文本列上的Pyspark DataFrame UDF

Question

我正在尝试在PySpark DataFrame中对一些Unicode列进行一些NLP文本清理。 我已经尝试过Spark 1.3,1.5和1.6，似乎无法让事情在我的生活中发挥作用。 我也尝试过使用Python 2.7和Python 3.4。

我已经创建了一个非常简单的udf，如下所示，它应该为新列中的每个记录返回一个字符串。 其他函数将操作文本，然后将更改的文本返回到新列中。

import pyspark
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark.sql import SQLContext
from pyspark.sql.functions import udf

def dummy_function(data_str):
    cleaned_str = 'dummyData' 
    return cleaned_str

dummy_function_udf = udf(dummy_function, StringType())

一些样本数据可以从这里解压缩。

这是我用来导入数据然后应用udf的代码。

# Load a text file and convert each line to a Row.
lines = sc.textFile("classified_tweets.txt")
parts = lines.map(lambda l: l.split("\t"))
training = parts.map(lambda p: (p[0], p[1]))

# Create dataframe
training_df = sqlContext.createDataFrame(training, ["tweet", "classification"])

training_df.show(5)
+--------------------+--------------+
|               tweet|classification|
+--------------------+--------------+
|rt @jiffyclub: wi...|        python|
|rt @arnicas: ipyt...|        python|
|rt @treycausey: i...|        python|
|what's my best op...|        python|
|rt @raymondh: #py...|        python|
+--------------------+--------------+

# Apply UDF function
df = training_df.withColumn("dummy", dummy_function_udf(training_df['tweet']))
df.show(5)

当我运行df.show（5）时，我收到以下错误。 我知道这个问题最有可能不是来自show（），但跟踪并没有给我太多帮助。

 ---------------------------------------------------------------------------Py4JJavaError                             Traceback (most recent call last)<ipython-input-19-0b21c233c724> in <module>()
      1 df = training_df.withColumn("dummy", dummy_function_udf(training_df['tweet']))
----> 2 df.show(5)
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/sql/dataframe.py in show(self, n, truncate)
    255         +---+-----+
    256         """
--> 257         print(self._jdf.showString(n, truncate))
    258 
    259     def __repr__(self):
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
    811         answer = self.gateway_client.send_command(command)
    812         return_value = get_return_value(
--> 813             answer, self.gateway_client, self.target_id, self.name)
    814 
    815         for temp_arg in temp_args:
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/sql/utils.py in deco(*a, **kw)
     43     def deco(*a, **kw):
     44         try:
---> 45             return f(*a, **kw)
     46         except py4j.protocol.Py4JJavaError as e:
     47             s = e.java_exception.toString()
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    306                 raise Py4JJavaError(
    307                     "An error occurred while calling {0}{1}{2}.\n".
--> 308                     format(target_id, ".", name), value)
    309             else:
    310                 raise Py4JError(
Py4JJavaError: An error occurred while calling o474.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 10.0 failed 1 times, most recent failure: Lost task 0.0 in stage 10.0 (TID 10, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/worker.py", line 111, in main
    process()
  File "/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/worker.py", line 106, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/serializers.py", line 263, in dump_stream
    vs = list(itertools.islice(iterator, batch))
  File "<ipython-input-12-4bc30395aac5>", line 4, in <lambda>
IndexError: list index out of range

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
    at org.apache.spark.api.python.PythonRunner$$anon$1.next(PythonRDD.scala:129)
    at org.apache.spark.api.python.PythonRunner$$anon$1.next(PythonRDD.scala:125)
    at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$GroupedIterator.takeDestructively(Iterator.scala:913)
    at scala.collection.Iterator$GroupedIterator.go(Iterator.scala:929)
    at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:968)
    at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:972)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:452)
    at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:280)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1741)
    at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:239)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:212)
    at org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:165)
    at org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174)
    at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1538)
    at org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1538)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
    at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2125)
    at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$execute$1(DataFrame.scala:1537)
    at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$collect(DataFrame.scala:1544)
    at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1414)
    at org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1413)
    at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2138)
    at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1413)
    at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1495)
    at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:171)
    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:497)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:209)
    at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/worker.py", line 111, in main
    process()
  File "/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/worker.py", line 106, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/serializers.py", line 263, in dump_stream
    vs = list(itertools.islice(iterator, batch))
  File "<ipython-input-12-4bc30395aac5>", line 4, in <lambda>
IndexError: list index out of range

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
    at org.apache.spark.api.python.PythonRunner$$anon$1.next(PythonRDD.scala:129)
    at org.apache.spark.api.python.PythonRunner$$anon$1.next(PythonRDD.scala:125)
    at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$GroupedIterator.takeDestructively(Iterator.scala:913)
    at scala.collection.Iterator$GroupedIterator.go(Iterator.scala:929)
    at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:968)
    at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:972)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:452)
    at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:280)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1741)
    at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:239)

我正在尝试的实际功能：

def tag_and_remove(data_str):
    cleaned_str = ' '
    # noun tags
    nn_tags = ['NN', 'NNP', 'NNP', 'NNPS', 'NNS']
    # adjectives
    jj_tags = ['JJ', 'JJR', 'JJS']
    # verbs
    vb_tags = ['VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ']
    nltk_tags = nn_tags + jj_tags + vb_tags

    # break string into 'words'
    text = data_str.split()

    # tag the text and keep only those with the right tags
    tagged_text = pos_tag(text)
    for tagged_word in tagged_text:
        if tagged_word[1] in nltk_tags:
            cleaned_str += tagged_word[0] + ' '

    return cleaned_str


tag_and_remove_udf = udf(tag_and_remove, StringType())

Answer 1

您的数据集不干净。 985行split('\\t')仅split('\\t')为一个值：

>>> from operator import add
>>> lines = sc.textFile("classified_tweets.txt")
>>> parts = lines.map(lambda l: l.split("\t"))
>>> parts.map(lambda l: (len(l), 1)).reduceByKey(add).collect()
[(2, 149195), (1, 985)]
>>> parts.filter(lambda l: len(l) == 1).take(5)
[['"show me the money!”  at what point do you start trying to monetize your #startup? tweet us with #startuplife.'],
 ['a good pitch can mean money in the bank for your #startup. see how body language plays a key role:  (via: ajalumnify)'],
 ['100+ apps in five years? @2359media did it using microsoft #azure:  #azureapps'],
 ['does buying better coffee make you a better leader? little things can make a big difference:  (via: @jmbrandonbb)'],
 ['.@msftventures graduates pitched\xa0#homeautomation #startups to #vcs! check out how they celebrated: ']]

所以将代码更改为：

>>> training = parts.filter(lambda l: len(l) == 2).map(lambda p: (p[0], p[1].strip()))
>>> training_df = sqlContext.createDataFrame(training, ["tweet", "classification"])
>>> df = training_df.withColumn("dummy", dummy_function_udf(training_df['tweet']))
>>> df.show(5)
+--------------------+--------------+---------+
|               tweet|classification|    dummy|
+--------------------+--------------+---------+
|rt @jiffyclub: wi...|        python|dummyData|
|rt @arnicas: ipyt...|        python|dummyData|
|rt @treycausey: i...|        python|dummyData|
|what's my best op...|        python|dummyData|
|rt @raymondh: #py...|        python|dummyData|
+--------------------+--------------+---------+
only showing top 5 rows

Answer 2

我认为你错误地定义了这个问题，并且为了这个问题可能会简化你的lambda，但却隐藏了真正的问题。

您的堆栈跟踪读取

File "<ipython-input-12-4bc30395aac5>", line 4, in <lambda>
IndexError: list index out of range

当我运行此代码时它工作正常：

import pyspark
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark.sql import SQLContext
from pyspark.sql.functions import udf

training_df = sqlContext.sql("select 'foo' as tweet, 'bar' as classification")

def dummy_function(data_str):
     cleaned_str = 'dummyData'
     return cleaned_str

dummy_function_udf = udf(dummy_function, StringType())
df = training_df.withColumn("dummy", dummy_function_udf(training_df['tweet']))
df.show()

+-----+--------------+---------+
|tweet|classification|    dummy|
+-----+--------------+---------+
|  foo|           bar|dummyData|
+-----+--------------+---------+

你确定你的dummy_function_udf没有其他的bug吗？ 你使用的'真实'udf是什么 - 除了这个样本版本？

Answer 3

下面一个使用spark2，

import hashlib
import uuid
import datetime
from pyspark.sql.types import StringType

def customencoding(s):
    m = hashlib.md5()
    m.update(s.encode('utf-8'))
    d = m.hexdigest()
    return d

spark.udf.register("udf_customhashing32adadf", customencoding, StringType())

spark.sql("SELECT udf_customhashing32adadf('test') as rowid").show(10, False)

您可以以相同的方式实现它。