我正在尝试将 s3 与安装在 ec2 集群上的 spark 连接起来。后者由一台主机和两台从机组成，它们都具有 6GB RAM 并且位于中欧(Fankfourt)AWS 区域。 我已经安装了 AWSCLI 并配置了我的 key 并将它们导出到 env 中。 我正在使用 pyspark。
我开始使用:
pyspark --master spark://my_ip:7077 --executor-memory 1G --packages org.apache.hadoop:hadoop-aws:2.7.3,com.amazonaws:aws-java-sdk- pom:1.11.274,com.databricks:spark-csv_2.10:1.1.0
packge hadoop-aws: 2.7.3 与 Spark 上的 hadoop-common-2.7.3.jar 版本相同。
进入 pyspark 后，我编写以下内容来设置 s3 配置:
sc._jsc.hadoopConfiguration (). set ("com.amazonaws.services.s3.enableV4", "true")
sc._jsc.hadoopConfiguration (). set ("fs.s3.awsAccessKeyId", "my_key")
sc._jsc.hadoopConfiguration (). set ("fs.s3.awsSecretAccessKey", "my_secret_key")
sc._jsc.hadoopConfiguration (). set ("fs.s3a.endpoint", "s3.eu-central-1.amazonaws.com")
然后我去写以下内容:
bucket = "my_bucket"
textFile = sc.textFile ("s3a: //" + bucket + "/tmp/small_file.csv")
textFile.take(5)
Python 抛出以下错误:
Py4JJavaError: An error occurred while calling o36.partitions.
: com.amazonaws.services.s3.model.AmazonS3Exception: Status Code: 400, AWS Service: Amazon S3, AWS Request ID: my_request_id, AWS Error Code: null, AWS Error Message: Bad Request, S3 Extended Request ID: my_extend_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$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.api.java.JavaRDDLike$class.partitions(JavaRDDLike.scala:61)
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:244)
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:748)
我错过了什么吗？
请您参考如下方法:
您可以更改配置
sc._jsc.hadoopConfiguration().set("com.amazonaws.services.s3.enableV4", "true")
到
sc.setSystemProperty("com.amazonaws.services.s3.enableV4", "true")
