beliefer commented on a change in pull request #33341:
URL: https://github.com/apache/spark/pull/33341#discussion_r670986772
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/DataFrameTimeWindowingSuite.scala
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@@ -17,184 +17,249 @@
package org.apache.spark.sql
-import org.apache.spark.sql.catalyst.plans.logical.Expand
+import java.time.LocalDateTime
+
+import org.apache.spark.sql.catalyst.expressions.AttributeReference
+import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, Expand}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.test.SharedSparkSession
-import org.apache.spark.sql.types.StringType
+import org.apache.spark.sql.types._
class DataFrameTimeWindowingSuite extends QueryTest with SharedSparkSession {
import testImplicits._
test("simple tumbling window with record at window start") {
- val df = Seq(
- ("2016-03-27 19:39:30", 1, "a")).toDF("time", "value", "id")
-
- checkAnswer(
- df.groupBy(window($"time", "10 seconds"))
- .agg(count("*").as("counts"))
- .orderBy($"window.start".asc)
- .select($"window.start".cast("string"), $"window.end".cast("string"),
$"counts"),
- Seq(
- Row("2016-03-27 19:39:30", "2016-03-27 19:39:40", 1)
+ val df1 = Seq(("2016-03-27 19:39:30", 1, "a")).toDF("time", "value", "id")
Review comment:
Yes. The string type will implict cast to the old timestamp type first.
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