Java – use Apache spark to write RDD as a text file

• Java

I'm exploring spark for batch processing I use stand-alone mode to run spark. Com on my local computer

I tried to convert spark RDD to a single file [final output] using the savetextfile() method, but it didn't work

For example, if I have multiple partitions, we can use a file as the final output

to update:

I tried the following method, but I got a null pointer exception

person.coalesce(1).toJavaRDD().saveAsTextFile("C://Java_All//output");
person.repartition(1).toJavaRDD().saveAsTextFile("C://Java_All//output");

The exceptions are:

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

Greetings, Shankar

Solution

You can save to a single file using the coalesce method Your code will look like this:

val myFile = sc.textFile("file.txt")
val finalRdd = doStuff(myFile)
finalRdd.coalesce(1).saveAsTextFile("newfile")

There is another way to repartition to do the same thing, but it will lead to a potentially expensive shuffle, and the merger will try to avoid the shuffle

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