I installed the custom as a standalone mode as normal. The master and slaves
started successfully.
However, I got error when I ran a job. It seems to me from the error message
the some library was compiled against hadoop1, but my spark was compiled
against hadoop2.
15/01/08 23:27:36 INFO ClientCnxn: Opening socket connection to server
master/10.191.41.253:2181. Will not attempt to authenticate using SASL
(unknown error)
15/01/08 23:27:36 INFO ClientCnxn: Socket connection established to
master/10.191.41.253:2181, initiating session
15/01/08 23:27:36 INFO ClientCnxn: Session establishment complete on server
master/10.191.41.253:2181, sessionid = 0x14acbdae7e60022, negotiated timeout
= 60000
Traceback (most recent call last):
File "/root/workspace/test/sparkhbase.py", line 23, in <module>
conf=conf2)
File "/root/spark/python/pyspark/context.py", line 530, in newAPIHadoopRDD
jconf, batchSize)
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.newAPIHadoopRDD.
: java.lang.IncompatibleClassChangeError: Found interface
org.apache.hadoop.mapreduce.JobContext, but class was expected
at
org.apache.hadoop.hbase.mapreduce.TableInputFormatBase.getSplits(TableInputFormatBase.java:157)
at
org.apache.spark.rdd.NewHadoopRDD.getPartitions(NewHadoopRDD.scala:98)
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$.newAPIHadoopRDD(PythonRDD.scala:500)
at
org.apache.spark.api.python.PythonRDD.newAPIHadoopRDD(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)
If I understand correctly, the org.apache.hadoop.mapreduce.JobContext in
hadoop1 is a class, but is a interface in hadoop2. My question is which
library could cause this problem.
Thanks.
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