当我尝试使用 pyspark 从 S3 获取数据时，出现空指针异常。我正在使用 hadoop 2.4 运行 spark 1.6.1。 我尝试同时使用 s3n 和 s3a。 也尝试通过以下方式设置配置:
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")
确保存储桶具有经过身份验证的用户的权限。
>>> 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)
最佳答案
目前还不清楚是什么导致了失败； ine where the exception was raised没有显示任何明显的东西。
我的建议是切换到 s3a，这是我们在 ASF 项目中目前正在维护的 S3 连接器； s3n 作为 100% bug-for-bug 向后兼容的连接器被单独留下。
s3a 不会工作，因为它不在 Hadoop-2.4 中；它与 Hadoop-2.6 一起出现，并在 Hadoop 2.7.1 时达到生产就绪状态。捕获一个针对它构建的 spark 版本，你应该会更好地看到你的生活。而且，如果没有:您可以针对 issues.apache.org 提交错误报告，这些错误报告不会作为 WONTFIX 关闭。
附言。如果您已在配置中设置属性，则无需在 URL 中包含 AWS user:secret；这将有助于将您的 secret 排除在日志之外。
关于python - 尝试使用 pyspark 从 S3 获取数据时出现空指针异常，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/40540485/