Bug: AttributeError: Can only use .dt accessor with datetimelike values
Status: Resolved | Priority: Major | Resolution: Cannot Reproduce
Created: 30/Jun/20 11:50
Affects Version/s: 2.4.5

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)