[
https://issues.apache.org/jira/browse/SPARK-18381?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Nicholas Chammas resolved SPARK-18381.
--------------------------------------
Resolution: Cannot Reproduce
As of Spark 4.2 this no longer seems to be an issue. Not sure when exactly it
was fixed.
{code:python}
>>> 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())
...
/opt/homebrew/Cellar/apache-spark/4.2.0/libexec/python/pyspark/sql/udf.py:120:
RuntimeWarning: Arrow optimization failed to enable because PyArrow or Pandas
is not installed. Falling back to a non-Arrow-optimized UDF.
warnings.warn(
+-----------+------+----------+
|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|
+-----------+------+----------+
+-----------+------+----------+
|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='0002-01-01', number=1, date_cast=datetime.date(2, 1, 1)),
Row(date_string='1581-01-01', number=2, date_cast=datetime.date(1581, 1, 1)),
Row(date_string='1582-01-01', number=3, date_cast=datetime.date(1582, 1, 1)),
Row(date_string='1583-01-01', number=4, date_cast=datetime.date(1583, 1, 1)),
Row(date_string='1584-01-01', number=5, date_cast=datetime.date(1584, 1, 1)),
Row(date_string='2012-01-21', number=6, date_cast=datetime.date(2012, 1, 21))]
[Row(date_string='0002-01-01', number=1, date_cast=datetime.date(2, 1, 1)),
Row(date_string='1581-01-01', number=2, date_cast=datetime.date(1581, 1, 1)),
Row(date_string='1582-01-01', number=3, date_cast=datetime.date(1582, 1, 1)),
Row(date_string='1583-01-01', number=4, date_cast=datetime.date(1583, 1, 1)),
Row(date_string='1584-01-01', number=5, date_cast=datetime.date(1584, 1, 1)),
Row(date_string='2012-01-21', number=6, date_cast=datetime.date(2012, 1, 21))]
[Row(date_string='0001-01-01', number=1, date_cast=datetime.date(1, 1, 1))]
{code}
> 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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