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https://issues.apache.org/jira/browse/FLINK-10566?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16655683#comment-16655683
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Maximilian Michels commented on FLINK-10566:
--------------------------------------------
Just to clarify, this happens during Plan creation which is specific to the
batch execution. Afterwards, the Plan is converted into a JobGraph which is
send to the JobMaster for scheduling.
[~robertwb] Have you run a similar experiment for the DataStream API? Asking
because streaming has a different way of creating the JobGraph.
[~fhueske] or [~till.rohrmann] Do you have an idea how we could fix this? This
blocks us from running TFX pipelines on Flink.
> Flink Planning is exponential in the number of stages
> -----------------------------------------------------
>
> Key: FLINK-10566
> URL: https://issues.apache.org/jira/browse/FLINK-10566
> Project: Flink
> Issue Type: Bug
> Components: Optimizer
> Affects Versions: 1.5.4
> Reporter: Robert Bradshaw
> Priority: Major
> Attachments: chart.png
>
>
> This makes it nearly impossible to run graphs with 100 or more stages. (The
> execution itself is still sub-second, but the job submission takes
> increasingly long.)
> I can reproduce this with the following pipeline, which resembles my
> real-world workloads (with depth up to 10 and width up, and past, 50). On
> Flink it seems getting width beyond width 10 is problematic (times out after
> hours). Note the log scale on the chart for time.
>
> {code:java}
> public static void runPipeline(int depth, int width) throws Exception {
> final ExecutionEnvironment env =
> ExecutionEnvironment.getExecutionEnvironment();
> DataSet<String> input = env.fromElements("a", "b", "c");
> DataSet<String> stats = null;
> for (int i = 0; i < depth; i++) {
> stats = analyze(input, stats, width / (i + 1) + 1);
> }
> stats.writeAsText("out.txt");
> env.execute("depth " + depth + " width " + width);
> }
> public static DataSet<String> analyze(DataSet<String> input,
> DataSet<String> stats, int branches) {
> System.out.println("analyze " + branches);
> for (int i = 0; i < branches; i++) {
> final int ii = i;
> if (stats != null) {
> input = input.map(new RichMapFunction<String, String>() {
> @Override
> public void open(Configuration parameters) throws Exception {
> Collection<String> broadcastSet =
> getRuntimeContext().getBroadcastVariable("stats");
> }
> @Override
> public String map(String value) throws Exception {
> return value;
> }
> }).withBroadcastSet(stats.map(s -> "(" + s + ").map"), "stats");
> }
> DataSet<String> branch = input
> .map(s -> new Tuple2<Integer, String>(0, s +
> ii))
> .groupBy(0)
> .minBy(1)
> .map(kv -> kv.f1);
> if (stats == null) {
> stats = branch;
> } else {
> stats = stats.union(branch);
> }
> }
> return stats.map(s -> "(" + s + ").stats");
> }
> {code}
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