[SPARK-16186] [SQL] Support partition batch pruning with `IN` predicate in InMemoryTableScanExec

## What changes were proposed in this pull request?

One of the most frequent usage patterns for Spark SQL is using **cached tables**. This PR improves `InMemoryTableScanExec` to handle `IN` predicate efficiently by pruning partition batches. Of course, the performance improvement varies over the queries and the datasets. But, for the following simple query, the query duration in Spark UI goes from 9 seconds to 50~90ms. It's about over 100 times faster.

**Before**
```scala
$ bin/spark-shell --driver-memory 6G
scala> val df = spark.range(2000000000)
scala> df.createOrReplaceTempView("t")
scala> spark.catalog.cacheTable("t")
scala> sql("select id from t where id = 1").collect()    // About 2 mins
scala> sql("select id from t where id = 1").collect()    // less than 90ms
scala> sql("select id from t where id in (1,2,3)").collect()  // 9 seconds
```

**After**
```scala
scala> sql("select id from t where id in (1,2,3)").collect() // less than 90ms
```

This PR has impacts over 35 queries of TPC-DS if the tables are cached.
Note that this optimization is applied for `IN`.  To apply `IN` predicate having more than 10 items, `spark.sql.optimizer.inSetConversionThreshold` option should be increased.

## How was this patch tested?

Pass the Jenkins tests (including new testcases).

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13887 from dongjoon-hyun/SPARK-16186.

sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala

@@ -79,6 +79,10 @@ private[sql] case class InMemoryTableScanExec(
 
     case IsNull(a: Attribute) => statsFor(a).nullCount > 0
     case IsNotNull(a: Attribute) => statsFor(a).count - statsFor(a).nullCount > 0
+
+    case In(a: AttributeReference, list: Seq[Expression]) if list.forall(_.isInstanceOf[Literal]) =>
+      list.map(l => statsFor(a).lowerBound <= l.asInstanceOf[Literal] &&
+        l.asInstanceOf[Literal] <= statsFor(a).upperBound).reduce(_ || _)
   }
 
   val partitionFilters: Seq[Expression] = {

sql/core/src/test/scala/org/apache/spark/sql/execution/columnar/PartitionBatchPruningSuite.scala

@@ -65,11 +65,18 @@ class PartitionBatchPruningSuite
     }, 5).toDF()
     pruningData.createOrReplaceTempView("pruningData")
     spark.catalog.cacheTable("pruningData")
+
+    val pruningStringData = sparkContext.makeRDD((100 to 200).map { key =>
+      StringData(key.toString)
+    }, 5).toDF()
+    pruningStringData.createOrReplaceTempView("pruningStringData")
+    spark.catalog.cacheTable("pruningStringData")
   }
 
   override protected def afterEach(): Unit = {
     try {
       spark.catalog.uncacheTable("pruningData")
+      spark.catalog.uncacheTable("pruningStringData")
     } finally {
       super.afterEach()
     }
@@ -110,9 +117,23 @@ class PartitionBatchPruningSuite
     88 to 100
   }
 
-  // With unsupported predicate
+  // Support `IN` predicate
+  checkBatchPruning("SELECT key FROM pruningData WHERE key IN (1)", 1, 1)(Seq(1))
+  checkBatchPruning("SELECT key FROM pruningData WHERE key IN (1, 2)", 1, 1)(Seq(1, 2))
+  checkBatchPruning("SELECT key FROM pruningData WHERE key IN (1, 11)", 1, 2)(Seq(1, 11))
+  checkBatchPruning("SELECT key FROM pruningData WHERE key IN (1, 21, 41, 61, 81)", 5, 5)(
+    Seq(1, 21, 41, 61, 81))
+  checkBatchPruning("SELECT CAST(s AS INT) FROM pruningStringData WHERE s = '100'", 1, 1)(Seq(100))
+  checkBatchPruning("SELECT CAST(s AS INT) FROM pruningStringData WHERE s < '102'", 1, 1)(
+    Seq(100, 101))
+  checkBatchPruning(
+    "SELECT CAST(s AS INT) FROM pruningStringData WHERE s IN ('99', '150', '201')", 1, 1)(
+      Seq(150))
+
+  // With unsupported `InSet` predicate
   {
     val seq = (1 to 30).mkString(", ")
+    checkBatchPruning(s"SELECT key FROM pruningData WHERE key IN ($seq)", 5, 10)(1 to 30)
     checkBatchPruning(s"SELECT key FROM pruningData WHERE NOT (key IN ($seq))", 5, 10)(31 to 100)
     checkBatchPruning(s"SELECT key FROM pruningData WHERE NOT (key IN ($seq)) AND key > 88", 1, 2) {
       89 to 100