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https://issues.apache.org/jira/browse/SPARK-32137?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17179562#comment-17179562
]
rahul bhatia commented on SPARK-32137:
--------------------------------------
Hi [~WaterKnight], I have been trying to resolve this issue for hours, but no
progress, did you find a solution?
> 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
> Priority: Major
>
> 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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