[SPARK-31918][R] Ignore S4 generic methods under SparkR namespace in …

…closure cleaning to support R 4.0.0+

### What changes were proposed in this pull request?

This PR proposes to ignore S4 generic methods under SparkR namespace in closure cleaning to support R 4.0.0+.

Currently, when you run the codes that runs R native codes, it fails as below with R 4.0.0:

```r
df <- createDataFrame(lapply(seq(100), function (e) list(value=e)))
count(dapply(df, function(x) as.data.frame(x[x$value < 50,]), schema(df)))
```

```
org.apache.spark.SparkException: R unexpectedly exited.
R worker produced errors: Error in lapply(part, FUN) : attempt to bind a variable to R_UnboundValue
```

The root cause seems to be related to when an S4 generic method is manually included into the closure's environment via `SparkR:::cleanClosure`. For example, when an RRDD is created via `createDataFrame` with calling `lapply` to convert, `lapply` itself:

https://github.com/apache/spark/blob/f53d8c63e80172295e2fbc805c0c391bdececcaa/R/pkg/R/RDD.R#L484

is added into the environment of the cleaned closure - because this is not an exposed namespace; however, this is broken in R 4.0.0+ for an unknown reason with an error message such as "attempt to bind a variable to R_UnboundValue".

Actually, we don't need to add the `lapply` into the environment of the closure because it is not supposed to be called in worker side. In fact, there is no private generic methods supposed to be called in worker side in SparkR at all from my understanding.

Therefore, this PR takes a simpler path to work around just by explicitly excluding the S4 generic methods under SparkR namespace to support R 4.0.0. in SparkR.

### Why are the changes needed?

To support R 4.0.0+ with SparkR, and unblock the releases on CRAN. CRAN requires the tests pass with the latest R.

### Does this PR introduce _any_ user-facing change?

Yes, it will support R 4.0.0 to end-users.

### How was this patch tested?

Manually tested. Both CRAN and tests with R 4.0.1:

```
══ testthat results  ═══════════════════════════════════════════════════════════
[ OK: 13 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 0 ]
✔ |  OK F W S | Context
✔ |  11       | binary functions [2.5 s]
✔ |   4       | functions on binary files [2.1 s]
✔ |   2       | broadcast variables [0.5 s]
✔ |   5       | functions in client.R
✔ |  46       | test functions in sparkR.R [6.3 s]
✔ |   2       | include R packages [0.3 s]
✔ |   2       | JVM API [0.2 s]
✔ |  75       | MLlib classification algorithms, except for tree-based algorithms [86.3 s]
✔ |  70       | MLlib clustering algorithms [44.5 s]
✔ |   6       | MLlib frequent pattern mining [3.0 s]
✔ |   8       | MLlib recommendation algorithms [9.6 s]
✔ | 136       | MLlib regression algorithms, except for tree-based algorithms [76.0 s]
✔ |   8       | MLlib statistics algorithms [0.6 s]
✔ |  94       | MLlib tree-based algorithms [85.2 s]
✔ |  29       | parallelize() and collect() [0.5 s]
✔ | 428       | basic RDD functions [25.3 s]
✔ |  39       | SerDe functionality [2.2 s]
✔ |  20       | partitionBy, groupByKey, reduceByKey etc. [3.9 s]
✔ |   4       | functions in sparkR.R
✔ |  16       | SparkSQL Arrow optimization [19.2 s]
✔ |   6       | test show SparkDataFrame when eager execution is enabled. [1.1 s]
✔ | 1175       | SparkSQL functions [134.8 s]
✔ |  42       | Structured Streaming [478.2 s]
✔ |  16       | tests RDD function take() [1.1 s]
✔ |  14       | the textFile() function [2.9 s]
✔ |  46       | functions in utils.R [0.7 s]
✔ |   0     1 | Windows-specific tests
────────────────────────────────────────────────────────────────────────────────
test_Windows.R:22: skip: sparkJars tag in SparkContext
Reason: This test is only for Windows, skipped
────────────────────────────────────────────────────────────────────────────────

══ Results ═════════════════════════════════════════════════════════════════════
Duration: 987.3 s

OK:       2304
Failed:   0
Warnings: 0
Skipped:  1
...
Status: OK
+ popd
Tests passed.
```

Note that I tested to build SparkR in R 4.0.0, and run the tests with R 3.6.3. It all passed. See also [the comment in the JIRA](https://issues.apache.org/jira/browse/SPARK-31918?focusedCommentId=17142837&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-17142837).

Closes #28907 from HyukjinKwon/SPARK-31918.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>

R/pkg/R/utils.R

@@ -529,7 +529,10 @@ processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) {
         # Namespaces other than "SparkR" will not be searched.
         if (!isNamespace(func.env) ||
             (getNamespaceName(func.env) == "SparkR" &&
-               !(nodeChar %in% getNamespaceExports("SparkR")))) {
+               !(nodeChar %in% getNamespaceExports("SparkR")) &&
+                  # Note that generic S4 methods should not be set to the environment of
+                  # cleaned closure. It does not work with R 4.0.0+. See also SPARK-31918.
+                  nodeChar != "" && !methods::isGeneric(nodeChar, func.env))) {
           # Only include SparkR internals.
 
           # Set parameter 'inherits' to FALSE since we do not need to search in

R/pkg/tests/fulltests/test_context.R

@@ -26,7 +26,9 @@ test_that("Check masked functions", {
                      "colnames", "colnames<-", "intersect", "rank", "rbind", "sample", "subset",
                      "summary", "transform", "drop", "window", "as.data.frame", "union", "not")
   version <- packageVersion("base")
-  if (as.numeric(version$major) >= 3 && as.numeric(version$minor) >= 3) {
+  is33Above <- as.numeric(version$major) >= 3 && as.numeric(version$minor) >= 3
+  is40Above <- as.numeric(version$major) >= 4
+  if (is33Above || is40Above) {
     namesOfMasked <- c("endsWith", "startsWith", namesOfMasked)
   }
   masked <- conflicts(detail = TRUE)$`package:SparkR`

R/pkg/tests/fulltests/test_mllib_classification.R

@@ -34,7 +34,7 @@ test_that("spark.svmLinear", {
   summary <- summary(model)
 
   # test summary coefficients return matrix type
-  expect_true(class(summary$coefficients) == "matrix")
+  expect_true(any(class(summary$coefficients) == "matrix"))
   expect_true(class(summary$coefficients[, 1]) == "numeric")
 
   coefs <- summary$coefficients[, "Estimate"]
@@ -130,7 +130,7 @@ test_that("spark.logit", {
   summary <- summary(model)
 
   # test summary coefficients return matrix type
-  expect_true(class(summary$coefficients) == "matrix")
+  expect_true(any(class(summary$coefficients) == "matrix"))
   expect_true(class(summary$coefficients[, 1]) == "numeric")
 
   versicolorCoefsR <- c(1.52, 0.03, -0.53, 0.04, 0.00)
@@ -242,8 +242,8 @@ test_that("spark.logit", {
   # Test binomial logistic regression against two classes with upperBoundsOnCoefficients
   # and upperBoundsOnIntercepts
   u <- matrix(c(1.0, 0.0, 1.0, 0.0), nrow = 1, ncol = 4)
-  model <- spark.logit(training, Species ~ ., upperBoundsOnCoefficients = u,
-                       upperBoundsOnIntercepts = 1.0)
+  model <- suppressWarnings(spark.logit(training, Species ~ ., upperBoundsOnCoefficients = u,
+                                        upperBoundsOnIntercepts = 1.0))
   summary <- summary(model)
   coefsR <- c(-11.13331, 1.00000, 0.00000, 1.00000, 0.00000)
   coefs <- summary$coefficients[, "Estimate"]
@@ -255,8 +255,8 @@ test_that("spark.logit", {
   # Test binomial logistic regression against two classes with lowerBoundsOnCoefficients
   # and lowerBoundsOnIntercepts
   l <- matrix(c(0.0, -1.0, 0.0, -1.0), nrow = 1, ncol = 4)
-  model <- spark.logit(training, Species ~ ., lowerBoundsOnCoefficients = l,
-                       lowerBoundsOnIntercepts = 0.0)
+  model <- suppressWarnings(spark.logit(training, Species ~ ., lowerBoundsOnCoefficients = l,
+                                        lowerBoundsOnIntercepts = 0.0))
   summary <- summary(model)
   coefsR <- c(0, 0, -1, 0, 1.902192)
   coefs <- summary$coefficients[, "Estimate"]
@@ -268,9 +268,9 @@ test_that("spark.logit", {
   # Test multinomial logistic regression with lowerBoundsOnCoefficients
   # and lowerBoundsOnIntercepts
   l <- matrix(c(0.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0, -1.0), nrow = 2, ncol = 4)
-  model <- spark.logit(training, Species ~ ., family = "multinomial",
-                       lowerBoundsOnCoefficients = l,
-                       lowerBoundsOnIntercepts = as.array(c(0.0, 0.0)))
+  model <- suppressWarnings(spark.logit(training, Species ~ ., family = "multinomial",
+                                        lowerBoundsOnCoefficients = l,
+                                        lowerBoundsOnIntercepts = as.array(c(0.0, 0.0))))
   summary <- summary(model)
   versicolorCoefsR <- c(42.639465, 7.258104, 14.330814, 16.298243, 11.716429)
   virginicaCoefsR <- c(0.0002970796, 4.79274, 7.65047, 25.72793, 30.0021)

R/pkg/tests/fulltests/test_mllib_clustering.R

@@ -171,7 +171,7 @@ test_that("spark.kmeans", {
   expect_equal(sort(collect(distinct(select(cluster, "prediction")))$prediction), c(0, 1))
 
   # test summary coefficients return matrix type
-  expect_true(class(summary.model$coefficients) == "matrix")
+  expect_true(any(class(summary.model$coefficients) == "matrix"))
   expect_true(class(summary.model$coefficients[1, ]) == "numeric")
 
   # Test model save/load

R/pkg/tests/fulltests/test_mllib_regression.R

@@ -116,7 +116,7 @@ test_that("spark.glm summary", {
   rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = dataset))
 
   # test summary coefficients return matrix type
-  expect_true(class(stats$coefficients) == "matrix")
+  expect_true(any(class(stats$coefficients) == "matrix"))
   expect_true(class(stats$coefficients[, 1]) == "numeric")
 
   coefs <- stats$coefficients