Yes, the distribution is certainly fine and built for Hadoop 2. It sounds like you are inadvertently including Spark code compiled for Hadoop 1 when you run your app. The general idea is to use the cluster's copy at runtime. Those with more pyspark experience might be able to give more useful directions about how to fix that.
On Wed, Jan 7, 2015 at 1:46 PM, Antony Mayi <antonym...@yahoo.com> wrote: > 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 <so...@cloudera.com> > 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 <antonym...@yahoo.com.invalid> > 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) > > > > > > >
