On 26 Apr 2016, at 18:49, Ted Yu <yuzhih...@gmail.com<mailto:yuzhih...@gmail.com>> wrote:
Looking at the cause of the error, it seems hadoop-aws-xx.jar (corresponding to the version of hadoop you use) was missing in classpath. yes, that s3n was moved from hadoop-common to the new hadoop-aws, and without realising it broke a lot of things. you'll need hadoop-aws and jets3t on the classpath If you are using Hadoop 2.7, I'd recommend s3a instead, which means hadoop-aws and the exact same amazon-sdk that comes bundled with the hadoop binaries your version of spark is built with (it's a bit brittle API-wise) FYI On Tue, Apr 26, 2016 at 9:06 AM, Jinan Alhajjaj <j.r.alhaj...@hotmail.com<mailto:j.r.alhaj...@hotmail.com>> wrote: Hi All, I am trying to read a file stored in Amazon S3. I wrote this code: import java.util.List; import java.util.Scanner; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.Function; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.Row; import org.apache.spark.sql.SQLContext; public class WordAnalysis { public static void main(String[] args) { int startYear=0; int endyear=0; Scanner input = new Scanner(System.in); System.out.println("Please, Enter 1 if you want 1599-2008 or enter 2 for specific range: "); int choice=input.nextInt(); if(choice==1) { startYear=1500; endyear=2008; } if(choice==2) { System.out.print("please,Enter the start year : "); startYear = input.nextInt(); System.out.print("please,Enter the end year : "); endyear = input.nextInt(); } SparkConf conf = new SparkConf().setAppName("jinantry").setMaster("local"); JavaSparkContext spark = new JavaSparkContext(conf); SQLContext sqlContext = new org.apache.spark.sql.SQLContext(spark); JavaRDD<Items> ngram = spark.textFile("s3n://google-books-ngram/1gram/googlebooks-eng-all-1gram-20120701-x.gz") .map(new Function<String, Items>() { public Items call(String line) throws Exception { String[] parts = line.split("\t"); Items item = new Items(); if (parts.length == 4) { item.setWord(parts[0]); item.setYear(Integer.parseInt(parts[1])); item.setCount(Integer.parseInt(parts[2])); item.setVolume(Integer.parseInt(parts[3])); return item; } else { item.setWord(" "); item.setYear(Integer.parseInt(" ")); item.setCount(Integer.parseInt(" ")); item.setVolume(Integer.parseInt(" ")); return item; } } }); DataFrame schemangram = sqlContext.createDataFrame(ngram, Items.class); schemangram.registerTempTable("ngram"); String sql="SELECT word,SUM(count) FROM ngram where year >="+startYear+" AND year<="+endyear+" And word LIKE '%_NOUN' GROUP BY word ORDER BY SUM(count) DESC"; DataFrame matchyear = sqlContext.sql(sql); List<Row> words=matchyear.collectAsList(); int i=1; for (Row scholar : words) { System.out.println(scholar); if(i==10) break; i++; } } } When I run it this error appear to me: Exception in thread "main" org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree: Exchange rangepartitioning(aggOrder#5L DESC,200), None +- ConvertToSafe +- TungstenAggregate(key=[word#2], functions=[(sum(cast(count#0 as bigint)),mode=Final,isDistinct=false)], output=[word#2,_c1#4L,aggOrder#5L]) +- TungstenExchange hashpartitioning(word#2,200), None +- TungstenAggregate(key=[word#2], functions=[(sum(cast(count#0 as bigint)),mode=Partial,isDistinct=false)], output=[word#2,sum#8L]) +- Project [word#2,count#0] +- Filter (((year#3 >= 1500) && (year#3 <= 1600)) && word#2 LIKE %_NOUN) +- Scan ExistingRDD[count#0,volume#1,word#2,year#3] at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49) at org.apache.spark.sql.execution.Exchange.doExecute(Exchange.scala:247) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130) at org.apache.spark.sql.execution.ConvertToUnsafe.doExecute(rowFormatConverters.scala:38) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130) at org.apache.spark.sql.execution.Sort.doExecute(Sort.scala:64) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130) at org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130) at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55) at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55) at org.apache.spark.sql.DataFrame.rdd$lzycompute(DataFrame.scala:1637) at org.apache.spark.sql.DataFrame.rdd(DataFrame.scala:1634) at org.apache.spark.sql.DataFrame$$anonfun$collectAsList$1$$anonfun$apply$12.apply(DataFrame.scala:1493) at org.apache.spark.sql.DataFrame$$anonfun$collectAsList$1$$anonfun$apply$12.apply(DataFrame.scala:1493) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56) at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086) at org.apache.spark.sql.DataFrame$$anonfun$collectAsList$1.apply(DataFrame.scala:1492) at org.apache.spark.sql.DataFrame$$anonfun$collectAsList$1.apply(DataFrame.scala:1491) at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2099) at org.apache.spark.sql.DataFrame.collectAsList(DataFrame.scala:1491) at WordAnalysis.main(WordAnalysis.java:60) Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree: TungstenAggregate(key=[word#2], functions=[(sum(cast(count#0 as bigint)),mode=Final,isDistinct=false)], output=[word#2,_c1#4L,aggOrder#5L]) +- TungstenExchange hashpartitioning(word#2,200), None +- TungstenAggregate(key=[word#2], functions=[(sum(cast(count#0 as bigint)),mode=Partial,isDistinct=false)], output=[word#2,sum#8L]) +- Project [word#2,count#0] +- Filter (((year#3 >= 1500) && (year#3 <= 1600)) && word#2 LIKE %_NOUN) +- Scan ExistingRDD[count#0,volume#1,word#2,year#3] at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49) at org.apache.spark.sql.execution.aggregate.TungstenAggregate.doExecute(TungstenAggregate.scala:80) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130) at org.apache.spark.sql.execution.ConvertToSafe.doExecute(rowFormatConverters.scala:56) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130) at org.apache.spark.sql.execution.Exchange.prepareShuffleDependency(Exchange.scala:164) at org.apache.spark.sql.execution.Exchange$$anonfun$doExecute$1.apply(Exchange.scala:254) at org.apache.spark.sql.execution.Exchange$$anonfun$doExecute$1.apply(Exchange.scala:248) at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:48) ... 33 more Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree: TungstenExchange hashpartitioning(word#2,200), None +- TungstenAggregate(key=[word#2], functions=[(sum(cast(count#0 as bigint)),mode=Partial,isDistinct=false)], output=[word#2,sum#8L]) +- Project [word#2,count#0] +- Filter (((year#3 >= 1500) && (year#3 <= 1600)) && word#2 LIKE %_NOUN) +- Scan ExistingRDD[count#0,volume#1,word#2,year#3] at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49) at org.apache.spark.sql.execution.Exchange.doExecute(Exchange.scala:247) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130) at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.apply(TungstenAggregate.scala:86) at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.apply(TungstenAggregate.scala:80) at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:48) ... 47 more Caused by: java.io.IOException: No FileSystem for scheme: s3n at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2584) at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2591) at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:91) at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2630) at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2612) at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:370) at org.apache.hadoop.fs.Path.getFileSystem(Path.java:296) at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:256) at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:228) at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:313) at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) at org.apache.spark.ShuffleDependency.<init>(Dependency.scala:91) at org.apache.spark.sql.execution.Exchange.prepareShuffleDependency(Exchange.scala:220) at org.apache.spark.sql.execution.Exchange$$anonfun$doExecute$1.apply(Exchange.scala:254) at org.apache.spark.sql.execution.Exchange$$anonfun$doExecute$1.apply(Exchange.scala:248) at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:48) ... 55 more could any one help me in this. Thank you
