[SPARK-25609][TESTS] Reduce time of test for SPARK-22226

## What changes were proposed in this pull request?

The PR changes the test introduced for SPARK-22226, so that we don't run analysis and optimization on the plan. The scope of the test is code generation and running the above mentioned operation is expensive and useless for the test.

The UT was also moved to the `CodeGenerationSuite` which is a better place given the scope of the test.

## How was this patch tested?

running the UT before SPARK-22226 fails, after it passes. The execution time is about 50% the original one. On my laptop this means that the test now runs in about 23 seconds (instead of 50 seconds).

Closes #22629 from mgaido91/SPARK-25609.

Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala

@@ -346,6 +346,16 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
     projection(row)
   }
 
+  test("SPARK-22226: splitExpressions should not generate codes beyond 64KB") {
+    val colNumber = 10000
+    val attrs = (1 to colNumber).map(colIndex => AttributeReference(s"_$colIndex", IntegerType)())
+    val lit = Literal(1000)
+    val exprs = attrs.flatMap { a =>
+      Seq(If(lit < a, lit, a), sqrt(a))
+    }
+    UnsafeProjection.create(exprs, attrs)
+  }
+
   test("SPARK-22543: split large predicates into blocks due to JVM code size limit") {
     val length = 600
 

sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala

@@ -2408,18 +2408,6 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
       Seq(Row(7, 1, 1), Row(7, 1, 2), Row(7, 2, 1), Row(7, 2, 2), Row(7, 3, 1), Row(7, 3, 2)))
   }
 
-  test("SPARK-22226: splitExpressions should not generate codes beyond 64KB") {
-    val colNumber = 10000
-    val input = spark.range(2).rdd.map(_ => Row(1 to colNumber: _*))
-    val df = sqlContext.createDataFrame(input, StructType(
-      (1 to colNumber).map(colIndex => StructField(s"_$colIndex", IntegerType, false))))
-    val newCols = (1 to colNumber).flatMap { colIndex =>
-      Seq(expr(s"if(1000 < _$colIndex, 1000, _$colIndex)"),
-        expr(s"sqrt(_$colIndex)"))
-    }
-    df.select(newCols: _*).collect()
-  }
-
   test("SPARK-22271: mean overflows and returns null for some decimal variables") {
     val d = 0.034567890
     val df = Seq(d, d, d, d, d, d, d, d, d, d).toDF("DecimalCol")