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Posted to issues@spark.apache.org by "Nicolas Tilmans (JIRA)" <ji...@apache.org> on 2019/02/25 22:53:00 UTC
[jira] [Commented] (SPARK-18381) Wrong date conversion between
spark and python for dates before 1583
[ https://issues.apache.org/jira/browse/SPARK-18381?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16777362#comment-16777362 ]
Nicolas Tilmans commented on SPARK-18381:
-----------------------------------------
Any updates on this issue? We are encountering it with the same use case as [~nchammas], a placeholder date from a client is blowing up pyspark pipelines. Although a change to the documentation is welcome, a recommendation for how to actually handle the error elegantly would also be appreciated. We could remap to a different, more compatible date, but that just seems hacky.
> Wrong date conversion between spark and python for dates before 1583
> --------------------------------------------------------------------
>
> Key: SPARK-18381
> URL: https://issues.apache.org/jira/browse/SPARK-18381
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.0.0
> Reporter: Luca Caniparoli
> Priority: Major
>
> Dates before 1538 (julian/gregorian calendar transition) are processed incorrectly.
> * With python udf (datetime.strptime), .show() returns wrong dates but .collect() returns correct dates
> * With pyspark.sql.functions.to_date, .show() shows correct dates but .collect() returns wrong dates. Additionally, collecting '0001-01-01' returns error when collecting dataframe.
> {code:none}
> from pyspark.sql.types import DateType
> from pyspark.sql.functions import to_date, udf
> from datetime import datetime
> strToDate = udf (lambda x: datetime.strptime(x, '%Y-%m-%d'), DateType())
> l = [('0002-01-01', 1), ('1581-01-01', 2), ('1582-01-01', 3), ('1583-01-01', 4), ('1584-01-01', 5), ('2012-01-21', 6)]
> l_older = [('0001-01-01', 1)]
> test_df = spark.createDataFrame(l, ["date_string", "number"])
> test_df_older = spark.createDataFrame(l_older, ["date_string", "number"])
> test_df_strptime = test_df.withColumn( "date_cast", strToDate(test_df["date_string"]))
> test_df_todate = test_df.withColumn( "date_cast", to_date(test_df["date_string"]))
> test_df_older_todate = test_df_older.withColumn( "date_cast", to_date(test_df_older["date_string"]))
> test_df_strptime.show()
> test_df_todate.show()
> print test_df_strptime.collect()
> print test_df_todate.collect()
> print test_df_older_todate.collect()
> {code}
> {noformat}
> +-----------+------+----------+
> |date_string|number| date_cast|
> +-----------+------+----------+
> | 0002-01-01| 1|0002-01-03|
> | 1581-01-01| 2|1580-12-22|
> | 1582-01-01| 3|1581-12-22|
> | 1583-01-01| 4|1583-01-01|
> | 1584-01-01| 5|1584-01-01|
> | 2012-01-21| 6|2012-01-21|
> +-----------+------+----------+
> +-----------+------+----------+
> |date_string|number| date_cast|
> +-----------+------+----------+
> | 0002-01-01| 1|0002-01-01|
> | 1581-01-01| 2|1581-01-01|
> | 1582-01-01| 3|1582-01-01|
> | 1583-01-01| 4|1583-01-01|
> | 1584-01-01| 5|1584-01-01|
> | 2012-01-21| 6|2012-01-21|
> +-----------+------+----------+
> [Row(date_string=u'0002-01-01', number=1, date_cast=datetime.date(2, 1, 1)), Row(date_string=u'1581-01-01', number=2, date_cast=datetime.date(1581, 1, 1)), Row(date_string=u'1582-01-01', number=3, date_cast=datetime.date(1582, 1, 1)), Row(date_string=u'1583-01-01', number=4, date_cast=datetime.date(1583, 1, 1)), Row(date_string=u'1584-01-01', number=5, date_cast=datetime.date(1584, 1, 1)), Row(date_string=u'2012-01-21', number=6, date_cast=datetime.date(2012, 1, 21))]
> [Row(date_string=u'0002-01-01', number=1, date_cast=datetime.date(1, 12, 30)), Row(date_string=u'1581-01-01', number=2, date_cast=datetime.date(1581, 1, 11)), Row(date_string=u'1582-01-01', number=3, date_cast=datetime.date(1582, 1, 11)), Row(date_string=u'1583-01-01', number=4, date_cast=datetime.date(1583, 1, 1)), Row(date_string=u'1584-01-01', number=5, date_cast=datetime.date(1584, 1, 1)), Row(date_string=u'2012-01-21', number=6, date_cast=datetime.date(2012, 1, 21))]
> Traceback (most recent call last):
> File "/tmp/zeppelin_pyspark-6043517212596195478.py", line 267, in <module>
> raise Exception(traceback.format_exc())
> Exception: Traceback (most recent call last):
> File "/tmp/zeppelin_pyspark-6043517212596195478.py", line 265, in <module>
> exec(code)
> File "<stdin>", line 15, in <module>
> File "/usr/local/spark/python/pyspark/sql/dataframe.py", line 311, in collect
> return list(_load_from_socket(port, BatchedSerializer(PickleSerializer())))
> File "/usr/local/spark/python/pyspark/rdd.py", line 142, in _load_from_socket
> for item in serializer.load_stream(rf):
> File "/usr/local/spark/python/pyspark/serializers.py", line 139, in load_stream
> yield self._read_with_length(stream)
> File "/usr/local/spark/python/pyspark/serializers.py", line 164, in _read_with_length
> return self.loads(obj)
> File "/usr/local/spark/python/pyspark/serializers.py", line 422, in loads
> return pickle.loads(obj)
> File "/usr/local/spark/python/pyspark/sql/types.py", line 1354, in <lambda>
> return lambda *a: dataType.fromInternal(a)
> File "/usr/local/spark/python/pyspark/sql/types.py", line 600, in fromInternal
> values = [f.fromInternal(v) for f, v in zip(self.fields, obj)]
> File "/usr/local/spark/python/pyspark/sql/types.py", line 439, in fromInternal
> return self.dataType.fromInternal(obj)
> File "/usr/local/spark/python/pyspark/sql/types.py", line 176, in fromInternal
> return datetime.date.fromordinal(v + self.EPOCH_ORDINAL)
> ValueError: ('ordinal must be >= 1', <function <lambda> at 0x7fa21bf7baa0>, (u'0001-01-01', 1, -719164))
> {noformat}
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