Interesting, how are you submitting your job? Are you using spark-submit with the "yarn-master" spark master? Is your main class extending CrunchTool? My thinking is that somehow the default configurations are not being inherited, and maybe you are working with a totally blank Configuration object.
On Mon, Jan 4, 2016 at 2:19 PM, Yan Yang <[email protected]> wrote: > Jeff, > > Thanks for the suggestion. After I switch the URL to s3 an almost > identical exception is now encountered: > > java.lang.IllegalArgumentException: AWS Access Key ID and Secret Access Key > must be specified as the username or password (respectively) of a s3 URL, or > by setting the *fs.s3.awsAccessKeyId* or *fs.s3.awsSecretAccessKey* > properties (respectively). > > > > On Mon, Jan 4, 2016 at 12:46 PM, Jeff Quinn <[email protected]> wrote: > >> Ah ok, I would try it with "s3://",and I think it should work as >> expected, assuming the machine role you are using for EMR has the proper >> permissions for writing to the bucket. >> >> You should not need to set fs.s3n.awsSecretAccessKey/fs.s3n.awsAccessKeyId >> or any other properties, EMR service should be taking care of that for you. >> >> On Mon, Jan 4, 2016 at 12:22 PM, Yan Yang <[email protected]> wrote: >> >>> Hi Jeff, >>> >>> We are using s3n://bucket/path >>> >>> Thanks >>> Yan >>> >>> On Mon, Jan 4, 2016 at 12:19 PM, Jeff Quinn <[email protected]> wrote: >>> >>>> Hey Yan, >>>> >>>> Just a hunch but from that stacktrace it looks like you might be using >>>> the outdated s3-hadoop filesystem, is the url you are trying to write to of >>>> the form s3://bucket/path or s3n://bucket/path? >>>> >>>> Thanks! >>>> >>>> Jeff >>>> >>>> On Mon, Jan 4, 2016 at 12:15 PM, Yan Yang <[email protected]> wrote: >>>> >>>>> Hi >>>>> >>>>> I have tried to set up a Sparkpipeline to run within AWS EMR. >>>>> >>>>> The code is as below: >>>>> >>>>> SparkConf sparkConf = new SparkConf().setAppName("JavaSparkPi"); >>>>> JavaSparkContext jsc = new JavaSparkContext(sparkConf); >>>>> SparkPipeline pipeline = new SparkPipeline(jsc, "spark-app"); >>>>> >>>>> PCollection<Input> input = pipeline.read(From.avroFile(inputPaths, >>>>> Input.class)); >>>>> PCollection<Output> output = process(input); >>>>> pipeline.write(output, To.avroFile(outputPath)); >>>>> >>>>> The read works and a simple spark write such as calling >>>>> saveAsTextFile() on an RDD object also works. >>>>> >>>>> However write using pipeline.write() hits below exceptions. I have >>>>> tried to set fs.s3n.awsAccessKeyId and fs.s3n.awsSecretAccessKey in >>>>> sparkConf >>>>> with the same result: >>>>> >>>>> java.lang.IllegalArgumentException: AWS Access Key ID and Secret Access >>>>> Key must be specified as the username or password (respectively) of a s3n >>>>> URL, or by setting the fs.s3n.awsAccessKeyId or fs.s3n.awsSecretAccessKey >>>>> properties (respectively). >>>>> at >>>>> org.apache.hadoop.fs.s3.S3Credentials.initialize(S3Credentials.java:70) >>>>> at >>>>> org.apache.hadoop.fs.s3native.Jets3tNativeFileSystemStore.initialize(Jets3tNativeFileSystemStore.java:80) >>>>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>>>> at >>>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) >>>>> at >>>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>>>> at java.lang.reflect.Method.invoke(Method.java:606) >>>>> at >>>>> org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187) >>>>> at >>>>> org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102) >>>>> at org.apache.hadoop.fs.s3native.$Proxy9.initialize(Unknown Source) >>>>> at >>>>> org.apache.hadoop.fs.s3native.NativeS3FileSystem.initialize(NativeS3FileSystem.java:326) >>>>> at >>>>> org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2644) >>>>> at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:90) >>>>> at >>>>> org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2678) >>>>> at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2660) >>>>> at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:374) >>>>> at org.apache.hadoop.fs.Path.getFileSystem(Path.java:296) >>>>> at org.apache.avro.mapred.FsInput.<init>(FsInput.java:37) >>>>> at >>>>> org.apache.crunch.types.avro.AvroRecordReader.initialize(AvroRecordReader.java:54) >>>>> at >>>>> org.apache.crunch.impl.mr.run.CrunchRecordReader.initialize(CrunchRecordReader.java:150) >>>>> at >>>>> org.apache.spark.rdd.NewHadoopRDD$$anon$1.<init>(NewHadoopRDD.scala:153) >>>>> at org.apache.spark.rdd.NewHadoopRDD.compute(NewHadoopRDD.scala:124) >>>>> at org.apache.spark.rdd.NewHadoopRDD.compute(NewHadoopRDD.scala:65) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) >>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>>>> at >>>>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) >>>>> at >>>>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) >>>>> at org.apache.spark.scheduler.Task.run(Task.scala:88) >>>>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) >>>>> at >>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>>>> at >>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>>>> at java.lang.Thread.run(Thread.java:745) >>>>> >>>>> Thanks >>>>> Yan >>>>> >>>> >>>> >>> >> >
