CDH 5.5 only provides Spark 1.5. Are you managing your pySpark install
separately?
For something like your example, you will get significantly better
performance using coalesce with a lit, like so:
from pyspark.sql.functions import lit, coalesce
def replace_empty(icol):
return coalesce(col(icol), lit("")).alias(icol)
and use it similarly to what you are doing (I would build a function around
your if logic, its easier to understand):
def _if_not_in_processing(icol):
return icol if (icol not in colprocessing) else replace_empty(icol)
dfTotaleNormalize53 = dfTotaleNormalize52.select([_if_not_in_processing(i) for
i in dfTotaleNormalize52.columns])
Otherwise there isn't anything obvious to me as to why it isn't working. If
you actually do have pySpark 1.5 and not 1.6 I know it handles UDF
registration differently.
Hope this helps.
Nicholas Szandor Hakobian, Ph.D.
Senior Data Scientist
Rally Health
nicholas.hakob...@rallyhealth.com
On Wed, Apr 19, 2017 at 5:13 AM, issues solution <issues.solut...@gmail.com>
wrote:
> Pyspark 1.6 On cloudera 5.5 (yearn)
>
> 2017-04-19 13:42 GMT+02:00 issues solution <issues.solut...@gmail.com>:
>
>> Hi ,
>> somone can tell me why i get the folowing error with udf apply like
>> udf
>>
>> def replaceCempty(x):
>> if x is None :
>> return ""
>> else :
>> return x.encode('utf-8')
>> udf_replaceCempty = F.udf(replaceCempty,StringType())
>>
>> dfTotaleNormalize53 = dfTotaleNormalize52.select([i if i not in
>> colprocessing else udf_replaceCempty(F.col(i)).alias(i) for i in
>> dfTotaleNormalize52.columns])
>>
>>
>> java.lang.java.lang.UnsupportedOperationException
>>
>> Cannot evaluate expression: PythonUDF#replaceCempty(input[77,string])
>>
>> ??
>> regards
>>
>>
>>
>>
>
