JoanFM opened a new pull request #26523: [SPARK-29877][GRAPHX] static PageRank allow checkPoint from previous computations URL: https://github.com/apache/spark/pull/26523 ### What changes were proposed in this pull request? Add an optional parameter to the staticPageRank computation with the result of a previous PageRank computation. This would make the algorithm start from a different starting point closer to the convergence configuration ### Why are the changes needed? https://issues.apache.org/jira/browse/SPARK-29877 It would be really helpful to have the possibility, when computing staticPageRank to use a previous computation as a checkpoint to continue the iterations. ### Does this PR introduce any user-facing change? Yes, it allows to start the static page Rank computation from the point where an earlier one finished. Example: Compute 10 iteration first, and continue for 3 more iterations ```scala val partialPageRank = graph.ops.staticPageRank(numIter=10, resetProb=0.15) val continuationPageRank = graph.ops.staticPageRank(numIter=3, resetProb=0.15, Some(partialPageRank)) ``` ### How was this patch tested? Yes, some tests were added. Testing was done as follow: - Check how many iterations it takes for a static Page Rank computation to converge - Run the static Page Rank computation for half of these iterations and take result as checkpoint - Restart computation and check that number of iterations it takes to converge. It never has to be larger than the original one and in most of the cases it is much smaller. Due to the presence of sinks and the normalization done in [[SPARK-18847]] it is not exactly equivalent to compute static page rank for 2 iterations, take the result at checkpoint and run for 2 more iterations than to compute directly for 4 iterations. However this checkpointing can give the algorithm a hint about the true distribution of pageRanks in the graph
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