David Lacalle Castillo created SPARK-32137:
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Summary: AttributeError: Can only use .dt accessor with
datetimelike values
Key: SPARK-32137
URL: https://issues.apache.org/jira/browse/SPARK-32137
Project: Spark
Issue Type: Bug
Components: PySpark, SQL
Affects Versions: 2.4.5
Reporter: David Lacalle Castillo
I was using a pandas udf with a dataframe containing a date object. I was using
the lastversion of pyarrow, 0.17.0.
I setup this variable on zeppelin spark interpreter:
ARROW_PRE_0_15_IPC_FORMAT=1
However, I was getting the following error:
Job aborted due to stage failure: Task 0 in stage 19.0 failed 4 times, most
recent failure: Lost task 0.3 in stage 19.0 (TID 1619, 10.20.0.5, executor 1):
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/opt/spark/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
process()
File "/opt/spark/python/lib/pyspark.zip/pyspark/worker.py", line 372, in
process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/opt/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 290, in
dump_stream
for series in iterator:
File "/opt/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 311, in
load_stream
yield [self.arrow_to_pandas(c) for c in
pa.Table.from_batches([batch]).itercolumns()]
File "/opt/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 311, in
<listcomp>
yield [self.arrow_to_pandas(c) for c in
pa.Table.from_batches([batch]).itercolumns()]
File "/opt/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 278, in
arrow_to_pandas
s = _check_series_convert_date(s, from_arrow_type(arrow_column.type))
File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/types.py", line 1692, in
_check_series_convert_date
return series.dt.date
File "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py", line
5270, in getattr
return object.getattribute(self, name)
File "/usr/local/lib/python3.7/dist-packages/pandas/core/accessor.py", line
187, in get
accessor_obj = self._accessor(obj)
File
"/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/accessors.py", line
338, in new
raise AttributeError("Can only use .dt accessor with datetimelike values")
AttributeError: Can only use .dt accessor with datetimelike values
at
org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
at
org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172)
at
org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)
at
org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:858)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:858)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
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