spark udf问题，同时通过转换现有列之一添加array< string>类型的新列

ix0qys7i  于 2021-05-16  发布在  Spark
关注(0)|答案(1)|浏览(474)

spark-2.4.4SparkNLP-2.6.4 python=3.7.0

transformed_df.show(10)
+-----+--------------------+--------------------+
|   id|                text|      finished_lemma|
+-----+--------------------+--------------------+
|73471|Patriots Day Is B...|[Patriots, Day, B...|
|73472|A Break in the Se...|[Break, Search, O...|
|73474|Obama’s Ingenious...|[Obama’s, Ingenio...|
|73475|Donald Trump Meet...|[Donald, Trump, M...|
|73476|Trump: ’I Think’ ...|[Trump:, ’I, Thin...|
|73477|Seth Meyers Quest...|[Seth, Meyers, Qu...|
|73478|Obama Frames His ...|[Obama, Frames, E...|
|73479|The Trump Adminis...|[Trump, Administr...|
|73484|The Longstanding ...|[Longstanding, Cr...|
|73485|The Atlantic Dail...|[Atlantic, Daily:...|
+-----+--------------------+--------------------+

我想通过只选取长度>=3的100个单词来过滤完成的\u引理列

def filter100words(row):
    wordList = row.finished_lemma
    newWordList = []
    i = 0

    while (len(newWordList) < 100):

        if i >= len(wordList):
            break
        if len(wordList[i]) >= 3:
            newWordList.append(wordList[i])
        i += 1 

    return newWordList

from pyspark.sql.types import *
udfSomeFunc = F.udf(filter100words, ArrayType(StringType()))

df3 = transformed_df.withColumn("final_words", udfSomeFunc("finished_lemma"))

这似乎奏效了，甚至创造了一个新的专栏。

df3.printSchema()
root
 |-- id: string (nullable = true)
 |-- text: string (nullable = true)
 |-- finished_lemma: array (nullable = true)
 |    |-- element: string (containsNull = true)
 |-- final_words: array (nullable = true)
 |    |-- element: string (containsNull = true)

但是当我试图访问它的行时，我得到了一个错误

df3.show()
---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-135-22bb2fff2002> in <module>()
----> 1 df3.show()

~/spark/python/pyspark/sql/dataframe.py in show(self, n, truncate, vertical)
    378         """
    379         if isinstance(truncate, bool) and truncate:
--> 380             print(self._jdf.showString(n, 20, vertical))
    381         else:
    382             print(self._jdf.showString(n, int(truncate), vertical))

~/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

~/spark/python/pyspark/sql/utils.py in deco(*a,**kw)
     61     def deco(*a,**kw):
     62         try:
---> 63             return f(*a,**kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

~/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o1875.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 60.0 failed 1 times, most recent failure: Lost task 0.0 in stage 60.0 (TID 98, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 345, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 334, in _batched
    for item in iterator:
  File "<string>", line 1, in <lambda>
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/worker.py", line 85, in <lambda>
    return lambda *a: f(*a)
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/util.py", line 99, in wrapper
    return f(*args,**kwargs)
  File "<ipython-input-121-1aab53473371>", line 6, in filter100words
AttributeError: 'list' object has no attribute 'finished_lemma'

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
    at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:81)
    at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:64)
    at org.apache.spark.api.python.BasePythonRunner$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 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$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$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)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
    at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
    at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
    at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
    at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)
    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:3369)
    at org.apache.spark.sql.Dataset.head(Dataset.scala:2550)
    at org.apache.spark.sql.Dataset.take(Dataset.scala:2764)
    at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)
    at org.apache.spark.sql.Dataset.showString(Dataset.scala:291)
    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 "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 345, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 334, in _batched
    for item in iterator:
  File "<string>", line 1, in <lambda>
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/worker.py", line 85, in <lambda>
    return lambda *a: f(*a)
  File "/home/sankethbk7777/spark/python/lib/pyspark.zip/pyspark/util.py", line 99, in wrapper
    return f(*args,**kwargs)
  File "<ipython-input-121-1aab53473371>", line 6, in filter100words
AttributeError: 'list' object has no attribute 'finished_lemma'

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
    at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:81)
    at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:64)
    at org.apache.spark.api.python.BasePythonRunner$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 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$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$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)
    ... 1 more
apache-sparkpysparkapache-spark-sqluser-defined-functions

1条答案

按热度按时间
lokaqttq

lokaqttq1#

正如错误所说-

AttributeError: 'list' object has no attribute 'finished_lemma'

因为你用

udfSomeFunc("finished_lemma")

udf接收每行的“finished\u lemma”列，即一个数组作为其参数，而不是一个row对象。所以

wordList = row.finished_lemma

应替换为

wordList = row
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