【发布时间】：2020-01-26 08:19:04
【问题描述】：
我已经设置了本地 PySpark，但每次尝试使用 s3a 协议读取文件 s3 时，它都会返回 403 AccessDenied 错误。我尝试连接的帐户仅支持 AWS 假设角色，它为我提供了一个临时 Access_key、Secret_key 和 session_token
我正在使用 spark 2.4.4、Hadoop 2.7.3 和 aws-java-sdk-1.7.4 jar 文件。我知道问题不在于我的安全令牌，因为我可以在 boto3 中使用相同的凭据来查询同一个存储桶。我正在按如下方式设置我的 Spark 会话：
spark.sparkContext._conf.setAll([
[('fs.s3a.impl', 'org.apache.hadoop.fs.s3a.S3AFileSystem'),
('fs.s3a.aws.credentials.provider','org.apache.hadoop.fs.s3a.TemporaryAWSCredentialsProvider'),
("fs.s3a.endpoint", "s3-ap-southeast-2.amazonaws.com"),
('fs.s3a.access.key', "..."),
('fs.s3a.secret.key', "..."),
('fs.s3a.session.token', "...")])
])
spark_01 = spark.builder.config(conf=conf).appName('s3connection').getOrCreate()
df = spark_01.read.load('s3a://<some bucket>')
我得到的错误是：
com.amazonaws.services.s3.model.AmazonS3Exception: Status Code: 403, AWS Service: Amazon S3, AWS Request ID: ... , AWS Error Code
更新： 完整的错误堆栈：
19/10/08 16:37:17 WARN FileStreamSink: Error while looking for metadata directory.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/spark-2.4.4-bin-hadoop2.7/python/pyspark/sql/readwriter.py", line 166, in load
return self._df(self._jreader.load(path))
File "/usr/local/spark-2.4.4-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
File "/usr/local/spark-2.4.4-bin-hadoop2.7/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/usr/local/spark-2.4.4-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o47.load.
: com.amazonaws.services.s3.model.AmazonS3Exception: Status Code: 403, AWS Service: Amazon S3, AWS Request ID: DFF18E66D647F534, AWS Error Code: null, AWS Error Message: Forbidden, S3 Extended Request ID: ye5NgB5wRhmHpn37tghQ0EuO9K6vPDE/1+Y6m3Y5sGqxD9iFOktFUjdqzn6hd/aHoakEXmafA9o=
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.getObjectMetadata(AmazonS3Client.java:976)
at com.amazonaws.services.s3.AmazonS3Client.getObjectMetadata(AmazonS3Client.java:956)
at org.apache.hadoop.fs.s3a.S3AFileSystem.getFileStatus(S3AFileSystem.java:892)
at org.apache.hadoop.fs.s3a.S3AFileSystem.getFileStatus(S3AFileSystem.java:77)
at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1426)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:557)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:545)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.immutable.List.foreach(List.scala:392)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.immutable.List.flatMap(List.scala:355)
at org.apache.spark.sql.execution.datasources.DataSource.org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary(DataSource.scala:545)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:359)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:223)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
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:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)```
【问题讨论】：
-
可能是签名版本的问题？
-
@Lamanus 您能否详细说明签名版本的含义？谢谢
-
s3 访问可以通过两种类型来完成，它们是用于客户端和 S3 通信的 V2 和 V4 签名版本。因此，您必须检查您的存储桶是否需要 V4。如果它仅对 V4 有效，那么您必须将您的客户端设置为使用 V4 签名。
-
@Lamanus 我明白要设置以下内容，但我仍然遇到同样的错误。 ：
conf = (SparkConf().set("spark.executor.extraJavaOptions","-Dcom.amazonaws.services.s3.enableV4=true").set("spark.driver.extraJavaOptions","-Dcom.amazonaws.services.s3.enableV4=true")) scT=SparkContext(conf=conf) scT.setSystemProperty("com.amazonaws.services.s3.enableV4", "true") -
我也关注了你在这篇文章中的回答：stackoverflow.com/questions/57477385/… 并运行以下参数：
spark-submit main.py -Dcom.amazonaws.services.s3.enableV4=true -Dcom.amazonaws.services.s3.enableV4=true但我仍然遇到同样的错误
标签： python-3.x apache-spark amazon-s3 pyspark