嘗試從S3使用pyspark獲取數據時出現空指針異常

Question

當我試圖從S3使用pyspark獲取數據時，出現空指針異常。我用hadoop 2.4運行spark 1.6.1。 我試過使用s3n和s3a。 試圖通過以下方式設置配置以及：

hadoopConf = sc._jsc.hadoopConfiguration() 
hadoopConf.set("fs.s3.impl",  "org.apache.hadoop.fs.s3native.NativeS3FileSystem") 
hadoopConf.set("fs.s3n.awsAccessKeyId", "aws-key") 
hadoopConf.set("fs.s3n.awsSecretAccessKey", "aws-secret-key") 

確認在鬥過驗證的用戶權限。

>>> myRDD = sc.textFile("s3n://aws-key:aws-secret-key@my-bucket/data.csv-000").count() 

16/11/10 18:37:50 INFO MemoryStore: Block broadcast_10 stored as values in memory (estimated size 157.2 KB, free 1755.2 KB) 
16/11/10 18:37:50 INFO MemoryStore: Block broadcast_10_piece0 stored as bytes in memory (estimated size 17.0 KB, free 1772.2 KB) 
16/11/10 18:37:50 INFO BlockManagerInfo: Added broadcast_10_piece0 in memory on localhost:61806 (size: 17.0 KB, free: 510.9 MB) 
16/11/10 18:37:50 INFO SparkContext: Created broadcast 10 from textFile at NativeMethodAccessorImpl.java:-2 
Traceback (most recent call last): 
    File "<stdin>", line 1, in <module> 
    File "/Users/skalyanpur/spark-1.6.1-bin-hadoop2.4/python/pyspark/rdd.py", line 1004, in count 
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() 
    File "/Users/skalyanpur/spark-1.6.1-bin-hadoop2.4/python/pyspark/rdd.py", line 995, in sum 
    return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add) 
    File "/Users/skalyanpur/spark-1.6.1-bin-hadoop2.4/python/pyspark/rdd.py", line 869, in fold 
    vals = self.mapPartitions(func).collect() 
    File "/Users/skalyanpur/spark-1.6.1-bin-hadoop2.4/python/pyspark/rdd.py", line 771, in collect 
    port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) 
    File "/Users/skalyanpur/spark-1.6.1-bin-hadoop2.4/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", line 813, in __call__ 

    File "/Users/skalyanpur/spark-1.6.1-bin-hadoop2.4/python/pyspark/sql/utils.py", line 45, in deco 
    return f(*a, **kw) 
    File "/Users/skalyanpur/spark-1.6.1-bin-hadoop2.4/python/lib/py4j-0.9-src.zip/py4j/protocol.py", line 308, in get_return_value 
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe. 
: java.lang.NullPointerException 
    at org.apache.hadoop.fs.s3native.NativeS3FileSystem.getFileStatus(NativeS3FileSystem.java:433) 
    at org.apache.hadoop.fs.Globber.getFileStatus(Globber.java:57) 
    at org.apache.hadoop.fs.Globber.glob(Globber.java:248) 
    at org.apache.hadoop.fs.FileSystem.globStatus(FileSystem.java:1642) 
    at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:257) 
    at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:228) 
    at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:304) 
    at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199) 
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) 
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) 
    at scala.Option.getOrElse(Option.scala:120) 
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) 
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35) 
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) 
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) 
    at scala.Option.getOrElse(Option.scala:120) 
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) 
    at org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:58) 
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239) 
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237) 
    at scala.Option.getOrElse(Option.scala:120) 
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:237) 
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929) 
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927) 
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) 
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) 
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) 
    at org.apache.spark.rdd.RDD.collect(RDD.scala:926) 
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:405) 
    at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala) 
    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:231) 
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381) 
    at py4j.Gateway.invoke(Gateway.java:259) 
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133) 
    at py4j.commands.CallCommand.execute(CallCommand.java:79) 
    at py4j.GatewayConnection.run(GatewayConnection.java:209) 
    at java.lang.Thread.run(Thread.java:745) 

Answer 1

不清楚是什麼引起了故障; ine where the exception was raised沒有顯示任何明顯的。

我的建議是切換到s3a，它是我們在ASF項目當前維護的S3連接器; s3n被單獨留作100％bug-for-bug向後兼容連接器。

s3a不會工作，因爲它不在Hadoop-2.4中;它使用Hadoop-2.6，並通過Hadoop 2.7.1達到了生產就緒狀態。抓住一個與此相反的火花版本，你應該看到你的生活更好。而且，如果沒有：您可以針對issues.apache.org提交bug報告，該報告不會像WONTFIX那樣關閉。

ps。如果您在配置中設置了屬性，則不需要在URL中包含AWS用戶：祕密;這將有助於保持您的祕密不受日誌影響。