2016-11-20 27 views
1

我一直在嘗試使用本地計算機上的Spark訪問Amazon s3上的數據。我可以S3N訪問數據,但不能與S3A,下面是配置PySpark:AWS s3n正在工作,但s3a不工作

星火: - 2.0.1預建用Hadoop 2.7

spark-defauts.conf parameters :- 
spark.jars.packages    com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.1 
spark.hadoop.fs.s3a.impl  org.apache.hadoop.fs.s3a.S3AFileSystem 
spark.hadoop.fs.s3a.access.key accesskey 
spark.hadoop.fs.s3a.secret.key secretkey 
spark.hadoop.fs.s3a.fast.upload true 

收到錯誤: -

Py4JJavaError: An error occurred while calling o235.partitions. 
: com.amazonaws.services.s3.model.AmazonS3Exception: Status Code: 400, AWS Service: Amazon S3, AWS Request ID: , AWS Error Code: null, AWS Error Message: Bad Request, S3 Extended Request ID: 
    at com.amazonaws.http.AmazonHttpClient.handleErrorResponse(AmazonHttpClient.java:798) 
    at com.amazonaws.http.AmazonHttpClient.executeHelper(AmazonHttpClient.java:421) 
    at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:232) 
    at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:3528) 
    at com.amazonaws.services.s3.AmazonS3Client.headBucket(AmazonS3Client.java:1031) 
    at com.amazonaws.services.s3.AmazonS3Client.doesBucketExist(AmazonS3Client.java:994) 
    at org.apache.hadoop.fs.s3a.S3AFileSystem.initialize(S3AFileSystem.java:297) 
    at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2669) 
    at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:94) 
    at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703) 
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685) 
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373) 
    at org.apache.hadoop.fs.Path.getFileSystem(Path.java:295) 
    at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:258) 
    at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:229) 
    at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:315) 
    at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199) 
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248) 
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246) 
    at scala.Option.getOrElse(Option.scala:121) 
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:246) 
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) 
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248) 
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246) 
    at scala.Option.getOrElse(Option.scala:121) 
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:246) 
    at org.apache.spark.api.java.JavaRDDLike$class.partitions(JavaRDDLike.scala:60) 
    at org.apache.spark.api.java.AbstractJavaRDDLike.partitions(JavaRDDLike.scala:45) 
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) 
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) 
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) 
    at java.lang.reflect.Method.invoke(Method.java:498) 
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237) 
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) 
    at py4j.Gateway.invoke(Gateway.java:280) 
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) 
    at py4j.commands.CallCommand.execute(CallCommand.java:79) 
    at py4j.GatewayConnection.run(GatewayConnection.java:214) 
    at java.lang.Thread.run(Thread.java:745) 

哪有我修復這個錯誤?

+0

也許相關:[使用sc.textFile(「s3n:// ...)]從S3讀取文件(http://stackoverflow.com/questions/30851244/spark-read-file-from-s3-using- sc-textfile-s3n)和[通過Spark訪問存儲在Amazon S3中的數據](http://www.cloudera.com/documentation/enterprise/5-5-x/topics/spark_s3.html) –

回答

0

您可能正在嘗試與首爾,法蘭克福或其他存儲區在V4-auth-only區域合作,但仍將端點設置爲默認的美國東區。

將fs.s3a.endpoint的值更改爲適當的值。請參閱「在不同地區提着水桶工作」

https://github.com/apache/hadoop/blob/trunk/hadoop-tools/hadoop-aws/src/site/markdown/tools/hadoop-aws/index.md

PS:非常小心的跟在Hadoop的2.7快速的上傳;除非您調整隊列長度,否則容易出現OOM。 Hadoop 2.8完全重寫,默認緩存存儲在硬盤中。

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