zhangyt26 opened a new pull request, #47571:
URL: https://github.com/apache/spark/pull/47571
This will create a stable hash for the plan if there are more than one
projection to break down
### What changes were proposed in this pull request?
Use LinkedHashSet so projections order is always stable
### Why are the changes needed?
The same spark plan should compare equal even after analyzer phase.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
```
import spark.implicits._
import org.apache.spark.sql.{Row, functions => SparkFuncs}
import org.apache.spark.sql.types._
import scala.collection.JavaConverters._
import scala.collection.immutable.ListMap
val df1 = Seq((1, 2, 3, 4), (5, 6, 7, 8), (9, 10, 11, 12))
.toDF("a", "b", "c", "d")
.select(SparkFuncs.struct("a", "b", "c", "d").alias("df1_struct"))
val df2 =
df1.groupBy().agg(SparkFuncs.first("df1_struct").as("df1_struct_first")).withColumn("e",
SparkFuncs.lit("100"))
val df3 = spark
.createDataFrame(List(Row(0)).asJava, StructType(Seq(StructField("EMPTY",
IntegerType))))
.withColumn("num1", SparkFuncs.lit(1000))
.withColumn("num2", SparkFuncs.lit(2000))
.withColumn("num3", SparkFuncs.lit(3000))
.withColumn("num4", SparkFuncs.lit(4000))
.select(SparkFuncs.struct("num1", "num2", "num3",
"num4").alias("df3_struct"))
.cache()
val df4 = df2
.join(
df2
.join(df3, Seq(), "inner")
.groupBy()
.agg(SparkFuncs.first("df3_struct").as("df3_struct_first")),
Seq(),
"left")
.select(
SparkFuncs.col("*"),
SparkFuncs
.posexplode(SparkFuncs.array(SparkFuncs.lit("x"),
SparkFuncs.lit("y"), SparkFuncs.lit("z")))
.as(Seq("pos", "val"))
)
// run this multiple times and it is unstable on master, but stable after
the patch
df4
.withColumns(
ListMap(
"col1" -> SparkFuncs.lit(100),
"col2" -> SparkFuncs
.when(
SparkFuncs.col("val").eqNullSafe(SparkFuncs.lit("x")),
SparkFuncs.col("df1_struct_first").getItem("a"))
.otherwise(
SparkFuncs
.when(
SparkFuncs.col("val").eqNullSafe(SparkFuncs.lit("y")),
SparkFuncs.col("df1_struct_first").getItem("b"))
.otherwise(SparkFuncs
.when(
SparkFuncs.col("val").eqNullSafe(SparkFuncs.lit("z")),
SparkFuncs.col("df1_struct_first").getItem("c")))),
"col3" -> SparkFuncs.col("col2").plus(SparkFuncs.lit(1000)),
"col4" ->
SparkFuncs.col("df3_struct_first").getItem("num1").multiply(SparkFuncs.lit(10)),
"col5" ->
SparkFuncs.col("df3_struct_first").getItem("num2").multiply(SparkFuncs.lit(10)),
"col6" ->
SparkFuncs.col("df3_struct_first").getItem("num3").multiply(SparkFuncs.lit(10)),
"col7" ->
SparkFuncs.col("df3_struct_first").getItem("num4").multiply(SparkFuncs.lit(10)),
"col8" -> SparkFuncs.col("col4").minus(SparkFuncs.col("col5")),
"col9" -> SparkFuncs.col("col8").plus(SparkFuncs.lit(1000))
))
.semanticHash()
```
### Was this patch authored or co-authored using generative AI tooling?
no
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