Cannot trigger skew join optimization of AQE in spark 3.0.0

from pyspark.sql.functions import *

spark.conf.set("spark.sql.autoBroadcastJoinThreshold","-1")
spark.conf.set("spark.sql.shuffle.partitions","3")
spark.conf.set("spark.sql.adaptive.skewJoin.skewedPartitionThresholdInBytes","-1")

df1 = spark.range(10000000000).withColumn("id",lit("x"))

extravalues=spark.range(4).withColumn("id",lit("y"))

more = spark.range(4).withColumn("id",lit("z"))

df1=df1.union(extravalues).union(more)

df2 = spark.range(1000000).withColumn("id",lit("x"))

df2_extra = spark.range(10).withColumn("id",lit("y"))

df2_more = spark.range(10).withColumn("id",lit("z"))

df2=df2.union(df2_extra).union(df2_more)


output = df1.join(df2,df1.id==df2.id).select(df1.id)
output.write.parquet('s3a://...',mode='overwrite')

spark.sql.adaptive.skewJoin.skewedPartitionFactor = 2 spark.sql.adaptive.localShuffleReader.enabled = true

skew join failed