Return-Path: X-Original-To: apmail-spark-issues-archive@minotaur.apache.org Delivered-To: apmail-spark-issues-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 61B8C1840C for ; Mon, 9 Nov 2015 13:24:11 +0000 (UTC) Received: (qmail 49148 invoked by uid 500); 9 Nov 2015 13:24:11 -0000 Delivered-To: apmail-spark-issues-archive@spark.apache.org Received: (qmail 49106 invoked by uid 500); 9 Nov 2015 13:24:11 -0000 Mailing-List: contact issues-help@spark.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Delivered-To: mailing list issues@spark.apache.org Received: (qmail 48990 invoked by uid 99); 9 Nov 2015 13:24:11 -0000 Received: from arcas.apache.org (HELO arcas) (140.211.11.28) by apache.org (qpsmtpd/0.29) with ESMTP; Mon, 09 Nov 2015 13:24:11 +0000 Received: from arcas.apache.org (localhost [127.0.0.1]) by arcas (Postfix) with ESMTP id 1D2A22C0452 for ; Mon, 9 Nov 2015 13:24:11 +0000 (UTC) Date: Mon, 9 Nov 2015 13:24:11 +0000 (UTC) From: "Apache Spark (JIRA)" To: issues@spark.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Assigned] (SPARK-11594) Cannot create UDAF in REPL MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ https://issues.apache.org/jira/browse/SPARK-11594?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-11594: ------------------------------------ Assignee: Apache Spark > Cannot create UDAF in REPL > -------------------------- > > Key: SPARK-11594 > URL: https://issues.apache.org/jira/browse/SPARK-11594 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 1.5.1, 1.6.0 > Environment: Latest Spark Master > JVM 1.8.0_66-b17 > Reporter: Herman van Hovell > Assignee: Apache Spark > Priority: Minor > > If you try to define the a UDAF in the REPL, an internal error is thrown by Java. The following code for example: > {noformat} > import org.apache.spark.sql.Row > import org.apache.spark.sql.types.{DataType, LongType, StructType} > import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction} > class LongProductSum extends UserDefinedAggregateFunction { > def inputSchema: StructType = new StructType() > .add("a", LongType) > .add("b", LongType) > def bufferSchema: StructType = new StructType() > .add("product", LongType) > def dataType: DataType = LongType > def deterministic: Boolean = true > def initialize(buffer: MutableAggregationBuffer): Unit = { > buffer(0) = 0L > } > def update(buffer: MutableAggregationBuffer, input: Row): Unit = { > if (!(input.isNullAt(0) || input.isNullAt(1))) { > buffer(0) = buffer.getLong(0) + input.getLong(0) * input.getLong(1) > } > } > def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = { > buffer1(0) = buffer1.getLong(0) + buffer2.getLong(0) > } > def evaluate(buffer: Row): Any = > buffer.getLong(0) > } > sqlContext.udf.register("longProductSum", new LongProductSum) > val data2 = Seq[(Integer, Integer, Integer)]( > (1, 10, -10), > (null, -60, 60), > (1, 30, -30), > (1, 30, 30), > (2, 1, 1), > (3, null, null)).toDF("key", "value1", "value2") > data2.registerTempTable("agg2") > val q = sqlContext.sql(""" > |SELECT > | key, > | count(distinct value1, value2), > | longProductSum(distinct value1, value2) > |FROM agg2 > |GROUP BY key > """.stripMargin) > q.show > {noformat} > Will throw the following error: > {noformat} > java.lang.InternalError: Malformed class name > at java.lang.Class.getSimpleName(Class.java:1330) > at org.apache.spark.sql.execution.aggregate.ScalaUDAF.toString(udaf.scala:455) > at org.apache.spark.sql.execution.SparkStrategies$Aggregation$$anonfun$9.apply(SparkStrategies.scala:211) > at org.apache.spark.sql.execution.SparkStrategies$Aggregation$$anonfun$9.apply(SparkStrategies.scala:209) > at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) > at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) > at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > at scala.collection.TraversableLike$class.map(TraversableLike.scala:244) > at scala.collection.AbstractTraversable.map(Traversable.scala:105) > at org.apache.spark.sql.execution.SparkStrategies$Aggregation$.apply(SparkStrategies.scala:209) > at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58) > at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58) > at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) > at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59) > at org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:54) > at org.apache.spark.sql.execution.SparkStrategies$BasicOperators$.apply(SparkStrategies.scala:445) > at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58) > at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58) > at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) > at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59) > at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:51) > at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:49) > at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:56) > at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:56) > at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2092) > at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1419) > at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1488) > at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:171) > at org.apache.spark.sql.DataFrame.show(DataFrame.scala:404) > at org.apache.spark.sql.DataFrame.show(DataFrame.scala:365) > at org.apache.spark.sql.DataFrame.show(DataFrame.scala:373) > at .(:52) > at .() > at .(:7) > at .() > at $print() > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) > at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:497) > at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:734) > at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:983) > at scala.tools.nsc.interpreter.IMain.loadAndRunReq$1(IMain.scala:573) > at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:604) > at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:568) > at scala.tools.nsc.interpreter.ILoop.reallyInterpret$1(ILoop.scala:760) > at scala.tools.nsc.interpreter.ILoop.interpretStartingWith(ILoop.scala:805) > at scala.tools.nsc.interpreter.ILoop.command(ILoop.scala:717) > at scala.tools.nsc.interpreter.ILoop.processLine$1(ILoop.scala:581) > at scala.tools.nsc.interpreter.ILoop.innerLoop$1(ILoop.scala:588) > at scala.tools.nsc.interpreter.ILoop.loop(ILoop.scala:591) > at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply$mcZ$sp(ILoop.scala:882) > at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:837) > at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:837) > at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135) > at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:837) > at scala.tools.nsc.interpreter.ILoop.main(ILoop.scala:904) > at xsbt.ConsoleInterface.run(ConsoleInterface.scala:62) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) > at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:497) > at sbt.compiler.AnalyzingCompiler.call(AnalyzingCompiler.scala:101) > at sbt.compiler.AnalyzingCompiler.console(AnalyzingCompiler.scala:76) > at sbt.Console.sbt$Console$$console0$1(Console.scala:22) > at sbt.Console$$anonfun$apply$2$$anonfun$apply$1.apply$mcV$sp(Console.scala:23) > at sbt.Console$$anonfun$apply$2$$anonfun$apply$1.apply(Console.scala:23) > at sbt.Console$$anonfun$apply$2$$anonfun$apply$1.apply(Console.scala:23) > at sbt.Logger$$anon$4.apply(Logger.scala:85) > at sbt.TrapExit$App.run(TrapExit.scala:248) > at java.lang.Thread.run(Thread.java:745) > {noformat} > Seems like my JVM (1.8.0_66-b17) does not like REPL created classes. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org For additional commands, e-mail: issues-help@spark.apache.org