Github user wesolowskim commented on the issue: https://github.com/apache/spark/pull/14137 Whole computation is iterative and depends on previous state of sccWorkGraph. It iterates on sccWorkGraph.numVertices which is action under the hood and without caching whole algorithm would be extremely slow, because with every iteration sccWorkGraph would be computed all over again. Before I came up with this solution I tried to unpersist intermediary sccWorkGraphs, and colleague of mine tried to remove caches. Non of it worked, because of the reasons mentioned above. Initially i thought that caching should be removed but there are two reasons that is not the case: 1. Scc algorithm requires knowledge of data (numVertices) that requires action in spark 2. Every iteration is depended on previous state of graph What is more pregel itself is implemented with caching and immediate materialization. If I cannot remove caches (I think I can't) additional ones have to be added. I tested few other solutions and that is the one that turned to be the best in terms of performance. I hope to find an optimal solution.
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