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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2022/06/21 08:56:55 UTC
[GitHub] [spark] beliefer commented on a diff in pull request #36295: [SPARK-38978][SQL] DS V2 supports push down OFFSET operator
beliefer commented on code in PR #36295:
URL: https://github.com/apache/spark/pull/36295#discussion_r902351005
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sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCV2Suite.scala:
##########
@@ -206,6 +207,355 @@ class JDBCV2Suite extends QueryTest with SharedSparkSession with ExplainSuiteHel
checkAnswer(df5, Seq(Row(10000.00, 1000.0, "amy")))
}
+ private def checkOffsetRemoved(df: DataFrame, removed: Boolean = true): Unit = {
+ val offsets = df.queryExecution.optimizedPlan.collect {
+ case offset: Offset => offset
+ }
+ if (removed) {
+ assert(offsets.isEmpty)
+ } else {
+ assert(offsets.nonEmpty)
+ }
+ }
+
+ test("simple scan with OFFSET") {
+ val df1 = spark.read
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .offset(1)
+ checkOffsetRemoved(df1)
+ checkPushedInfo(df1,
+ "PushedFilters: [DEPT IS NOT NULL, DEPT = 1], PushedOffset: OFFSET 1,")
+ checkAnswer(df1, Seq(Row(1, "cathy", 9000.00, 1200.0, false)))
+
+ val df2 = spark.read
+ .option("pushDownOffset", "false")
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .offset(1)
+ checkOffsetRemoved(df2, false)
+ checkPushedInfo(df2,
+ "PushedFilters: [DEPT IS NOT NULL, DEPT = 1], ReadSchema:")
+ checkAnswer(df2, Seq(Row(1, "cathy", 9000.00, 1200.0, false)))
+
+ val df3 = spark.read
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .sort($"salary")
+ .offset(1)
+ checkOffsetRemoved(df3, false)
+ checkPushedInfo(df3,
+ "PushedFilters: [DEPT IS NOT NULL, DEPT = 1], ReadSchema:")
+ checkAnswer(df3, Seq(Row(1, "amy", 10000.00, 1000.0, true)))
+
+ val df4 = spark.read
+ .option("partitionColumn", "dept")
+ .option("lowerBound", "0")
+ .option("upperBound", "2")
+ .option("numPartitions", "2")
+ .table("h2.test.employee")
+ .filter($"dept" > 1)
+ .offset(1)
+ checkOffsetRemoved(df4, false)
+ checkPushedInfo(df4, "PushedFilters: [DEPT IS NOT NULL, DEPT > 1], ReadSchema:")
+ checkAnswer(df4, Seq(Row(2, "david", 10000, 1300, true), Row(6, "jen", 12000, 1200, true)))
+
+ val df5 = spark.read
+ .table("h2.test.employee")
+ .groupBy("DEPT").sum("SALARY")
+ .offset(1)
+ checkOffsetRemoved(df5, false)
+ checkPushedInfo(df5,
+ "PushedAggregates: [SUM(SALARY)], PushedFilters: [], PushedGroupByExpressions: [DEPT], ")
+ checkAnswer(df5, Seq(Row(2, 22000.00), Row(6, 12000.00)))
+
+ val name = udf { (x: String) => x.matches("cat|dav|amy") }
+ val sub = udf { (x: String) => x.substring(0, 3) }
+ val df6 = spark.read
+ .table("h2.test.employee")
+ .select($"SALARY", $"BONUS", sub($"NAME").as("shortName"))
+ .filter(name($"shortName"))
+ .offset(1)
+ checkOffsetRemoved(df6, false)
+ // OFFSET is pushed down only if all the filters are pushed down
+ checkPushedInfo(df6, "PushedFilters: [], ")
+ checkAnswer(df6, Seq(Row(10000.00, 1300.0, "dav"), Row(9000.00, 1200.0, "cat")))
+ }
+
+ test("simple scan with LIMIT and OFFSET") {
+ val df1 = spark.read
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df1)
+ checkOffsetRemoved(df1)
+ checkPushedInfo(df1,
+ "PushedFilters: [DEPT IS NOT NULL, DEPT = 1], PushedLimit: LIMIT 2, PushedOffset: OFFSET 1,")
+ checkAnswer(df1, Seq(Row(1, "cathy", 9000.00, 1200.0, false)))
+
+ val df2 = spark.read
+ .option("pushDownLimit", "false")
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df2, false)
+ checkOffsetRemoved(df2, false)
+ checkPushedInfo(df2,
+ "PushedFilters: [DEPT IS NOT NULL, DEPT = 1], ReadSchema:")
+ checkAnswer(df2, Seq(Row(1, "cathy", 9000.00, 1200.0, false)))
+
+ val df3 = spark.read
+ .option("pushDownOffset", "false")
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df3)
+ checkOffsetRemoved(df3, false)
+ checkPushedInfo(df3,
+ "PushedFilters: [DEPT IS NOT NULL, DEPT = 1], PushedLimit: LIMIT 2, ReadSchema:")
+ checkAnswer(df3, Seq(Row(1, "cathy", 9000.00, 1200.0, false)))
+
+ val df4 = spark.read
+ .option("pushDownLimit", "false")
+ .option("pushDownOffset", "false")
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df4, false)
+ checkOffsetRemoved(df4, false)
+ checkPushedInfo(df4,
+ "PushedFilters: [DEPT IS NOT NULL, DEPT = 1], ReadSchema:")
+ checkAnswer(df4, Seq(Row(1, "cathy", 9000.00, 1200.0, false)))
+
+ val df5 = spark.read
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .sort($"salary")
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df5)
+ checkOffsetRemoved(df5)
+ checkPushedInfo(df5, "PushedFilters: [DEPT IS NOT NULL, DEPT = 1], " +
+ "PushedOffset: OFFSET 1, PushedTopN: ORDER BY [SALARY ASC NULLS FIRST] LIMIT 2, ReadSchema:")
+ checkAnswer(df5, Seq(Row(1, "amy", 10000.00, 1000.0, true)))
+
+ val df6 = spark.read
+ .option("pushDownLimit", "false")
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .sort($"salary")
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df6, false)
+ checkOffsetRemoved(df6, false)
+ checkPushedInfo(df6, "PushedFilters: [DEPT IS NOT NULL, DEPT = 1], ReadSchema:")
+ checkAnswer(df6, Seq(Row(1, "amy", 10000.00, 1000.0, true)))
+
+ val df7 = spark.read
+ .option("pushDownOffset", "false")
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .sort($"salary")
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df7)
+ checkOffsetRemoved(df7, false)
+ checkPushedInfo(df7, "PushedFilters: [DEPT IS NOT NULL, DEPT = 1]," +
+ " PushedTopN: ORDER BY [SALARY ASC NULLS FIRST] LIMIT 2, ReadSchema:")
+ checkAnswer(df7, Seq(Row(1, "amy", 10000.00, 1000.0, true)))
+
+ val df8 = spark.read
+ .option("pushDownLimit", "false")
+ .option("pushDownOffset", "false")
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .sort($"salary")
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df8, false)
+ checkOffsetRemoved(df8, false)
+ checkPushedInfo(df8, "PushedFilters: [DEPT IS NOT NULL, DEPT = 1], ReadSchema:")
+ checkAnswer(df8, Seq(Row(1, "amy", 10000.00, 1000.0, true)))
+
+ val df9 = spark.read
+ .option("partitionColumn", "dept")
+ .option("lowerBound", "0")
+ .option("upperBound", "2")
+ .option("numPartitions", "2")
+ .table("h2.test.employee")
+ .filter($"dept" > 1)
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df9, false)
+ checkOffsetRemoved(df9, false)
+ checkPushedInfo(df9,
+ "PushedFilters: [DEPT IS NOT NULL, DEPT > 1], PushedLimit: LIMIT 2, ReadSchema:")
+ checkAnswer(df9, Seq(Row(2, "david", 10000.00, 1300.0, true)))
+
+ val df10 = spark.read
+ .table("h2.test.employee")
+ .groupBy("DEPT").sum("SALARY")
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df10, false)
+ checkOffsetRemoved(df10, false)
+ checkPushedInfo(df10,
+ "PushedAggregates: [SUM(SALARY)], PushedFilters: [], PushedGroupByExpressions: [DEPT], ")
+ checkAnswer(df10, Seq(Row(2, 22000.00)))
+
+ val name = udf { (x: String) => x.matches("cat|dav|amy") }
+ val sub = udf { (x: String) => x.substring(0, 3) }
+ val df11 = spark.read
+ .table("h2.test.employee")
+ .select($"SALARY", $"BONUS", sub($"NAME").as("shortName"))
+ .filter(name($"shortName"))
+ .limit(2)
+ .offset(1)
+ checkLimitRemoved(df11, false)
+ checkOffsetRemoved(df11, false)
+ checkPushedInfo(df11, "PushedFilters: [], ")
+ checkAnswer(df11, Seq(Row(9000.00, 1200.0, "cat")))
+ }
+
+ test("simple scan with OFFSET and LIMIT") {
+ val df1 = spark.read
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .offset(1)
+ .limit(1)
+ checkLimitRemoved(df1)
+ checkOffsetRemoved(df1)
+ checkPushedInfo(df1,
+ "[DEPT IS NOT NULL, DEPT = 1], PushedLimit: LIMIT 2, PushedOffset: OFFSET 1,")
+ checkAnswer(df1, Seq(Row(1, "cathy", 9000.00, 1200.0, false)))
+
+ val df2 = spark.read
+ .option("pushDownOffset", "false")
+ .table("h2.test.employee")
+ .where($"dept" === 1)
+ .offset(1)
+ .limit(1)
+ checkLimitRemoved(df2, false)
Review Comment:
Yeah. Push down `limit 2` completely and then Spark only need do `offset(1)`.
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