Thanks Ayan! *Daniel Lopes* Chief Data and Analytics Officer | OneMatch c: +55 (18) 99764-2733 | https://www.linkedin.com/in/dslopes
www.onematch.com.br <http://www.onematch.com.br/?utm_source=EmailSignature&utm_term=daniel-lopes> On Thu, Sep 8, 2016 at 7:54 PM, ayan guha <guha.a...@gmail.com> wrote: > Another way of debugging would be writing another UDF, returning string. > Also, in that function, put something useful in catch block, so you can > filter those records from df. > On 9 Sep 2016 03:41, "Daniel Lopes" <dan...@onematch.com.br> wrote: > >> Thanks Mike, >> >> A good way to debug! Was that already! >> >> Best, >> >> *Daniel Lopes* >> Chief Data and Analytics Officer | OneMatch >> c: +55 (18) 99764-2733 | https://www.linkedin.com/in/dslopes >> >> www.onematch.com.br >> <http://www.onematch.com.br/?utm_source=EmailSignature&utm_term=daniel-lopes> >> >> On Thu, Sep 8, 2016 at 2:26 PM, Mike Metzger <m...@flexiblecreations.com> >> wrote: >> >>> My guess is there's some row that does not match up with the expected >>> data. While slower, I've found RDDs to be easier to troubleshoot this kind >>> of thing until you sort out exactly what's happening. >>> >>> Something like: >>> >>> raw_data = sc.textFile("<path to text file(s)>") >>> rowcounts = raw_data.map(lambda x: (len(x.split(",")), >>> 1)).reduceByKey(lambda x,y: x+y) >>> rowcounts.take(5) >>> >>> badrows = raw_data.filter(lambda x: len(x.split(",")) != <expected >>> number of columns>) >>> if badrows.count() > 0: >>> badrows.saveAsTextFile("<path to malformed.csv>") >>> >>> >>> You should be able to tell if there are any rows with column counts that >>> don't match up (the thing that usually bites me with CSV conversions). >>> Assuming these all match to what you want, I'd try mapping the unparsed >>> date column out to separate fields and try to see if a year field isn't >>> matching the expected values. >>> >>> Thanks >>> >>> Mike >>> >>> >>> On Thu, Sep 8, 2016 at 8:15 AM, Daniel Lopes <dan...@onematch.com.br> >>> wrote: >>> >>>> Thanks, >>>> >>>> I *tested* the function offline and works >>>> Tested too with select * from after convert the data and see the new >>>> data good >>>> *but* if I *register as temp table* to *join other table* stilll shows *the >>>> same error*. >>>> >>>> ValueError: year out of range >>>> >>>> Best, >>>> >>>> *Daniel Lopes* >>>> Chief Data and Analytics Officer | OneMatch >>>> c: +55 (18) 99764-2733 | https://www.linkedin.com/in/dslopes >>>> >>>> www.onematch.com.br >>>> <http://www.onematch.com.br/?utm_source=EmailSignature&utm_term=daniel-lopes> >>>> >>>> On Thu, Sep 8, 2016 at 9:43 AM, Marco Mistroni <mmistr...@gmail.com> >>>> wrote: >>>> >>>>> Daniel >>>>> Test the parse date offline to make sure it returns what you expect >>>>> If it does in spark shell create a df with 1 row only and run ur >>>>> UDF. U should b able to see issue >>>>> If not send me a reduced CSV file at my email and I give it a try this >>>>> eve ....hopefully someone else will b able to assist in meantime >>>>> U don't need to run a full spark app to debug issue >>>>> Ur problem. Is either in the parse date or in what gets passed to the >>>>> UDF >>>>> Hth >>>>> >>>>> On 8 Sep 2016 1:31 pm, "Daniel Lopes" <dan...@onematch.com.br> wrote: >>>>> >>>>>> Thanks Marco for your response. >>>>>> >>>>>> The field came encoded by SQL Server in locale pt_BR. >>>>>> >>>>>> The code that I am formating is: >>>>>> >>>>>> -------------------------- >>>>>> def parse_date(argument, format_date='%Y-%m%d %H:%M:%S'): >>>>>> try: >>>>>> locale.setlocale(locale.LC_TIME, 'pt_BR.utf8') >>>>>> return datetime.strptime(argument, format_date) >>>>>> except: >>>>>> return None >>>>>> >>>>>> convert_date = funcspk.udf(lambda x: parse_date(x, '%b %d %Y %H:%M'), >>>>>> TimestampType()) >>>>>> >>>>>> transacoes = transacoes.withColumn('tr_Vencimento', >>>>>> convert_date(transacoes.*tr_Vencimento*)) >>>>>> >>>>>> -------------------------- >>>>>> >>>>>> the sample is >>>>>> >>>>>> ------------------------- >>>>>> +-----------------+----------------+-----------------+------ >>>>>> --+------------------+-----------+-----------------+-------- >>>>>> -------------+------------------+--------------+------------ >>>>>> ----+-------------+-------------+----------------------+---- >>>>>> ------------------------+--------------------+--------+----- >>>>>> ---+------------------+----------------+--------+----------+ >>>>>> -----------------+----------+ >>>>>> |tr_NumeroContrato|tr_TipoDocumento| *tr_Vencimento* >>>>>> |tr_Valor|tr_DataRecebimento|tr_TaxaMora|tr_De >>>>>> scontoMaximo|tr_DescontoMaximoCorr|tr_ValorAtualizado|tr_Com >>>>>> Garantia|tr_ValorDesconto|tr_ValorJuros|tr_ValorMulta|tr_Dat >>>>>> aDevolucaoCheque|tr_ValorCorrigidoContratante| >>>>>> tr_DataNotificacao|tr_Banco|tr_Praca|tr_DescricaoAlinea|tr_ >>>>>> Enquadramento|tr_Linha|tr_Arquivo|tr_DataImportacao|tr_Agencia| >>>>>> +-----------------+----------------+-----------------+------ >>>>>> --+------------------+-----------+-----------------+-------- >>>>>> -------------+------------------+--------------+------------ >>>>>> ----+-------------+-------------+----------------------+---- >>>>>> ------------------------+--------------------+--------+----- >>>>>> ---+------------------+----------------+--------+----------+ >>>>>> -----------------+----------+ >>>>>> | 0000992600153001| |*Jul 20 2015 12:00*| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35|2015-07-20 12:00:...| >>>>>> null| null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |*Abr 20 2015 12:00*| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35| null| >>>>>> null| null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Nov 20 2015 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35|2015-11-20 12:00:...| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Dez 20 2015 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35| null| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Fev 20 2016 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35| null| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Fev 20 2015 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35| null| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Jun 20 2015 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35|2015-06-20 12:00:...| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Ago 20 2015 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35| null| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Jan 20 2016 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35|2016-01-20 12:00:...| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Jan 20 2015 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35|2015-01-20 12:00:...| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Set 20 2015 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35| null| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Mai 20 2015 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35| null| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Out 20 2015 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35| null| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> | 0000992600153001| |Mar 20 2015 12:00| 254.35| >>>>>> null| null| null| null| >>>>>> null| 0| null| null| null| >>>>>> null| 254.35|2015-03-20 12:00:...| >>>>>> null| >>>>>> null| null| null| null| null| >>>>>> null| null| >>>>>> +-----------------+----------------+-----------------+------ >>>>>> --+------------------+-----------+-----------------+-------- >>>>>> -------------+------------------+--------------+------------ >>>>>> ----+-------------+-------------+----------------------+---- >>>>>> ------------------------+--------------------+--------+----- >>>>>> ---+------------------+----------------+--------+----------+ >>>>>> -----------------+----------+ >>>>>> >>>>>> ------------------------- >>>>>> >>>>>> *Daniel Lopes* >>>>>> Chief Data and Analytics Officer | OneMatch >>>>>> c: +55 (18) 99764-2733 | https://www.linkedin.com/in/dslopes >>>>>> >>>>>> www.onematch.com.br >>>>>> <http://www.onematch.com.br/?utm_source=EmailSignature&utm_term=daniel-lopes> >>>>>> >>>>>> On Thu, Sep 8, 2016 at 5:33 AM, Marco Mistroni <mmistr...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> Pls paste code and sample CSV >>>>>>> I m guessing it has to do with formatting time? >>>>>>> Kr >>>>>>> >>>>>>> On 8 Sep 2016 12:38 am, "Daniel Lopes" <dan...@onematch.com.br> >>>>>>> wrote: >>>>>>> >>>>>>>> Hi, >>>>>>>> >>>>>>>> I'm* importing a few CSV*s with spark-csv package, >>>>>>>> Always when I give a select at each one looks ok >>>>>>>> But when i join then with sqlContext.sql give me this error >>>>>>>> >>>>>>>> all tables has fields timestamp >>>>>>>> >>>>>>>> joins are not with this dates >>>>>>>> >>>>>>>> >>>>>>>> *Py4JJavaError: An error occurred while calling o643.showString.* >>>>>>>> : org.apache.spark.SparkException: Job aborted due to stage >>>>>>>> failure: Task 54 in stage 92.0 failed 10 times, most recent failure: >>>>>>>> Lost >>>>>>>> task 54.9 in stage 92.0 (TID 6356, yp-spark-dal09-env5-0036): >>>>>>>> org.apache.spark.api.python.PythonException: Traceback (most >>>>>>>> recent call last): >>>>>>>> File "/usr/local/src/spark160master/spark-1.6.0-bin-2.6.0/python/ >>>>>>>> lib/pyspark.zip/pyspark/worker.py", line 111, in main >>>>>>>> process() >>>>>>>> File "/usr/local/src/spark160master/spark-1.6.0-bin-2.6.0/python/ >>>>>>>> lib/pyspark.zip/pyspark/worker.py", line 106, in process >>>>>>>> serializer.dump_stream(func(split_index, iterator), outfile) >>>>>>>> File "/usr/local/src/spark160master/spark-1.6.0-bin-2.6.0/python/ >>>>>>>> lib/pyspark.zip/pyspark/serializers.py", line 263, in dump_stream >>>>>>>> vs = list(itertools.islice(iterator, batch)) >>>>>>>> File >>>>>>>> "/usr/local/src/spark160master/spark/python/pyspark/sql/functions.py", >>>>>>>> line 1563, in <lambda> >>>>>>>> func = lambda _, it: map(lambda x: >>>>>>>> returnType.toInternal(f(*x)), it) >>>>>>>> File "/usr/local/src/spark160master/spark-1.6.0-bin-2.6.0/python/ >>>>>>>> lib/pyspark.zip/pyspark/sql/types.py", line 191, in toInternal >>>>>>>> else time.mktime(dt.timetuple())) >>>>>>>> *ValueError: year out of range * >>>>>>>> >>>>>>>> Any one knows this problem? >>>>>>>> >>>>>>>> Best, >>>>>>>> >>>>>>>> *Daniel Lopes* >>>>>>>> Chief Data and Analytics Officer | OneMatch >>>>>>>> c: +55 (18) 99764-2733 | https://www.linkedin.com/in/dslopes >>>>>>>> >>>>>>>> www.onematch.com.br >>>>>>>> <http://www.onematch.com.br/?utm_source=EmailSignature&utm_term=daniel-lopes> >>>>>>>> >>>>>>> >>>>>> >>>> >>> >>
