Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/12836#issuecomment-216703595
Hi @davies ,
That's a very good question. Thank you for asking.
Here is my answer:
gapply should be able to do grouping across partitions.
Example - even if I set the number of partitions to 3 - the answer should
be correct.
df <- createDataFrame (
sqlContext,
list(list(1L, 2, "1", 0.1), list(3L, 1, "2", 0.2), list(1L, 3, "3",
0.3)),
c("a", "b", "c", "d"))
df <- repartition(df, 3)
schema <- structType(structField("a", "integer"), structField("avg",
"double"))
df1 <- gapply(
df,
function(x) {
y <- (data.frame(x$a[1], mean(x$b)))
},
schema, "a")
collect(df1)
Result
------------------------
a avg
1 1 2.5
2 3 1.0
In this case I do not care about the size and the number of the partition.
I might understood your question wrongly. Please let me know if I've
answered to your question.
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