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From Jeff Quinn <j...@nuna.com>
Subject Re: Sparkpipeline hit credentials issue when trying to write to S3
Date Mon, 04 Jan 2016 22:27:26 GMT
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 <yan@wealthfront.com> 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 <jeff@nuna.com> 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 <yan@wealthfront.com> wrote:
>>
>>> Hi Jeff,
>>>
>>> We are using s3n://bucket/path
>>>
>>> Thanks
>>> Yan
>>>
>>> On Mon, Jan 4, 2016 at 12:19 PM, Jeff Quinn <jeff@nuna.com> 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 the outdated s3-hadoop filesystem, is the url you are trying to write to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 <yan@wealthfront.com> 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
>>>>>
>>>>
>>>>
>>>
>>
>

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