![]() ![]() ![]() Since we are not handling null with UDF function, using this on DataFrame returns below error. ![]() Note that from the above snippet, record with “Seqno 4” has value “None” for “name” column. Spark.sql("select convertUDF(Name) from NAME_TABLE2") \ for example, when you have a column that contains the value null on some recordsĭf2 = spark.createDataFrame(data=data,schema=columns)ĭf2.createOrReplaceTempView("NAME_TABLE2") UDF’s are error-prone when not designed carefully. "where Name is not null and convertUDF(Name) like '%John%'") \ Spark.sql("select Seqno, convertUDF(Name) as Name from NAME_TABLE " \ If convertUDF(Name) like '%John%' execute first then No guarantee Name is not null will execute first So when you are designing and using UDF, you have to be very careful especially with null handling as these results runtime exceptions. PySpark reorders the execution for query optimization and planning hence, AND, OR, WHERE and HAVING expression will have side effects. One thing to aware is in PySpark/Spark does not guarantee the order of evaluation of subexpressions meaning expressions are not guarantee to evaluated left-to-right or in any other fixed order. This results same output as section 3.2 5. In the previous sections, you have learned creating a UDF is a 2 step process, first, you need to create a Python function, second convert function to UDF using SQL udf() function, however, you can avoid these two steps and create it with just a single step by using upperCase(str):ĭf.withColumn("Cureated Name", upperCase(col("Name"))) \ This yields the same output as 3.1 example. Spark.sql("select Seqno, convertUDF(Name) as Name from NAME_TABLE") \ In order to use convertCase() function on PySpark SQL, you need to register the function with PySpark by using (). UpperCaseUDF = udf(lambda z:upperCase(z),StringType())ĭf.withColumn("Cureated Name", upperCaseUDF(col("Name"))) \ģ.3 Registering PySpark UDF
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