Hi Ashish, How many threads at a time executing putAll jobs in a single client (spark job?)... Do you see read timeout exception in client logs...If so, can you try increasing the read timeout value. Or reducing the putAll size.
In case of PutAll for partitioned region; the putAll (entries) size is broken down and sent to respective servers based on its data affinity; the reason its working with partitioned region. You can find more detail on how client-server connection works at: https://geode.apache.org/docs/guide/14/topologies_and_comm/topology_concepts/how_the_pool_manages_connections.html -Anil. On Mon, Jun 24, 2019 at 10:04 AM aashish choudhary < aashish.choudha...@gmail.com> wrote: > Hi, > > We have been experiencing issues while connect to geode using putAll API > with spark. Issue is specific to one particular spark job which tries to > load data to a replicated region. Exception we see in the server side is > that default limit of 800 gets maxed out and on client side we see retry > attempt to each server but gets failed even though when we re ran the same > job it gets completed without any issue. > > In the code problem I could see is that we are connecting to geode using > client cache in forEachPartition which I think could be the issue. So for > each partition we are making a connection to geode. In stats file we could > see that connections getting timeout and there is thread burst also > sometimes >4000. > > What is the recommended way to connect to geode using spark? > > But this one specific job which gets failed most of the times and is a > replicated region. Also when we change the type of region to partitioned > then job gets completed. We have enabled disk persistence for both type of > regions. > > Thoughts? > > > > With best regards, > Ashish >
