amazon-web-services - 使用 Hadoop 版本 2.7.2 从 Spark 使用 S3a 协议(protocol)访问 S3

Question

我正在尝试从 pyspark(版本 2.2.0)访问 s3(s3a 协议(protocol))，但我遇到了一些困难。

我正在使用 Hadoop 和 AWS SDK 包。

pyspark --packages com.amazonaws:aws-java-sdk-pom:1.10.34,org.apache.hadoop:hadoop-aws:2.7.2

这是我的代码:

sc._jsc.hadoopConfiguration().set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
sc._jsc.hadoopConfiguration().set("fs.s3a.access.key", AWS_ACCESS_KEY_ID)
sc._jsc.hadoopConfiguration().set("fs.s3a.secret.key", AWS_SECRET_ACCESS_KEY)

rdd = sc.textFile('s3a://spark-test-project/large-file.csv')
print(rdd.first().show())

我明白了:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/attazadeh/DataEngine/env/lib/python3.4/site-packages/pyspark/rdd.py", line 1361, in first
    rs = self.take(1)
  File "/Users/attazadeh/DataEngine/env/lib/python3.4/site-packages/pyspark/rdd.py", line 1313, in take
    totalParts = self.getNumPartitions()
  File "/Users/attazadeh/DataEngine/env/lib/python3.4/site-packages/pyspark/rdd.py", line 385, in getNumPartitions
    return self._jrdd.partitions().size()
  File "/Users/attazadeh/DataEngine/env/lib/python3.4/site-packages/pyspark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
  File "/Users/attazadeh/DataEngine/env/lib/python3.4/site-packages/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/Users/attazadeh/DataEngine/env/lib/python3.4/site-packages/pyspark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o34.partitions.
: com.amazonaws.services.s3.model.AmazonS3Exception: Status Code: 400, AWS Service: Amazon S3, AWS Request ID: 32750D3DED4067BD, AWS Error Code: null, AWS Error Message: Bad Request, S3 Extended Request ID: jAhO0tWTblPEUehF1Bul9WZj/9G7woaHFVxb8gzsOpekam82V/Rm9zLgdLDNsGZ6mPizGZmo6xI=
    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:194)
    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)

这是 AWS Java SDK 的错误吗？我是 spark 的新手，所以我不知道除了 AWS Error Code: null

之外是否还有其他方法可以从 AWS 获取更好的日志记录信息

Answer 1

对于它的值(value)，我在 aws 上的 spark-defaults.conf 文件中有这一行:

spark.jars.packages com.amazonaws:aws-java-sdk:1.11.99,org.apache.hadoop:hadoop-aws:2.7.2

我还确保我在设置 EC2 时使用的安全组可以访问 s3。

在这两件事之后，我从 s3 读取文件就没有问题了:

%pyspark
df = spark.read.csv("s3a://my_bucket/name/")

或者，如果您使用 AWS EMR，您应该能够立即访问 s3:

%pyspark
df = spark.read.csv("s3://my_bucket/name/")