I'm in the second chapter of Advanced Analytics with Spark, second edition. I use
val parsed = spark.read.csv("linkage")
and get this error:
18/01/16 12:09:32 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0) java.lang.ArrayIndexOutOfBoundsException: 63 at org.apache.spark.unsafe.types.UTF8String.numBytesForFirstByte(UTF8String.java:190) at org.apache.spark.unsafe.types.UTF8String.numChars(UTF8String.java:205) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:108) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:748) 18/01/16 12:09:32 WARN TaskSetManager: Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): java.lang.ArrayIndexOutOfBoundsException: 63 at org.apache.spark.unsafe.types.UTF8String.numBytesForFirstByte(UTF8String.java:190) at org.apache.spark.unsafe.types.UTF8String.numChars(UTF8String.java:205) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:108) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:748) 18/01/16 12:09:32 ERROR TaskSetManager: Task 0 in stage 0.0 failed 1 times; aborting job org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): java.lang.ArrayIndexOutOfBoundsException: 63 at org.apache.spark.unsafe.types.UTF8String.numBytesForFirstByte(UTF8String.java:190) at org.apache.spark.unsafe.types.UTF8String.numChars(UTF8String.java:205) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:108) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:748) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:336) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:2853) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2153) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2153) at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2837) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2836) at org.apache.spark.sql.Dataset.head(Dataset.scala:2153) at org.apache.spark.sql.Dataset.take(Dataset.scala:2366) at org.apache.spark.sql.execution.datasources.csv.TextInputCSVDataSource$.infer(CSVDataSource.scala:147) at org.apache.spark.sql.execution.datasources.csv.CSVDataSource.inferSchema(CSVDataSource.scala:62) at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.inferSchema(CSVFileFormat.scala:57) at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:177) at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:177) at scala.Option.orElse(Option.scala:289) at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:176) at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:366) at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178) at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:533) at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:412) ... 48 elided Caused by: java.lang.ArrayIndexOutOfBoundsException: 63 at org.apache.spark.unsafe.types.UTF8String.numBytesForFirstByte(UTF8String.java:190) at org.apache.spark.unsafe.types.UTF8String.numChars(UTF8String.java:205) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:108) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:748)
At first I thought there was a problem in my dataset, so I checked to see if there is the same number of commas between the elements for each line of all files. There are 11 commas:
scala> import scala.io.Source import scala.io.Source scala> Source.fromFile("linkage/block_1.csv").getLines take 10 foreach println "id_1","id_2","cmp_fname_c1","cmp_fname_c2","cmp_lname_c1","cmp_lname_c2","cmp_sex","cmp_bd","cmp_bm","cmp_by","cmp_plz","is_match" 37291,53113,0.833333333333333,?,1,?,1,1,1,1,0,TRUE 39086,47614,1,?,1,?,1,1,1,1,1,TRUE 70031,70237,1,?,1,?,1,1,1,1,1,TRUE 84795,97439,1,?,1,?,1,1,1,1,1,TRUE 36950,42116,1,?,1,1,1,1,1,1,1,TRUE 42413,48491,1,?,1,?,1,1,1,1,1,TRUE 25965,64753,1,?,1,?,1,1,1,1,1,TRUE 49451,90407,1,?,1,?,1,1,1,1,0,TRUE 39932,40902,1,?,1,?,1,1,1,1,1,TRUE scala> val fileNames = (1 to 10) map (num => s"block_$num.csv") fileNames: scala.collection.immutable.IndexedSeq[String] = Vector(block_1.csv, block_2.csv, block_3.csv, block_4.csv, block_5.csv, block_6.csv, block_7.csv, block_8.csv, block_9.csv, block_10.csv) scala> fileNames foreach { fn => | val lines = Source.fromFile(s"linkage/$fn").getLines.toList | val notMatching = lines filterNot (line => ((line count(c => c == ',')) == 11)) | notMatching foreach println | println("\n") | }
The result of this was just "\ n", so there are always 11 commas between the values for each line.
I use the following versions:
Spark version: 2.2.0 Scala version: 2.11.8 Hadoop version: 2.8.2
I run the spark shell on the local machine as follows:
spark-shell --master local[*] --driver-memory 2g
The link directory is located directly under the place where I launch the spark shell.
Any ideas? Maybe I'm doing something stupid since this is my first time with Spark / Scala. I am also not familiar with Java if this could be a problem. Thanks.