Error when parsing html in Spark Dataframe

Question

I have a spark dataframe. I am using a mapping function on each Row of the data frame to parse the text column using HTML parsing library and then save the parsed html along with 2 other columns as new RDD.

In the end I want to save the RDD as new Spark Dataframe. Here is the code for the same.

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()

When I do ret_list.take(2) I do get the mapping result correct. The same works for sdf_cleaned.show() etc.

The mapping function works fine as I get the right RDD. See below result of the returned RDD of the mapping function.

[(-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"),

However when I do count on both these it throws error.

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

This happens because you don't correct for NULL (SQL) / None (Python) values and has nothing to do with counting. When parser gets None instead of text it will fail with the exception you see:

BeautifulSoup(None, "lxml")

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

Depending on your requirements you can drop:

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

or fill:

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

NULL values before mapping.

add explicit exception handling:

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

check for None before parsing:

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

or default to empty string:

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

You should also consider using udf to simplify the process:

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|
+---+-----+-------+

Assuming you have well formed XML and enabled Hive support you could use xpath* UDFs but there are significantly less robust than BeautifulSoup.