this is official cloudera compiled stack cdh 5.3.0 - nothing has been done by
me and I presume they are pretty good in building it so I still suspect it now
gets the classpath resolved in different way?
thx,Antony.
On Wednesday, 7 January 2015, 18:55, Sean Owen <[email protected]> wrote:
Problems like this are always due to having code compiled for Hadoop 1.x run
against Hadoop 2.x, or vice versa. Here, you compiled for 1.x but at runtime
Hadoop 2.x is used.
A common cause is actually bundling Spark / Hadoop classes with your app, when
the app should just use the Spark / Hadoop provided by the cluster. It could
also be that you're pairing Spark compiled for Hadoop 1.x with a 2.x cluster.
On Wed, Jan 7, 2015 at 9:38 AM, Antony Mayi <[email protected]>
wrote:
Hi,
I am using newAPIHadoopRDD to load RDD from hbase (using pyspark running as
yarn-client) - pretty much the standard case demonstrated in the
hbase_inputformat.py from examples... the thing is the when trying the very
same code on spark 1.2 I am getting the error bellow which based on similar
cases on another forums suggest incompatibility between MR1 and MR2.
why would this now start happening? is that due to some changes in resolving
the classpath which now picks up MR2 jars first while before it was MR1?
is there any workaround for this?
thanks,Antony.
the error:
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:158)
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)
