[
https://issues.apache.org/jira/browse/SPARK-9131?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Reynold Xin updated SPARK-9131:
-------------------------------
Description:
I am having some troubles when using a custom udf in dataframes with pyspark
1.4.
I have rewritten the udf to simplify the problem and it gets even weirder. The
udfs I am using do absolutely nothing, they just receive some value and output
the same value with the same format.
I show you my code below:
{code}
c= a.join(b, a['ID'] == b['ID_new'], 'inner')
c.filter(c['ID'] == '6000000002698917').show()
udf_A = UserDefinedFunction(lambda x: x, DateType())
udf_B = UserDefinedFunction(lambda x: x, DateType())
udf_C = UserDefinedFunction(lambda x: x, DateType())
d = c.select(c['ID'], c['t1'].alias('ta'),
udf_A(vinc_muestra['t2']).alias('tb'), udf_B(vinc_muestra['t1']).alias('tc'),
udf_C(vinc_muestra['t2']).alias('td'))
d.filter(d['ID'] == '6000000002698917').show()
{code}
I am showing here the results from the outputs:
{code}
+----------------+----------------+----------+----------+
| ID | ID_new | t1 | t2 |
+----------------+----------------+----------+----------+
|6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
|6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
|6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
|6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
|6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
|6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
|6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
|6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
+----------------+----------------+----------+----------+
+----------------+---------------+---------------+------------+------------+
| ID | ta | tb | tc | td
|
+----------------+---------------+---------------+------------+------------+
|6000000002698917| 2012-02-28| 2007-03-05| 2003-03-05|
2014-02-28|
|6000000002698917| 2012-02-20| 2007-02-15| 2002-02-15|
2013-02-20|
|6000000002698917| 2012-02-28| 2007-03-10| 2005-03-10|
2014-02-28|
|6000000002698917| 2012-02-20| 2007-03-05| 2003-03-05|
2013-02-20|
|6000000002698917| 2012-02-20| 2013-08-02| 2013-01-02|
2013-02-20|
|6000000002698917| 2012-02-28| 2007-02-15| 2002-02-15|
2014-02-28|
|6000000002698917| 2012-02-28| 2007-02-15| 2002-02-15|
2014-02-28|
|6000000002698917| 2012-02-20| 2014-01-02| 2013-01-02|
2013-02-20|
+----------------+---------------+---------------+------------+------------+
{code}
The problem here is that values at columns 'tb', 'tc' and 'td' in dataframe 'd'
are completely different from values 't1' and 't2' in dataframe c even when my
udfs are doing nothing. It seems like if values were somehow got from other
registers (or just invented). Results are different between executions
(apparently random).
Thanks in advance
was:
I am having some troubles when using a custom udf in dataframes with pyspark
1.4.
I have rewritten the udf to simplify the problem and it gets even weirder. The
udfs I am using do absolutely nothing, they just receive some value and output
the same value with the same format.
I show you my code below:
c= a.join(b, a['ID'] == b['ID_new'], 'inner')
c.filter(c['ID'] == '6000000002698917').show()
udf_A = UserDefinedFunction(lambda x: x, DateType())
udf_B = UserDefinedFunction(lambda x: x, DateType())
udf_C = UserDefinedFunction(lambda x: x, DateType())
d = c.select(c['ID'], c['t1'].alias('ta'),
udf_A(vinc_muestra['t2']).alias('tb'), udf_B(vinc_muestra['t1']).alias('tc'),
udf_C(vinc_muestra['t2']).alias('td'))
d.filter(d['ID'] == '6000000002698917').show()
I am showing here the results from the outputs:
+----------------+----------------+----------+----------+
| ID | ID_new | t1 | t2 |
+----------------+----------------+----------+----------+
|6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
|6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
|6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
|6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
|6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
|6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
|6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
|6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
+----------------+----------------+----------+----------+
+----------------+---------------+---------------+------------+------------+
| ID | ta | tb | tc | td
|
+----------------+---------------+---------------+------------+------------+
|6000000002698917| 2012-02-28| 2007-03-05| 2003-03-05|
2014-02-28|
|6000000002698917| 2012-02-20| 2007-02-15| 2002-02-15|
2013-02-20|
|6000000002698917| 2012-02-28| 2007-03-10| 2005-03-10|
2014-02-28|
|6000000002698917| 2012-02-20| 2007-03-05| 2003-03-05|
2013-02-20|
|6000000002698917| 2012-02-20| 2013-08-02| 2013-01-02|
2013-02-20|
|6000000002698917| 2012-02-28| 2007-02-15| 2002-02-15|
2014-02-28|
|6000000002698917| 2012-02-28| 2007-02-15| 2002-02-15|
2014-02-28|
|6000000002698917| 2012-02-20| 2014-01-02| 2013-01-02|
2013-02-20|
+----------------+---------------+---------------+------------+------------+
The problem here is that values at columns 'tb', 'tc' and 'td' in dataframe 'd'
are completely different from values 't1' and 't2' in dataframe c even when my
udfs are doing nothing. It seems like if values were somehow got from other
registers (or just invented). Results are different between executions
(apparently random).
Thanks in advance
> UDFs change data values
> -----------------------
>
> Key: SPARK-9131
> URL: https://issues.apache.org/jira/browse/SPARK-9131
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 1.4.0, 1.4.1
> Environment: Pyspark 1.4 and 1.4.1
> Reporter: Luis Guerra
> Priority: Critical
>
> I am having some troubles when using a custom udf in dataframes with pyspark
> 1.4.
> I have rewritten the udf to simplify the problem and it gets even weirder.
> The udfs I am using do absolutely nothing, they just receive some value and
> output the same value with the same format.
> I show you my code below:
> {code}
> c= a.join(b, a['ID'] == b['ID_new'], 'inner')
> c.filter(c['ID'] == '6000000002698917').show()
> udf_A = UserDefinedFunction(lambda x: x, DateType())
> udf_B = UserDefinedFunction(lambda x: x, DateType())
> udf_C = UserDefinedFunction(lambda x: x, DateType())
> d = c.select(c['ID'], c['t1'].alias('ta'),
> udf_A(vinc_muestra['t2']).alias('tb'), udf_B(vinc_muestra['t1']).alias('tc'),
> udf_C(vinc_muestra['t2']).alias('td'))
> d.filter(d['ID'] == '6000000002698917').show()
> {code}
> I am showing here the results from the outputs:
> {code}
> +----------------+----------------+----------+----------+
> | ID | ID_new | t1 | t2 |
> +----------------+----------------+----------+----------+
> |6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
> |6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
> |6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
> |6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
> |6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
> |6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
> |6000000002698917| 6000000002698917| 2012-02-28| 2014-02-28|
> |6000000002698917| 6000000002698917| 2012-02-20| 2013-02-20|
> +----------------+----------------+----------+----------+
> +----------------+---------------+---------------+------------+------------+
> | ID | ta | tb | tc | td
> |
> +----------------+---------------+---------------+------------+------------+
> |6000000002698917| 2012-02-28| 2007-03-05| 2003-03-05|
> 2014-02-28|
> |6000000002698917| 2012-02-20| 2007-02-15| 2002-02-15|
> 2013-02-20|
> |6000000002698917| 2012-02-28| 2007-03-10| 2005-03-10|
> 2014-02-28|
> |6000000002698917| 2012-02-20| 2007-03-05| 2003-03-05|
> 2013-02-20|
> |6000000002698917| 2012-02-20| 2013-08-02| 2013-01-02|
> 2013-02-20|
> |6000000002698917| 2012-02-28| 2007-02-15| 2002-02-15|
> 2014-02-28|
> |6000000002698917| 2012-02-28| 2007-02-15| 2002-02-15|
> 2014-02-28|
> |6000000002698917| 2012-02-20| 2014-01-02| 2013-01-02|
> 2013-02-20|
> +----------------+---------------+---------------+------------+------------+
> {code}
> The problem here is that values at columns 'tb', 'tc' and 'td' in dataframe
> 'd' are completely different from values 't1' and 't2' in dataframe c even
> when my udfs are doing nothing. It seems like if values were somehow got from
> other registers (or just invented). Results are different between executions
> (apparently random).
> Thanks in advance
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