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Github user adrian555 commented on the issue: https://github.com/apache/spark/pull/22455 > I'm not sure about the new changes. IMO we are mixing S3 and S4 convention here as per @adrian555. The implemented code is still conforming to S4 object standard that `show()` is the method to display the object. > IMO, we should have an option for show to return a data.frame, if we are running in an IDE. then we can leverage any custom print logic the IDE has `show()` ideally should return an invisible NULL to be consistent with other R objects. CC @falaki for any further discussion on this topic. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user falaki commented on the issue: https://github.com/apache/spark/pull/22455 @adrian555 yes, that looks good. Thank you! --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user adrian555 commented on the issue:
https://github.com/apache/spark/pull/22455
Thanks @falaki. We are taking out the `print` method out so the requirement
seems clear now. Yes, for Jupyter with IRkernel, two options
`jupyter.rich_display` and jupyter.base_display_func` are used to display the
data.frame in a nicer way, as you mentioned. And since it is a kernel with a
backend, the automatic print is not really calling the `show` method. Instead,
the data is evaluated and the resulted message is returned as a response.
Hence, the `df` and `show(df)` have different output formats.
I think you want to have a common option in SparkR that other framework can
use it instead of creating its own. Will `sparkr.SparkDataFrame.base_show_func`
work for you? And I will modify the `show()` method for `SparkDataFrame` as
follow
```r
setMethod("show", "SparkDataFrame",
function(object) {
showFunc <- getOption("sparkr.SparkDataFrame.base_show_func")
if (!is.null(showFunc)) {
stopifnot(typeof(showFunc) == "closure")
showFunc(object)
} else {
allConf <- sparkR.conf()
prop <- allConf[["spark.sql.repl.eagerEval.enabled"]]
if (!is.null(prop) && identical(prop, "true")) {
argsList <- list()
argsList$x <- object
prop <- allConf[["spark.sql.repl.eagerEval.maxNumRows"]]
if (!is.null(prop)) {
numRows <- as.numeric(prop)
if (numRows > 0) {
argsList$numRows <- numRows
}
}
prop <- allConf[["spark.sql.repl.eagerEval.truncate"]]
if (!is.null(prop)) {
truncate <- as.numeric(prop)
if (truncate > 0) {
argsList$truncate <- truncate
}
}
do.call(showDF, argsList)
} else {
cols <- lapply(dtypes(object), function(l) {
paste(l, collapse = ":")
})
s <- paste(cols, collapse = ", ")
cat(paste(class(object), "[", s, "]\n", sep = ""))
}
}
})
```
Basically if that option is set to a predefined function, it will run that
function to show. This approach also makes the `df` and `show(df)` act the same.
Please have a look. Thanks.
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[GitHub] spark issue #22455: [SPARK-24572][SPARKR] "eager execution" for R shell, IDE
Github user falaki commented on the issue: https://github.com/apache/spark/pull/22455 @adrian555 These all great points. My high-level was enabling other platforms (e.g., Jupyter) to plugin more advanced (custom) functions for displaying SparkDataFrame. If the framework does not set anything, SparkR would use `show()` by default. But if the notebook environment has a nicer way of displaying, they can simply set a config to use that by default. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22455: [SPARK-24572][SPARKR] "eager execution" for R shell, IDE
Github user adrian555 commented on the issue:
https://github.com/apache/spark/pull/22455
Another flavor will be this
```r
print.SparkDataFrame <- function(x, ...) {
if ((length(list(...)) > 0) &&
identical(sparkR.conf("spark.sql.repl.eagerEval.enabled", "false")[[1]],
"true")) {
# TODO: limit the maxNumRows and truncate
df <- collect(x)
print(df, ...)
} else {
show(x)
}
}
```
this allows the other `print` formats to be taken. For example, when eager
execution is enabled, `print(createDataFrame(iris), quote=TRUE)` will have such
output
```
Sepal_Length Sepal_Width Petal_Length Petal_Width Species
1 "5.1" "3.5""1.4" "0.2" "setosa"
2 "4.9" "3.0""1.4" "0.2" "setosa"
3 "4.7" "3.2""1.3" "0.2" "setosa"
4 "4.6" "3.1""1.5" "0.2" "setosa"
```
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[GitHub] spark issue #22455: [SPARK-24572][SPARKR] "eager execution" for R shell, IDE
Github user adrian555 commented on the issue:
https://github.com/apache/spark/pull/22455
@falaki trying to understand what you were asking for. I know that `print`
takes some arguments like `digit` and `quote` that can further format the
output. `print` is for S3 object and `show` is for S4 object. So, are you
asking for `print.SparkDataFrame` function as follow
```
print.SparkDataFrame <- function(x, ...) {
argsList <- list(...)
if (!is.null(argsList$eager) && !argsList$eager) {
cols <- lapply(dtypes(x), function(l) {
paste(l, collapse = ":")
})
s <- paste(cols, collapse = ", ")
cat(paste(class(x), "[", s, "]\n", sep = ""))
} else {
show(x)
}
}
```
With this,
* `print(df)` will have the same behavior as `show(df)`.
* `print(df, eager=FALSE)` will continue to display just the SparkDataFrame
class info, even eager execution is enabled.
* `print(df, eager=TRUE)` will be the same as `show()` when eager execution
is enabled. But if eager execution is disable, the passed in `eager=TRUE` is
ignored. (This way, we guarantee only the session's SQLConf is in effect.)
Also, are you also thinking of passing the `maxNumRows` and `truncate` as
arguments to `print.SparkDataFrame` and replacing
`spark.sql.repl.eagerEval.maxNumRows` and `spark.sql.repl.eagerEval.truncate`?
Please let me know if this is on par with your thought then I will push a
commit.
Thanks.
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[GitHub] spark issue #22455: [SPARK-24572][SPARKR] "eager execution" for R shell, IDE
Github user falaki commented on the issue: https://github.com/apache/spark/pull/22455 @adrian555 thanks for submitting this. Can we have a config to set the default `print` function in eager mode. It can default to `show`, but I can imagine it is useful to make it configurable. For example in Jupyter there are ricer ways of displaying R data.frame. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user felixcheung commented on the issue: https://github.com/apache/spark/pull/22455 > Looks like the test was terminated in the middle, not likely related to the any code change. Could someone please ask for retest? Thanks. appveyor one? you need to close and re-open this PR to trigger it --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user viirya commented on the issue: https://github.com/apache/spark/pull/22455 Let's also update the doc of `REPL_EAGER_EVAL_ENABLED` in `SQLConf`. After this patch, eager evaluation is not only supported in PySpark. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] spark issue #22455: [SPARK-24572][SparkR] "eager execution" for R shell, IDE
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[GitHub] spark issue #22455: [SPARK-24572][SparkR] "eager execution" for R shell, IDE
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[GitHub] spark issue #22455: [SPARK-24572][SparkR] "eager execution" for R shell, IDE
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[GitHub] spark issue #22455: [SPARK-24572][SparkR] "eager execution" for R shell, IDE
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