spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Mark Heimann <>
Subject Re: What is the Effect of Serialization within Stages?
Date Thu, 13 Aug 2015 13:53:52 GMT
Thanks a lot guys, that's exactly what I hoped for :-).


2015-08-13 6:35 GMT+02:00 Hemant Bhanawat <>:

> A chain of map and flatmap does not cause any
> serialization-deserialization.
> On Wed, Aug 12, 2015 at 4:02 PM, Mark Heimann <>
> wrote:
>> 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

View raw message