Re: java.lang.UnsupportedOperationException: empty collection
I've tried running your code through spark-shell on both 1.3.0 (pre-built for Hadoop 2.4 and above) and a recently built snapshot of master. Both work fine. Running on OS X yosemite. What's your configuration? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/java-lang-UnsupportedOperationException-empty-collection-tp22677p22686.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
java.lang.UnsupportedOperationException: empty collection
I am running following code on Spark 1.3.0. It is from https://spark.apache.org/docs/1.3.0/ml-guide.html On running val model1 = lr.fit(training.toDF) I get java.lang.UnsupportedOperationException: empty collection what could be the reason? import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.ml.classification.LogisticRegression import org.apache.spark.ml.param.ParamMap import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.sql.{Row, SQLContext} val conf = new SparkConf().setAppName("SimpleParamsExample") val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) import sqlContext.implicits._ // Prepare training data. // We use LabeledPoint, which is a case class. Spark SQL can convert RDDs of case classes // into DataFrames, where it uses the case class metadata to infer the schema. val training = sc.parallelize(Seq( LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)), LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)), LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)), LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5 // Create a LogisticRegression instance. This instance is an Estimator. val lr = new LogisticRegression() // Print out the parameters, documentation, and any default values. println("LogisticRegression parameters:\n" + lr.explainParams() + "\n") // We may set parameters using setter methods. lr.setMaxIter(10) .setRegParam(0.01) // Learn a LogisticRegression model. This uses the parameters stored in lr. *val model1 = lr.fit(training.toDF)* *Some more information:* scala> training.toDF res26: org.apache.spark.sql.DataFrame = [label: double, features: vecto] scala> training.toDF.collect() res27: Array[org.apache.spark.sql.Row] = Array([1.0,[0.0,1.1,0.1]], [0.0,[2.0,1.0,-1.0]], [0.0,[2.0,1.3,1.0]], [1.0,[0.0,1.2,-0.5]]) -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/java-lang-UnsupportedOperationException-empty-collection-tp22677.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
RE: spark left outer join with java.lang.UnsupportedOperationException: empty collection
OK. I think I have to use "None" instead null, then it works. Still switching from Java. I can also just use the field name as what I assume. Great experience. From: java8...@hotmail.com To: user@spark.apache.org Subject: spark left outer join with java.lang.UnsupportedOperationException: empty collection Date: Thu, 12 Feb 2015 18:06:43 -0500 Hi, I am using Spark 1.2.0 with Hadoop 2.2. Now I have to 2 csv files, but have 8 fields. I know that the first field from both files are IDs. I want to find all the IDs existed in the first file, but NOT in the 2nd file. I am coming with the following code in spark-shell. case class origAsLeft (id: String)case class newAsRight (id: String)val OrigData = sc.textFile("hdfs://firstfile").map(_.split(",")).map( r=>(r(0), origAsLeft(r(0val NewData = sc.textFile("hdfs://secondfile").map(_.split(",")).map( r=>(r(0), newAsRight(r(0val output = OrigData.leftOuterJoin(NewData).filter{ case (k, v) => v._2 == null } Find what I understand, after OrigData left outer join with NewData, it will use the id as the key, and a tuple with (leftObject, RightObject).Since I want to find out all the IDs existed in the first file, but not in the 2nd one, so the output RDD will be the one I want, as it will filter out only when there is no newAsRight object from NewData. Then I run output.first Spark does start to run, but give me the following error message:15/02/12 16:43:38 INFO scheduler.DAGScheduler: Job 4 finished: first at :21, took 78.303549 sjava.lang.UnsupportedOperationException: empty collection at org.apache.spark.rdd.RDD.first(RDD.scala:1095) at $iwC$$iwC$$iwC$$iwC.(:21) at $iwC$$iwC$$iwC.(:26) at $iwC$$iwC.(:28) at $iwC.(:30)at (:32) at .(:36) at .() at .(:7) at .() at $print()at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:94) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:55) at java.lang.reflect.Method.invoke(Method.java:619) at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125) at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674) at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705) at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669) at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:828) at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:873) at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:785) at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:628) at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:636) at org.apache.spark.repl.SparkILoop.loop(SparkILoop.scala:641) at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:968) at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:916) at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:916) at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135) at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:916) at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1011) at org.apache.spark.repl.Main$.main(Main.scala:31) at org.apache.spark.repl.Main.main(Main.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:94) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:55) at java.lang.reflect.Method.invoke(Method.java:619) at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:358) at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75) at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) Did I do anything wrong? What is the way to find all the id in the first file, but not in the 2nd file? Second question is how can I use the object field to do the compare in this case? For example, if I define: case class origAsLeft (id: String, name: String)case class newAsRight (id: String, name: String)val OrigData = sc.textFile("hdfs://firstfile").map(_.split(",")).map( r=>(r(0), origAsLeft(r(0), r(1val NewData = sc.textFile("hdfs://secondfile").map(_.split(",")).map( r=>(r(0), newAsRight(r(0), r(1// in this case, I want to list all the data in the first file which has the same ID as in the 2nd file, but with different value in name, I want to do something like below: val output = OrigData.join(NewData).filter{ case (k, v) => v._1.name != v._2.name } But what is the syntax to use the field in the case class I defined? Thanks Yong
spark left outer join with java.lang.UnsupportedOperationException: empty collection
Hi, I am using Spark 1.2.0 with Hadoop 2.2. Now I have to 2 csv files, but have 8 fields. I know that the first field from both files are IDs. I want to find all the IDs existed in the first file, but NOT in the 2nd file. I am coming with the following code in spark-shell. case class origAsLeft (id: String)case class newAsRight (id: String)val OrigData = sc.textFile("hdfs://firstfile").map(_.split(",")).map( r=>(r(0), origAsLeft(r(0val NewData = sc.textFile("hdfs://secondfile").map(_.split(",")).map( r=>(r(0), newAsRight(r(0val output = OrigData.leftOuterJoin(NewData).filter{ case (k, v) => v._2 == null } Find what I understand, after OrigData left outer join with NewData, it will use the id as the key, and a tuple with (leftObject, RightObject).Since I want to find out all the IDs existed in the first file, but not in the 2nd one, so the output RDD will be the one I want, as it will filter out only when there is no newAsRight object from NewData. Then I run output.first Spark does start to run, but give me the following error message:15/02/12 16:43:38 INFO scheduler.DAGScheduler: Job 4 finished: first at :21, took 78.303549 sjava.lang.UnsupportedOperationException: empty collection at org.apache.spark.rdd.RDD.first(RDD.scala:1095) at $iwC$$iwC$$iwC$$iwC.(:21) at $iwC$$iwC$$iwC.(:26) at $iwC$$iwC.(:28) at $iwC.(:30)at (:32) at .(:36) at .() at .(:7) at .() at $print()at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:94) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:55) at java.lang.reflect.Method.invoke(Method.java:619) at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125) at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674) at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705) at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669) at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:828) at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:873) at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:785) at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:628) at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:636) at org.apache.spark.repl.SparkILoop.loop(SparkILoop.scala:641) at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:968) at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:916) at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:916) at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135) at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:916) at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1011) at org.apache.spark.repl.Main$.main(Main.scala:31) at org.apache.spark.repl.Main.main(Main.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:94) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:55) at java.lang.reflect.Method.invoke(Method.java:619) at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:358) at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75) at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) Did I do anything wrong? What is the way to find all the id in the first file, but not in the 2nd file? Second question is how can I use the object field to do the compare in this case? For example, if I define: case class origAsLeft (id: String, name: String)case class newAsRight (id: String, name: String)val OrigData = sc.textFile("hdfs://firstfile").map(_.split(",")).map( r=>(r(0), origAsLeft(r(0), r(1val NewData = sc.textFile("hdfs://secondfile").map(_.split(",")).map( r=>(r(0), newAsRight(r(0), r(1// in this case, I want to list all the data in the first file which has the same ID as in the 2nd file, but with different value in name, I want to do something like below: val output = OrigData.join(NewData).filter{ case (k, v) => v._1.name != v._2.name } But what is the syntax to use the field in the case class I defined? Thanks Yong