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Posted to reviews@spark.apache.org by NarineK <gi...@git.apache.org> on 2016/05/06 22:09:16 UTC
[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
GitHub user NarineK opened a pull request:
https://github.com/apache/spark/pull/12966
[SPARK-15196][SparkR] Add a wrapper for dapply(repartiition(col,...), ... )
## What changes were proposed in this pull request?
As mentioned in :
https://github.com/apache/spark/pull/12836#issuecomment-217338855
We would like to create a wrapper for: dapply(repartiition(col,...), ... )
This will allow to run aggregate functions on groups which are identified by a list of grouping columns.
I called the wrapper method gapply. We can rename it if we want to call it differently.
We could also have:
` setMethod("dapply", signature(x = "SparkDataFrame", func = "function", schema = "structType", col = "Column"),` , however, dapply already has many examples in the doc and if we add new examples for aggregate functions that will make the documentation longer and less clear.
## How was this patch tested?
Unit tests
1. Group by a column and compute mean
2. Group by a column and train linear model
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/NarineK/spark repartitionWithDapply
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/12966.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #12966
----
commit be5de6a42e50c42b4af15b87623bc7b49aecb353
Author: NarineK <na...@us.ibm.com>
Date: 2016-05-06T21:51:35Z
Add a wrapper for dapply(repartiition(col,...), ... )
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217611301
**[Test build #58059 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58059/consoleFull)** for PR 12966 at commit [`057ff9b`](https://github.com/apache/spark/commit/057ff9b30e56de4172c957e467b9b35dd932999a).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217610810
**[Test build #58059 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58059/consoleFull)** for PR 12966 at commit [`057ff9b`](https://github.com/apache/spark/commit/057ff9b30e56de4172c957e467b9b35dd932999a).
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217936323
Thanks, @davies !
This means that we cannot implement group-apply using repartitioning.
What would you suggest in this case ?
My previous pull request works fine for one key.
We can try another implementation with groupBy-> agg . In this case I understand that we will need to implement imperative or declarative aggregate which most probably will collect the rows in a buffer with the same grouping column and pass that buffer with the rows to R worker.
Is this what you'd prefer ?
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by shivaram <gi...@git.apache.org>.
Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/12966#discussion_r62399743
--- Diff: R/pkg/R/DataFrame.R ---
@@ -1214,6 +1214,77 @@ setMethod("dapply",
dataFrame(sdf)
})
+#' gapply
+#'
+#' Apply a function to each group of a DataFrame. The group is defined by an input
+#' grouping column(s).
+#'
+#' @param x A SparkDataFrame
+#' @param func A function to be applied to each group partition specified by grouping
+#' column(s) of the SparkDataFrame.
+#' The output of func is a local R data.frame.
+#' @param schema The schema of the resulting SparkDataFrame after the function is applied.
+#' It must match the output of func.
+#' @family SparkDataFrame functions
+#' @rdname gapply
+#' @name gapply
+#' @export
+#' @examples
+#'
+#' \dontrun{
+#'
+#' Computes the arithmetic mean of `Sepal_Width` by grouping
+#' on `Species`. Output the grouping value and the average.
+#'
+#' df <- createDataFrame (sqlContext, iris)
+#' schema <- structType(structField("Species", "string"), structField("Avg", "double"))
+#' df1 <- gapply(
+#' df,
+#' function(x) {
+#' data.frame(x$Species[1], mean(x$Sepal_Width), stringsAsFactors = FALSE)
+#' },
+#' schema, col=df$"Species")
+#' collect(df1)
+#'
+#' Species Avg
+#' -----------------
+#' virginica 2.974
+#' versicolor 2.770
+#' setosa 3.428
+#'
+#' Fits linear models on iris dataset by grouping on the `Species` column and
+#' using `Sepal_Length` as a target variable, `Sepal_Width`, `Petal_Length`
+#' and `Petal_Width` as training features.
+#'
+#' df <- createDataFrame (sqlContext, iris)
+#' schema <- structType(structField("(Intercept)", "double"),
+#' structField("Sepal_Width", "double"), structField("Petal_Length", "double"),
+#' structField("Petal_Width", "double"))
+#' df1 <- gapply(
+#' df,
+#' function(x) {
+#' model <- suppressWarnings(lm(Sepal_Length ~
+#' Sepal_Width + Petal_Length + Petal_Width, x))
+#' data.frame(t(coef(model)))
+#' }, schema, df$"Species")
+#' collect(df1)
+#'
+#'Result
+#'---------
+#' Model (Intercept) Sepal_Width Petal_Length Petal_Width
+#' 1 0.699883 0.3303370 0.9455356 -0.1697527
+#' 2 1.895540 0.3868576 0.9083370 -0.6792238
+#' 3 2.351890 0.6548350 0.2375602 0.2521257
+#'
+#'}
+setMethod("gapply",
+ signature(x = "SparkDataFrame", func = "function", schema = "structType",
+ col = "Column"),
--- End diff --
can we handle multiple columns now that we are using repartition ?
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217611334
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217602308
**[Test build #58050 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58050/consoleFull)** for PR 12966 at commit [`bf3a74d`](https://github.com/apache/spark/commit/bf3a74d34b21eaa6c3d1422c1135658d9be58a8a).
* This patch **fails SparkR unit tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217739297
Thanks, @sun-rui !
Yes, It seems to be the case @sun-rui .
I've recently hit the case where the number of partitions was less than the number of actual groups.
I've tried the same thing on my previous implementation with groupByKey -> flatMap and it works perfectly fine with any repartitioning.
Maybe @davies, has some suggestions about this.
If there is any repartitioner which will guarantee that each group will be in a single partition then we can use it otherwise this won't give us the expected result.
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217588851
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217593698
**[Test build #58043 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58043/consoleFull)** for PR 12966 at commit [`9704956`](https://github.com/apache/spark/commit/97049564433607544beef439ddce272f607298d9).
* This patch **fails SparkR unit tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
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https://github.com/apache/spark/pull/12966#issuecomment-217578938
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217602310
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by sun-rui <gi...@git.apache.org>.
Github user sun-rui commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217699516
@NarineK, it is guaranteed that all items in a same group will be in a same partition. But it is not guaranteed that there is only single group in a partition. There could be multiple groups in a partition.
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on a diff in the pull request:
https://github.com/apache/spark/pull/12966#discussion_r62400586
--- Diff: R/pkg/R/DataFrame.R ---
@@ -1214,6 +1214,77 @@ setMethod("dapply",
dataFrame(sdf)
})
+#' gapply
+#'
+#' Apply a function to each group of a DataFrame. The group is defined by an input
+#' grouping column(s).
+#'
+#' @param x A SparkDataFrame
+#' @param func A function to be applied to each group partition specified by grouping
+#' column(s) of the SparkDataFrame.
+#' The output of func is a local R data.frame.
+#' @param schema The schema of the resulting SparkDataFrame after the function is applied.
+#' It must match the output of func.
+#' @family SparkDataFrame functions
+#' @rdname gapply
+#' @name gapply
+#' @export
+#' @examples
+#'
+#' \dontrun{
+#'
+#' Computes the arithmetic mean of `Sepal_Width` by grouping
+#' on `Species`. Output the grouping value and the average.
+#'
+#' df <- createDataFrame (sqlContext, iris)
+#' schema <- structType(structField("Species", "string"), structField("Avg", "double"))
+#' df1 <- gapply(
+#' df,
+#' function(x) {
+#' data.frame(x$Species[1], mean(x$Sepal_Width), stringsAsFactors = FALSE)
+#' },
+#' schema, col=df$"Species")
+#' collect(df1)
+#'
+#' Species Avg
+#' -----------------
+#' virginica 2.974
+#' versicolor 2.770
+#' setosa 3.428
+#'
+#' Fits linear models on iris dataset by grouping on the `Species` column and
+#' using `Sepal_Length` as a target variable, `Sepal_Width`, `Petal_Length`
+#' and `Petal_Width` as training features.
+#'
+#' df <- createDataFrame (sqlContext, iris)
+#' schema <- structType(structField("(Intercept)", "double"),
+#' structField("Sepal_Width", "double"), structField("Petal_Length", "double"),
+#' structField("Petal_Width", "double"))
+#' df1 <- gapply(
+#' df,
+#' function(x) {
+#' model <- suppressWarnings(lm(Sepal_Length ~
+#' Sepal_Width + Petal_Length + Petal_Width, x))
+#' data.frame(t(coef(model)))
+#' }, schema, df$"Species")
+#' collect(df1)
+#'
+#'Result
+#'---------
+#' Model (Intercept) Sepal_Width Petal_Length Petal_Width
+#' 1 0.699883 0.3303370 0.9455356 -0.1697527
+#' 2 1.895540 0.3868576 0.9083370 -0.6792238
+#' 3 2.351890 0.6548350 0.2375602 0.2521257
+#'
+#'}
+setMethod("gapply",
+ signature(x = "SparkDataFrame", func = "function", schema = "structType",
+ col = "Column"),
--- End diff --
I might have an issue in the signature, I'll fix it
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217611333
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217958625
Well, since the user provides the R function, I think he/she should provide the aggregate too.
Instead of providing:
```
function(x) {
data.frame(x$Species[1], mean(x$Sepal_Width), stringsAsFactors = FALSE)
}
```
They will provide:
```
function(x) {
data.frame(aggregate(x$Sepal_Width, by = list(x$Species), FUN = mean), stringsAsFactors = FALSE)
}
```
This is my understanding, there may be also other ways ...
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217593702
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217588848
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217592573
**[Test build #58043 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58043/consoleFull)** for PR 12966 at commit [`9704956`](https://github.com/apache/spark/commit/97049564433607544beef439ddce272f607298d9).
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217587290
**[Test build #58041 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58041/consoleFull)** for PR 12966 at commit [`204a105`](https://github.com/apache/spark/commit/204a1053dabd74d39a25e725276e31bb3a592917).
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217580578
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217580569
**[Test build #58037 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58037/consoleFull)** for PR 12966 at commit [`30693c2`](https://github.com/apache/spark/commit/30693c2b40ab459a9fe252a2d00595c8190f2094).
* This patch **fails SparkR unit tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217593705
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by shivaram <gi...@git.apache.org>.
Github user shivaram commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217611993
Change LGTM. Thanks @NarineK
cc @sun-rui @felixcheung @davies - any other comments on this ?
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217577994
**[Test build #58037 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58037/consoleFull)** for PR 12966 at commit [`30693c2`](https://github.com/apache/spark/commit/30693c2b40ab459a9fe252a2d00595c8190f2094).
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217945476
I see, that's sounds good too, but, I'm not sure how user friendly it will be.
I guess for R side we need something like this:
df <- createDataFrame (sqlContext, iris)
schema <- structType(structField("Species", "string"), structField("avg", "double"))
df <- repartition(df, col=df$"Species")
df1 <- dapply(
df,
function(x) {
data.frame(aggregate(x$Sepal_Width, by = list(x$Species), FUN = mean), stringsAsFactors = FALSE)
},
schema)
collect(df1)
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217602311
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on a diff in the pull request:
https://github.com/apache/spark/pull/12966#discussion_r62400077
--- Diff: R/pkg/R/DataFrame.R ---
@@ -1214,6 +1214,77 @@ setMethod("dapply",
dataFrame(sdf)
})
+#' gapply
+#'
+#' Apply a function to each group of a DataFrame. The group is defined by an input
+#' grouping column(s).
+#'
+#' @param x A SparkDataFrame
+#' @param func A function to be applied to each group partition specified by grouping
+#' column(s) of the SparkDataFrame.
+#' The output of func is a local R data.frame.
+#' @param schema The schema of the resulting SparkDataFrame after the function is applied.
+#' It must match the output of func.
+#' @family SparkDataFrame functions
+#' @rdname gapply
+#' @name gapply
+#' @export
+#' @examples
+#'
+#' \dontrun{
+#'
+#' Computes the arithmetic mean of `Sepal_Width` by grouping
+#' on `Species`. Output the grouping value and the average.
+#'
+#' df <- createDataFrame (sqlContext, iris)
+#' schema <- structType(structField("Species", "string"), structField("Avg", "double"))
+#' df1 <- gapply(
+#' df,
+#' function(x) {
+#' data.frame(x$Species[1], mean(x$Sepal_Width), stringsAsFactors = FALSE)
+#' },
+#' schema, col=df$"Species")
+#' collect(df1)
+#'
+#' Species Avg
+#' -----------------
+#' virginica 2.974
+#' versicolor 2.770
+#' setosa 3.428
+#'
+#' Fits linear models on iris dataset by grouping on the `Species` column and
+#' using `Sepal_Length` as a target variable, `Sepal_Width`, `Petal_Length`
+#' and `Petal_Width` as training features.
+#'
+#' df <- createDataFrame (sqlContext, iris)
+#' schema <- structType(structField("(Intercept)", "double"),
+#' structField("Sepal_Width", "double"), structField("Petal_Length", "double"),
+#' structField("Petal_Width", "double"))
+#' df1 <- gapply(
+#' df,
+#' function(x) {
+#' model <- suppressWarnings(lm(Sepal_Length ~
+#' Sepal_Width + Petal_Length + Petal_Width, x))
+#' data.frame(t(coef(model)))
+#' }, schema, df$"Species")
+#' collect(df1)
+#'
+#'Result
+#'---------
+#' Model (Intercept) Sepal_Width Petal_Length Petal_Width
+#' 1 0.699883 0.3303370 0.9455356 -0.1697527
+#' 2 1.895540 0.3868576 0.9083370 -0.6792238
+#' 3 2.351890 0.6548350 0.2375602 0.2521257
+#'
+#'}
+setMethod("gapply",
+ signature(x = "SparkDataFrame", func = "function", schema = "structType",
+ col = "Column"),
--- End diff --
yes, absolutely!
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217578915
**[Test build #58035 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58035/consoleFull)** for PR 12966 at commit [`be5de6a`](https://github.com/apache/spark/commit/be5de6a42e50c42b4af15b87623bc7b49aecb353).
* This patch **fails SparkR unit tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217955181
What do you think, @shivaram , @sun-rui , @felixcheung ?
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by sun-rui <gi...@git.apache.org>.
Github user sun-rui commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-221144808
@NarineK, I think we can close this PR?
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by shivaram <gi...@git.apache.org>.
Github user shivaram commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217956433
I am not sure why it should affect SparkR users. Users will still give us the same function to `gapply` as before but we will implement this inside SparkR side using the `aggregate` code snippet above ?
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-221157067
sure!
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by sun-rui <gi...@git.apache.org>.
Github user sun-rui commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217632210
@NarineK , @shivaram . sorry for missing the previous discussion. The problem is that repartition() can have multiple groups in a partition. This is not gapply() meant to handle with. Do I miss something?
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217588838
**[Test build #58041 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58041/consoleFull)** for PR 12966 at commit [`204a105`](https://github.com/apache/spark/commit/204a1053dabd74d39a25e725276e31bb3a592917).
* This patch **fails SparkR unit tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217578934
Merged build finished. Test FAILed.
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217612497
Thanks, @shivaram !
I have one question regarding factor datatypes. It seems that SparkR doesn't support it and we have to set: `stringsAsFactors = FALSE` in order to avoid "UnsupportedType" exceptions in data.frame.
Is it possible to map factor to maybe to list or string ... ?
R's data.frame converts strings to factor ... , but a factor in general doesn't have to be a string.
here is a snapshot from R's documentation:
`Character variables passed to data.frame are converted to factor columns unless protected by I or argument stringsAsFactors is false.
`
https://stat.ethz.ch/R-manual/R-devel/library/base/html/data.frame.html
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217580580
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by davies <gi...@git.apache.org>.
Github user davies commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217939409
@NarineK After repartition(), all the row with same grouping key are in the same partition, so we could have another groupBy() in R for single partition, then do the aggregate.
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by davies <gi...@git.apache.org>.
Github user davies commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217932852
@NarineK There is no such partitioner right now (it can't be cheap).
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217971884
This is similar to the following test case for repatition:
https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala#L1238
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by sun-rui <gi...@git.apache.org>.
Github user sun-rui commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-218022565
i suggest we follow the original design. i will take a detailed look at previous PR soon
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK closed the pull request at:
https://github.com/apache/spark/pull/12966
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217575956
**[Test build #58035 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58035/consoleFull)** for PR 12966 at commit [`be5de6a`](https://github.com/apache/spark/commit/be5de6a42e50c42b4af15b87623bc7b49aecb353).
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217601769
**[Test build #58050 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/58050/consoleFull)** for PR 12966 at commit [`bf3a74d`](https://github.com/apache/spark/commit/bf3a74d34b21eaa6c3d1422c1135658d9be58a8a).
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[GitHub] spark pull request: [SPARK-15196][SparkR] Add a wrapper for dapply...
Posted by NarineK <gi...@git.apache.org>.
Github user NarineK commented on the pull request:
https://github.com/apache/spark/pull/12966#issuecomment-217662459
Hi @sun-rui ,
I think it depends on how we do the repartitioning. It shouldn't be the case when we do it by :
https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala#L2191
@davies , can it be the case ?
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