python - 无法在 pyspark 中应用 pandas_udf

Question

我正在附加到 AWS EMR 实例的 jupyter notebook 上尝试一些与 pyspark 相关的实验。我有一个 spark 数据框，它从 s3 读取数据，然后过滤掉一些东西。使用 df1.printSchema() 打印模式输出如下:

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)

我正在尝试在此数据帧上应用 pandas_udf(示例 here)。我的 udf 是:

@pandas_udf(df1.schema, PandasUDFType.GROUPED_MAP)
def normalize(pdf):
    hour = pdf.hour
    return pdf.assign(hour=(hour - hour.mean()) / hour.std())

调用是这样的:

df2 = df1.groupBy('idvalue') \
        .apply(normalize).show()   

不幸的是，这是抛出错误，说:

    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

我不明白为什么会抛出与日期时间相关的错误。我正在做的所有操作都与此无关。任何帮助表示赞赏。

Answer 1

我认为 pandas_udf 还不支持所有的 spark 类型，而且您的 date_time 列似乎有问题。

任何 udf 的一个问题是，所有数据都必须为您的 udf 实现，即使 udf 忽略了这些值，这也可能导致此类问题，或者至少导致性能下降。在其他条件相同的情况下，您应该尝试减少传递给 udf 的列数。例如，通过在您的 groupby 之前添加一个选择。

df2 = df1.select('idvalue', 'hour').groupBy('idvalue').apply(normalize).show()