I'm trying to batch-process 30-ish files from HDFS, but I see that data is distributed very badly across slots. 4 out of 32 slots get 4/5ths of the data, another 3 slots get about 1/5th and a last slot just a few records. This probably triggers disk spillover on these slots and slows down the job immensely. The data has many many unique keys and processing could be done in a highly parallel manner. From what I understand, HDFS data locality governs which splits are assigned to which subtask.
Does the statement of input split assignment ring true? Is the fact that data isn't redistributed an effort from Flink to have high data locality, even if this means disk spillover for a few slots/tms and idleness for others? Is there any use for parallelism if work isn't distributed anyway?
Thanks for your time, Reinier