wankunde commented on code in PR #38649:
URL: https://github.com/apache/spark/pull/38649#discussion_r1044038097
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/expressions.scala:
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@@ -762,10 +762,40 @@ object LikeSimplification extends Rule[LogicalPlan] with
PredicateHelper {
val and = buildBalancedPredicate(replacements.map(Not(_)), And)
if (remainPatterns.nonEmpty) And(and, l.copy(patterns =
remainPatterns)) else and
case l: LikeAny =>
- val or = buildBalancedPredicate(replacements, Or)
+ val equalPatterns = MutableHashset[Any]()
Review Comment:
Added benchmark result.
benchmark code:
```
object LikeAnyBenchmark extends SqlBasedBenchmark {
import spark.implicits._
private val numRows = 10000
private val width = 5
def withTempTable(tableNames: String*)(f: => Unit): Unit = {
try f finally tableNames.foreach(spark.catalog.dropTempView)
}
private def saveAsTable(df: DataFrame, dir: File): Unit = {
val parquetPath = dir.getCanonicalPath + "/parquet"
df.write.mode("overwrite").parquet(parquetPath)
spark.read.parquet(parquetPath).createOrReplaceTempView("parquetTable")
}
override def runBenchmarkSuite(mainArgs: Array[String]): Unit = {
withTempPath { dir =>
withTempTable("parquetTable") {
val selectExpr = (1 to width).map(i => s"CAST(value + 1000000 AS
STRING) c$i")
val df = spark.range(0, numRows, 1, 100)
.map(_ => Random.nextLong).selectExpr(selectExpr: _*)
saveAsTable(df, dir)
val benchmark =
new Benchmark("Multi like query", numRows, minNumIters = 3, output
= output)
benchmark.addCase("Query with LikeAny simplification", numIters = 3)
{ _ =>
val likeAnyExpr =
Random.shuffle(Range(1000, 1300).map(i =>
if (i < 1100) s"'$i%'" else if (i < 1200) s"'%$i'" else s"'$i'"
)).mkString("c1 like any(", ", ", ")")
spark.sql(s"SELECT * FROM parquetTable WHERE $likeAnyExpr").noop()
}
benchmark.run()
}
}
}
}
```
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