在初始化Spark上下文后，在运行时期间更改pyspark的Hadoop配置中的aws凭据

ybzsozfc  于 2022-12-13  发布在  Apache
关注(0)|答案(2)|浏览(109)

我已经在Stack Overflow上寻找了相关问题的解决方案，但似乎这一个是相当独特的。对于上下文，由于公司程序，我需要每小时刷新一次AWS安全凭据，我正在努力将新刷新的安全凭据添加到Spark。在第一个小时内，一切都正常（我可以从s3等访问和读取表），但在第一个小时结束并刷新凭据后，我无法成功更改我的aws凭据。
一旦我刷新了我的aws证书，下面是我用来更新spark的代码，使他们使用新的aws证书：

sc = spark.sparkContext

def getAWSKeys(profile):
   awsCreds = {}
   Config = ConfigParser.ConfigParser()
   Config.read(os.path.join(os.getenv("HOME"), '.aws', 'credentials'))
   if profile in Config.sections():
       awsCreds["aws_access_key_id"] = Config.get(
           profile, "aws_access_key_id")
       awsCreds["aws_secret_access_key"] = Config.get(
           profile, "aws_secret_access_key")
       awsCreds["aws_session_token"] = Config.get(
           profile, "aws_session_token")
   return awsCreds

awsKeys = getAWSKeys(profile)
sc._jsc.hadoopConfiguration().set("fs.s3.awsAccessKeyId",
                                 awsKeys["aws_access_key_id"])
sc._jsc.hadoopConfiguration().set("fs.s3.awsSecretAccessKey",
                                 awsKeys["aws_secret_access_key"])
sc._jsc.hadoopConfiguration().set("fs.s3.session.token",
                                 awsKeys["aws_session_token"])
sc._jsc.hadoopConfiguration().set("fs.s3.enableServerSideEncryption", "true")
sc._jsc.hadoopConfiguration().set("fs.s3.access.key",
                                 awsKeys["aws_access_key_id"])
sc._jsc.hadoopConfiguration().set("fs.s3.secret.key",
                                 awsKeys["aws_secret_access_key"])
sc._jsc.hadoopConfiguration().set("fs.s3.endpoint",
                                 "s3.us-east-1.amazonaws.com")

sc._jsc.hadoopConfiguration().set("fs.s3a.awsAccessKeyId",
                                 awsKeys["aws_access_key_id"])
sc._jsc.hadoopConfiguration().set("fs.s3a.awsSecretAccessKey",
                                 awsKeys["aws_secret_access_key"])
sc._jsc.hadoopConfiguration().set("fs.s3a.session.token",
                                 awsKeys["aws_session_token"])
sc._jsc.hadoopConfiguration().set("fs.s3a.enableServerSideEncryption", "true")
sc._jsc.hadoopConfiguration().set("fs.s3a.access.key",
                                 awsKeys["aws_access_key_id"])
sc._jsc.hadoopConfiguration().set("fs.s3a.secret.key",
                                 awsKeys["aws_secret_access_key"])
sc._jsc.hadoopConfiguration().set("fs.s3a.endpoint",
                                 "s3.us-east-1.amazonaws.com")

sc._jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId",
                                 awsKeys["aws_access_key_id"])
sc._jsc.hadoopConfiguration().set("fs.s3n.awsSecretAccessKey",
                                 awsKeys["aws_secret_access_key"])
sc._jsc.hadoopConfiguration().set("fs.s3n.session.token",
                                 awsKeys["aws_session_token"])
sc._jsc.hadoopConfiguration().set("fs.s3n.enableServerSideEncryption", "true")
sc._jsc.hadoopConfiguration().set("fs.s3n.access.key",
                                 awsKeys["aws_access_key_id"])
sc._jsc.hadoopConfiguration().set("fs.s3n.secret.key",
                                 awsKeys["aws_secret_access_key"])
sc._jsc.hadoopConfiguration().set("fs.s3n.endpoint",
                                 "s3.us-east-1.amazonaws.com")

sc.setSystemProperty("com.amazonaws.services.s3.enableV4", "true")
sc.setSystemProperty("com.amazonaws.services.s3n.enableV4", "true")
sc.setSystemProperty("com.amazonaws.services.s3a.enableV4", "true")

# sc._jsc.hadoopConfiguration().set("fs.s3.aws.credentials.provider",
#                                  "org.apache.hadoop.fs.s3.TemporaryAWSCredentialsProvider")

os.environ['AWS_ACCESS_KEY_ID'] = awsKeys["aws_access_key_id"]
os.environ['AWS_SECRET_ACCESS_KEY'] = awsKeys["aws_secret_access_key"]
os.environ['AWS_SESSION_TOKEN'] = awsKeys["aws_session_token"]

我试图在我的方法是详尽的，但可悲的是没有工作。我得到的错误是：

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-57-674174eca978> in <module>()
      3 table = (
      4     spark.read.option("delimiter", "|")
----> 5     .csv(f"s3n://{s3_path}/{file1}", header = True, inferSchema=True)
      6     .select("col1", "col2", "col3", "col4")
      7 )

/usr/lib/spark/python/pyspark/sql/readwriter.py in csv(self, path, schema, sep, encoding, quote, escape, comment, header, inferSchema, ignoreLeadingWhiteSpace, ignoreTrailingWhiteSpace, nullValue, nanValue, positiveInf, negativeInf, dateFormat, timestampFormat, maxColumns, maxCharsPerColumn, maxMalformedLogPerPartition, mode, columnNameOfCorruptRecord, multiLine)
    408         if isinstance(path, basestring):
    409             path = [path]
--> 410         return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path)))
    411 
    412     @since(1.5)

/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1131         answer = self.gateway_client.send_command(command)
   1132         return_value = get_return_value(
-> 1133             answer, self.gateway_client, self.target_id, self.name)
   1134 
   1135         for temp_arg in temp_args:

/usr/lib/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    317                 raise Py4JJavaError(
    318                     "An error occurred while calling {0}{1}{2}.\n".
--> 319                     format(target_id, ".", name), value)
    320             else:
    321                 raise Py4JError(

Py4JJavaError: An error occurred while calling o12923.csv.
: com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.model.AmazonS3Exception: Bad Request (Service: Amazon S3; Status Code: 400; Error Code: 400 Bad Request; Request ID: 9A4F6DDEA3BD8AA6), S3 Extended Request ID: xg9ZiPjfV3h4rGgs5emsUiWl8xQdv0OMhK/91qdAs/iIvapWgIlWh9m1qLTGj3ODFM9MtEnuueg=
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.handleErrorResponse(AmazonHttpClient.java:1588)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1258)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1030)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:742)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:716)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:699)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:667)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:649)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:513)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:4169)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:4116)
    at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.getObjectMetadata(AmazonS3Client.java:1237)
    at com.amazon.ws.emr.hadoop.fs.s3.lite.call.GetObjectMetadataCall.perform(GetObjectMetadataCall.java:24)
    at com.amazon.ws.emr.hadoop.fs.s3.lite.call.GetObjectMetadataCall.perform(GetObjectMetadataCall.java:10)
    at com.amazon.ws.emr.hadoop.fs.s3.lite.executor.GlobalS3Executor.execute(GlobalS3Executor.java:82)
    at com.amazon.ws.emr.hadoop.fs.s3.lite.AmazonS3LiteClient.invoke(AmazonS3LiteClient.java:176)
    at com.amazon.ws.emr.hadoop.fs.s3.lite.AmazonS3LiteClient.getObjectMetadata(AmazonS3LiteClient.java:94)
    at com.amazon.ws.emr.hadoop.fs.s3.lite.AbstractAmazonS3Lite.getObjectMetadata(AbstractAmazonS3Lite.java:39)
    at com.amazon.ws.emr.hadoop.fs.s3n.Jets3tNativeFileSystemStore.retrieveMetadata(Jets3tNativeFileSystemStore.java:211)
    at sun.reflect.GeneratedMethodAccessor42.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:191)
    at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
    at com.sun.proxy.$Proxy36.retrieveMetadata(Unknown Source)
    at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.getFileStatus(S3NativeFileSystem.java:768)
    at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1430)
    at com.amazon.ws.emr.hadoop.fs.EmrFileSystem.exists(EmrFileSystem.java:311)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:359)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:348)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
    at scala.collection.immutable.List.flatMap(List.scala:344)
    at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:348)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
    at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:533)
    at sun.reflect.GeneratedMethodAccessor118.invoke(Unknown Source)
    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)

重申一下，在第一个小时一切正常，但是当我刷新aws凭据时，我得到了400 Bad Request错误。我尝试过将这些新的aws凭据添加到spark，但是没有任何效果。

apache-spark

2条答案

按热度按时间
2ledvvac

2ledvvac1#

我看不出有什么简单的方法可以做到这一点，因为这些凭据被绑定到文件系统，然后被冻结。
如果我尝试这样做，我会编写自己的AWSCredentialsProvider实现，它为AWS调用提供凭据。默认链如下所示：spark config，env vars，对EC2元数据服务的GET请求。您可以添加一个新的服务，它 * 不知何故 * 获得了新的值。您需要想出一种方法将新的会话凭据传播到集群中的每台主机，尽管......很辛苦
另一件事是要知道，AWS假定角色的最长寿命已从1小时增加到12小时，因此，如果您能让IT团队将分配给您的角色增加到12小时，您可能就能度过这一天。
先试试这个。
ps：CSV“inferSchema=true”表示“只读取整个CSV文件一次以计算出架构”。避免

赞(0）分享  回复(0）举报  2022-12-13
67up9zun

67up9zun2#

最近偶然发现了这一条;在深入研究hadoop-awsjava代码后，发现：

  • 存在高速缓存（高速缓存条目：URI，hadoopconf）的凭据提供程序，不知何故不工作;当更改hadoop conf（以更改凭据）并阅读新文件时，它会在不应使用提供程序缓存条目的情况下使用提供程序缓存条目，因此会使用过时的凭据
  • 有一个fs.<scheme>.impl.disable.cache（在我们例子中是scheme=s3a）来禁用这个缓存;这将每次示例化一个新的凭据提供程序，但至少您可以使用新凭据创建一个凭据提供程序
赞(0）分享  回复(0）举报  2022-12-13
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