Sorry for the confusion. I actually meant that in your scenario update operations are not collocated and will imply network trips (unlike Hadoop which will write data locally unless you run out of space). So the way you split the process most likely will not give you performance improvement - you're minimizing the amount of data transferred from S3 to nodes, but the greater part of it will still be transferred between nodes.
Since you're loading from the remote storage, I think the best way is to use CacheStore to load the data, like described in [1]. In this case each node will have to read the whole file, but all updates will be local. Let me know if it helps. [1] https://apacheignite.readme.io/docs/data-loading#ignitecacheloadcache -- View this message in context: http://apache-ignite-users.70518.x6.nabble.com/Super-slow-data-loading-performance-when-more-nodes-added-tp1105p1119.html Sent from the Apache Ignite Users mailing list archive at Nabble.com.
