python – pyspark：使用spark-submit运送jar依赖项

我写了一个pyspark脚本,它读取两个json文件,coGroup它们并将结果发送到elasticsearch集群;当我在本地运行它时,一切都按预期工作(大部分),我为org.elasticsearch.hadoop.mr.EsOutputFormat和org.elasticsearch.hadoop.mr.LinkedMapWritable类下载了elasticsearch-hadoop jar文件,然后运行我的作业pyspark使用–jars参数,我可以看到弹性搜索集群中出现的文档.

但是,当我尝试在spark集群上运行它时,我收到此错误：

Traceback (most recent call last):
  File "/root/spark/spark_test.py",line 141,in <module>
    conf=es_write_conf
  File "/root/spark/python/pyspark/rdd.py",line 1302,in saveAsNewAPIHadoopFile
    keyConverter,valueConverter,jconf)
  File "/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py",line 538,in __call__
  File "/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py",line 300,in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.saveAsNewAPIHadoopFile.
: java.lang.ClassNotFoundException: org.elasticsearch.hadoop.mr.LinkedMapWritable
    at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:274)
    at org.apache.spark.util.Utils$.classForName(Utils.scala:157)
    at org.apache.spark.api.python.PythonRDD$$anonfun$getKeyValueTypes$1$$anonfun$apply$9.apply(PythonRDD.scala:611)
    at org.apache.spark.api.python.PythonRDD$$anonfun$getKeyValueTypes$1$$anonfun$apply$9.apply(PythonRDD.scala:610)
    at scala.Option.map(Option.scala:145)
    at org.apache.spark.api.python.PythonRDD$$anonfun$getKeyValueTypes$1.apply(PythonRDD.scala:610)
    at org.apache.spark.api.python.PythonRDD$$anonfun$getKeyValueTypes$1.apply(PythonRDD.scala:609)
    at scala.Option.flatMap(Option.scala:170)
    at org.apache.spark.api.python.PythonRDD$.getKeyValueTypes(PythonRDD.scala:609)
    at org.apache.spark.api.python.PythonRDD$.saveAsNewAPIHadoopFile(PythonRDD.scala:701)
    at org.apache.spark.api.python.PythonRDD.saveAsNewAPIHadoopFile(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:745)

对我来说这似乎很清楚：在工人身上没有弹性研究 – hadoop jar;所以问题是：我如何将它与我的应用程序一起发送？我可以使用sc.addPyFile作为python依赖项,但它不适用于jar,并且使用spark-submit的–jars参数也无济于事.

./bin/spark-submit <options> scriptname

./bin/spark-submit --jars /path/to/my.jar myscript.py

./bin/spark-submit --jars /path/to/my.jar myscript.py --do-magic=true