Hello everyone, I am wondering what the effect of serialization is within a stage.
My understanding of Spark as an execution engine is that the data flow graph is divided into stages and a new stage always starts after an operation/transformation that cannot be pipelined (such as groupBy or join) because it can only be completed after the whole data set has "been taken care off". At the end of a stage shuffle files are written and at the beginning of the next stage they are read from. Within a stage my understanding is that pipelining is used, therefore I wonder whether there is any serialization overhead involved when there is no shuffling taking place. I am also assuming that my data set fits into memory and must not be spilled to disk. So if I would chain multiple *map* or *flatMap* operations and they end up in the same stage, will there be any serialization overhead for piping the result of the first *map* operation as a parameter into the following *map* operation? Any ideas and feedback appreciated, thanks a lot. Best regards, Mark
