koert kuipers created SPARK-35079: ------------------------------------- Summary: Transform with udf gives incorrect result Key: SPARK-35079 URL: https://issues.apache.org/jira/browse/SPARK-35079 Project: Spark Issue Type: Bug Components: SQL Affects Versions: 3.1.1 Reporter: koert kuipers
i think this is a correctness bug in spark 3.1.1 the behavior is correct in spark 3.0.1 in spark 3.0.1: {code:java} scala> import spark.implicits._ scala> import org.apache.spark.sql.functions._ scala> val x = Seq(Seq("11", "22", "33")).toDF x: org.apache.spark.sql.DataFrame = [value: array<string>] scala> x.select(transform(col("value"), col => udf((_: String).drop(1)).apply(col))).show +---------------------------------------------------+ |transform(value, lambdafunction(UDF(lambda 'x), x))| +---------------------------------------------------+ | [1, 2, 3]| +---------------------------------------------------+ {code} in spark 3.1.1: {code:java} scala> import spark.implicits._ scala> import org.apache.spark.sql.functions._ scala> val x = Seq(Seq("11", "22", "33")).toDF x: org.apache.spark.sql.DataFrame = [value: array<string>] scala> x.select(transform(col("value"), col => udf((_: String).drop(1)).apply(col))).show +---------------------------------------------------+ |transform(value, lambdafunction(UDF(lambda 'x), x))| +---------------------------------------------------+ | [3, 3, 3]| +---------------------------------------------------+ {code} -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org