Github user mridulm commented on the pull request: https://github.com/apache/spark/pull/2366#issuecomment-55472830 What happens when there is recomputation which results in same blockId getting regenerated (unpersist followed by recomputation/persist or block drop followed by recomputation or something else ) ? It will now go to some random node potentially not same as previously selected ? Resulting in over-replication ? A more corner case is if the computation was not idempotent ... and resulted in a changed dataset for the block - earlier it will get overwritten as part of replication : will we will now have two nodes with same data and a third (initially replicated to) which can diverge ? Btw, from what I saw, node loss is not handled right ? So a block can get under replicated ? Would be nice if we added that in some day ... Streaming is not the only application for replication :-) We use it in conjunction with locality wait levels to speed up computation when speculative execution is enabled.
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