.timeZone`
No regression because the previous two PRs were just merged to be unreleased master branch.
## How was this patch tested?
Added the test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19939 from gatorsmile/timezoneUpdate.
## What changes were proposed in this pull request?
Before we deliver the Hive compatibility mode, we plan to write a set of test cases that can be easily run in both Spark and Hive sides. We can easily compare whether they are the same or not. When new typeCoercion rules are added, we also can easily track the changes. These test cases can also be backported to the previous Spark versions for determining the changes we made.
This PR is the first attempt for improving the test coverage for type coercion compatibility. We generate these test cases for our binary comparison and ImplicitTypeCasts based on the Apache Derby test cases in https://github.com/apache/derby/blob/10.14/java/testing/org/apache/derbyTesting/functionTests/tests/lang/implicitConversions.sql
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19918 from gatorsmile/typeCoercionTests.
