Я испытываю некоторые эксперименты, связанные с pyspark, на ноутбуке jupyter, подключенном к экземпляру AWS EMR. У меня есть спарк dataframe, который читает данные из s3, а затем отфильтровывает некоторые вещи. Печать схемы с использованием df1.printSchema() выводов, подобных этому:
root
|-- idvalue: string (nullable = true)
|-- locationaccuracyhorizontal: float (nullable = true)
|-- hour: integer (nullable = true)
|-- day: integer (nullable = true)
|-- date: date (nullable = true)
|-- is_weekend: boolean (nullable = true)
|-- locationlatrad: float (nullable = true)
|-- locationlonrad: float (nullable = true)
|-- epochtimestamp: integer (nullable = true)
Я пытаюсь применить pandas_udf к этому фрейму данных (примеры здесь ). Мой udf-код:
@pandas_udf(df1.schema, PandasUDFType.GROUPED_MAP)
def normalize(pdf):
hour = pdf.hour
return pdf.assign(hour=(hour - hour.mean()) / hour.std())
Вызов выглядит так:
df2 = df1.groupBy('idvalue') \
.apply(normalize).show()
К сожалению, это ошибка с сообщением:
An error occurred while calling o522.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 11.0 failed 4 times, most recent failure: Lost task 0.3 in stage 11.0 (TID 31, x.x.x.x, executor 7): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/usr/local/lib64/python3.6/site-packages/pandas/core/indexes/accessors.py", line 256, in _make_accessor
return maybe_to_datetimelike(data)
File "/usr/local/lib64/python3.6/site-packages/pandas/core/indexes/accessors.py", line 82, in maybe_to_datetimelike
"datetimelike index".format(type(data)))
TypeError: cannot convert an object of type <class 'pandas.core.series.Series'> to a datetimelike index
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/mnt1/yarn/usercache/livy/appcache/application_1555045880196_0210/container_1555045880196_0210_01_000013/pyspark.zip/pyspark/worker.py", line 372, in main
process()
File "/mnt1/yarn/usercache/livy/appcache/application_1555045880196_0210/container_1555045880196_0210_01_000013/pyspark.zip/pyspark/worker.py", line 367, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/mnt1/yarn/usercache/livy/appcache/application_1555045880196_0210/container_1555045880196_0210_01_000013/pyspark.zip/pyspark/serializers.py", line 283, in dump_stream
for series in iterator:
File "/mnt1/yarn/usercache/livy/appcache/application_1555045880196_0210/container_1555045880196_0210_01_000013/pyspark.zip/pyspark/serializers.py", line 301, in load_stream
yield [self.arrow_to_pandas(c) for c in pa.Table.from_batches([batch]).itercolumns()]
File "/mnt1/yarn/usercache/livy/appcache/application_1555045880196_0210/container_1555045880196_0210_01_000013/pyspark.zip/pyspark/serializers.py", line 301, in <listcomp>
yield [self.arrow_to_pandas(c) for c in pa.Table.from_batches([batch]).itercolumns()]
File "/mnt1/yarn/usercache/livy/appcache/application_1555045880196_0210/container_1555045880196_0210_01_000013/pyspark.zip/pyspark/serializers.py", line 271, in arrow_to_pandas
s = _check_series_convert_date(s, from_arrow_type(arrow_column.type))
File "/mnt1/yarn/usercache/livy/appcache/application_1555045880196_0210/container_1555045880196_0210_01_000013/pyspark.zip/pyspark/sql/types.py", line 1692, in _check_series_convert_date
return series.dt.date
File "/usr/local/lib64/python3.6/site-packages/pandas/core/generic.py", line 3610, in __getattr__
return object.__getattribute__(self, name)
File "/usr/local/lib64/python3.6/site-packages/pandas/core/accessor.py", line 54, in __get__
return self.construct_accessor(instance)
File "/usr/local/lib64/python3.6/site-packages/pandas/core/indexes/accessors.py", line 258, in _make_accessor
raise AttributeError("Can only use .dt accessor with "
AttributeError: Can only use .dt accessor with datetimelike values
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
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:406)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
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:836)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2039)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2027)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2026)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2026)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:966)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2260)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2209)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2198)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:777)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3384)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3365)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3364)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2545)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2759)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:255)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:292)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/usr/local/lib64/python3.6/site-packages/pandas/core/indexes/accessors.py", line 256, in _make_accessor
return maybe_to_datetimelike(data)
File "/usr/local/lib64/python3.6/site-packages/pandas/core/indexes/accessors.py", line 82, in maybe_to_datetimelike
"datetimelike index".format(type(data)))
TypeError: cannot convert an object of type <class 'pandas.core.series.Series'> to a datetimelike index
Я не понимаю, почему выдается ошибка, связанная с датой и временем. Ни одна из операций, которые я делаю, не связана с этим. Любая помощь приветствуется.