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())
某些示例数据可以从here.
解压缩以下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。我知道问题最有可能不是来自show（），但是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())
答案 0 :(得分：8)
您的数据集不干净。 985行split('\t')只有一个值：
>>> 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: ']]
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
答案 1 :(得分：7)
我认为你错误地解决了这个问题，并且为了这个问题的目的可能会简化你的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，它可以正常工作：
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|
+-----+--------------+---------+
您确定dummy_function_udf中没有其他错误吗？除了这个样本版本之外，你使用的'真实'udf是什么？
答案 2 :(得分：0)
下面是与spark2配合使用的
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)
您可以用相同的方式实现它。