nyingping commented on a change in pull request #35526:
URL: https://github.com/apache/spark/pull/35526#discussion_r808594997
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/DataFrameTimeWindowingSuite.scala
##########
@@ -490,4 +490,81 @@ class DataFrameTimeWindowingSuite extends QueryTest with
SharedSparkSession {
assert(attributeReference.dataType == tuple._2)
}
}
+
+ test("No need to filter data when the sliding window length is not
redundant") {
+ // check the value column
+ val df1 = Seq(
+ ("2022-02-15 19:39:34", 1, "a"),
+ ("2022-02-15 19:39:56", 2, "a"),
+ ("2022-02-15 19:39:27", 4, "b")).toDF("time", "value", "id")
+ .select(window($"time", "9 seconds", "3 seconds", "0 second"), $"value")
+ .orderBy($"window.start".asc, $"value".desc).select("value")
+ val df2 = Seq(
+ (LocalDateTime.parse("2022-02-15T19:39:34"), 1, "a"),
+ (LocalDateTime.parse("2022-02-15T19:39:56"), 2, "a"),
+ (LocalDateTime.parse("2022-02-15T19:39:27"), 4, "b")).toDF("time",
"value", "id")
+ .select(window($"time", "9 seconds", "3 seconds", "0 second"), $"value")
+ .orderBy($"window.start".asc, $"value".desc).select("value")
+
+ val df3 = Seq(
+ ("2022-02-15 19:39:34", 1, "a"),
+ ("2022-02-15 19:39:56", 2, "a"),
+ ("2022-02-15 19:39:27", 4, "b")).toDF("time", "value", "id")
+ .select(window($"time", "9 seconds", "3 seconds", "-2 second"), $"value")
+ .orderBy($"window.start".asc, $"value".desc).select("value")
+ val df4 = Seq(
+ (LocalDateTime.parse("2022-02-15T19:39:34"), 1, "a"),
+ (LocalDateTime.parse("2022-02-15T19:39:56"), 2, "a"),
+ (LocalDateTime.parse("2022-02-15T19:39:27"), 4, "b")).toDF("time",
"value", "id")
+ .select(window($"time", "9 seconds", "3 seconds", "2 second"), $"value")
+ .orderBy($"window.start".asc, $"value".desc).select("value")
+
+ Seq(df1, df2).foreach { df =>
+ val filter = df.queryExecution.optimizedPlan.find(_.isInstanceOf[Filter])
+ val exist = filter.get.constraints.iterator.toStream.filter(e =>
+ e.toString.contains(">=") || e.toString.contains("<"))
+ assert(exist.isEmpty, "No need to filter data between " +
+ "window.start and window.end when the sliding window length is not
redundant")
+
+ checkAnswer(
+ df,
+ Seq(Row(4), Row(4), Row(4), Row(1), Row(1), Row(1), Row(2), Row(2),
Row(2))
+ )
+ }
+
+ Seq(df3, df4).foreach { df =>
+ val filter = df.queryExecution.optimizedPlan.find(_.isInstanceOf[Filter])
+ val exist = filter.get.constraints.iterator.toStream.filter(e =>
+ e.toString.contains(">=") || e.toString.contains("<"))
+ assert(exist.isEmpty, "No need to filter data between " +
+ "window.start and window.end when the sliding window length is not
redundant")
+
+ checkAnswer(
+ df,
+ Seq(Row(4), Row(4), Row(4), Row(1), Row(1), Row(1), Row(2), Row(2),
Row(2))
+ )
+ }
+
+ // check produces right windows
Review comment:
Most of the existing tests for sliding windows are under the condition
of "`windowduration % slideduration ! = 0`". The only exception is the test
case called "`millisecond precision sliding windows`", but it focuses on
"`millisecond precision`". So I'm not sure if I need to add new case to verify
that the change still produces right windows.
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