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[SPARK-19342][SPARKR] bug fixed in collect method for collecting timestamp column #16689

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[SPARK-19342][SPARKR] bug fixed in collect method for collecting timestamp column #16689

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a51c2eb
SPARK-19342 bug fixed in collect method for collecting timestamp column
titicaca Jan 24, 2017
5829ddd
SPARK-19342 bug fixed in collect method for collecting timestamp column
titicaca Jan 24, 2017
6a0eb3f
SPARK-19342 bug fixed in collect method for collecting timestamp colu…
titicaca Jan 25, 2017
09d4dfb
SPARK-19342 fix R code style error in test
titicaca Jan 25, 2017
43e334a
SPARK-19342 If vec is an vector with only NAs, the type will be cast …
titicaca Jan 25, 2017
7903bb3
SPARK-19342 fix for R style checks
titicaca Jan 31, 2017
8379c38
SPARK-19342 remove the comment
titicaca Jan 31, 2017
407c625
SPARK-19342 modify PRIMITIVE_TYPES for type timestamp
titicaca Feb 1, 2017
d6d454e
SPARK-19342 fix for method coltypes in test_sparkSQL.R#2262
titicaca Feb 4, 2017
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5 changes: 5 additions & 0 deletions R/pkg/R/DataFrame.R
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Original file line number Diff line number Diff line change
Expand Up @@ -1138,6 +1138,11 @@ setMethod("collect",
if (!is.null(PRIMITIVE_TYPES[[colType]]) && colType != "binary") {
vec <- do.call(c, col)
stopifnot(class(vec) != "list")
class(vec) <-
if (colType == "timestamp")
c("POSIXct", "POSIXt")
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@felixcheung felixcheung Jan 31, 2017

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why should the class be c("POSIXct", "POSIXt") in this case?

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@titicaca titicaca Jan 31, 2017

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Because PRIMITIVE_TYPES[["timestamp"]] is POSIXct, it usually comes with POSIXt together. POSIXt is virtual class used to allow operations such as subtraction to mix the two classes POSIXct and POSIXlt.
The previous convertion will also convert timestamp to c("POSIXct", "POSIXt").

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@felixcheung felixcheung Jan 31, 2017

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Should PRIMITIVE_TYPES[["timestamp"]] be changed then
https://github.com/apache/spark/blob/master/R/pkg/R/types.R#L32

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@titicaca titicaca Feb 1, 2017

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It looks better if it won't affect other methods. I will try it. Thanks for the advice.

else
PRIMITIVE_TYPES[[colType]]
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@felixcheung felixcheung Jan 31, 2017

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by setting these instead of having it inferred - does this break any existing behavior? does any type differ because of this line of change?

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@titicaca titicaca Jan 31, 2017

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Currently all tests are passed, except for the two modified tests with NA types as discussed before. The followings are the all type convertions from SparkDataframe to R data.frame, which have been tested in the existing tests in test_sparkSQL.R.

PRIMITIVE_TYPES <- as.environment(list(
  "tinyint" = "integer",
  "smallint" = "integer",
  "int" = "integer",
  "bigint" = "numeric",
  "float" = "numeric",
  "double" = "numeric",
  "decimal" = "numeric",
  "string" = "character",
  "binary" = "raw",
  "boolean" = "logical",
  "timestamp" = "POSIXct",
  "date" = "Date",
  # following types are not SQL types returned by dtypes(). They are listed here for usage
  # by checkType() in schema.R.
  # TODO: refactor checkType() in schema.R.
  "byte" = "integer",
  "integer" = "integer"
  ))

df[[colIndex]] <- vec
} else {
df[[colIndex]] <- col
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42 changes: 40 additions & 2 deletions R/pkg/inst/tests/testthat/test_sparkSQL.R
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Expand Up @@ -1277,9 +1277,9 @@ test_that("column functions", {

# Test first(), last()
df <- read.json(jsonPath)
expect_equal(collect(select(df, first(df$age)))[[1]], NA)
expect_equal(collect(select(df, first(df$age)))[[1]], NA_real_)
expect_equal(collect(select(df, first(df$age, TRUE)))[[1]], 30)
expect_equal(collect(select(df, first("age")))[[1]], NA)
expect_equal(collect(select(df, first("age")))[[1]], NA_real_)
expect_equal(collect(select(df, first("age", TRUE)))[[1]], 30)
expect_equal(collect(select(df, last(df$age)))[[1]], 19)
expect_equal(collect(select(df, last(df$age, TRUE)))[[1]], 19)
Expand Down Expand Up @@ -2748,6 +2748,44 @@ test_that("Call DataFrameWriter.load() API in Java without path and check argume
"Unnamed arguments ignored: 2, 3, a.")
})

test_that("Collect on DataFrame when NAs exists at the top of a timestamp column", {
ldf <- data.frame(col1 = c(0, 1, 2),
col2 = c(as.POSIXct("2017-01-01 00:00:01"),
NA,
as.POSIXct("2017-01-01 12:00:01")),
col3 = c(as.POSIXlt("2016-01-01 00:59:59"),
NA,
as.POSIXlt("2016-01-01 12:01:01")))
sdf1 <- createDataFrame(ldf)
ldf1 <- collect(sdf1)
expect_equal(dtypes(sdf1), list(c("col1", "double"),
c("col2", "timestamp"),
c("col3", "timestamp")))
expect_equal(class(ldf1$col1), "numeric")
expect_equal(class(ldf1$col2), c("POSIXct", "POSIXt"))
expect_equal(class(ldf1$col3), c("POSIXct", "POSIXt"))

# Columns with NAs at the top
sdf2 <- filter(sdf1, "col1 > 1")
ldf2 <- collect(sdf2)
expect_equal(dtypes(sdf2), list(c("col1", "double"),
c("col2", "timestamp"),
c("col3", "timestamp")))
expect_equal(class(ldf2$col1), "numeric")
expect_equal(class(ldf2$col2), c("POSIXct", "POSIXt"))
expect_equal(class(ldf2$col3), c("POSIXct", "POSIXt"))

# Columns with only NAs, the type will also be cast to PRIMITIVE_TYPE
sdf3 <- filter(sdf1, "col1 == 0")
ldf3 <- collect(sdf3)
expect_equal(dtypes(sdf3), list(c("col1", "double"),
c("col2", "timestamp"),
c("col3", "timestamp")))
expect_equal(class(ldf3$col1), "numeric")
expect_equal(class(ldf3$col2), c("POSIXct", "POSIXt"))
expect_equal(class(ldf3$col3), c("POSIXct", "POSIXt"))
})

unlink(parquetPath)
unlink(orcPath)
unlink(jsonPath)
Expand Down