Hi Maximilian,
Thank you for the response.
Yeah its possible to break up global state but its very tricky to merge two
local state variables and also I have to refactor my algorithm logic.
Is there way where I can create a new object every time in reduce function
so that I can assign the compute
Hi Ravikumar,
In short: No, you can't use closures to maintain a global state. If
you want to keep an always global state, you'll have to use
parallelism 1 or an external data store to keep that global state.
Is it possible to break up your global state into a set of local
states which can be com
Hi Fabian, Thank you for your help.
I want my Flink application to be distributed as well as I want
the facility to support the update of the variable [Coefficients of
LinearRegression].
How you would do in that case?
The problem with iteration is that it expects Dataset with same type to be
fe
Hi,
1) Yes, that is correct. If you set the parallelism of an operator to 1 it
is only executed on a single node. It depends on your application, if you
need a global state or whether multiple local states are OK.
2) Flink programs follow the concept a data flow. There is no communication
between
Hi Fabian, Thank you for your answers,
1) If there is only single instance of that function, then it will defeat
the purpose of distributed correct me if I am wrong, so If I run
parallelism with 1 on cluster does that mean it will execute on only one
node?
2) I mean to say, when a map operator re
Hi Ravikumar,
I'll try to answer your questions:
1) If you set the parallelism of a map function to 1, there will be only a
single instance of that function regardless whether it is execution locally
or remotely in a cluster.
2) Flink does also support aggregations, (reduce, groupReduce, combine,
Hi Till, Thank you for your answer, I have couple of questions
1) Setting parallelism on a single map function in local is fine but on
distributed will it work as local execution?
2) Is there any other way apart from setting parallelism? Like spark
aggregate function?
3) Is it necessary that aft
Hi Ravikumar,
Flink's operators are stateful. So you can simply create a variable in your
mapper to keep the state around. But every mapper instance will have it's
own state. This state is determined by the records which are sent to this
mapper instance. If you need a global state, then you have t
