apache-spark - 在Hive-S3表的情况下pyspark命令行错误

Question

我有一个Hive表(由S3存储桶提供)，可以像魅力一样在Hive shell 上工作。
我想继续使用pyspark。我可以从pyspark shell访问HDFS表，但是当我想访问S3中的数据时，会收到此错误消息

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/Cellar/apache-spark/2.4.5/libexec/python/pyspark/sql/dataframe.py", line 380, in show
    print(self._jdf.showString(n, 20, vertical))
  File "/usr/local/Cellar/apache-spark/2.4.5/libexec/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
  File "/usr/local/Cellar/apache-spark/2.4.5/libexec/python/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/usr/local/Cellar/apache-spark/2.4.5/libexec/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 o41.showString.
: java.lang.NoSuchMethodError: org.apache.hadoop.conf.Configuration.getTimeDuration(Ljava/lang/String;Ljava/lang/String;Ljava/util/concurrent/TimeUnit;)J
    at org.apache.hadoop.fs.s3a.S3ARetryPolicy.<init>(S3ARetryPolicy.java:114)
    at org.apache.hadoop.fs.s3a.S3AFileSystem.initialize(S3AFileSystem.java:263)
    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:204)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:342)
    at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
    at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
    at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
    at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369)
    at org.apache.spark.sql.Dataset.head(Dataset.scala:2550)
    at org.apache.spark.sql.Dataset.take(Dataset.scala:2764)
    at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)
    at org.apache.spark.sql.Dataset.showString(Dataset.scala:291)
    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)

有人可以帮我解决这个问题吗？

Answer 1

我被困在org.apache.hadoop.conf.Configuration.getTimeDuration错误上了好几个星期!spark-submit随附的pyspark和pip install pyspark可执行文件似乎已损坏。
我从Spark download site下载了预构建的版本，提取了tarball和voilá，它起作用了!
我用以下方法进行了测试:

操作系统:Ubuntu和Amazon Linux 2

Java:OpenJDK 8和OpenJDK 11和Amazon Corretto 11

Python:3.6和3.7

Spark:为Hadoop 3.2预先构建的3.0.0

在所有情况下，使用pip随附的 spark-submit和 pyspark可执行文件时，我都会遇到相同的错误。
在所有情况下，当我使用下载的tarball随附的spark可执行文件时，错误消失了。
只需确保设置以下变量(您的值当然会有所不同):

export SPARK_HOME=/opt/Spark/spark-3.0.0-bin-hadoop3.2
export SPARK_CONF_DIR=/opt/Spark/spark-3.0.0-bin-hadoop3.2/conf
export PATH=$SPARK_HOME/bin:$PATH

并使用 --packages com.amazonaws:aws-java-sdk-bundle:1.11.816,org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0,org.apache.hadoop:hadoop-client:3.2.0启动您的spark-submit / pyspark