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[SPARK-12104][SPARKR] collect() does not handle multiple columns with same name. #10118

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[SPARK-12104][SPARKR] collect() does not handle multiple columns with same name. #10118

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[SPARK-12104][SPARKR] collect() does not handle multiple columns with…
Dec 3, 2015
3760410
add a test case for join().
Dec 4, 2015
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8 changes: 4 additions & 4 deletions R/pkg/R/DataFrame.R
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Original file line number Diff line number Diff line change
Expand Up @@ -822,21 +822,21 @@ setMethod("collect",
# Get a column of complex type returns a list.
# Get a cell from a column of complex type returns a list instead of a vector.
col <- listCols[[colIndex]]
colName <- dtypes[[colIndex]][[1]]
if (length(col) <= 0) {
df[[colName]] <- col
df[[colIndex]] <- col
} else {
colType <- dtypes[[colIndex]][[2]]
# Note that "binary" columns behave like complex types.
if (!is.null(PRIMITIVE_TYPES[[colType]]) && colType != "binary") {
vec <- do.call(c, col)
stopifnot(class(vec) != "list")
df[[colName]] <- vec
df[[colIndex]] <- vec
} else {
df[[colName]] <- col
df[[colIndex]] <- col
}
}
}
names(df) <- names(x)
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@falaki falaki Dec 3, 2015

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This is slightly different from 1.5. We will get exact same column names in local data.frame. In Spark 1.5 subsequent instances of the same name are appended with numbers. I am not sure which one is better. In fact I slightly prefer your suggested behavior. But just in case others want to chime in: cc @shivaram

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@felixcheung felixcheung Dec 3, 2015

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I think the current behavior in 1.6 is actually an unintentional change from a recent change in the collect() code
Matching back to 1.5.x seems to make sense

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@sun-rui sun-rui Dec 3, 2015

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I tested with Spark 1.4.1 and 1.5.1, both just have the same names instead of making the duplicated names unique. So this PR's behavior is backward-compatible.

>df <- createDataFrame(sqlContext, list(list(1, 2)), schema = c("a", "a"))
>collect(df)
  a a
1 1 2

Actually, it is very easy to make unique column names, like:

names(df) <- make.names(names(x), unique = TRUE)

But we need discussion is this preferred behavior?

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@shivaram shivaram Dec 3, 2015

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Yeah lets keep the local DF names consistent with the schema in SQL. (i.e. duplicated name, name is fine). If this is a breaking change we can add a note in the release notes

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@shivaram shivaram Dec 3, 2015

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@falaki Just curious: what is the query you used to create the numbered columns ?

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@falaki falaki Dec 3, 2015

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I was using a left outer join and then collecting it.

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@shivaram shivaram Dec 4, 2015

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@sun-rui Can we add a test with left-outer join and then collect ?

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@falaki falaki Dec 4, 2015

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BTW: as I said, I like this behavior.

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@shivaram shivaram Dec 4, 2015

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Yeah this behavior is fine. I just want to make sure that example doesn't trigger some other code path etc.

df
}
})
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6 changes: 6 additions & 0 deletions R/pkg/inst/tests/test_sparkSQL.R
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Expand Up @@ -530,6 +530,11 @@ test_that("collect() returns a data.frame", {
expect_equal(names(rdf)[1], "age")
expect_equal(nrow(rdf), 0)
expect_equal(ncol(rdf), 2)

# collect() correctly handles multiple columns with same name
df <- createDataFrame(sqlContext, list(list(1, 2)), schema = c("name", "name"))
ldf <- collect(df)
expect_equal(names(ldf), c("name", "name"))
})

test_that("limit() returns DataFrame with the correct number of rows", {
Expand Down Expand Up @@ -1197,6 +1202,7 @@ test_that("join() and merge() on a DataFrame", {
joined <- join(df, df2)
expect_equal(names(joined), c("age", "name", "name", "test"))
expect_equal(count(joined), 12)
expect_equal(names(collect(joined)), c("age", "name", "name", "test"))

joined2 <- join(df, df2, df$name == df2$name)
expect_equal(names(joined2), c("age", "name", "name", "test"))
Expand Down