[SPARK-8420] [SQL] Fix comparision of timestamps/dates with strings (branch-1.4)

This is branch 1.4 backport of https://github.com/apache/spark/pull/6888.

Below is the original description.

In earlier versions of Spark SQL we casted `TimestampType` and `DataType` to `StringType` when it was involved in a binary comparison with a `StringType`.  This allowed comparing a timestamp with a partial date as a user would expect.
 - `time > "2014-06-10"`
 - `time > "2014"`

In 1.4.0 we tried to cast the String instead into a Timestamp.  However, since partial dates are not a valid complete timestamp this results in `null` which results in the tuple being filtered.

This PR restores the earlier behavior.  Note that we still special case equality so that these comparisons are not affected by not printing zeros for subsecond precision.

Author: Michael Armbrust <michaeldatabricks.com>

Closes #6888 from marmbrus/timeCompareString and squashes the following commits:

bdef29c [Michael Armbrust] test partial date
1f09adf [Michael Armbrust] special handling of equality
1172c60 [Michael Armbrust] more test fixing
4dfc412 [Michael Armbrust] fix tests
aaa9508 [Michael Armbrust] newline
04d908f [Michael Armbrust] [SPARK-8420][SQL] Fix comparision of timestamps/dates with strings

Conflicts:
	sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
	sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala

Author: Michael Armbrust <michael@databricks.com>

Closes #6914 from yhuai/timeCompareString-1.4 and squashes the following commits:

9882915 [Michael Armbrust] [SPARK-8420] [SQL] Fix comparision of timestamps/dates with strings

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

@@ -242,7 +242,16 @@ trait HiveTypeCoercion {
       case a: BinaryArithmetic if a.right.dataType == StringType =>
         a.makeCopy(Array(a.left, Cast(a.right, DoubleType)))
 
-      // we should cast all timestamp/date/string compare into string compare
+      // For equality between string and timestamp we cast the string to a timestamp
+      // so that things like rounding of subsecond precision does not affect the comparison.
+      case p @ Equality(left @ StringType(), right @ TimestampType()) =>
+        p.makeCopy(Array(Cast(left, TimestampType), right))
+      case p @ Equality(left @ TimestampType(), right @ StringType()) =>
+        p.makeCopy(Array(left, Cast(right, TimestampType)))
+
+      // We should cast all relative timestamp/date/string comparison into string comparisions
+      // This behaves as a user would expect because timestamp strings sort lexicographically.
+      // i.e. TimeStamp(2013-01-01 00:00 ...) < "2014" = true
       case p: BinaryComparison if p.left.dataType == StringType &&
                                   p.right.dataType == DateType =>
         p.makeCopy(Array(p.left, Cast(p.right, StringType)))
@@ -251,10 +260,12 @@ trait HiveTypeCoercion {
         p.makeCopy(Array(Cast(p.left, StringType), p.right))
       case p: BinaryComparison if p.left.dataType == StringType &&
                                   p.right.dataType == TimestampType =>
-        p.makeCopy(Array(Cast(p.left, TimestampType), p.right))
+        p.makeCopy(Array(p.left, Cast(p.right, StringType)))
       case p: BinaryComparison if p.left.dataType == TimestampType &&
                                   p.right.dataType == StringType =>
-        p.makeCopy(Array(p.left, Cast(p.right, TimestampType)))
+        p.makeCopy(Array(Cast(p.left, StringType), p.right))
+
+      // Comparisons between dates and timestamps.
       case p: BinaryComparison if p.left.dataType == TimestampType &&
                                   p.right.dataType == DateType =>
         p.makeCopy(Array(Cast(p.left, StringType), Cast(p.right, StringType)))

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala

@@ -175,6 +175,15 @@ abstract class BinaryComparison extends BinaryExpression with Predicate {
   self: Product =>
 }
 
+/** An extractor that matches both standard 3VL equality and null-safe equality. */
+private[sql] object Equality {
+  def unapply(e: BinaryComparison): Option[(Expression, Expression)] = e match {
+    case EqualTo(l, r) => Some((l, r))
+    case EqualNullSafe(l, r) => Some((l, r))
+    case _ => None
+  }
+}
+
 case class EqualTo(left: Expression, right: Expression) extends BinaryComparison {
   override def symbol: String = "="
 

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

@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import java.sql.{Date, Timestamp}
+
+class DataFrameDateTimeSuite extends QueryTest {
+
+  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+  import ctx.implicits._
+
+  test("timestamp comparison with date strings") {
+    val df = Seq(
+      (1, Timestamp.valueOf("2015-01-01 00:00:00")),
+      (2, Timestamp.valueOf("2014-01-01 00:00:00"))).toDF("i", "t")
+
+    checkAnswer(
+      df.select("t").filter($"t" <= "2014-06-01"),
+      Row(Timestamp.valueOf("2014-01-01 00:00:00")) :: Nil)
+
+
+    checkAnswer(
+      df.select("t").filter($"t" >= "2014-06-01"),
+      Row(Timestamp.valueOf("2015-01-01 00:00:00")) :: Nil)
+  }
+
+  test("date comparison with date strings") {
+    val df = Seq(
+      (1, Date.valueOf("2015-01-01")),
+      (2, Date.valueOf("2014-01-01"))).toDF("i", "t")
+
+    checkAnswer(
+      df.select("t").filter($"t" <= "2014-06-01"),
+      Row(Date.valueOf("2014-01-01")) :: Nil)
+
+
+    checkAnswer(
+      df.select("t").filter($"t" >= "2015"),
+      Row(Date.valueOf("2015-01-01")) :: Nil)
+  }
+}

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

@@ -19,6 +19,8 @@ package org.apache.spark.sql
 
 import org.scalatest.BeforeAndAfterAll
 
+import java.sql.Timestamp
+
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.GeneratedAggregate
@@ -311,6 +313,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
   }
 
   test("SPARK-3173 Timestamp support in the parser") {
+    (0 to 3).map(i => Tuple1(new Timestamp(i))).toDF("time").registerTempTable("timestamps")
+
     checkAnswer(sql(
       "SELECT time FROM timestamps WHERE time='1969-12-31 16:00:00.0'"),
       Row(java.sql.Timestamp.valueOf("1969-12-31 16:00:00")))

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

@@ -174,12 +174,6 @@ object TestData {
       "3, C3, true, null" ::
       "4, D4, true, 2147483644" :: Nil)
 
-  case class TimestampField(time: Timestamp)
-  val timestamps = TestSQLContext.sparkContext.parallelize((0 to 3).map { i =>
-    TimestampField(new Timestamp(i))
-  })
-  timestamps.toDF().registerTempTable("timestamps")
-
   case class IntField(i: Int)
   // An RDD with 4 elements and 8 partitions
   val withEmptyParts = TestSQLContext.sparkContext.parallelize((1 to 4).map(IntField), 8)

sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala

@@ -90,15 +90,18 @@ class InMemoryColumnarQuerySuite extends QueryTest {
   }
 
   test("SPARK-2729 regression: timestamp data type") {
+    val timestamps = (0 to 3).map(i => Tuple1(new Timestamp(i))).toDF("time")
+    timestamps.registerTempTable("timestamps")
+
     checkAnswer(
       sql("SELECT time FROM timestamps"),
-      timestamps.collect().toSeq.map(Row.fromTuple))
+      timestamps.collect().toSeq)
 
     cacheTable("timestamps")
 
     checkAnswer(
       sql("SELECT time FROM timestamps"),
-      timestamps.collect().toSeq.map(Row.fromTuple))
+      timestamps.collect().toSeq)
   }
 
   test("SPARK-3320 regression: batched column buffer building should work with empty partitions") {