[SPARK-32001][SQL]Create JDBC authentication provider developer API #29024
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I've created a WIP PR because the MariaDB integration test fails on master as well so I can't tell this change is good or not: I've started to analyze this issue separately. |
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After some analysis I've filed SPARK-32211 which provides the solution. |
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I've double tested and w/ the mentioned change |
gaborgsomogyi
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Test build #125218 has finished for PR 29024 at commit
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gaborgsomogyi
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The failure seems related so checking it... |
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retest this please |
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Test build #125234 has finished for PR 29024 at commit
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Found the issue. |
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Test build #125331 has finished for PR 29024 at commit
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retest this please |
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maropu
reviewed
Jul 9, 2020
| * Options for the JDBC data source. | ||
| */ | ||
| @DeveloperApi |
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This is an internal class, so could you avoid exposing it? How about using Map[String, String] instead in the provider?
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Good idea, this way the API is less exposed to changes.
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I've done the initial steps but I see bad and worse tradeoffs. The API is less dependent on JDBCOptions changes but the provider side implementation becames more complicated with either duplicated code or duplicated instantiation. Let me explain in examples:
Option1: Re-instantiate JDBCOptions where duplicated instantiation worries me + the general concern down below.
override def canHandle(driver: Driver, options: Map[String, String]): Boolean = {
val jdbcOptions = new JDBCOptions(options)
jdbcOptions.keytab == null || jdbcOptions.principal == null
}
Option2: Find out the needed parameters on by own where re-inventing the wheel feeling worries me + the general concern down below.
override def canHandle(driver: Driver, options: Map[String, String]): Boolean = {
val keytab = {
val keytabParam = options.getOrElse(JDBC_KEYTAB, null)
if (keytabParam != null && FilenameUtils.getPath(keytabParam).isEmpty) {
val result = SparkFiles.get(keytabParam)
logDebug(s"Keytab path not found, assuming --files, file name used on executor: $result")
result
} else {
logDebug("Keytab path found, assuming manual upload")
keytabParam
}
}
val principal = options.getOrElse(JDBC_PRINCIPAL, null)
keytab == null || principal == null
}
General concern: Both cases Spark can be good because in the first case new JDBCOptions instantiation, in the second case copy paste moved into a base class can fill the gap. However considering myself as a 3rd-party developer I have not much options (since JDBCOptions is not exposed as developer API):
- Copy and paste the code from
JDBCOptionswhich may change over time - Implement the parameter parsing on my own which may contain bugs
Considering these findings I think it's better to keep JDBCOptions. WDYT?
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Option 1 looks better. But, 3rd-party developers need to use JDBCOptions there? Or, could we just pass the two params only?
override def canHandle(driver: Driver, keytab: String, principal: String): Boolean = {
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We could pass the 2 params but then we limit further implementation possibilities so I would vote on the map.
At the moment there is no need other params other than keytab and principal but later providers may need further things. It's not a strong opinion, just don't want to close later possibilities. If we agree on the way I'll do the changes.
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The example I've given in the other open question here can show how parameters other than keytab and principal can be used. Not passing the whole map would close this possibility.
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I agree with @maropu here. JDBCOptions is under execution package and meant to be private.
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@HyukjinKwon thanks for having a look!
I agree that JDBCOptions mustn't be exposed. Let me change the code to show option 1. As said passing only keytab: String, principal: String is not enough because not all but some of the providers need further configurations. I've started to work on this this change (unless anybody has better option).
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I've removed JDBCOptions from the API but keeping this open if further discussion needed.
core/src/main/scala/org/apache/spark/security/SecurityConfigurationLock.scala
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retest this please |
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Test build #125590 has finished for PR 29024 at commit
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Gosh, jenkins still not stable. Sigh. |
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retest this please |
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retest this please |
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Test build #129192 has finished for PR 29024 at commit
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Will update the example provider to show the usage tomorrow... |
HyukjinKwon
reviewed
Sep 29, 2020
sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcConnectionProvider.scala
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HyukjinKwon
approved these changes
Sep 29, 2020
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I've just re-executed all the integration tests and seems like SPARK-32211 appeared again because it's not yet exists in this branch. Merging master into this branch and re-executing all integration tests again. |
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I've just re-executed all the integration tests and all has passed. |
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I've just updated the example to show the usage: https://github.com/gaborgsomogyi/spark-jdbc-connection-provider |
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Kubernetes integration test starting |
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Kubernetes integration test starting |
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Kubernetes integration test status success |
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I'll merge in few days if there are no more comments. |
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Kubernetes integration test status failure |
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Test build #129228 has finished for PR 29024 at commit
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retest this please |
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Test build #129227 has finished for PR 29024 at commit
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Kubernetes integration test starting |
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Kubernetes integration test status success |
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Test build #129234 has finished for PR 29024 at commit
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Merged to master. |
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Thanks all of you guys to invest your time! |
MaxGekk
reviewed
Oct 7, 2020
| providers += provider | ||
| } catch { | ||
| case t: Throwable => | ||
| logError(s"Failed to load built in provider.", t) |
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I am getting the following exception on my console permanently while running JDBC tests. Should it be really logged as an error?
14:31:25.070 ERROR org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProvider: Failed to load built in provider.
java.util.ServiceConfigurationError: org.apache.spark.sql.jdbc.JdbcConnectionProvider: Provider org.apache.spark.sql.execution.datasources.jdbc.connection.IntentionallyFaultyConnectionProvider could not be instantiated
at java.util.ServiceLoader.fail(ServiceLoader.java:232)
at java.util.ServiceLoader.access$100(ServiceLoader.java:185)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:384)
at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
at org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProvider$.loadProviders(ConnectionProvider.scala:41)
at org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProvider$.<init>(ConnectionProvider.scala:31)
at org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProvider$.<clinit>(ConnectionProvider.scala)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$createConnectionFactory$1(JdbcUtils.scala:66)
at org.apache.spark.sql.execution.datasources.v2.jdbc.JDBCTableCatalog.withConnection(JDBCTableCatalog.scala:156)
at org.apache.spark.sql.execution.datasources.v2.jdbc.JDBCTableCatalog.listTables(JDBCTableCatalog.scala:58)
at org.apache.spark.sql.execution.datasources.v2.ShowTablesExec.run(ShowTablesExec.scala:42)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:39)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:39)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:45)
at org.apache.spark.sql.Dataset.$anonfun$logicalPlan$1(Dataset.scala:229)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3675)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3673)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:229)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:100)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:97)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:612)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
at org.apache.spark.sql.test.SQLTestUtilsBase.$anonfun$sql$1(SQLTestUtils.scala:231)
at org.apache.spark.sql.execution.datasources.v2.jdbc.JDBCTableCatalogSuite.$anonfun$new$2(JDBCTableCatalogSuite.scala:67)
at org.apache.spark.sql.QueryTest.checkAnswer(QueryTest.scala:134)
at org.apache.spark.sql.execution.datasources.v2.jdbc.JDBCTableCatalogSuite.$anonfun$new$1(JDBCTableCatalogSuite.scala:67)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.funsuite.AnyFunSuiteLike$$anon$1.apply(AnyFunSuiteLike.scala:189)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:176)
at org.scalatest.funsuite.AnyFunSuiteLike.invokeWithFixture$1(AnyFunSuiteLike.scala:187)
at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTest$1(AnyFunSuiteLike.scala:199)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
at org.scalatest.funsuite.AnyFunSuiteLike.runTest(AnyFunSuiteLike.scala:199)
at org.scalatest.funsuite.AnyFunSuiteLike.runTest$(AnyFunSuiteLike.scala:181)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(SparkFunSuite.scala:61)
at org.scalatest.BeforeAndAfterEach.runTest(BeforeAndAfterEach.scala:234)
at org.scalatest.BeforeAndAfterEach.runTest$(BeforeAndAfterEach.scala:227)
at org.apache.spark.SparkFunSuite.runTest(SparkFunSuite.scala:61)
at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTests$1(AnyFunSuiteLike.scala:232)
at org.scalatest.SuperEngine.$anonfun$runTestsInBranch$1(Engine.scala:413)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
at org.scalatest.SuperEngine.runTestsInBranch(Engine.scala:396)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:475)
at org.scalatest.funsuite.AnyFunSuiteLike.runTests(AnyFunSuiteLike.scala:232)
at org.scalatest.funsuite.AnyFunSuiteLike.runTests$(AnyFunSuiteLike.scala:231)
at org.scalatest.funsuite.AnyFunSuite.runTests(AnyFunSuite.scala:1562)
at org.scalatest.Suite.run(Suite.scala:1112)
at org.scalatest.Suite.run$(Suite.scala:1094)
at org.scalatest.funsuite.AnyFunSuite.org$scalatest$funsuite$AnyFunSuiteLike$$super$run(AnyFunSuite.scala:1562)
at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$run$1(AnyFunSuiteLike.scala:236)
at org.scalatest.SuperEngine.runImpl(Engine.scala:535)
at org.scalatest.funsuite.AnyFunSuiteLike.run(AnyFunSuiteLike.scala:236)
at org.scalatest.funsuite.AnyFunSuiteLike.run$(AnyFunSuiteLike.scala:235)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:61)
at org.scalatest.BeforeAndAfterAll.liftedTree1$1(BeforeAndAfterAll.scala:213)
at org.scalatest.BeforeAndAfterAll.run(BeforeAndAfterAll.scala:210)
at org.scalatest.BeforeAndAfterAll.run$(BeforeAndAfterAll.scala:208)
at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:61)
at org.scalatest.tools.SuiteRunner.run(SuiteRunner.scala:45)
at org.scalatest.tools.Runner$.$anonfun$doRunRunRunDaDoRunRun$13(Runner.scala:1320)
at org.scalatest.tools.Runner$.$anonfun$doRunRunRunDaDoRunRun$13$adapted(Runner.scala:1314)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.tools.Runner$.doRunRunRunDaDoRunRun(Runner.scala:1314)
at org.scalatest.tools.Runner$.$anonfun$runOptionallyWithPassFailReporter$24(Runner.scala:993)
at org.scalatest.tools.Runner$.$anonfun$runOptionallyWithPassFailReporter$24$adapted(Runner.scala:971)
at org.scalatest.tools.Runner$.withClassLoaderAndDispatchReporter(Runner.scala:1480)
at org.scalatest.tools.Runner$.runOptionallyWithPassFailReporter(Runner.scala:971)
at org.scalatest.tools.Runner$.run(Runner.scala:798)
at org.scalatest.tools.Runner.run(Runner.scala)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.runScalaTest2or3(ScalaTestRunner.java:40)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.main(ScalaTestRunner.java:27)
Caused by: java.lang.IllegalArgumentException: Intentional Exception
at org.apache.spark.sql.execution.datasources.jdbc.connection.IntentionallyFaultyConnectionProvider.<init>(IntentionallyFaultyConnectionProvider.scala:26)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at java.lang.Class.newInstance(Class.java:442)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:380)
... 82 more
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We can decrease it to warning. The main message is to notify the user.
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Is it okay to ignore the error case where it fails to load builtin providers? DataSource throws an exception if it fails to load builtin datasources:
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How do you mean ignore? Providers must be loaded independently so we need to catch and ignore the exception.
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If the suggestion is let the exception fire then I would say it's bad idea. If a provider is not able to be loaded then the rest must go. We've had similar issue and expectation in hadoop delegation token area.
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Yea, the policy looks okay for secure connections, but how about the basic one, BasicConnectionProvider? At least, until Spark v3.0, creating basic JDBC connections does not fail because of the loading failure.
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At least, until Spark v3.0, creating basic JDBC connections does not fail because of the loading failure.
Here it's the same since BasicConnectionProvider is built-in and no pre-load inside. The main issue would come when one adds a new provider which unable to be loaded. That failure would make all the rest workload fail if we don't load them independently.
dongjoon-hyun
pushed a commit
that referenced
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Nov 25, 2020
…n't consider case-sensitivity for properties ### What changes were proposed in this pull request? This PR fixes an issue that `BasicConnectionProvider` doesn't consider case-sensitivity for properties. For example, the property `oracle.jdbc.mapDateToTimestamp` should be considered case-sensitivity but it is not considered. ### Why are the changes needed? This is a bug introduced by #29024 . Caused by this issue, `OracleIntegrationSuite` doesn't pass. ``` [info] - SPARK-16625: General data types to be mapped to Oracle *** FAILED *** (32 seconds, 129 milliseconds) [info] types.apply(9).equals(org.apache.spark.sql.types.DateType) was false (OracleIntegrationSuite.scala:238) [info] org.scalatest.exceptions.TestFailedException: [info] at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:472) [info] at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:471) [info] at org.scalatest.Assertions$.newAssertionFailedException(Assertions.scala:1231) [info] at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:1295) [info] at org.apache.spark.sql.jdbc.OracleIntegrationSuite.$anonfun$new$4(OracleIntegrationSuite.scala:238) [info] at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85) [info] at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83) [info] at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104) [info] at org.scalatest.Transformer.apply(Transformer.scala:22) [info] at org.scalatest.Transformer.apply(Transformer.scala:20) [info] at org.scalatest.funsuite.AnyFunSuiteLike$$anon$1.apply(AnyFunSuiteLike.scala:190) [info] at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:176) [info] at org.scalatest.funsuite.AnyFunSuiteLike.invokeWithFixture$1(AnyFunSuiteLike.scala:188) [info] at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTest$1(AnyFunSuiteLike.scala:200) [info] at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306) [info] at org.scalatest.funsuite.AnyFunSuiteLike.runTest(AnyFunSuiteLike.scala:200) [info] at org.scalatest.funsuite.AnyFunSuiteLike.runTest$(AnyFunSuiteLike.scala:182) [info] at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(SparkFunSuite.scala:61) [info] at org.scalatest.BeforeAndAfterEach.runTest(BeforeAndAfterEach.scala:234) [info] at org.scalatest.BeforeAndAfterEach.runTest$(BeforeAndAfterEach.scala:227) [info] at org.apache.spark.SparkFunSuite.runTest(SparkFunSuite.scala:61) [info] at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTests$1(AnyFunSuiteLike.scala:233) [info] at org.scalatest.SuperEngine.$anonfun$runTestsInBranch$1(Engine.scala:413) [info] at scala.collection.immutable.List.foreach(List.scala:392) [info] at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401) [info] at org.scalatest.SuperEngine.runTestsInBranch(Engine.scala:396) [info] at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:475) [info] at org.scalatest.funsuite.AnyFunSuiteLike.runTests(AnyFunSuiteLike.scala:233) [info] at org.scalatest.funsuite.AnyFunSuiteLike.runTests$(AnyFunSuiteLike.scala:232) [info] at org.scalatest.funsuite.AnyFunSuite.runTests(AnyFunSuite.scala:1563) [info] at org.scalatest.Suite.run(Suite.scala:1112) [info] at org.scalatest.Suite.run$(Suite.scala:1094) [info] at org.scalatest.funsuite.AnyFunSuite.org$scalatest$funsuite$AnyFunSuiteLike$$super$run(AnyFunSuite.scala:1563) [info] at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$run$1(AnyFunSuiteLike.scala:237) [info] at org.scalatest.SuperEngine.runImpl(Engine.scala:535) [info] at org.scalatest.funsuite.AnyFunSuiteLike.run(AnyFunSuiteLike.scala:237) [info] at org.scalatest.funsuite.AnyFunSuiteLike.run$(AnyFunSuiteLike.scala:236) [info] at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:61) [info] at org.scalatest.BeforeAndAfterAll.liftedTree1$1(BeforeAndAfterAll.scala:213) [info] at org.scalatest.BeforeAndAfterAll.run(BeforeAndAfterAll.scala:210) [info] at org.scalatest.BeforeAndAfterAll.run$(BeforeAndAfterAll.scala:208) [info] at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:61) [info] at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:318) [info] at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:513) [info] at sbt.ForkMain$Run.lambda$runTest$1(ForkMain.java:413) [info] at java.util.concurrent.FutureTask.run(FutureTask.java:266) [info] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) [info] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) [info] at java.lang.Thread.run(Thread.java:748) ``` ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? With this change, I confirmed that `OracleIntegrationSuite` passes with the following command. ``` $ git clone https://github.com/oracle/docker-images.git $ cd docker-images/OracleDatabase/SingleInstance/dockerfiles $ ./buildDockerImage.sh -v 18.4.0 -x $ ORACLE_DOCKER_IMAGE_NAME=oracle/database:18.4.0-xe build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver "testOnly org.apache.spark.sql.jdbc.OracleIntegrationSuite" ``` Closes #30485 from sarutak/fix-oracle-integration-suite. Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
ahshahid
added a commit
to ahshahid/spark
that referenced
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Dec 8, 2020
* [SPARK-33287][SS][UI] Expose state custom metrics information on SS UI
### What changes were proposed in this pull request?
Structured Streaming UI is not containing state custom metrics information. In this PR I've added it.
### Why are the changes needed?
Missing state custom metrics information.
### Does this PR introduce _any_ user-facing change?
Additional UI elements appear.
### How was this patch tested?
Existing unit tests + manual test.
```
#Compile Spark
echo "spark.sql.streaming.ui.enabledCustomMetricList stateOnCurrentVersionSizeBytes" >> conf/spark-defaults.conf
sbin/start-master.sh
sbin/start-worker.sh spark://gsomogyi-MBP16:7077
./bin/spark-submit --master spark://gsomogyi-MBP16:7077 --deploy-mode client --class com.spark.Main ../spark-test/target/spark-test-1.0-SNAPSHOT-jar-with-dependencies.jar
```
<img width="1119" alt="Screenshot 2020-11-18 at 12 45 36" src="https://user-images.githubusercontent.com/18561820/99527506-2f979680-299d-11eb-9187-4ae7fbd2596a.png">
Closes #30336 from gaborgsomogyi/SPARK-33287.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
* [SPARK-33457][PYTHON] Adjust mypy configuration
### What changes were proposed in this pull request?
This pull request:
- Adds following flags to the main mypy configuration:
- [`strict_optional`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-strict_optional)
- [`no_implicit_optional`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-no_implicit_optional)
- [`disallow_untyped_defs`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-disallow_untyped_calls)
These flags are enabled only for public API and disabled for tests and internal modules.
Additionally, these PR fixes missing annotations.
### Why are the changes needed?
Primary reason to propose this changes is to use standard configuration as used by typeshed project. This will allow us to be more strict, especially when interacting with JVM code. See for example https://github.com/apache/spark/pull/29122#pullrequestreview-513112882
Additionally, it will allow us to detect cases where annotations have unintentionally omitted.
### Does this PR introduce _any_ user-facing change?
Annotations only.
### How was this patch tested?
`dev/lint-python`.
Closes #30382 from zero323/SPARK-33457.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33252][PYTHON][DOCS] Migration to NumPy documentation style in MLlib (pyspark.mllib.*)
### What changes were proposed in this pull request?
This PR proposes migration of `pyspark.mllib` to NumPy documentation style.
### Why are the changes needed?
To improve documentation style.
Before:
After:
### Does this PR introduce _any_ user-facing change?
Yes, this changes both rendered HTML docs and console representation (SPARK-33243).
### How was this patch tested?
`dev/lint-python` and manual inspection.
Closes #30413 from zero323/SPARK-33252.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33494][SQL][AQE] Do not use local shuffle reader for repartition
### What changes were proposed in this pull request?
This PR updates `ShuffleExchangeExec` to carry more information about how much we can change the partitioning. For `repartition(col)`, we should preserve the user-specified partitioning and don't apply the AQE local shuffle reader.
### Why are the changes needed?
Similar to `repartition(number, col)`, we should respect the user-specified partitioning.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
a new test
Closes #30432 from cloud-fan/aqe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-33543][SQL] Migrate SHOW COLUMNS command to use UnresolvedTableOrView to resolve the identifier
### What changes were proposed in this pull request?
This PR proposes to migrate `SHOW COLUMNS` to use `UnresolvedTableOrView` to resolve the table/view identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
Note that `SHOW COLUMNS` is not yet supported for v2 tables.
### Why are the changes needed?
To use `UnresolvedTableOrView` for table/view resolution. Note that `ShowColumnsCommand` internally resolves to a temp view first, so there is no resolution behavior change with this PR.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Updated existing tests.
Closes #30490 from imback82/show_columns.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-33224][SS][WEBUI] Add watermark gap information into SS UI page
### What changes were proposed in this pull request?
This PR proposes to add the watermark gap information in SS UI page. Please refer below screenshots to see what we'd like to show in UI.
Please note that this PR doesn't plot the watermark value - knowing the gap between actual wall clock and watermark looks more useful than the absolute value.
### Why are the changes needed?
Watermark is the one of major metrics the end users need to track for stateful queries. Watermark defines "when" the output will be emitted for append mode, hence knowing how much gap between wall clock and watermark (input data) is very helpful to make expectation of the output.
### Does this PR introduce _any_ user-facing change?
Yes, SS UI query page will contain the watermark gap information.
### How was this patch tested?
Basic UT added. Manually tested with two queries:
> simple case
You'll see consistent watermark gap with (15 seconds + a) = 10 seconds are from delay in watermark definition, 5 seconds are trigger interval.
```
import org.apache.spark.sql.streaming.Trigger
spark.conf.set("spark.sql.shuffle.partitions", "10")
val query = spark
.readStream
.format("rate")
.option("rowsPerSecond", 1000)
.option("rampUpTime", "10s")
.load()
.selectExpr("timestamp", "mod(value, 100) as mod", "value")
.withWatermark("timestamp", "10 seconds")
.groupBy(window($"timestamp", "1 minute", "10 seconds"), $"mod")
.agg(max("value").as("max_value"), min("value").as("min_value"), avg("value").as("avg_value"))
.writeStream
.format("console")
.trigger(Trigger.ProcessingTime("5 seconds"))
.outputMode("append")
.start()
query.awaitTermination()
```
> complicated case
This randomizes the timestamp, hence producing random watermark gap. This won't be smaller than 15 seconds as I described earlier.
```
import org.apache.spark.sql.streaming.Trigger
spark.conf.set("spark.sql.shuffle.partitions", "10")
val query = spark
.readStream
.format("rate")
.option("rowsPerSecond", 1000)
.option("rampUpTime", "10s")
.load()
.selectExpr("*", "CAST(CAST(timestamp AS BIGINT) - CAST((RAND() * 100000) AS BIGINT) AS TIMESTAMP) AS tsMod")
.selectExpr("tsMod", "mod(value, 100) as mod", "value")
.withWatermark("tsMod", "10 seconds")
.groupBy(window($"tsMod", "1 minute", "10 seconds"), $"mod")
.agg(max("value").as("max_value"), min("value").as("min_value"), avg("value").as("avg_value"))
.writeStream
.format("console")
.trigger(Trigger.ProcessingTime("5 seconds"))
.outputMode("append")
.start()
query.awaitTermination()
```
Closes #30427 from HeartSaVioR/SPARK-33224.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
* [SPARK-33533][SQL] Fix the regression bug that ConnectionProviders don't consider case-sensitivity for properties
### What changes were proposed in this pull request?
This PR fixes an issue that `BasicConnectionProvider` doesn't consider case-sensitivity for properties.
For example, the property `oracle.jdbc.mapDateToTimestamp` should be considered case-sensitivity but it is not considered.
### Why are the changes needed?
This is a bug introduced by #29024 .
Caused by this issue, `OracleIntegrationSuite` doesn't pass.
```
[info] - SPARK-16625: General data types to be mapped to Oracle *** FAILED *** (32 seconds, 129 milliseconds)
[info] types.apply(9).equals(org.apache.spark.sql.types.DateType) was false (OracleIntegrationSuite.scala:238)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:472)
[info] at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:471)
[info] at org.scalatest.Assertions$.newAssertionFailedException(Assertions.scala:1231)
[info] at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:1295)
[info] at org.apache.spark.sql.jdbc.OracleIntegrationSuite.$anonfun$new$4(OracleIntegrationSuite.scala:238)
[info] at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
[info] at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
[info] at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
[info] at org.scalatest.Transformer.apply(Transformer.scala:22)
[info] at org.scalatest.Transformer.apply(Transformer.scala:20)
[info] at org.scalatest.funsuite.AnyFunSuiteLike$$anon$1.apply(AnyFunSuiteLike.scala:190)
[info] at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:176)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.invokeWithFixture$1(AnyFunSuiteLike.scala:188)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTest$1(AnyFunSuiteLike.scala:200)
[info] at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.runTest(AnyFunSuiteLike.scala:200)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.runTest$(AnyFunSuiteLike.scala:182)
[info] at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(SparkFunSuite.scala:61)
[info] at org.scalatest.BeforeAndAfterEach.runTest(BeforeAndAfterEach.scala:234)
[info] at org.scalatest.BeforeAndAfterEach.runTest$(BeforeAndAfterEach.scala:227)
[info] at org.apache.spark.SparkFunSuite.runTest(SparkFunSuite.scala:61)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTests$1(AnyFunSuiteLike.scala:233)
[info] at org.scalatest.SuperEngine.$anonfun$runTestsInBranch$1(Engine.scala:413)
[info] at scala.collection.immutable.List.foreach(List.scala:392)
[info] at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
[info] at org.scalatest.SuperEngine.runTestsInBranch(Engine.scala:396)
[info] at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:475)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.runTests(AnyFunSuiteLike.scala:233)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.runTests$(AnyFunSuiteLike.scala:232)
[info] at org.scalatest.funsuite.AnyFunSuite.runTests(AnyFunSuite.scala:1563)
[info] at org.scalatest.Suite.run(Suite.scala:1112)
[info] at org.scalatest.Suite.run$(Suite.scala:1094)
[info] at org.scalatest.funsuite.AnyFunSuite.org$scalatest$funsuite$AnyFunSuiteLike$$super$run(AnyFunSuite.scala:1563)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$run$1(AnyFunSuiteLike.scala:237)
[info] at org.scalatest.SuperEngine.runImpl(Engine.scala:535)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.run(AnyFunSuiteLike.scala:237)
[info] at org.scalatest.funsuite.AnyFunSuiteLike.run$(AnyFunSuiteLike.scala:236)
[info] at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:61)
[info] at org.scalatest.BeforeAndAfterAll.liftedTree1$1(BeforeAndAfterAll.scala:213)
[info] at org.scalatest.BeforeAndAfterAll.run(BeforeAndAfterAll.scala:210)
[info] at org.scalatest.BeforeAndAfterAll.run$(BeforeAndAfterAll.scala:208)
[info] at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:61)
[info] at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:318)
[info] at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:513)
[info] at sbt.ForkMain$Run.lambda$runTest$1(ForkMain.java:413)
[info] at java.util.concurrent.FutureTask.run(FutureTask.java:266)
[info] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
[info] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
[info] at java.lang.Thread.run(Thread.java:748)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
With this change, I confirmed that `OracleIntegrationSuite` passes with the following command.
```
$ git clone https://github.com/oracle/docker-images.git
$ cd docker-images/OracleDatabase/SingleInstance/dockerfiles
$ ./buildDockerImage.sh -v 18.4.0 -x
$ ORACLE_DOCKER_IMAGE_NAME=oracle/database:18.4.0-xe build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver "testOnly org.apache.spark.sql.jdbc.OracleIntegrationSuite"
```
Closes #30485 from sarutak/fix-oracle-integration-suite.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [SPARK-33477][SQL] Hive Metastore support filter by date type
### What changes were proposed in this pull request?
Hive Metastore supports strings and integral types in filters. It could also support dates. Please see [HIVE-5679](https://github.com/apache/hive/commit/5106bf1c8671740099fca8e1a7d4b37afe97137f) for more details.
This pr add support it.
### Why are the changes needed?
Improve query performance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes #30408 from wangyum/SPARK-33477.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33549][SQL] Remove configuration spark.sql.legacy.allowCastNumericToTimestamp
### What changes were proposed in this pull request?
Remove SQL configuration spark.sql.legacy.allowCastNumericToTimestamp
### Why are the changes needed?
In the current master branch, there is a new configuration `spark.sql.legacy.allowCastNumericToTimestamp` which controls whether to cast Numeric types to Timestamp or not. The default value is true.
After https://github.com/apache/spark/pull/30260, the type conversion between Timestamp type and Numeric type is disallowed in ANSI mode. So, we don't need to a separate configuration `spark.sql.legacy.allowCastNumericToTimestamp` for disallowing the conversion. Users just need to set `spark.sql.ansi.enabled` for the behavior.
As the configuration is not in any released yet, we should remove the configuration to make things simpler.
### Does this PR introduce _any_ user-facing change?
No, since the configuration is not released yet.
### How was this patch tested?
Existing test cases
Closes #30493 from gengliangwang/LEGACY_ALLOW_CAST_NUMERIC_TO_TIMESTAMP.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-33509][SQL] List partition by names from a V2 table which supports partition management
### What changes were proposed in this pull request?
1. Add new method `listPartitionByNames` to the `SupportsPartitionManagement` interface. It allows to list partitions by partition names and their values.
2. Implement new method in `InMemoryPartitionTable` which is used in DSv2 tests.
### Why are the changes needed?
Currently, the `SupportsPartitionManagement` interface exposes only `listPartitionIdentifiers` which allows to list partitions by partition values. And it requires to specify all values for partition schema fields in the prefix. This restriction does not allow to list partitions by some of partition names (not all of them).
For example, the table `tableA` is partitioned by two column `year` and `month`
```
CREATE TABLE tableA (price int, year int, month int)
USING _
partitioned by (year, month)
```
and has the following partitions:
```
PARTITION(year = 2015, month = 1)
PARTITION(year = 2015, month = 2)
PARTITION(year = 2016, month = 2)
PARTITION(year = 2016, month = 3)
```
If we want to list all partitions with `month = 2`, we have to specify `year` for **listPartitionIdentifiers()** which not always possible as we don't know all `year` values in advance. New method **listPartitionByNames()** allows to specify partition values only for `month`, and get two partitions:
```
PARTITION(year = 2015, month = 2)
PARTITION(year = 2016, month = 2)
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the affected test suite `SupportsPartitionManagementSuite`.
Closes #30452 from MaxGekk/column-names-listPartitionIdentifiers.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-27194][SPARK-29302][SQL] Fix commit collision in dynamic partition overwrite mode
### What changes were proposed in this pull request?
When using dynamic partition overwrite, each task has its working dir under staging dir like `stagingDir/.spark-staging-{jobId}`, each task commits to `outputPath/.spark-staging-{jobId}/{partitionId}/part-{taskId}-{jobId}{ext}`.
When speculation enable, multiple task attempts would be setup for one task, **they have same task id and they would commit to same file concurrently**. Due to host done or node preemption, the partly-committed files aren't cleaned up, a FileAlreadyExistsException would be raised in this situation, resulting in job failure.
I don't try to change task commit process for dynamic partition overwrite, like adding attempt id to task working dir for each attempts and committing to final output dir via a new outputCommitCoordinator, here is reason:
1. `FileOutputCommitter` already has commit coordinator for each task attempts, we can leverage it rather than build a new one.
2. To say the least, we implement a coordinator solving task attempts commit conflict, suppose a severe case, application master failover, tasks with same attempt id and same task id would commit to same files, the `FileAlreadyExistsException` risk still exists
In this pr, I leverage FileOutputCommitter to solve the problem:
1. when initing a write job description, set `outputPath/.spark-staging-{jobId}` as the output dir
2. each task attempt writes output to `outputPath/.spark-staging-{jobId}/_temporary/${appAttemptId}/_temporary/${taskAttemptId}/{partitionId}/part-{taskId}-{jobId}{ext}`
3. leverage `FileOutputCommitter` coordinator, write job firstly commits output to `outputPath/.spark-staging-{jobId}/{partitionId}`
4. for dynamic partition overwrite, write job finally move `outputPath/.spark-staging-{jobId}/{partitionId}` to `outputPath/{partitionId}`
### Why are the changes needed?
Without this pr, dynamic partition overwrite would fail
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
added UT.
Closes #29000 from WinkerDu/master-fix-dynamic-partition-multi-commit.
Authored-by: duripeng <duripeng@baidu.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-31257][SPARK-33561][SQL] Unify create table syntax
### What changes were proposed in this pull request?
* Unify the create table syntax in the parser by merging Hive and DataSource clauses
* Add `SerdeInfo` and `external` boolean to statement plans and update AstBuilder to produce them
* Add conversion from create statement plan to v1 create plans in ResolveSessionCatalog
* Support new statement clauses in ResolveCatalogs conversion to v2 create plans
* Remove SparkSqlParser rules for Hive syntax
* Add "option." namespace to distinguish SERDEPROPERTIES and OPTIONS in table properties
### Why are the changes needed?
* Current behavior is confusing.
* A way to pass the Hive create options to DSv2 is needed for a Hive source.
### Does this PR introduce any user-facing change?
Not by default, but v2 sources will be able to handle STORED AS and other Hive clauses.
### How was this patch tested?
Existing tests validate there are no behavior changes.
Update unit tests for using a statement plan for Hive create syntax:
* Move create tests from spark-sql DDLParserSuite into PlanResolutionSuite
* Add parser tests to spark-catalyst DDLParserSuite
Closes #28026 from rdblue/unify-create-table.
Lead-authored-by: Ryan Blue <blue@apache.org>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-33496][SQL] Improve error message of ANSI explicit cast
### What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/30260, there are some type conversions disallowed under ANSI mode.
We should tell users what they can do if they have to use the disallowed casting.
### Why are the changes needed?
Make it more user-friendly.
### Does this PR introduce _any_ user-facing change?
Yes, the error message is improved on casting failure when ANSI mode is enabled
### How was this patch tested?
Unit tests.
Closes #30440 from gengliangwang/improveAnsiCastErrorMSG.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
* [SPARK-33540][SQL] Subexpression elimination for interpreted predicate
### What changes were proposed in this pull request?
This patch proposes to support subexpression elimination for interpreted predicate.
### Why are the changes needed?
Similar to interpreted projection, there are use cases when codegen predicate is not able to work, e.g. too complex schema, non-codegen expression, etc. When there are frequently occurring expressions (subexpressions) among predicate expression, the performance is quite bad as we need to re-compute same expressions. We should be able to support subexpression elimination for interpreted predicate like interpreted projection.
### Does this PR introduce _any_ user-facing change?
No, this doesn't change user behavior.
### How was this patch tested?
Unit test and benchmark.
Closes #30497 from viirya/SPARK-33540.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [SPARK-31257][SPARK-33561][SQL][FOLLOWUP] Fix Scala 2.13 compilation
### What changes were proposed in this pull request?
This PR is a follow-up to fix Scala 2.13 compilation.
### Why are the changes needed?
To support Scala 2.13 in Apache Spark 3.1.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the GitHub Action Scala 2.13 compilation job.
Closes #30502 from dongjoon-hyun/SPARK-31257.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [SPARK-33525][SQL] Update hive-service-rpc to 3.1.2
### What changes were proposed in this pull request?
We supported Hive metastore are 0.12.0 through 3.1.2, but we supported hive-jdbc are 0.12.0 through 2.3.7. It will throw `TProtocolException` if we use hive-jdbc 3.x:
```
[rootspark-3267648 apache-hive-3.1.2-bin]# bin/beeline -u jdbc:hive2://localhost:10000/default
Connecting to jdbc:hive2://localhost:10000/default
Connected to: Spark SQL (version 3.1.0-SNAPSHOT)
Driver: Hive JDBC (version 3.1.2)
Transaction isolation: TRANSACTION_REPEATABLE_READ
Beeline version 3.1.2 by Apache Hive
0: jdbc:hive2://localhost:10000/default> create table t1(id int) using parquet;
Unexpected end of file when reading from HS2 server. The root cause might be too many concurrent connections. Please ask the administrator to check the number of active connections, and adjust hive.server2.thrift.max.worker.threads if applicable.
Error: org.apache.thrift.transport.TTransportException (state=08S01,code=0)
```
```
org.apache.thrift.protocol.TProtocolException: Missing version in readMessageBegin, old client?
at org.apache.thrift.protocol.TBinaryProtocol.readMessageBegin(TBinaryProtocol.java:234)
at org.apache.thrift.TBaseProcessor.process(TBaseProcessor.java:27)
at org.apache.hive.service.auth.TSetIpAddressProcessor.process(TSetIpAddressProcessor.java:53)
at org.apache.thrift.server.TThreadPoolServer$WorkerProcess.run(TThreadPoolServer.java:310)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1130)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:630)
at java.base/java.lang.Thread.run(Thread.java:832)
```
This pr upgrade hive-service-rpc to 3.1.2 to fix this issue.
### Why are the changes needed?
To support hive-jdbc 3.x.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual test:
```
[rootspark-3267648 apache-hive-3.1.2-bin]# bin/beeline -u jdbc:hive2://localhost:10000/default
Connecting to jdbc:hive2://localhost:10000/default
Connected to: Spark SQL (version 3.1.0-SNAPSHOT)
Driver: Hive JDBC (version 3.1.2)
Transaction isolation: TRANSACTION_REPEATABLE_READ
Beeline version 3.1.2 by Apache Hive
0: jdbc:hive2://localhost:10000/default> create table t1(id int) using parquet;
+---------+
| Result |
+---------+
+---------+
No rows selected (1.051 seconds)
0: jdbc:hive2://localhost:10000/default> insert into t1 values(1);
+---------+
| Result |
+---------+
+---------+
No rows selected (2.08 seconds)
0: jdbc:hive2://localhost:10000/default> select * from t1;
+-----+
| id |
+-----+
| 1 |
+-----+
1 row selected (0.605 seconds)
```
Closes #30478 from wangyum/SPARK-33525.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [SPARK-33565][BUILD][PYTHON] remove python3.8 and fix breakage
### What changes were proposed in this pull request?
remove python 3.8 from python/run-tests.py and stop build breaks
### Why are the changes needed?
the python tests are running against the bare-bones system install of python3, rather than an anaconda environment.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
via jenkins
Closes #30506 from shaneknapp/remove-py38.
Authored-by: shane knapp <incomplete@gmail.com>
Signed-off-by: shane knapp <incomplete@gmail.com>
* [SPARK-33523][SQL][TEST][FOLLOWUP] Fix benchmark case name in SubExprEliminationBenchmark
### What changes were proposed in this pull request?
Fix the wrong benchmark case name.
### Why are the changes needed?
The last commit to refactor the benchmark code missed a change of case name.
### Does this PR introduce _any_ user-facing change?
No, dev only.
### How was this patch tested?
Unit test.
Closes #30505 from viirya/SPARK-33523-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [SPARK-33562][UI] Improve the style of the checkbox in executor page
### What changes were proposed in this pull request?
1. Remove the fixed width style of class `container-fluid-div`. So that the UI looks clean when the text is long.
2. Add one space between a checkbox and the text on the right side, which is consistent with the stage page.
### Why are the changes needed?
The width of class `container-fluid-div` is set as 200px after https://github.com/apache/spark/pull/21688 . This makes the checkbox in the executor page messy.
We should remove the width limit.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manual test.
After the changes:
Closes #30500 from gengliangwang/reviseStyle.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33565][INFRA][FOLLOW-UP] Keep the test coverage with Python 3.8 in GitHub Actions
### What changes were proposed in this pull request?
This PR proposes to keep the test coverage with Python 3.8 in GitHub Actions. It is not tested for now in Jenkins due to an env issue.
**Before this change in GitHub Actions:**
```
========================================================================
Running PySpark tests
========================================================================
Running PySpark tests. Output is in /__w/spark/spark/python/unit-tests.log
Will test against the following Python executables: ['python3.6', 'pypy3']
...
```
**After this change in GitHub Actions:**
```
========================================================================
Running PySpark tests
========================================================================
Running PySpark tests. Output is in /__w/spark/spark/python/unit-tests.log
Will test against the following Python executables: ['python3.6', 'python3.8', 'pypy3']
```
### Why are the changes needed?
To keep the test coverage with Python 3.8 in GitHub Actions.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
GitHub Actions in this build will test.
Closes #30510 from HyukjinKwon/SPARK-33565.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33551][SQL] Do not use custom shuffle reader for repartition
### What changes were proposed in this pull request?
This PR fixes an AQE issue where local shuffle reader, partition coalescing, or skew join optimization can be mistakenly applied to a shuffle introduced by repartition or a regular shuffle that logically replaces a repartition shuffle.
The proposed solution checks for the presence of any repartition shuffle and filters out not applicable optimization rules for the final stage in an AQE plan.
### Why are the changes needed?
Without the change, the output of a repartition query may not be correct.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added UT.
Closes #30494 from maryannxue/csr-repartition.
Authored-by: Maryann Xue <maryann.xue@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
* [SPARK-33563][PYTHON][R][SQL] Expose inverse hyperbolic trig functions in PySpark and SparkR
### What changes were proposed in this pull request?
This PR adds the following functions (introduced in Scala API with SPARK-33061):
- `acosh`
- `asinh`
- `atanh`
to Python and R.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
New functions.
### How was this patch tested?
New unit tests.
Closes #30501 from zero323/SPARK-33563.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33566][CORE][SQL][SS][PYTHON] Make unescapedQuoteHandling option configurable when read CSV
### What changes were proposed in this pull request?
There are some differences between Spark CSV, opencsv and commons-csv, the typical case are described in SPARK-33566, When there are both unescaped quotes and unescaped qualifier in value, the results of parsing are different.
The reason for the difference is Spark use `STOP_AT_DELIMITER` as default `UnescapedQuoteHandling` to build `CsvParser` and it not configurable.
On the other hand, opencsv and commons-csv use the parsing mechanism similar to `STOP_AT_CLOSING_QUOTE ` by default.
So this pr make `unescapedQuoteHandling` option configurable to get the same parsing result as opencsv and commons-csv.
### Why are the changes needed?
Make unescapedQuoteHandling option configurable when read CSV to make parsing more flexible。
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the Jenkins or GitHub Action
- Add a new case similar to that described in SPARK-33566
Closes #30518 from LuciferYang/SPARK-33566.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33575][SQL] Fix misleading exception for "ANALYZE TABLE ... FOR COLUMNS" on temporary views
### What changes were proposed in this pull request?
This PR proposes to fix the exception message for `ANALYZE TABLE ... FOR COLUMNS` on temporary views.
The current behavior throws `NoSuchTableException` even if the temporary view exists:
```
sql("CREATE TEMP VIEW t AS SELECT 1 AS id")
sql("ANALYZE TABLE t COMPUTE STATISTICS FOR COLUMNS id")
org.apache.spark.sql.catalyst.analysis.NoSuchTableException: Table or view 't' not found in database 'db';
at org.apache.spark.sql.execution.command.AnalyzeColumnCommand.analyzeColumnInTempView(AnalyzeColumnCommand.scala:76)
at org.apache.spark.sql.execution.command.AnalyzeColumnCommand.run(AnalyzeColumnCommand.scala:54)
```
After this PR, more reasonable exception is thrown:
```
org.apache.spark.sql.AnalysisException: Temporary view `testView` is not cached for analyzing columns.;
[info] at org.apache.spark.sql.execution.command.AnalyzeColumnCommand.analyzeColumnInTempView(AnalyzeColumnCommand.scala:74)
[info] at org.apache.spark.sql.execution.command.AnalyzeColumnCommand.run(AnalyzeColumnCommand.scala:54)
```
### Why are the changes needed?
To fix a misleading exception.
### Does this PR introduce _any_ user-facing change?
Yes, the exception thrown is changed as shown above.
### How was this patch tested?
Updated existing test.
Closes #30519 from imback82/analyze_table_message.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-33522][SQL] Improve exception messages while handling UnresolvedTableOrView
### What changes were proposed in this pull request?
This PR proposes to improve the exception messages while `UnresolvedTableOrView` is handled based on this suggestion: https://github.com/apache/spark/pull/30321#discussion_r521127001.
Currently, when an identifier is resolved to a temp view when a table/permanent view is expected, the following exception message is displayed (e.g., for `SHOW CREATE TABLE`):
```
t is a temp view not table or permanent view.
```
After this PR, the message will be:
```
t is a temp view. 'SHOW CREATE TABLE' expects a table or permanent view.
```
Also, if an identifier is not resolved, the following exception message is currently used:
```
Table or view not found: t
```
After this PR, the message will be:
```
Table or permanent view not found for 'SHOW CREATE TABLE': t
```
or
```
Table or view not found for 'ANALYZE TABLE ... FOR COLUMNS ...': t
```
### Why are the changes needed?
To improve the exception message.
### Does this PR introduce _any_ user-facing change?
Yes, the exception message will be changed as described above.
### How was this patch tested?
Updated existing tests.
Closes #30475 from imback82/unresolved_table_or_view.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-28645][SQL] ParseException is thrown when the window is redefined
### What changes were proposed in this pull request?
Currently in Spark one could redefine a window. For instance:
`select count(*) OVER w FROM tenk1 WINDOW w AS (ORDER BY unique1), w AS (ORDER BY unique1);`
The window `w` is defined two times. In PgSQL, on the other hand, a thrown will happen:
`ERROR: window "w" is already defined`
### Why are the changes needed?
The current implement gives the following window definitions a higher priority. But it wasn't Spark's intention and users can't know from any document of Spark.
This PR fixes the bug.
### Does this PR introduce _any_ user-facing change?
Yes.
There is an example query output with/without this fix.
```
SELECT
employee_name,
salary,
first_value(employee_name) OVER w highest_salary,
nth_value(employee_name, 2) OVER w second_highest_salary
FROM
basic_pays
WINDOW
w AS (ORDER BY salary DESC ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING),
w AS (ORDER BY salary DESC ROWS BETWEEN UNBOUNDED PRECEDING AND 2 FOLLOWING)
ORDER BY salary DESC
```
The output before this fix:
```
Larry Bott 11798 Larry Bott Gerard Bondur
Gerard Bondur 11472 Larry Bott Gerard Bondur
Pamela Castillo 11303 Larry Bott Gerard Bondur
Barry Jones 10586 Larry Bott Gerard Bondur
George Vanauf 10563 Larry Bott Gerard Bondur
Loui Bondur 10449 Larry Bott Gerard Bondur
Mary Patterson 9998 Larry Bott Gerard Bondur
Steve Patterson 9441 Larry Bott Gerard Bondur
Julie Firrelli 9181 Larry Bott Gerard Bondur
Jeff Firrelli 8992 Larry Bott Gerard Bondur
William Patterson 8870 Larry Bott Gerard Bondur
Diane Murphy 8435 Larry Bott Gerard Bondur
Leslie Jennings 8113 Larry Bott Gerard Bondur
Gerard Hernandez 6949 Larry Bott Gerard Bondur
Foon Yue Tseng 6660 Larry Bott Gerard Bondur
Anthony Bow 6627 Larry Bott Gerard Bondur
Leslie Thompson 5186 Larry Bott Gerard Bondur
```
The output after this fix:
```
struct<>
-- !query output
org.apache.spark.sql.catalyst.parser.ParseException
The definition of window 'w' is repetitive(line 8, pos 0)
```
### How was this patch tested?
Jenkins test.
Closes #30512 from beliefer/SPARK-28645.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-33498][SQL] Datetime parsing should fail if the input string can't be parsed, or the pattern string is invalid
### What changes were proposed in this pull request?
Datetime parsing should fail if the input string can't be parsed, or the pattern string is invalid, when ANSI mode is enable. This patch should update GetTimeStamp, UnixTimeStamp, ToUnixTimeStamp and Cast.
### Why are the changes needed?
For ANSI mode.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT and Existing UT.
Closes #30442 from leanken/leanken-SPARK-33498.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-33141][SQL] Capture SQL configs when creating permanent views
### What changes were proposed in this pull request?
This PR makes CreateViewCommand/AlterViewAsCommand capturing runtime SQL configs and store them as view properties. These configs will be applied during the parsing and analysis phases of the view resolution. Users can set `spark.sql.legacy.useCurrentConfigsForView` to `true` to restore the behavior before.
### Why are the changes needed?
This PR is a sub-task of [SPARK-33138](https://issues.apache.org/jira/browse/SPARK-33138) that proposes to unify temp view and permanent view behaviors. This PR makes permanent views mimicking the temp view behavior that "fixes" view semantic by directly storing resolved LogicalPlan. For example, if a user uses spark 2.4 to create a view that contains null values from division-by-zero expressions, she may not want that other users' queries which reference her view throw exceptions when running on spark 3.x with ansi mode on.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
added UT + existing UTs (improved)
Closes #30289 from luluorta/SPARK-33141.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* Spelling r common dev mlib external project streaming resource managers python
### What changes were proposed in this pull request?
This PR intends to fix typos in the sub-modules:
* `R`
* `common`
* `dev`
* `mlib`
* `external`
* `project`
* `streaming`
* `resource-managers`
* `python`
Split per srowen https://github.com/apache/spark/pull/30323#issuecomment-728981618
NOTE: The misspellings have been reported at https://github.com/jsoref/spark/commit/706a726f87a0bbf5e31467fae9015218773db85b#commitcomment-44064356
### Why are the changes needed?
Misspelled words make it harder to read / understand content.
### Does this PR introduce _any_ user-facing change?
There are various fixes to documentation, etc...
### How was this patch tested?
No testing was performed
Closes #30402 from jsoref/spelling-R_common_dev_mlib_external_project_streaming_resource-managers_python.
Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
* [SPARK-33570][SQL][TESTS] Set the proper version of gssapi plugin automatically for MariaDBKrbIntegrationSuite
### What changes were proposed in this pull request?
This PR changes mariadb_docker_entrypoint.sh to set the proper version automatically for mariadb-plugin-gssapi-server.
The proper version is based on the one of mariadb-server.
Also, this PR enables to use arbitrary docker image by setting the environment variable `MARIADB_CONTAINER_IMAGE_NAME`.
### Why are the changes needed?
For `MariaDBKrbIntegrationSuite`, the version of `mariadb-plugin-gssapi-server` is currently set to `10.5.5` in `mariadb_docker_entrypoint.sh` but it's no longer available in the official apt repository and `MariaDBKrbIntegrationSuite` doesn't pass for now.
It seems that only the most recent three versions are available for each major version and they are `10.5.6`, `10.5.7` and `10.5.8` for now.
Further, the release cycle of MariaDB seems to be very rapid (1 ~ 2 months) so I don't think it's a good idea to set to an specific version for `mariadb-plugin-gssapi-server`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Confirmed that `MariaDBKrbIntegrationSuite` passes with the following commands.
```
$ build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.MariaDBKrbIntegrationSuite"
```
In this case, we can see what version of `mariadb-plugin-gssapi-server` is going to be installed in the following container log message.
```
Installing mariadb-plugin-gssapi-server=1:10.5.8+maria~focal
```
Or, we can set MARIADB_CONTAINER_IMAGE_NAME for a specific version of MariaDB.
```
$ MARIADB_DOCKER_IMAGE_NAME=mariadb:10.5.6 build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.MariaDBKrbIntegrationSuite"
```
```
Installing mariadb-plugin-gssapi-server=1:10.5.6+maria~focal
```
Closes #30515 from sarutak/fix-MariaDBKrbIntegrationSuite.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
* [SPARK-33580][CORE] resolveDependencyPaths should use classifier attribute of artifact
### What changes were proposed in this pull request?
This patch proposes to use classifier attribute to construct artifact path instead of type.
### Why are the changes needed?
`resolveDependencyPaths` now takes artifact type to decide to add "-tests" postfix. However, the path pattern of ivy in `resolveMavenCoordinates` is `[organization]_[artifact][revision](-[classifier]).[ext]`. We should use classifier instead of type to construct file path.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test. Manual test.
Closes #30524 from viirya/SPARK-33580.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [MINOR][SQL] Remove `getTables()` from `r.SQLUtils`
### What changes were proposed in this pull request?
Remove the unused method `getTables()` from `r.SQLUtils`. The method was used before the changes https://github.com/apache/spark/pull/17483 but R's `tables.default` was rewritten using `listTables()`: https://github.com/apache/spark/pull/17483/files#diff-2c01472a7bcb1d318244afcd621d726e00d36cd15dffe7e44fa96c54fce4cd9aR220-R223
### Why are the changes needed?
To improve code maintenance, and remove the dead code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By R tests.
Closes #30527 from MaxGekk/remove-getTables-in-r-SQLUtils.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [SPARK-33581][SQL][TEST] Refactor HivePartitionFilteringSuite
### What changes were proposed in this pull request?
This pr refactor HivePartitionFilteringSuite.
### Why are the changes needed?
To make it easy to maintain.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes #30525 from wangyum/SPARK-33581.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
* [SPARK-33590][DOCS][SQL] Add missing sub-bullets in Spark SQL Guide
### What changes were proposed in this pull request?
Add the missing sub-bullets in the left side of `Spark SQL Guide`
### Why are the changes needed?
The three sub-bullets in the left side is not consistent with the contents (five bullets) in the right side.
### Does this PR introduce _any_ user-facing change?
Yes, you can see more lines in the left menu.
### How was this patch tested?
Manually build the doc as follows. This can be verified as attached:
```
cd docs
SKIP_API=1 jekyll build
firefox _site/sql-pyspark-pandas-with-arrow.html
```
Closes #30537 from kiszk/SPARK-33590.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [SPARK-33587][CORE] Kill the executor on nested fatal errors
### What changes were proposed in this pull request?
Currently we will kill the executor when hitting a fatal error. However, if the fatal error is wrapped by another exception, such as
- java.util.concurrent.ExecutionException, com.google.common.util.concurrent.UncheckedExecutionException, com.google.common.util.concurrent.ExecutionError when using Guava cache or Java thread pool.
- SparkException thrown from https://github.com/apache/spark/blob/cf98a761de677c733f3c33230e1c63ddb785d5c5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormatWriter.scala#L231 or https://github.com/apache/spark/blob/cf98a761de677c733f3c33230e1c63ddb785d5c5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormatWriter.scala#L296
We will still keep the executor running. Fatal errors are usually unrecoverable (such as OutOfMemoryError), some components may be in a broken state when hitting a fatal error and it's hard to predicate the behaviors of a broken component. Hence, it's better to detect the nested fatal error as well and kill the executor. Then we can rely on Spark's fault tolerance to recover.
### Why are the changes needed?
Fatal errors are usually unrecoverable (such as OutOfMemoryError), some components may be in a broken state when hitting a fatal error and it's hard to predicate the behaviors of a broken component. Hence, it's better to detect the nested fatal error as well and kill the executor. Then we can rely on Spark's fault tolerance to recover.
### Does this PR introduce _any_ user-facing change?
Yep. There is a slight internal behavior change on when to kill an executor. We will kill the executor when detecting a nested fatal error in the exception chain. `spark.executor.killOnFatalError.depth` is added to allow users to turn off this change if the slight behavior change impacts them.
### How was this patch tested?
The new method `Executor.isFatalError` is tested by `spark.executor.killOnNestedFatalError`.
Closes #30528 from zsxwing/SPARK-33587.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [SPARK-33588][SQL] Respect the `spark.sql.caseSensitive` config while resolving partition spec in v1 `SHOW TABLE EXTENDED`
### What changes were proposed in this pull request?
Perform partition spec normalization in `ShowTablesCommand` according to the table schema before getting partitions from the catalog. The normalization via `PartitioningUtils.normalizePartitionSpec()` adjusts the column names in partition specification, w.r.t. the real partition column names and case sensitivity.
### Why are the changes needed?
Even when `spark.sql.caseSensitive` is `false` which is the default value, v1 `SHOW TABLE EXTENDED` is case sensitive:
```sql
spark-sql> CREATE TABLE tbl1 (price int, qty int, year int, month int)
> USING parquet
> partitioned by (year, month);
spark-sql> INSERT INTO tbl1 PARTITION(year = 2015, month = 1) SELECT 1, 1;
spark-sql> SHOW TABLE EXTENDED LIKE 'tbl1' PARTITION(YEAR = 2015, Month = 1);
Error in query: Partition spec is invalid. The spec (YEAR, Month) must match the partition spec (year, month) defined in table '`default`.`tbl1`';
```
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, the `SHOW TABLE EXTENDED` command respects the SQL config. And for example above, it returns correct result:
```sql
spark-sql> SHOW TABLE EXTENDED LIKE 'tbl1' PARTITION(YEAR = 2015, Month = 1);
default tbl1 false Partition Values: [year=2015, month=1]
Location: file:/Users/maximgekk/spark-warehouse/tbl1/year=2015/month=1
Serde Library: org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe
InputFormat: org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat
Storage Properties: [serialization.format=1, path=file:/Users/maximgekk/spark-warehouse/tbl1]
Partition Parameters: {transient_lastDdlTime=1606595118, totalSize=623, numFiles=1}
Created Time: Sat Nov 28 23:25:18 MSK 2020
Last Access: UNKNOWN
Partition Statistics: 623 bytes
```
### How was this patch tested?
By running the modified test suite `v1/ShowTablesSuite`
Closes #30529 from MaxGekk/show-table-case-sensitive-spec.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [SPARK-33585][SQL][DOCS] Fix the comment for `SQLContext.tables()` and mention the `database` column
### What changes were proposed in this pull request?
Change the comments for `SQLContext.tables()` to "The returned DataFrame has three columns, database, tableName and isTemporary".
### Why are the changes needed?
Currently, the comment mentions only 2 columns but `tables()` returns 3 columns actually:
```scala
scala> spark.range(10).createOrReplaceTempView("view1")
scala> val tables = spark.sqlContext.tables()
tables: org.apache.spark.sql.DataFrame = [database: string, tableName: string ... 1 more field]
scala> tables.printSchema
root
|-- database: string (nullable = false)
|-- tableName: string (nullable = false)
|-- isTemporary: boolean (nullable = false)
scala> tables.show
+--------+---------+-----------+
|database|tableName|isTemporary|
+--------+---------+-----------+
| default| t1| false|
| default| t2| false|
| default| ymd| false|
| | view1| true|
+--------+---------+-----------+
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running `./dev/scalastyle`
Closes #30526 from MaxGekk/sqlcontext-tables-doc.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
* [SPARK-33517][SQL][DOCS] Fix the correct menu items and page links in PySpark Usage Guide for Pandas with Apache Arrow
### What changes were proposed in this pull request?
Change "Apache Arrow in Spark" to "Apache Arrow in PySpark"
and the link to “/sql-pyspark-pandas-with-arrow.html#apache-arrow-in-pyspark”
### Why are the changes needed?
When I click on the menu item it doesn't point to the correct page, and from the parent menu I can infer that the correct menu item name and link should be "Apache Arrow in PySpark".
like this:
image
### Does this PR introduce any user-facing change?
Yes, clicking on the menu item will take you to the correct guide page
### How was this patch tested?
Manually build the doc. This can be verified as below:
cd docs
SKIP_API=1 jekyll build
open _site/sql-pyspark-pandas-with-arrow.html
Closes #30466 from liucht-inspur/master.
Authored-by: liucht <liucht@inspur.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33589][SQL] Close opened session if the initialization fails
### What changes were proposed in this pull request?
This pr add try catch when opening session.
### Why are the changes needed?
Close opened session if the initialization fails.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual test.
Before this pr:
```
[rootspark-3267648 spark]# bin/beeline -u jdbc:hive2://localhost:10000/db_not_exist
NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark classes ahead of assembly.
Connecting to jdbc:hive2://localhost:10000/db_not_exist
log4j:WARN No appenders could be found for logger (org.apache.hive.jdbc.Utils).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Error: Could not open client transport with JDBC Uri: jdbc:hive2://localhost:10000/db_not_exist: Database 'db_not_exist' not found; (state=08S01,code=0)
Beeline version 2.3.7 by Apache Hive
beeline>
```
After this pr:
```
[rootspark-3267648 spark]# bin/beeline -u jdbc:hive2://localhost:10000/db_not_exist
NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark classes ahead of assembly.
log4j:WARN No appenders could be found for logger (org.apache.hadoop.util.Shell).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Connecting to jdbc:hive2://localhost:10000/db_not_exist
Error: Could not open client transport with JDBC Uri: jdbc:hive2://localhost:10000/db_not_exist: Failed to open new session: org.apache.spark.sql.catalyst.analysis.NoSuchDatabaseException: Database 'db_not_exist' not found; (state=08S01,code=0)
Beeline version 2.3.7 by Apache Hive
beeline>
```
Closes #30536 from wangyum/SPARK-33589.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33582][SQL] Hive Metastore support filter by not-equals
### What changes were proposed in this pull request?
This pr make partition predicate pushdown into Hive metastore support not-equals operator.
Hive related changes:
https://github.com/apache/hive/blob/b8bd4594bef718b1eeac9fceb437d7df7b480ed1/itests/hive-unit/src/test/java/org/apache/hadoop/hive/metastore/TestHiveMetaStore.java#L2194-L2207
https://issues.apache.org/jira/browse/HIVE-2702
### Why are the changes needed?
Improve query performance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes #30534 from wangyum/SPARK-33582.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33567][SQL] DSv2: Use callback instead of passing Spark session and v2 relation for refreshing cache
### What changes were proposed in this pull request?
This replaces Spark session and `DataSourceV2Relation` in V2 write plans by replacing them with a callback `afterWrite`.
### Why are the changes needed?
Per discussion in #30429, it's better to not pass Spark session and `DataSourceV2Relation` through Spark plans. Instead we can use a callback which makes the interface cleaner.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes #30491 from sunchao/SPARK-33492-followup.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [MINOR] Spelling bin core docs external mllib repl
### What changes were proposed in this pull request?
This PR intends to fix typos in the sub-modules:
* `bin`
* `core`
* `docs`
* `external`
* `mllib`
* `repl`
* `pom.xml`
Split per srowen https://github.com/apache/spark/pull/30323#issuecomment-728981618
NOTE: The misspellings have been reported at https://github.com/jsoref/spark/commit/706a726f87a0bbf5e31467fae9015218773db85b#commitcomment-44064356
### Why are the changes needed?
Misspelled words make it harder to read / understand content.
### Does this PR introduce _any_ user-facing change?
There are various fixes to documentation, etc...
### How was this patch tested?
No testing was performed
Closes #30530 from jsoref/spelling-bin-core-docs-external-mllib-repl.
Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
* [SPARK-32976][SQL] Support column list in INSERT statement
### What changes were proposed in this pull request?
#### JIRA expectations
```
INSERT currently does not support named column lists.
INSERT INTO <table> (col1, col2,…) VALUES( 'val1', 'val2', … )
Note, we assume the column list contains all the column names. Issue an exception if the list is not complete. The column order could be different from the column order defined in the table definition.
```
#### implemetations
In this PR, we add a column list as an optional part to the `INSERT OVERWRITE/INTO` statements:
```
/**
* {{{
* INSERT OVERWRITE TABLE tableIdentifier [partitionSpec [IF NOT EXISTS]]? [identifierList] ...
* INSERT INTO [TABLE] tableIdentifier [partitionSpec] [identifierList] ...
* }}}
*/
```
The column list represents all expected columns with an explicit order that you want to insert to the target table. **Particularly**, we assume the column list contains all the column names in the current implementation, it will fail when the list is incomplete.
In **Analyzer**, we add a code path to resolve the column list in the `ResolveOutputRelation` rule before it is transformed to v1 or v2 command. It will fail here if the list has any field that not belongs to the target table.
Then, for v2 command, e.g. `AppendData`, we use the resolved column list and output of the target table to resolve the output of the source query `ResolveOutputRelation` rule. If the list has duplicated columns, we fail. If the list is not empty but the list size does not match the target table, we fail. If no other exceptions occur, we use the column list to map the output of the source query to the output of the target table. The column list will be set to Nil and it will not hit the rule again after it is resolved.
for v1 command, those all happen in the `PreprocessTableInsertion` rule
### Why are the changes needed?
new feature support
### Does this PR introduce _any_ user-facing change?
yes, insert into/overwrite table support specify column list
### How was this patch tested?
new tests
Closes #29893 from yaooqinn/SPARK-32976.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-33448][SQL] Support CACHE/UNCACHE TABLE commands for v2 tables
### What changes were proposed in this pull request?
This PR proposes to support `CHACHE/UNCACHE TABLE` commands for v2 tables.
In addtion, this PR proposes to migrate `CACHE/UNCACHE TABLE` to use `UnresolvedTableOrView` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
To support `CACHE/UNCACHE TABLE` commands for v2 tables.
Note that `CACHE/UNCACHE TABLE` for v1 tables/views go through `SparkSession.table` to resolve identifier, which resolves temp views first, so there is no change in the behavior by moving to the new framework.
### Does this PR introduce _any_ user-facing change?
Yes. Now the user can run `CACHE/UNCACHE TABLE` commands on v2 tables.
### How was this patch tested?
Added/updated existing tests.
Closes #30403 from imback82/cache_table.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
* [SPARK-33498][SQL][FOLLOW-UP] Deduplicate the unittest by using checkCastWithParseError
### What changes were proposed in this pull request?
Dup code removed in SPARK-33498 as follow-up.
### Why are the changes needed?
Nit.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes #30540 from leanken/leanken-SPARK-33498.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-28646][SQL] Fix bug of Count so as consistent with mainstream databases
### What changes were proposed in this pull request?
Currently, Spark allows calls to `count` even for non parameterless aggregate function. For example, the following query actually works:
`SELECT count() FROM tenk1;`
On the other hand, mainstream databases will throw an error.
**Oracle**
`> ORA-00909: invalid number of arguments`
**PgSQL**
`ERROR: count(*) must be used to call a parameterless aggregate function`
**MySQL**
`> 1064 - You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')`
### Why are the changes needed?
Fix a bug so that consistent with mainstream databases.
There is an example query output with/without this fix.
`SELECT count() FROM testData;`
The output before this fix:
`0`
The output after this fix:
```
org.apache.spark.sql.AnalysisException
cannot resolve 'count()' due to data type mismatch: count requires at least one argument.; line 1 pos 7
```
### Does this PR introduce _any_ user-facing change?
Yes.
If not specify parameter for `count`, will throw an error.
### How was this patch tested?
Jenkins test.
Closes #30541 from beliefer/SPARK-28646.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* [SPARK-33480][SQL] Support char/varchar type
### What changes were proposed in this pull request?
This PR adds the char/varchar type which is kind of a variant of string type:
1. Char type is fixed-length string. When comparing char type values, we need to pad the shorter one to the longer length.
2. Varchar type is string with a length limitation.
To implement the char/varchar semantic, this PR:
1. Do string length check when writing to char/varchar type columns.
2. Do string padding when reading char type columns. We don't do it at the writing side to save storage space.
3. Do string padding when comparing char type column with string literal or another char type column. (string literal is fixed length so should be treated as char type as well)
To simplify the implementation, this PR doesn't propagate char/varchar type info through functions/operator…
tdg5
reviewed
Nov 22, 2021
| require(filteredProviders.size == 1, | ||
| "JDBC connection initiated but not exactly one connection provider found which can handle " + | ||
| s"it. Found active providers: ${filteredProviders.mkString(", ")}") | ||
| SecurityConfigurationLock.synchronized { |
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😭
I believe this synchronization introduces a significant performance bottleneck for applications that rely on being able to establish many connections simultaneously for performance reasons. This change forces concurrency to 1 when establishing database connections for a given JDBC driver and that strikes me as a significant user impacting change.
Can anyone propose a workaround for this? I have an app that makes connections to thousands of databases and I can't upgrade to any version >=3.1.x because of this significant bottleneck.
https://issues.apache.org/jira/browse/SPARK-37391
That screenshot only shows a handful of blocked threads, but in total I have 98 threads blocked waiting for the SecurityConfigurationLock.
I do not have this issue when running Spark 3.0.1 with no code changes.
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@gaborgsomogyi is getConnection() not thread-safe?
Or is there any possibility of finer-grained locking on the actual object that getConnection() is called on?
We could move the filteredProviders.head out of the block but doubt that does anything.
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is getConnection() not thread-safe?
Simply no.
This is well thought initially even if it introduces bottlenecks. The main problem is that JVM has a single security context. In order to make a connection one MUST modify the security context, otherwise the JDBC connector is not able to provide the security credentials (TGT, keytab or whatever). Simply saying JDBC connectors are able to get credentials from the single security context.
Since multiple connections are made concurrently (sometimes same DB type w/ different credentials) this must be synchronized not to have a malformed security context (we've made load test and added 10+ DBs and corrupted the security context pretty fast w/o sync and it was horror to find out what's going on).
Workload are coming and going, nobody can tell in advance what kind of security context need to be created at the very beginning of JVM creation. If we would pre-bake the security context (not sure how?!) then the following issues would come:
- How to handle 2 same type of databases (for example MariaDB) with 2 different credentials?
- How to handle if a DB login credentials changed over time?
I personally think the first one can't be solved, the second one could be cured w/ all JVM restarts but I think it's just not user friendly.
If somebody has an excellent idea I would like to hear it.
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What I can imagine is to pre-set the JVM JAAS config for databases from file like this: java -Djava.security.auth.login.config=jaas.conf.
And then a new flag can be introduced like: spark.jdbc.doNotSyncronize which is default true.
That case security credentials MUSTN'T be provided by Spark's JDBC properties but only from JAAS file.
However this would be super risky and for advanced users only. That said I've spent at least a month to debug the JVM security context what's going on...
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I don't know anything about the underlying problem, but it had crossed my mind that allowing the synchronization to be optional could be one path forward.
@gaborgsomogyi may I trouble you for some references related to
The main problem is that JVM has a single security context. In order to make a connection one MUST modify the security context, otherwise the JDBC connector is not able to provide the security credentials (TGT, keytab or whatever)
so I can better familiarize myself with the problem that you are describing?
Beyond this particular issue, what you've shared suggests that the concurrency utilized by my app could be causing us to crosswire data which would be a major problem.
I guess I'd also ask, is there more to it than you described? It sounds like I should either have some cross wired data or if that's not the case then there is some missing piece of the puzzle that means the synchronization is not always required.
Thanks in advance!
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Unless somebody has a ground breaking idea providers must be synced where security enabled, we can free up some time when no authentication is in place.
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The proposed solution seems straightforward to me and I'm happy to take a stab at it. Dumb question though, how would I eventually tweak the spark class loader so it would detect a custom JdbcConnectionProvider in my library code?
I tried baking such a class into the uberjar containing the driver app, but I didn't seem to be able to get ConnectionProvider to notice my clone of BasicConnectionProvider. I was guessing this was due to a class loader disagreement.
My spark-submit looks roughly like so:
spark-submit \
--class theThing \
--master 'local[*]' \
--driver-memory 2G \
--driver-class-path theUberjar.jar
theUberjar.jar
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@gaborgsomogyi thanks! The META-INF manifest is what I was missing 👍🏻
Here's my stab at what you suggested: #34745
I marked it a WIP, but it is complete as far as I am concerned and aware. I just want to make sure it matches up with what you had in mind before I open it up to broader review.
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What changes were proposed in this pull request?
At the moment only the baked in JDBC connection providers can be used but there is a need to support additional databases and use-cases. In this PR I'm proposing a new developer API name
JdbcConnectionProvider. To show how an external JDBC connection provider can be implemented I've created an example here.The PR contains the following changes:
SecurityConfigurationLockinto a central place because other areas will change global JVM security configWhy are the changes needed?
No custom authentication possibility.
Does this PR introduce any user-facing change?
No.
How was this patch tested?