[SPARK-25454][SQL] add a new config for picking minimum precision for integral literals

## What changes were proposed in this pull request?

apache#20023 proposed to allow precision lose during decimal operations, to reduce the possibilities of overflow. This is a behavior change and is protected by the DECIMAL_OPERATIONS_ALLOW_PREC_LOSS config. However, that PR introduced another behavior change: pick a minimum precision for integral literals, which is not protected by a config. This PR add a new config for it: `spark.sql.literal.pickMinimumPrecision`.

This can allow users to work around issue in SPARK-25454, which is caused by a long-standing bug of negative scale.

## How was this patch tested?

a new test

Closes apache#22494 from cloud-fan/decimal.

Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>

Author: cloud-fan

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

@@ -290,11 +290,13 @@ object DecimalPrecision extends TypeCoercionRule {
         // potentially loosing 11 digits of the fractional part. Using only the precision needed
         // by the Literal, instead, the result would be DECIMAL(38 + 1 + 1, 18), which would
         // become DECIMAL(38, 16), safely having a much lower precision loss.
-        case (l: Literal, r) if r.dataType.isInstanceOf[DecimalType]
-          && l.dataType.isInstanceOf[IntegralType] =>
+        case (l: Literal, r) if r.dataType.isInstanceOf[DecimalType] &&
+            l.dataType.isInstanceOf[IntegralType] &&
+            SQLConf.get.literalPickMinimumPrecision =>
           b.makeCopy(Array(Cast(l, DecimalType.fromLiteral(l)), r))
-        case (l, r: Literal) if l.dataType.isInstanceOf[DecimalType]
-          && r.dataType.isInstanceOf[IntegralType] =>
+        case (l, r: Literal) if l.dataType.isInstanceOf[DecimalType] &&
+            r.dataType.isInstanceOf[IntegralType] &&
+            SQLConf.get.literalPickMinimumPrecision =>
           b.makeCopy(Array(l, Cast(r, DecimalType.fromLiteral(r))))
         // Promote integers inside a binary expression with fixed-precision decimals to decimals,
         // and fixed-precision decimals in an expression with floats / doubles to doubles

sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

@@ -1331,6 +1331,15 @@ object SQLConf {
       .booleanConf
       .createWithDefault(true)
 
+  val LITERAL_PICK_MINIMUM_PRECISION =
+    buildConf("spark.sql.legacy.literal.pickMinimumPrecision")
+      .internal()
+      .doc("When integral literal is used in decimal operations, pick a minimum precision " +
+        "required by the literal if this config is true, to make the resulting precision and/or " +
+        "scale smaller. This can reduce the possibility of precision lose and/or overflow.")
+      .booleanConf
+      .createWithDefault(true)
+
   val SQL_OPTIONS_REDACTION_PATTERN =
     buildConf("spark.sql.redaction.options.regex")
       .doc("Regex to decide which keys in a Spark SQL command's options map contain sensitive " +
@@ -1925,6 +1934,8 @@ class SQLConf extends Serializable with Logging {
 
   def decimalOperationsAllowPrecisionLoss: Boolean = getConf(DECIMAL_OPERATIONS_ALLOW_PREC_LOSS)
 
+  def literalPickMinimumPrecision: Boolean = getConf(LITERAL_PICK_MINIMUM_PRECISION)
+
   def continuousStreamingExecutorQueueSize: Int = getConf(CONTINUOUS_STREAMING_EXECUTOR_QUEUE_SIZE)
 
   def continuousStreamingExecutorPollIntervalMs: Long =

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

@@ -2849,6 +2849,13 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     val result = ds.flatMap(_.bar).distinct
     result.rdd.isEmpty
   }
+
+  test("SPARK-25454: decimal division with negative scale") {
+    // TODO: completely fix this issue even when LITERAL_PRECISE_PRECISION is true.
+    withSQLConf(SQLConf.LITERAL_PICK_MINIMUM_PRECISION.key -> "false") {
+      checkAnswer(sql("select 26393499451 / (1e6 * 1000)"), Row(BigDecimal("26.3934994510000")))
+    }
+  }
 }
 
 case class Foo(bar: Option[String])