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From HyukjinKwon <>
Subject [GitHub] spark issue #18320: [SPARK-21093][R] Terminate R's worker processes in the p...
Date Thu, 22 Jun 2017 08:29:13 GMT
Github user HyukjinKwon commented on the issue:
    **jobs with many stages**:
    I tested the codes below:
    df <- createDataFrame(list(list(1L, 1, "1", 0.1)), c("a", "b", "c", "d"))
    for(i in 0:90) {
      df <- (gapply(df, "a", function(key, x) { x }, schema(df)))
    More iteration produced `StackOverflowError` in my local and CentOS. This created 18201
tasks with 92 stages.
    **jobs with long stages**:
    I made the change as below:
    df <- createDataFrame(list(list(1L, 1, "1", 0.1)), c("a", "b", "c", "d"))
    collect(dapply(repartition(df, 8), function(x) { x }, schema(df)))
    after manual changes as below:
     outputCon <- socketConnection(
         port = port, blocking = TRUE, open = "wb", timeout = connectionTimeout)
     # read the index of the current partition inside the RDD
     partition <- SparkR:::readInt(inputCon)
    This took 10 mins (default cores in executors was 8). 
    It looks both were fine. Would this address your concern enough @felixcheung?

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