SPRKPY1014

pyspark.sql.functions.asinh

Message: pyspark.sql.functions.asinh has a workaround

Category: Warning.

Description

This issue appears when the tool detects the usage of which has a workaround.

Scenario

Input

On this example pyspark calculates the asinh for a dataframe by using .

from pyspark.sql import SparkSession
from pyspark.sql.functions import asinh
spark = SparkSession.builder.getOrCreate()
data = [['V1', 3.0],
        ['V2', 60.0],
        ['V3', 14.0],
        ['V4', 3.1]]

columns = ['Paremeter', 'value']
df = spark.createDataFrame(data, columns)
df_result = df.withColumn("asinh_value", asinh(df["value"]))

Output

SMA returns the EWI SPRKPY1014 over the line where asinh is used, so you can use to identify where to fix.

from snowflake.snowpark import Session

spark = Session.builder.getOrCreate()
spark.update_query_tag({"origin":"sf_sit","name":"sma","version":{"major":0,"minor":0,"patch":0},"attributes":{"language":"Python"}})
data = [['V1', 3.0],
        ['V2', 60.0],
        ['V3', 14.0],
        ['V4', 3.1]]

columns = ['Paremeter', 'value']
df = spark.createDataFrame(data, columns)
#EWI: SPRKPY1014 => pyspark.sql.functions.asinh has a workaround, see documentation for more info
df_result = df.withColumn("asinh_value", asinh(df["value"]))

Recomended fix

import snowflake.snowpark as snowpark
from snowflake.snowpark import Session
from snowflake.snowpark.functions import call_function, col

spark = Session.builder.getOrCreate()
spark.update_query_tag({"origin":"sf_sit","name":"sma","version":{"major":0,"minor":0,"patch":0},"attributes":{"language":"Python"}})
data = [['V1', 3.0],
        ['V2', 60.0],
        ['V3', 14.0],
        ['V4', 3.1]]

columns = ['Paremeter', 'value']
df = spark.createDataFrame(data, columns)
df_result = df.select(call_function('asinh', col('value')))

Additional recommendations

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Last updated 5 months ago

from pyspark.sql import SparkSession from pyspark.sql.functions import asinh spark = SparkSession.builder.getOrCreate() data = [['V1', 3.0], ['V2', 60.0], ['V3', 14.0], ['V4', 3.1]] columns = ['Paremeter', 'value'] df = spark.createDataFrame(data, columns) df_result = df.withColumn("asinh_value", asinh(df["value"]))

from snowflake.snowpark import Session spark = Session.builder.getOrCreate() spark.update_query_tag({"origin":"sf_sit","name":"sma","version":{"major":0,"minor":0,"patch":0},"attributes":{"language":"Python"}}) data = [['V1', 3.0], ['V2', 60.0], ['V3', 14.0], ['V4', 3.1]] columns = ['Paremeter', 'value'] df = spark.createDataFrame(data, columns) #EWI: SPRKPY1014 => pyspark.sql.functions.asinh has a workaround, see documentation for more info df_result = df.withColumn("asinh_value", asinh(df["value"]))

import snowflake.snowpark as snowpark from snowflake.snowpark import Session from snowflake.snowpark.functions import call_function, col spark = Session.builder.getOrCreate() spark.update_query_tag({"origin":"sf_sit","name":"sma","version":{"major":0,"minor":0,"patch":0},"attributes":{"language":"Python"}}) data = [['V1', 3.0], ['V2', 60.0], ['V3', 14.0], ['V4', 3.1]] columns = ['Paremeter', 'value'] df = spark.createDataFrame(data, columns) df_result = df.select(call_function('asinh', col('value')))