在Spark Dataframe中解析html时出错

Question

我有一个spark数据框。我在数据框的每一行上使用一个映射函数来使用HTML解析库解析文本列，然后将解析的html和其他两列保存为新的RDD。

最后我想将RDD保存为新的Spark Dataframe。这是相同的代码。

def htmlParsing(x): 
    """ This function takes the input text and cleans the HTML tags from it 

    """ 

    from bs4 import BeautifulSoup 
    row=x.asDict() 
    textcleaned='' 
    souptext=BeautifulSoup(row['desc']) 
    #souptext=BeautifulSoup(text) 
    p_tags=souptext.find_all('p') 
    for p in p_tags: 
     if p.string: 
      textcleaned+=p.string 
    ret_list= (int(row['id']),row['title'],textcleaned) 
    return ret_list 


ret_list=sdf_rss.map(htmlParsing) 

sdf_cleaned=sqlContext.createDataFrame(ret_list,['id','title','desc']) 
sdf_cleaned.count() 

当我做ret_list.take（2）我得到的映射结果是正确的。对于sdf_cleaned.show（）等的作品。

映射函数工作正常，因为我得到正确的RDD。请参阅下面的映射函数返回的RDD结果。

[(-33753621, 
    u'Royal Bank of Scotland is testing a robot that could solve your banking problems (RBS)', 
    u"If you hate dealing with bank tellers or customer service representatives, then the Royal Bank of Scotland might have a solution for you.If this program is successful, it could be a big step forward on the road to automated customer service through the use of AI, notes Laurie Beaver, research associate for BI Intelligence, Business Insider's premium research service.It's noteworthy that Luvo does not operate via a third-party app such as Facebook Messenger, WeChat, or Kik, all of which are currently trying to create bots that would assist in customer service within their respective platforms.Luvo would be available through the web and through smartphones. It would also use machine learning to learn from its mistakes, which should ultimately help with its response accuracy.Down the road, Luvo would become a supplement to the human staff. It can currently answer 20 set questions but as that number grows, it would allow the human employees to more complicated issues. If a problem is beyond Luvo's comprehension, then it would refer the customer to a bank employee; however,\xa0a user could choose to speak with a human instead of Luvo anyway.AI such as Luvo, if successful, could help businesses become more efficient and increase their productivity, while simultaneously improving customer service capacity, which would consequently\xa0save money that would otherwise go toward manpower.And this trend is already starting. Google, Microsoft, and IBM are investing significantly into AI research. Furthermore, the global AI market is estimated to grow from approximately $420 million in 2014 to $5.05 billion in 2020, according to a forecast by Research and Markets.\xa0The move toward AI would be just one more way in which the digital age is disrupting retail banking. Customers, particularly millennials, are increasingly moving toward digital banking, and as a result, they're walking into their banks' traditional brick-and-mortar branches less often than ever before."), 
(-761323061, 
    u'Teen sexting is prompting an overhaul in child pornography laws', 
    u"Rampant teen sexting has left politicians and law enforcement authorities around the country struggling to find some kind of legal middle ground between prosecuting students for child porn and letting them off the hook.Most states consider sexually explicit images of minors to be child pornography, meaning even teenagers who share nude selfies among themselves can, in theory at least, be hit with felony charges that can carry heavy prison sentences and require lifetime registration as a sex offender.Many authorities consider that overkill, however, and at least 20 states have adopted sexting laws with less-serious penalties, mostly within the past five years. Eleven states have made sexting between teens a misdemeanor; in some of those places, prosecutors can require youngsters to take courses on the dangers of social media instead of charging them with a crime.Hawaii passed a 2012 law saying youths can escape conviction if they take steps to delete explicit photos. Arkansas adopted a 2013 law sentencing first-time youth sexters to eight hours of community service. New Mexico last month removed criminal penalties altogether in such cases.At least 12 other states are considering sexting laws this year, many to create new a category of crime that would apply to young people.But one such proposal in Colorado has revealed deep divisions about how to treat the phenomenon. Though prosecutors and researchers agree that felony sex crimes shouldn't apply to a pair of 16-year-olds sending each other selfies, they disagree about whether sexting should be a crime at all.Colorado's bill was prompted by a scandal last year at a Canon City high school where more than 100 students were found with explicit images of other teens. The news sent shockwaves through the city of 16,000. Dozens of students were suspended, and the football team forfeited the final game of the season.Fremont County prosecutors ultimately decided against filing any criminal charges, saying Colorado law doesn't properly distinguish between adult sexual predators and misbehaving teenagers.In a similar case last year out Fayetteville, North Carolina, two dating teens who exchanged nude selfies at age 16 were charged as adults with a felony \u2014 sexual exploitation of a minor. After an uproar, the cha"), 

但是，当我指望这两个它会引发错误。

ret_list.count() 

/Users/i854319/spark/python/pyspark/rdd.pyc in count(self) 
    1002   3 
    1003   """ 
-> 1004   return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() 
    1005 
    1006  def stats(self): 

/Users/i854319/spark/python/pyspark/rdd.pyc in sum(self) 
    993   6.0 
    994   """ 
--> 995   return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add) 
    996 
    997  def count(self): 

/Users/i854319/spark/python/pyspark/rdd.pyc in fold(self, zeroValue, op) 
    867   # zeroValue provided to each partition is unique from the one provided 
    868   # to the final reduce call 
--> 869   vals = self.mapPartitions(func).collect() 
    870   return reduce(op, vals, zeroValue) 
    871 

/Users/i854319/spark/python/pyspark/rdd.pyc in collect(self) 
    769   """ 
    770   with SCCallSiteSync(self.context) as css: 
--> 771    port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) 
    772   return list(_load_from_socket(port, self._jrdd_deserializer)) 
    773 

/Users/i854319/spark/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/i854319/spark/python/pyspark/sql/utils.pyc 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/i854319/spark/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 z:org.apache.spark.api.python.PythonRDD.collectAndServe. 
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 129.0 failed 1 times, most recent failure: Lost task 2.0 in stage 129.0 (TID 189, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last): 
    File "/Users/i854319/spark/python/lib/pyspark.zip/pyspark/worker.py", line 111, in main 
    process() 
    File "/Users/i854319/spark/python/lib/pyspark.zip/pyspark/worker.py", line 106, in process 
    serializer.dump_stream(func(split_index, iterator), outfile) 
    File "/Users/i854319/spark/python/pyspark/rdd.py", line 2346, in pipeline_func 
    return func(split, prev_func(split, iterator)) 
    File "/Users/i854319/spark/python/pyspark/rdd.py", line 2346, in pipeline_func 
    return func(split, prev_func(split, iterator)) 
    File "/Users/i854319/spark/python/pyspark/rdd.py", line 2346, in pipeline_func 
    return func(split, prev_func(split, iterator)) 
    File "/Users/i854319/spark/python/pyspark/rdd.py", line 317, in func 
    return f(iterator) 
    File "/Users/i854319/spark/python/pyspark/rdd.py", line 1004, in <lambda> 
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() 
    File "/Users/i854319/spark/python/pyspark/rdd.py", line 1004, in <genexpr> 
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() 
    File "<ipython-input-173-694d23c67c86>", line 10, in htmlParsing 
    File "/Users/i854319/anaconda/lib/python2.7/site-packages/bs4/__init__.py", line 176, in __init__ 
    elif len(markup) <= 256: 
TypeError: object of type 'NoneType' has no len() 

Answer 1

这是因为你做NULL（SQL）不正确/ None（Python）的值，也无关计数。当解析器得到None不是文本的它将失败例外，你看：

BeautifulSoup(None, "lxml") 

TypeError   
... 
TypeError: object of type 'NoneType' has no len() 

根据您的需求，您可以删除：

sdf_rss.na.drop(subset=["desc"]).rdd.map(...) 

或填充：

sdf_rss.na.fill({"desc": ""}).rdd.map(...) 

NULL值映射之前。

添加明确的异常处理：

try: 
    souptext = BeautifulSoup(row['desc']) 
    ... 
except TypeError: 
    ... 

检查为None解析之前：

if row['desc'] is not None: 
    souptext = BeautifulSoup(row['desc']) 
    ... 
else: 
    ... 

或默认为空字符串：

souptext = BeautifulSoup(row['desc'] or '') 

你也应该考虑使用udf简化过程：

from pyspark.sql.functions import udf 
from pyspark.sql import Column 
from typing import Union 

def parse_html(col: str) -> Column: 
    def parse_html_(desc: Union[None, str]) -> Union[None, str]: 
     if desc is not None: 
      ps = BeautifulSoup(desc, "lxml").find_all('p') 
      return "".join(p.string for p in ps) 
    return udf(parse_html_)(col) 

(sc 
    .parallelize([ 
     (1, "foo", "<div><p>foo</p> <p>bar</p></div>",), 
     (2, "bar", None,)]) 
    .toDF(["id", "title", "desc"]) 
    .select("id","title", parse_html("desc").alias("desc"))) 

+---+-----+-------+ 
| id|title| desc| 
+---+-----+-------+ 
| 1| foo|foo bar| 
| 2| bar| null| 
+---+-----+-------+ 

假设你已经很好地形成XML和蜂巢的支持，你可以使用xpath* UDFs但有比BeautifulSoup显著不太可靠。