Jupyter and Spark applications are launched in gopaddle after subscribing the respective templates from the gopaddle marketplace.

When a Python3 Jupyter note book is executed, the following error message appears in the Jupyter container logs and the notebook is stuck in running state.

22/09/02 10:54:35 ERROR TransportRequestHandler: Error while invoking RpcHandler#receive() for one-way message. org.apache.spark.deploy.DeployMessages$ExecutorUpdated; local class incompatible: stream classdesc serialVersionUID = 1654279024112373855, local class serialVersionUID = -1971851081955655249 at at at at at at at at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:76) at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:109) at org.apache.spark.rpc.netty.NettyRpcEnv.$anonfun$deserialize$2(NettyRpcEnv.scala:299) at scala.util.DynamicVariable.withValue(DynamicVariable.scala:62) at org.apache.spark.rpc.netty.NettyRpcEnv.deserialize(NettyRpcEnv.scala:352) at org.apache.spark.rpc.netty.NettyRpcEnv.$anonfun$deserialize$1(NettyRpcEnv.scala:298) at scala.util.DynamicVariable.withValue(DynamicVariable.scala:62) at org.apache.spark.rpc.netty.NettyRpcEnv.deserialize(NettyRpcEnv.scala:298) at org.apache.spark.rpc.netty.RequestMessage$.apply(NettyRpcEnv.scala:647) at org.apache.spark.rpc.netty.NettyRpcHandler.internalReceive(NettyRpcEnv.scala:698) at org.apache.spark.rpc.netty.NettyRpcHandler.receive(NettyRpcEnv.scala:690) at at at at at at at at at io.netty.handler.timeout.IdleStateHandler.channelRead( at at at at io.netty.handler.codec.MessageToMessageDecoder.channelRead( at at at at at at at at$HeadContext.channelRead( at at at at$ at at at at at io.netty.util.concurrent.SingleThreadEventExecutor$ at io.netty.util.internal.ThreadExecutorMap$ at at


One of the reasons for this failure, is the pyspark version incompatibility between Jupyter and the Spark cluster.

Check the pyspark version in both Jupyter and Spark Cluster by opening the container terminal in gopaddle UI and executing the below command.

Get the pyspark version

# pyspark --version

For instance -

In the spark master container - the version is 3.0.1

In the Jupyter container - the version is 3.1.2 which is higher than the one in Spark cluster.

Temporary Resolution:

  1. In the Jupyter container terminal, downgrade the pyspark version to match the one in Spark Cluster.

    # pip3 install --force-reinstall pyspark==3.0.1

    This uninstalls the current pyspark version and installs the new version.

  2. Re-run the Jupyter notebook.

Did this answer your question?