[jira] [Commented] (SPARK-32547) Cant able to process Timestamp 0001-01-01T00:00:00.000+0000 with TimestampType
[
https://issues.apache.org/jira/browse/SPARK-32547?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17172199#comment-17172199
]
Manjunath H commented on SPARK-32547:
-
[~Qin Yao] timezone is UTC
spark.conf.get('spark.sql.session.timeZone')
Out[2]: 'Etc/UTC'
import datetime
LOCAL_TIMEZONE =
datetime.datetime.now(datetime.timezone(datetime.timedelta(0))).astimezone().tzinfo
print(LOCAL_TIMEZONE)
UTC
> Cant able to process Timestamp 0001-01-01T00:00:00.000+ with TimestampType
> --
>
> Key: SPARK-32547
> URL: https://issues.apache.org/jira/browse/SPARK-32547
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 3.0.0
>Reporter: Manjunath H
>Priority: Major
>
> Spark Version : 3.0.0
> Below is the sample code to reproduce the problem with TimestampType.
> {code:java}
> from pyspark.sql.functions import lit
> from pyspark.sql.types import TimestampType
> df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
> new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
> new_df.printSchema()
> root
> |-- id: long (nullable = true)
> |-- txt: string (nullable = true)
> |-- test_timestamp: timestamp (nullable = true)
> new_df.show()
> +---+---+---+
> | id|txt| test_timestamp|
> +---+---+---+
> | 1|foo|0001-01-01 00:00:00|
> | 2|bar|0001-01-01 00:00:00|
> +---+---+---+
> {code}
>
> new_df.rdd.isEmpty() operation is failing with *year 0 is out of range*
>
> {code:java}
> new_df.rdd.isEmpty()
> Py4JJavaError: An error occurred while calling
> z:org.apache.spark.api.python.PythonRDD.runJob.
> : org.apache.spark.SparkException: Job aborted due to stage failure:
> Traceback (most recent call last):
> File "/databricks/spark/python/pyspark/serializers.py", line 177, in
> _read_with_length
> return self.loads(obj)
> File "/databricks/spark/python/pyspark/serializers.py", line 466, in loads
> return pickle.loads(obj, encoding=encoding)
> File "/databricks/spark/python/pyspark/sql/types.py", line 1415, in
> return lambda *a: dataType.fromInternal(a)
> File "/databricks/spark/python/pyspark/sql/types.py", line 635, in
> fromInternal
> for f, v, c in zip(self.fields, obj, self._needConversion)]
> File "/databricks/spark/python/pyspark/sql/types.py", line 635, in
> for f, v, c in zip(self.fields, obj, self._needConversion)]
> File "/databricks/spark/python/pyspark/sql/types.py", line 447, in
> fromInternal
> return self.dataType.fromInternal(obj)
> File "/databricks/spark/python/pyspark/sql/types.py", line 201, in
> fromInternal
> return datetime.datetime.fromtimestamp(ts // 100).replace(microsecond=ts
> % 100)
> ValueError: year 0 is out of range{code}
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[jira] [Updated] (SPARK-32547) Cant able to process Timestamp 0001-01-01T00:00:00.000+0000 with TimestampType
[
https://issues.apache.org/jira/browse/SPARK-32547?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Manjunath H updated SPARK-32547:
Description:
Spark Version : 3.0.0
Below is the sample code to reproduce the problem with TimestampType.
{code:java}
from pyspark.sql.functions import lit
from pyspark.sql.types import TimestampType
df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
new_df.printSchema()
root
|-- id: long (nullable = true)
|-- txt: string (nullable = true)
|-- test_timestamp: timestamp (nullable = true)
new_df.show()
+---+---+---+
| id|txt| test_timestamp|
+---+---+---+
| 1|foo|0001-01-01 00:00:00|
| 2|bar|0001-01-01 00:00:00|
+---+---+---+
{code}
new_df.rdd.isEmpty() operation is failing with *year 0 is out of range*
{code:java}
new_df.rdd.isEmpty()
Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Traceback
(most recent call last):
File "/databricks/spark/python/pyspark/serializers.py", line 177, in
_read_with_length
return self.loads(obj)
File "/databricks/spark/python/pyspark/serializers.py", line 466, in loads
return pickle.loads(obj, encoding=encoding)
File "/databricks/spark/python/pyspark/sql/types.py", line 1415, in
return lambda *a: dataType.fromInternal(a)
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in fromInternal
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 447, in fromInternal
return self.dataType.fromInternal(obj)
File "/databricks/spark/python/pyspark/sql/types.py", line 201, in fromInternal
return datetime.datetime.fromtimestamp(ts // 100).replace(microsecond=ts %
100)
ValueError: year 0 is out of range{code}
was:
Spark Version : 3.0.0
Below is the sample code to reproduce the problem with TimestampType.
{code:java}
from pyspark.sql.functions import lit
from pyspark.sql.types import TimestampType
df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
new_df.printSchema()
root
|-- id: long (nullable = true)
|-- txt: string (nullable = true)
|-- test_timestamp: timestamp (nullable = true)
new_df.show()
+---+---+---+
| id|txt| test_timestamp|
+---+---+---+
| 1|foo|0001-01-01 00:00:00|
| 2|bar|0001-01-01 00:00:00|
+---+---+---+
{code}
df.rdd.isEmpty() operation is failing with *year 0 is out of range*
{code:java}
df.rdd.isEmpty()
Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Traceback
(most recent call last):
File "/databricks/spark/python/pyspark/serializers.py", line 177, in
_read_with_length
return self.loads(obj)
File "/databricks/spark/python/pyspark/serializers.py", line 466, in loads
return pickle.loads(obj, encoding=encoding)
File "/databricks/spark/python/pyspark/sql/types.py", line 1415, in
return lambda *a: dataType.fromInternal(a)
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in fromInternal
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 447, in fromInternal
return self.dataType.fromInternal(obj)
File "/databricks/spark/python/pyspark/sql/types.py", line 201, in fromInternal
return datetime.datetime.fromtimestamp(ts // 100).replace(microsecond=ts %
100)
ValueError: year 0 is out of range{code}
> Cant able to process Timestamp 0001-01-01T00:00:00.000+ with TimestampType
> --
>
> Key: SPARK-32547
> URL: https://issues.apache.org/jira/browse/SPARK-32547
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 3.0.0
>Reporter: Manjunath H
>Priority: Major
>
> Spark Version : 3.0.0
> Below is the sample code to reproduce the problem with TimestampType.
> {code:java}
> from pyspark.sql.functions import lit
> from pyspark.sql.types import TimestampType
> df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
> new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
> new_df.printSchema()
> root
> |-- id: long (nu
[jira] [Updated] (SPARK-32547) Cant able to process Timestamp 0001-01-01T00:00:00.000+0000 with TimestampType
[
https://issues.apache.org/jira/browse/SPARK-32547?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Manjunath H updated SPARK-32547:
Description:
Spark Version : 3.0.0
Below is the sample code to reproduce the problem with TimestampType.
{code:java}
from pyspark.sql.functions import lit
from pyspark.sql.types import TimestampType
df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
new_df.printSchema()
root
|-- id: long (nullable = true)
|-- txt: string (nullable = true)
|-- test_timestamp: timestamp (nullable = true)
new_df.show()
+---+---+---+
| id|txt| test_timestamp|
+---+---+---+
| 1|foo|0001-01-01 00:00:00|
| 2|bar|0001-01-01 00:00:00|
+---+---+---+
{code}
df.rdd.isEmpty() operation is failing with *year 0 is out of range*
{code:java}
df.rdd.isEmpty()
Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Traceback
(most recent call last):
File "/databricks/spark/python/pyspark/serializers.py", line 177, in
_read_with_length
return self.loads(obj)
File "/databricks/spark/python/pyspark/serializers.py", line 466, in loads
return pickle.loads(obj, encoding=encoding)
File "/databricks/spark/python/pyspark/sql/types.py", line 1415, in
return lambda *a: dataType.fromInternal(a)
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in fromInternal
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 447, in fromInternal
return self.dataType.fromInternal(obj)
File "/databricks/spark/python/pyspark/sql/types.py", line 201, in fromInternal
return datetime.datetime.fromtimestamp(ts // 100).replace(microsecond=ts %
100)
ValueError: year 0 is out of range{code}
was:
Spark Version : 3.0.0
Below is the sample code to reproduce the problem with TimestampType.
{code:java}
from pyspark.sql.functions import lit
from pyspark.sql.types import TimestampType
df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
new_df.printSchema()
root
|-- id: long (nullable = true)
|-- txt: string (nullable = true)
|-- test_timestamp: timestamp (nullable = true)
new_df.show()
+---+---+---+
| id|txt| test_timestamp|
+---+---+---+
| 1|foo|0001-01-01 00:00:00|
| 2|bar|0001-01-01 00:00:00|
+---+---+---
{code}
df.rdd.isEmpty() operation is failing with *year 0 is out of range*
{code:java}
df.rdd.isEmpty()
Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Traceback
(most recent call last):
File "/databricks/spark/python/pyspark/serializers.py", line 177, in
_read_with_length
return self.loads(obj)
File "/databricks/spark/python/pyspark/serializers.py", line 466, in loads
return pickle.loads(obj, encoding=encoding)
File "/databricks/spark/python/pyspark/sql/types.py", line 1415, in
return lambda *a: dataType.fromInternal(a)
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in fromInternal
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 447, in fromInternal
return self.dataType.fromInternal(obj)
File "/databricks/spark/python/pyspark/sql/types.py", line 201, in fromInternal
return datetime.datetime.fromtimestamp(ts // 100).replace(microsecond=ts %
100)
ValueError: year 0 is out of range{code}
> Cant able to process Timestamp 0001-01-01T00:00:00.000+ with TimestampType
> --
>
> Key: SPARK-32547
> URL: https://issues.apache.org/jira/browse/SPARK-32547
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 3.0.0
>Reporter: Manjunath H
>Priority: Major
>
> Spark Version : 3.0.0
> Below is the sample code to reproduce the problem with TimestampType.
> {code:java}
> from pyspark.sql.functions import lit
> from pyspark.sql.types import TimestampType
> df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
> new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
> new_df.printSchema()
> root
> |-- id: long (nullable = true)
[jira] [Updated] (SPARK-32547) Cant able to process Timestamp 0001-01-01T00:00:00.000+0000 with TimestampType
[
https://issues.apache.org/jira/browse/SPARK-32547?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Manjunath H updated SPARK-32547:
Description:
Spark Version : 3.0.0
Below is the sample code to reproduce the problem with TimestampType.
{code:java}
from pyspark.sql.functions import lit
from pyspark.sql.types import TimestampType
df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
new_df.printSchema()
root
|-- id: long (nullable = true)
|-- txt: string (nullable = true)
|-- test_date: timestamp (nullable = true)
|-- test_timestamp: timestamp (nullable = true)
new_df.show()
+---+---+---+
| id|txt| test_timestamp|
+---+---+---+
| 1|foo|0001-01-01 00:00:00|
| 2|bar|0001-01-01 00:00:00|
+---+---+---
{code}
df.rdd.isEmpty() operation is failing with *year 0 is out of range*
{code:java}
df.rdd.isEmpty()
Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Traceback
(most recent call last):
File "/databricks/spark/python/pyspark/serializers.py", line 177, in
_read_with_length
return self.loads(obj)
File "/databricks/spark/python/pyspark/serializers.py", line 466, in loads
return pickle.loads(obj, encoding=encoding)
File "/databricks/spark/python/pyspark/sql/types.py", line 1415, in
return lambda *a: dataType.fromInternal(a)
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in fromInternal
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 447, in fromInternal
return self.dataType.fromInternal(obj)
File "/databricks/spark/python/pyspark/sql/types.py", line 201, in fromInternal
return datetime.datetime.fromtimestamp(ts // 100).replace(microsecond=ts %
100)
ValueError: year 0 is out of range{code}
was:
Spark Version : 3.0.0
Below is the sample code to reproduce the problem with TimestampType.
{code:java}
from pyspark.sql.functions import lit
from pyspark.sql.types import TimestampType
df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
new_df.printSchema()
root
|-- id: long (nullable = true)
|-- txt: string (nullable = true)
|-- test_date: timestamp (nullable = true)
|-- test_timestamp: timestamp (nullable = true)
new_df.show()
+---+---+---+
| id|txt| test_date|
+---+---+---+
| 1|foo|0001-01-01 00:00:00|
| 2|bar|0001-01-01 00:00:00|
+---+---+---
{code}
df.rdd.isEmpty() operation is failing with *year 0 is out of range*
{code:java}
df.rdd.isEmpty()
Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Traceback
(most recent call last):
File "/databricks/spark/python/pyspark/serializers.py", line 177, in
_read_with_length
return self.loads(obj)
File "/databricks/spark/python/pyspark/serializers.py", line 466, in loads
return pickle.loads(obj, encoding=encoding)
File "/databricks/spark/python/pyspark/sql/types.py", line 1415, in
return lambda *a: dataType.fromInternal(a)
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in fromInternal
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 447, in fromInternal
return self.dataType.fromInternal(obj)
File "/databricks/spark/python/pyspark/sql/types.py", line 201, in fromInternal
return datetime.datetime.fromtimestamp(ts // 100).replace(microsecond=ts %
100)
ValueError: year 0 is out of range{code}
> Cant able to process Timestamp 0001-01-01T00:00:00.000+ with TimestampType
> --
>
> Key: SPARK-32547
> URL: https://issues.apache.org/jira/browse/SPARK-32547
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 3.0.0
>Reporter: Manjunath H
>Priority: Major
>
> Spark Version : 3.0.0
> Below is the sample code to reproduce the problem with TimestampType.
> {code:java}
> from pyspark.sql.functions import lit
> from pyspark.sql.types import TimestampType
> df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
> new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(Time
[jira] [Updated] (SPARK-32547) Cant able to process Timestamp 0001-01-01T00:00:00.000+0000 with TimestampType
[
https://issues.apache.org/jira/browse/SPARK-32547?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Manjunath H updated SPARK-32547:
Description:
Spark Version : 3.0.0
Below is the sample code to reproduce the problem with TimestampType.
{code:java}
from pyspark.sql.functions import lit
from pyspark.sql.types import TimestampType
df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
new_df.printSchema()
root
|-- id: long (nullable = true)
|-- txt: string (nullable = true)
|-- test_timestamp: timestamp (nullable = true)
new_df.show()
+---+---+---+
| id|txt| test_timestamp|
+---+---+---+
| 1|foo|0001-01-01 00:00:00|
| 2|bar|0001-01-01 00:00:00|
+---+---+---
{code}
df.rdd.isEmpty() operation is failing with *year 0 is out of range*
{code:java}
df.rdd.isEmpty()
Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Traceback
(most recent call last):
File "/databricks/spark/python/pyspark/serializers.py", line 177, in
_read_with_length
return self.loads(obj)
File "/databricks/spark/python/pyspark/serializers.py", line 466, in loads
return pickle.loads(obj, encoding=encoding)
File "/databricks/spark/python/pyspark/sql/types.py", line 1415, in
return lambda *a: dataType.fromInternal(a)
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in fromInternal
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 447, in fromInternal
return self.dataType.fromInternal(obj)
File "/databricks/spark/python/pyspark/sql/types.py", line 201, in fromInternal
return datetime.datetime.fromtimestamp(ts // 100).replace(microsecond=ts %
100)
ValueError: year 0 is out of range{code}
was:
Spark Version : 3.0.0
Below is the sample code to reproduce the problem with TimestampType.
{code:java}
from pyspark.sql.functions import lit
from pyspark.sql.types import TimestampType
df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
new_df.printSchema()
root
|-- id: long (nullable = true)
|-- txt: string (nullable = true)
|-- test_date: timestamp (nullable = true)
|-- test_timestamp: timestamp (nullable = true)
new_df.show()
+---+---+---+
| id|txt| test_timestamp|
+---+---+---+
| 1|foo|0001-01-01 00:00:00|
| 2|bar|0001-01-01 00:00:00|
+---+---+---
{code}
df.rdd.isEmpty() operation is failing with *year 0 is out of range*
{code:java}
df.rdd.isEmpty()
Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Traceback
(most recent call last):
File "/databricks/spark/python/pyspark/serializers.py", line 177, in
_read_with_length
return self.loads(obj)
File "/databricks/spark/python/pyspark/serializers.py", line 466, in loads
return pickle.loads(obj, encoding=encoding)
File "/databricks/spark/python/pyspark/sql/types.py", line 1415, in
return lambda *a: dataType.fromInternal(a)
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in fromInternal
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 635, in
for f, v, c in zip(self.fields, obj, self._needConversion)]
File "/databricks/spark/python/pyspark/sql/types.py", line 447, in fromInternal
return self.dataType.fromInternal(obj)
File "/databricks/spark/python/pyspark/sql/types.py", line 201, in fromInternal
return datetime.datetime.fromtimestamp(ts // 100).replace(microsecond=ts %
100)
ValueError: year 0 is out of range{code}
> Cant able to process Timestamp 0001-01-01T00:00:00.000+ with TimestampType
> --
>
> Key: SPARK-32547
> URL: https://issues.apache.org/jira/browse/SPARK-32547
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 3.0.0
>Reporter: Manjunath H
>Priority: Major
>
> Spark Version : 3.0.0
> Below is the sample code to reproduce the problem with TimestampType.
> {code:java}
> from pyspark.sql.functions import lit
> from pyspark.sql.types import TimestampType
> df=spark.createDataFrame([(1, 'foo'),(2, 'bar'),],['id', 'txt'])
> new_df=df.withColumn("test_timestamp",lit("0001-01-01T00:00:00.000+").cast(TimestampType()))
> new_df.printSchema()
> ro
