SPRKSCL1133

org.apache.spark.sql.functions.least

Message: org.apache.spark.sql.functions.least has a workaround, see documentation for more info

Category: Warning

Description

This issue appears when the SMA detects a use of the org.apache.spark.sql.functions.least function, which has a workaround.

Scenario

Input

Below is an example of the org.apache.spark.sql.functions.least function, first used with multiple column name as arguments and then with column objects.

val df = Seq((10, 20, 5), (15, 25, 30), (7, 14, 3)).toDF("value1", "value2", "value3")
val result1 = df.withColumn("least", least("value1", "value2", "value3"))
val result2 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))

Output

The SMA adds the EWI SPRKSCL1133 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq((10, 20, 5), (15, 25, 30), (7, 14, 3)).toDF("value1", "value2", "value3")
/*EWI: SPRKSCL1133 => org.apache.spark.sql.functions.least has a workaround, see documentation for more info*/
val result1 = df.withColumn("least", least("value1", "value2", "value3"))
/*EWI: SPRKSCL1133 => org.apache.spark.sql.functions.least has a workaround, see documentation for more info*/
val result2 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))

Recommended fix

Snowpark has an equivalent least function that receives multiple column objects as arguments. For that reason, the Spark overload that receives multiple column objects as arguments is directly supported by Snowpark and does not require any changes.

For the overload that receives multiple string arguments, you can convert the strings into column objects using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq((10, 20, 5), (15, 25, 30), (7, 14, 3)).toDF("value1", "value2", "value3")
val result1 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))
val result2 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))

Additional recommendations

For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

PreviousSPRKSCL1132 NextSPRKSCL1134

Last updated 7 months ago

val df = Seq((10, 20, 5), (15, 25, 30), (7, 14, 3)).toDF("value1", "value2", "value3") val result1 = df.withColumn("least", least("value1", "value2", "value3")) val result2 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))

val df = Seq((10, 20, 5), (15, 25, 30), (7, 14, 3)).toDF("value1", "value2", "value3") /*EWI: SPRKSCL1133 => org.apache.spark.sql.functions.least has a workaround, see documentation for more info*/ val result1 = df.withColumn("least", least("value1", "value2", "value3")) /*EWI: SPRKSCL1133 => org.apache.spark.sql.functions.least has a workaround, see documentation for more info*/ val result2 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))

val df = Seq((10, 20, 5), (15, 25, 30), (7, 14, 3)).toDF("value1", "value2", "value3") val result1 = df.withColumn("least", least(col("value1"), col("value2"), col("value3"))) val result2 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))