[SPARK-22076][SQL] Expand.projections should not be a Stream

## What changes were proposed in this pull request?

Spark with Scala 2.10 fails with a group by cube:
```
spark.range(1).select($"id" as "a", $"id" as "b").write.partitionBy("a").mode("overwrite").saveAsTable("rollup_bug")
spark.sql("select 1 from rollup_bug group by rollup ()").show
```

It can be traced back to #15484 , which made `Expand.projections` a lazy `Stream` for group by cube.

In scala 2.10 `Stream` captures a lot of stuff, and in this case it captures the entire query plan which has some un-serializable parts.

This change is also good for master branch, to reduce the serialized size of `Expand.projections`.

## How was this patch tested?

manually verified with Spark with Scala 2.10.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #19289 from cloud-fan/bug.

(cherry picked from commit ce6a71e)
Signed-off-by: gatorsmile <gatorsmile@gmail.com>

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala

@@ -280,9 +280,15 @@ class Analyzer(
      *  We need to get all of its subsets for a given GROUPBY expression, the subsets are
      *  represented as sequence of expressions.
      */
-    def cubeExprs(exprs: Seq[Expression]): Seq[Seq[Expression]] = exprs.toList match {
+    def cubeExprs(exprs: Seq[Expression]): Seq[Seq[Expression]] = {
+      // `cubeExprs0` is recursive and returns a lazy Stream. Here we call `toIndexedSeq` to
+      // materialize it and avoid serialization problems later on.
+      cubeExprs0(exprs).toIndexedSeq
+    }
+
+    def cubeExprs0(exprs: Seq[Expression]): Seq[Seq[Expression]] = exprs.toList match {
       case x :: xs =>
-        val initial = cubeExprs(xs)
+        val initial = cubeExprs0(xs)
         initial.map(x +: _) ++ initial
       case Nil =>
         Seq(Seq.empty)