Matching multiple regexes in a (py)Spark dataframe

Question

I have a Spark DataFrame that contains multiple columns with free text. Separately, I have a dictionary of regular expressions where each regex maps to a key.

For instance:

df = spark.sparkContext.parallelize([Row(**{'primary_loc': 'USA', 'description': 'PyCon happens annually in the United States, with satellite events in India, Brazil and Tokyo'}),
                                     Row(**{'primary_loc': 'Canada', 'description': 'The annual hockey championship has some events occurring in the US'})]).toDF()
keywords = {'united states': re.compile(r'\b(usa|us|united states|texas|washington|new york)\b', re.I),
            'india': re.compile(r'\b(india|bangalore|mumbai|delhi)\b', re.I),
            'canada': re.compile(r'\b(canada|winnipeg|toronto|ontario|vancouver)\b', re.I),
            'japan': re.compile(r'\b(japan|tokyo|kyoto)\b', re.I}

I want to be able to extract countries from the dataframe, such that I extract all countries from a set of columns (primary_loc and description in this case). So in this case, I'd get an output somewhat like

primary_loc   | description | country
--------------------------------------------
USA           | PyCon...    | united states
USA           | PyCon...    | india
USA           | PyCon...    | brazil
USA           | PyCon...    | japan
Canada        | The ann...  | canada
Canada        | The ann...  | united states

To get an idea of the scale of the problem, I have around 12-15k regexes and a dataframe with around 90 million rows.

I've tried using a Python UDF that looks somewhat like:

def get_countries(row):
  rd = row.asDict()
  rows_out = []
  
  for p, k in keywords.items():
    if k.search(rd['PRIMARY_LOC']) or k.search(rd['DESCRIPTION']):
      rows_out.append(Row(**{'product': p, **rd}))

  return rows_out

newDF = df.rdd.flatMap(lambda row: get_countries(row)).toDF()

but this is excruciatingly slow, even when operating on a subset of 10k or so rows.

If it matters, I'm using PySpark via DataBricks on Azure.

I'd suggest using a join for this purpose instead of matching regexes. You can create a dataframe with two columns, country and the corresponding patterns. Then join this dataframe to the original one on the condition that the columns in the original dataframe contains the patterns. This should be much more performant than matching regexes. — mck
– mck, Commented Jan 16, 2021 at 7:05
Spark ends up with a really horrible query plan for this - it does a full cartesian join before cutting down the number of rows. I end up with ~10^12 rows in an intermediate stage of the query. — Chinmay Kanchi
– Chinmay Kanchi, Commented Jan 19, 2021 at 0:11

Paul · Accepted Answer · 2021-01-18 08:55:43Z

2

Since you seem to only want to match exact words regex is way more expensive then just looking the words up. Assuming you only need to match whole words and not a complicated regular expression (e.g. numbers etc.) you can split the description into words and perform a lookup. If the words are saved in sets lookup will be O(1)

Code would look something like this

single_keywords = {'united states': {"usa", "us", "texas", "washington", "new york"},
            'india': {"india", "bangalore", "mumbai", "delhi"},
            'canada': {"canada", "winnipeg", "toronto", "ontario", "vancouver"},
            'japan': {"japan", "tokyo", "kyoto"},
}
multiword_keywords = {"united states": {("united", "states")}}

def get_countries(row):
  rd = row.asDict()
  rows_out = []
  
  words = rd['PRIMARY_LOC'].split(" ") + rd['DESCRIPTION'].split(" ")
  for p, k in single_keywords.items():
    if any((word in k for word in words)):
      rows_out.append(Row(**{'product': p, **rd}))
  
  for p, k in multiword_keywords.items():
    if any((all([word in t for word in words]) for t in k)):
      rows_out.append(Row(**{'product': p, **rd}))

  return rows_out

edited Jan 18, 2021 at 8:55

answered Jan 16, 2021 at 11:55

Paul

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4 Comments

Chinmay Kanchi Over a year ago

This will fail for "united states", plus can't deal with word boundaries other than space.

Paul Over a year ago

good point! maybe you can replace all non alphanumeric characters by a space first. Also for multiple word keywords you can have an extra dict. I've edited the answer to include this

Chinmay Kanchi Over a year ago

Let me try this out - if nothing else, if I can cut down the number of rows with approx matching (as long as there are no false negatives), I can always do another pass using regex on a much smaller dataset.

Paul Over a year ago

I think this approach should be way way faster than matching that long regular expressions. They get quite slow if they get long

Emer · Accepted Answer · 2021-01-19 08:56:22Z

As suggested by @mck, you can perform the regexp matching using the native API with the join strategy. I use UDF only as a last resource. The trick uses regexp_replace from the Scala API which allows input patterns from Columns. The function replaces the matched characters with an asterisk (it could be any char not present in your description column!) then contains checks for the asterisk and transforms the match to a boolean as a join condition.

Here is the example:

 val df_data = Seq(
      ("USA", "PyCon happens annually in the United States, with satellite events in India, Brazil and Tokyo"),
      ("Canada", "The annual hockey championship has some events occurring in the US")
    ).toDF("primary_loc", "description")

 val df_keywords = Seq(
      ("united states", "(?i)\\b(usa|us|united states|texas|washington|new york)\\b"),
      ("india", "(?i)\\b(india|bangalore|mumbai|delhi)\\b"),
      ("canada", "(?i)\\b(canada|winnipeg|toronto|ontario|vancouver)\\b"),
      ("japan", "(?i)\\b(japan|tokyo|kyoto)\\b"),
      ("brazil", "(?i)\\b(brazil)\\b"),
      ("spain", "(?i)\\b(spain|es|barcelona)\\b")
    ).toDF("country", "pattern")

 df_data.join(df_keywords, 
              regexp_replace(df_data("description"), df_keywords("pattern"), lit("*")).contains("*"), "inner")
        .show(truncate=false)

Result:

+-----------+---------------------------------------------------------------------------------------------+-------------+--------------------------------------------------------+
|primary_loc|description                                                                                  |country      |pattern                                                 |
+-----------+---------------------------------------------------------------------------------------------+-------------+--------------------------------------------------------+
|USA        |PyCon happens annually in the United States, with satellite events in India, Brazil and Tokyo|united states|(?i)\b(usa|us|united states|texas|washington|new york)\b|
|Canada     |The annual hockey championship has some events occurring in the US                           |united states|(?i)\b(usa|us|united states|texas|washington|new york)\b|
|USA        |PyCon happens annually in the United States, with satellite events in India, Brazil and Tokyo|india        |(?i)\b(india|bangalore|mumbai|delhi)\b                  |
|USA        |PyCon happens annually in the United States, with satellite events in India, Brazil and Tokyo|japan        |(?i)\b(japan|tokyo|kyoto)\b                             |
|USA        |PyCon happens annually in the United States, with satellite events in India, Brazil and Tokyo|brazil       |(?i)\b(brazil)\b                                        |
+-----------+---------------------------------------------------------------------------------------------+-------------+--------------------------------------------------------+

Unfortunatelly, I cound not make it work using the Python API. It returns a TypeError: Column is not iterable. Looks like the input patterns can only be strings. The patterns were also prefixed with (?i) to make them case insensitive. Also make sure the df_keywords is broadcasted to all workers. The explain output is:

== Physical Plan ==
BroadcastNestedLoopJoin BuildLeft, Inner, Contains(regexp_replace(description#307, pattern#400, *), *)
:- BroadcastExchange IdentityBroadcastMode
:  +- LocalTableScan [primary_loc#306, description#307]
+- LocalTableScan [country#399, pattern#400]

I tried something similar: df_data.join(df_keywords, F.expr('description rlike keywords')) and spark ended up trying to compute the full cartesian join - before applying the filter. Would regexp_replace be any better?
I think so, to get out of doubt compare both execution plans with explain. Performing a benchmark with 10k records would also be useful. If you are in Databricks Notebooks, use %scala in a cell to run scala code along with pyspark. Also keep an eye to the Spark UI. Check out the time per each task, maybe you also need to increase the number of partitions.

Chinmay Kanchi · Accepted Answer · 2021-01-20 07:12:58Z

1

For reference, I ended up solving the problem with a variant of Paul's answer. I built an Aho-Corasick automaton using pyahocorasick and pre-created the dictionary of keywords and a reverse lookup data structure. Since the Aho-Corasick algorithm doesn't deal with word boundaries etc., I still apply the corresponding regexes on any matches - but at least with my dataset, only a few (single-digit, typically) of the 10k regexes will result in a match, and this approach allows me to restrict myself to only those. My run-time for this problem went from 360,000 core-minutes (so 6000 hours on a single core) to around ~100 core-minutes with this approach.

So:

import ahocorasick
import re

def build_ahoacorasick_from_keywords(kw_dict):
  '''Build an automaton for searching for keywords in a haystack - also build an inverted dictionary of keyword -> locations and return both'''

  automaton = ahocorasick.Automaton()  
  inverted = {} 
  cnt = 0 
  for location, keyword_string in kw_dict.items(): 
      keywords = [_.lower() for _ in keyword_string.split(',') if _.strip()] 
      for kw in keywords: 
          automaton.add_word(kw, (cnt, kw)) 
          cnt += 1 
          if kw in inverted: 
              inverted[kw].append(location) 
          else: 
              inverted[kw] = [location] 
  automaton.make_automaton() 
  return automaton, inverted  


def get_locations(description, automaton, inverted_dict):
  description = description or ''
  haystack = description.lower().strip()
  locations = set()
  for _, (__, word) in automaton.iter(haystack):
    temp_re = r'\b{}\b'.format(re.escape(word))
    if re.search(temp_re, haystack):
      locations.update(inverted_dict[word])      
  return list(locations) if locations else None   

# kw_dict looks like {'united states': "usa,us,texas,washington,new york,united states", ...}
automaton, inverted = build_ahoacorasick_from_keywords(kw_dict)
my_udf = F.udf(lambda title, description: get_locations(description, automaton, inverted), ArrayType(StringType()))

new_df = df.withColumn('locations', my_udf(df.description))

# save new_df etc

answered Jan 20, 2021 at 7:12

Chinmay Kanchi

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3 Comments

jxc Over a year ago

as far as i see, your question is a simplified version of this post. based on the size of keywords, most likely, you just need using broadcast-join to avoid the cartesian-product of two dataframes.

Chinmay Kanchi Over a year ago

Did that and still got a cartesian join from spark. In any case, changing the algorithm made the entire thing so much faster that it's not worth quibbling over the other solutions.

Nebi M Aydin Over a year ago

I tried aho corasick approach as well, but still getting similar time as regex :(

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