Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/7280#discussion_r34226374
--- Diff: R/pkg/inst/tests/test_sparkSQL.R ---
@@ -108,6 +108,14 @@ test_that("create DataFrame from RDD", {
expect_equal(count(df), 10)
expect_equal(columns(df), c("a", "b"))
expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
+
+ localDF <- data.frame(name=c("John", "Smith", "Sarah"), age=c(19, 23,
18), height=c(164.10, 181.4, 173.7))
+ schema <- structType(structField("name", "string"), structField("age",
"integer"), structField("height", "float"))
+ df <- createDataFrame(sqlContext, localDF, schema)
--- End diff --
I checked this. The column is still `double` due to another problem I just
submitted in #7311. That is, in `createDataFrame`, the given `schema` will be
overwritten.
Although I solved that in #7311, I just found that with user defined
schema, it is possible to cause problem when collecting data from dataframe.
That is because we serialize `double` in R to `Double` in Java. If we
define a column as `float` in R and create a dataframe based on this schema.
The serialized and deserialized `Double` will be stored at the `float` column.
Then when we collect the data from it, it will throw error.
@shivaram How do you think? Do we need to fix #7311? Or you think it is up
to users to define correct schema?
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