使用Apache星火写RDD为文本文件 [英] Write RDD as textfile using Apache Spark
问题描述
我正在探索进行批处理的火花。我使用的单机模式下运行我的本地机器上的火花。
I am exploring Spark for batch processing. I am running the spark on my local machine using standalone mode.
我想星火RDD转换为单一文件[最终输出]使用saveTextFile()方法,但它不工作。
I am trying to convert the Spark RDD as single file [final output] using saveTextFile() method, but its not working.
例如,如果我有一个以上的分区,我们怎样才能得到一个单一的文件作为最终输出。
For example if i have more than one partition how we can get one single file as final output.
更新:
我试过下面的方法,但是我得到空指针异常。
I tried the below approaches, but i am getting null pointer exception.
person.coalesce(1).toJavaRDD().saveAsTextFile("C://Java_All//output");
person.repartition(1).toJavaRDD().saveAsTextFile("C://Java_All//output");
唯一的例外是:
15/06/23 18:25:27 INFO Executor: Running task 0.0 in stage 1.0 (TID 1)
15/06/23 18:25:27 INFO deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
15/06/23 18:25:27 INFO deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class
15/06/23 18:25:27 INFO deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class
15/06/23 18:25:27 INFO deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir
15/06/23 18:25:27 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 1)
java.lang.NullPointerException
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1012)
at org.apache.hadoop.util.Shell.runCommand(Shell.java:404)
at org.apache.hadoop.util.Shell.run(Shell.java:379)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:589)
at org.apache.hadoop.util.Shell.execCommand(Shell.java:678)
at org.apache.hadoop.util.Shell.execCommand(Shell.java:661)
at org.apache.hadoop.fs.RawLocalFileSystem.setPermission(RawLocalFileSystem.java:639)
at org.apache.hadoop.fs.FilterFileSystem.setPermission(FilterFileSystem.java:468)
at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:456)
at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:424)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:905)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:798)
at org.apache.hadoop.mapred.TextOutputFormat.getRecordWriter(TextOutputFormat.java:123)
at org.apache.spark.SparkHadoopWriter.open(SparkHadoopWriter.scala:90)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13.apply(PairRDDFunctions.scala:1104)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13.apply(PairRDDFunctions.scala:1095)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
at org.apache.spark.scheduler.Task.run(Task.scala:70)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
15/06/23 18:25:27 WARN TaskSetManager: Lost task 0.0 in stage 1.0 (TID 1, localhost): java.lang.NullPointerException
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1012)
at org.apache.hadoop.util.Shell.runCommand(Shell.java:404)
at org.apache.hadoop.util.Shell.run(Shell.java:379)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:589)
at org.apache.hadoop.util.Shell.execCommand(Shell.java:678)
at org.apache.hadoop.util.Shell.execCommand(Shell.java:661)
at org.apache.hadoop.fs.RawLocalFileSystem.setPermission(RawLocalFileSystem.java:639)
at org.apache.hadoop.fs.FilterFileSystem.setPermission(FilterFileSystem.java:468)
at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:456)
at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:424)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:905)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:798)
at org.apache.hadoop.mapred.TextOutputFormat.getRecordWriter(TextOutputFormat.java:123)
at org.apache.spark.SparkHadoopWriter.open(SparkHadoopWriter.scala:90)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13.apply(PairRDDFunctions.scala:1104)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13.apply(PairRDDFunctions.scala:1095)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
at org.apache.spark.scheduler.Task.run(Task.scala:70)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
15/06/23 18:25:27 ERROR TaskSetManager: Task 0 in stage 1.0 failed 1 times; aborting job
15/06/23 18:25:27 INFO TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks have all completed, from pool
15/06/23 18:25:27 INFO TaskSchedulerImpl: Cancelling stage 1
15/06/23 18:25:27 INFO DAGScheduler: ResultStage 1 (saveAsTextFile at TestSpark.java:40) failed in 0.249 s
15/06/23 18:25:28 INFO DAGScheduler: Job 0 failed: saveAsTextFile at TestSpark.java:40, took 0.952286 s
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1, localhost): java.lang.NullPointerException
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1012)
at org.apache.hadoop.util.Shell.runCommand(Shell.java:404)
at org.apache.hadoop.util.Shell.run(Shell.java:379)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:589)
at org.apache.hadoop.util.Shell.execCommand(Shell.java:678)
at org.apache.hadoop.util.Shell.execCommand(Shell.java:661)
at org.apache.hadoop.fs.RawLocalFileSystem.setPermission(RawLocalFileSystem.java:639)
at org.apache.hadoop.fs.FilterFileSystem.setPermission(FilterFileSystem.java:468)
at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:456)
at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:424)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:905)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:798)
at org.apache.hadoop.mapred.TextOutputFormat.getRecordWriter(TextOutputFormat.java:123)
at org.apache.spark.SparkHadoopWriter.open(SparkHadoopWriter.scala:90)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13.apply(PairRDDFunctions.scala:1104)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13.apply(PairRDDFunctions.scala:1095)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
at org.apache.spark.scheduler.Task.run(Task.scala:70)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1266)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1257)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1256)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1256)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:730)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1450)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1411)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
15/06/23 18:25:28 INFO SparkContext: Invoking stop() from shutdown hook
15/06/23 18:25:28 INFO SparkUI: Stopped Spark web UI at http://10.37.145.179:4040
15/06/23 18:25:28 INFO DAGScheduler: Stopping DAGScheduler
15/06/23 18:25:28 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
15/06/23 18:25:28 INFO Utils: path = C:\Users\crh537\AppData\Local\Temp\spark-a52371d8-ae6a-4567-b759-0a6c66c1908c\blockmgr-4d17a5b4-c8f8-4408-af07-0e88239794e8, already present as root for deletion.
15/06/23 18:25:28 INFO MemoryStore: MemoryStore cleared
15/06/23 18:25:28 INFO BlockManager: BlockManager stopped
15/06/23 18:25:28 INFO BlockManagerMaster: BlockManagerMaster stopped
15/06/23 18:25:28 INFO SparkContext: Successfully stopped SparkContext
15/06/23 18:25:28 INFO Utils: Shutdown hook called
问候,
尚卡尔
Regards, Shankar
推荐答案
您可以使用 COALESCE
方法来保存到一个文件中。这样你的code将是这样的:
You can use coalesce
method to save into a single file. This way your code will look like this:
val myFile = sc.textFile("file.txt")
val finalRdd = doStuff(myFile)
finalRdd.coalesce(1).saveAsTextFile("newfile")
也有另一种方法再分配
做同样的事情,但它会引起洗牌这可能是非常昂贵的,而COALESCE会尽量避免洗牌。
There is also another method repartition
to do the same thing, however it will cause a shuffle which is may be very expensive, while coalesce will try to avoid a shuffle.
这篇关于使用Apache星火写RDD为文本文件的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!