Thank you, that makes sense.
My use case is to match Spark dataframe functionality using only C# if
possible, without using Spark
Specifically we have CSV files we wish to load into the cache and then we
have compute functions that act on those rows, adding columns as they do, so
the cache will
Hello,
Cache with LOCAL mode is never distributed between nodes by design. Other
nodes doesn't even know that it exists, you can quickly test it by starting
two server nodes with simple code that creates LOCAL cache with same name on
each node, puts different values for the same key and gets this
Actually the code is adding data to each nodes individual LOCAL cache, its
just the affinity is not working as expected, all affinity jobs are run on
the node which is invoking them (in my case the client node) rather than
where the actual data exists.
--
Sent from:
Actually maybe I do not understand what a LOCAL cache is.
I create a LOCAL cache in my client and I have 2 server nodes. I send a
compute function to each node to load some data into the cache. I assumed as
the cache is LOCAL that the nodes would load the data into their own local
cache?
I have been using affinity keys with a PARTITIONED cache and then use those
keys to send computations to the nodes that have the data which all works as
expected.
I wanted to test LOCAL mode for performance but I found no calculations are
now sent to the nodes.
Is that expected behavior?
How
