pyspark:带有spark提交的jars依赖
我编写了一个pyspark脚本,该脚本读取两个json文件,coGroup
然后将它们发送到elasticsearch集群。当我在本地运行该elasticsearch-
hadoop文件时,一切都会正常运行(大部分情况下),我下载了org.elasticsearch.hadoop.mr.EsOutputFormat
和org.elasticsearch.hadoop.mr.LinkedMapWritable
类的jar文件,然后使用pyspark使用--jars
参数运行我的工作,并且可以看到在我的Elasticsearch集群中出现的文档。
但是,当我尝试在Spark群集上运行它时,出现此错误:
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)
在我看来,这很清楚:elasticsearch-
hadoop工人无法使用广口瓶;问题是:如何将其与应用程序一起发送?我可以将其sc.addPyFile
用于python依赖项,但不能与jars一起使用,并且使用--jars
参数spark-
submit也无济于事。
回答:
在--jars
刚刚作品; 问题是我一开始如何spark-submit
工作;正确的执行方式是:
./bin/spark-submit <options> scriptname
因此,--jars
必须将选项放在脚本之前:
./bin/spark-submit --jars /path/to/my.jar myscript.py
如果您认为这是将参数传递给脚本本身的唯一方法,那么这很明显,因为脚本名称后面的所有内容都将用作脚本的输入参数:
./bin/spark-submit --jars /path/to/my.jar myscript.py --do-magic=true
以上是 pyspark:带有spark提交的jars依赖 的全部内容, 来源链接: utcz.com/qa/433633.html