Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/18320
**jobs with many stages**:
I tested the codes below:
```R
```r
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)))
}
collect(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:
```r
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:
```r
outputCon <- socketConnection(
port = port, blocking = TRUE, open = "wb", timeout = connectionTimeout)
+Sys.sleep(600L)
+
# 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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