Hello,I'm facing a problem in an algorithm where I would like to constantly update a DataSet representing a graph, perform some computation, output one or more DataSink (such as a file on the local system) and then reuse the DataSet for a next iteration.I want to avoid spilling the results to disk at the end of an iteration and to read it back in the next iterations - the graph is very big and I do not wish to incur that time overhead.I want to reuse the full result DataSet of each iteration in the next one and I want to save to disk a small percentage of the produced DataSet for each iteration.The space complexity is rather constant - the number of edges in the graph increases by only 100 between iterations (which is an extremely low percentage of the original graph's edges) and is obtained using env.fromCollection(edgesToAdd). Although I am using Flink's Gelly API for graphs, I have no problem working directly with the underlying vertex and edge DataSet elements.Two ways to do this occur to me, but it seems both are currently not supported in Flink, as per Vasia's answer to this Stack Overflow question [1]:«Unfortunately, it is not currently possible to output intermediate results from a bulk iteration.
You can only output the final result at the end of the iteration.
Also, as you correctly noticed, Flink cannot efficiently unroll a while-loop or for-loop, so that won't work either.»1. I thought I could create a bulk iteration, perform the computation and between iterations, output the result to the file system.However, this is not possible, as per Vasia's answer, and produces the following exception on execution when I try (for example, to calculate a centrality metric for every vertex and dump the results to disk), as expected based on that information:org.apache.flink.api.common.InvalidProgramException: A data set that is part of an iteration was used as a sink or action. Did you forget to close the iteration? 2. Using a for loop in my own program and triggering sequential Flink job executions.Problem: in this scenario, while I am able to use a DataSet produced in an iteration's Flink job (and dump the desired output information to disk) and pass it to the next Flink job, the computation time increases constantly:(I also tried manually starting a session which is kept open with env.startNewSession() before the loop - no impact)Initial graph has 33511 vertices and 411578 edges.
Added 113 vertices and 100 edges.
1-th graph now has 33524 vertices and 411678 edges (2.543 s).
1-th graph algorithm produced 33524 elements. (20.96 s).
Added 222 vertices and 200 edges.
2-th graph now has 33536 vertices and 411778 edges (1.919 s).
2-th graph algorithm produced 33536 elements. (35.913 s).
Added 326 vertices and 300 edges.
3-th graph now has 33543 vertices and 411878 edges (1.825 s).
3-th graph algorithm produced 33543 elements. (49.624 s).
Added 436 vertices and 400 edges.
4-th graph now has 33557 vertices and 411978 edges (1.482 s).
4-th graph algorithm produced 33557 elements. (66.209 s).Note that the number of elements in the output DataSet is equal to the number of vertices in the graph.On iteration i in my program, the executed graph algorithm incorporates the result DataSet of iteration i - 1 by means of the g.joinWithVertices(previousResultDataSet, new RanksJoinFunction()) function.The VertexJoinFunction is a simple forwarding mechanism to set the previous values:@FunctionAnnotation.ForwardedFieldsFirst("*->*")
private static class RanksJoinFunction implements VertexJoinFunction<Double, Double> {
@Override
public Double vertexJoin(final Double vertexValue, final Double inputValue) throws Exception {
return inputValue;
}
}I have also used Flink's plan visualizer to check for discrepancies between the first iteration and the tenth (for example), but the layout of the plan remains exactly the same while the execution time continually increases for what should be the same amount of computations.Bottom-line: I was hoping someone could tell me how to overcome the performance bottleneck using the sequential job approach or enabling the output of intermediate results using Flink's Bulk Iterations.I believe others have stumbled upon this limitation before [2, 3].I have tested this on a dual-core i7 with 8 GB RAM on Java 8 64-bit using a local environment:final Configuration conf = new Configuration();final LocalEnvironment lenv = (LocalEnvironment) ExecutionEnvironment.createLocalEnvironmentWithWebUI(conf);
final ExecutionEnvironment env = lenv;
env.getConfig().disableSysoutLogging().setParallelism(1); I wish to execute in a cluster later on with a bigger dataset, so it would be essential that to maximize the ability to reuse the DataSets that are distributed by the Flink runtime.This would allow me to avoid the performance bottleneck that I described.Hopefully someone may shed light on this.Thanks for your attention.References:Miguel E. Coimbra
Email: [hidden email]
Skype: miguel.e.coimbra
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