从PySpark连接到S3数据

Question

我正在尝试从Amazon s3读取JSON文件以创建spark上下文并使用它来处理数据。

Spark基本上位于docker容器中。因此，将文件放在docker路径中也是PITA。因此将其推送到S3。

以下代码说明了其余内容。

from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName("first")
sc = SparkContext(conf=conf)

config_dict = {"fs.s3n.awsAccessKeyId":"**",
               "fs.s3n.awsSecretAccessKey":"**"}

bucket = "nonamecpp"
prefix = "dataset.json"
filename = "s3n://{}/{}".format(bucket, prefix)
rdd = sc.hadoopFile(filename,
                    'org.apache.hadoop.mapred.TextInputFormat',
                    'org.apache.hadoop.io.Text',
                    'org.apache.hadoop.io.LongWritable',
                    conf=config_dict)

我收到以下错误-

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-2-b94543fb0e8e> in <module>()
      9                     'org.apache.hadoop.io.Text',
     10                     'org.apache.hadoop.io.LongWritable',
---> 11                     conf=config_dict)
     12

/usr/local/spark/python/pyspark/context.pyc in hadoopFile(self, path, inputFormatClass, keyClass, valueClass, keyConverter, valueConverter, conf, batchSize)
    558         jrdd = self._jvm.PythonRDD.hadoopFile(self._jsc, path, inputFormatClass, keyClass,
    559                                               valueClass, keyConverter, valueConverter,
--> 560                                               jconf, batchSize)
    561         return RDD(jrdd, self)
    562

/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.pyc in __call__(self, *args)
    536         answer = self.gateway_client.send_command(command)
    537         return_value = get_return_value(answer, self.gateway_client,
--> 538                 self.target_id, self.name)
    539 
    540         for temp_arg in temp_args:

/usr/local/lib/python2.7/dist-packages/py4j/protocol.pyc in get_return_value(answer, gateway_client, target_id, name)
    298                 raise Py4JJavaError(
    299                     'An error occurred while calling {0}{1}{2}.\n'.
--> 300                     format(target_id, '.', name), value)
    301             else:
    302                 raise Py4JError(

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.hadoopFile.
: java.lang.IllegalArgumentException: AWS Access Key ID and Secret Access Key must be specified as the username or password (respectively) of a s3n URL, or by setting the fs.s3n.awsAccessKeyId or fs.s3n.awsSecretAccessKey properties (respectively).
    at org.apache.hadoop.fs.s3.S3Credentials.initialize(S3Credentials.java:70)
    at org.apache.hadoop.fs.s3native.Jets3tNativeFileSystemStore.initialize(Jets3tNativeFileSystemStore.java:73)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:190)
    at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:103)
    at org.apache.hadoop.fs.s3native.$Proxy20.initialize(Unknown Source)
    at org.apache.hadoop.fs.s3native.NativeS3FileSystem.initialize(NativeS3FileSystem.java:272)
    at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2397)
    at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:89)
    at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2431)
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2413)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:368)
    at org.apache.hadoop.fs.Path.getFileSystem(Path.java:296)
    at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:256)
    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:201)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:203)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:203)
    at org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:203)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:203)
    at org.apache.spark.rdd.RDD.take(RDD.scala:1060)
    at org.apache.spark.rdd.RDD.first(RDD.scala:1093)
    at org.apache.spark.api.python.SerDeUtil$.pairRDDToPython(SerDeUtil.scala:202)
    at org.apache.spark.api.python.PythonRDD$.hadoopFile(PythonRDD.scala:543)
    at org.apache.spark.api.python.PythonRDD.hadoopFile(PythonRDD.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
    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:207)
    at java.lang.Thread.run(Thread.java:744)

我已经清楚地提供了aswSecretAccessKey和awsAccessId。怎么了

Answer 1

我已经解决了添加--packages org.apache.hadoop:hadoop-aws:2.7.1到spark-submit命令中的问题。

它将下载所有缺少的hadoop软件包，这些软件包将使您能够使用S3执行spark作业。

然后，在您的工作中，您需要像这样设置AWS凭证：

sc._jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId", aws_id)
sc._jsc.hadoopConfiguration().set("fs.s3n.awsSecretAccessKey", aws_key)

关于设置凭据的其他选项是将它们定义为spark / conf / spark-env：

#!/usr/bin/env bash
AWS_ACCESS_KEY_ID='xxxx'
AWS_SECRET_ACCESS_KEY='xxxx'

SPARK_WORKER_CORES=1 # to set the number of cores to use on this machine
SPARK_WORKER_MEMORY=1g # to set how much total memory workers have to give executors (e.g. 1000m, 2g)
SPARK_EXECUTOR_INSTANCES=10 #, to set the number of worker processes per node

更多信息：

• 如何在AWS上运行PySpark
• AWS凭证