How to match/extract multi-line pattern from file in pysark

问题

I have a huge file of rdf triplets (subject predicate objects) as shown in the image below. The goals it extract the bold items and have the following output

  Item_Id | quantityAmount | quantityUnit | rank
    -----------------------------------------------
      Q31      24954         Meter       BestRank
      Q25       582         Kilometer    NormalRank  

I want to extract lines that follow the following pattern

• subject is given a pointer (<Q31> <prop/P1082> <Pointer_Q31-87RF> .)

• Pointer has a ranking (<Pointer_Q31-87RF> <rank> <BestRank> )
  and valuePointer (<Pointer_Q31-87RF> <prop/Pointer_value/P1082> <value/cebcf9> )

• The valuePointer in turn points to its Amount (<value/cebcf9> <quantityAmount> "24954") and Unit (<value/cebcf9> <quantityUnit> <Meter>)

The normal way is to read the file line by line and extract each one of these above patterns (using sc.textFile('inFile').flatMap(lambda x: extractFunc(x)) and then through different joins combine them such that it would provide the above table. Is there a better way to go after this? I am including the file sample below.

<Q31> <prop/P1082> <Pointer_Q31-87RF> .
<Pointer_Q31-87RF> <rank> <BestRank> .
<Pointer_Q31-87RF> <prop/Pointer_P1082> "+24954"^^<2001/XMLSchema#decimal> .
<Pointer_Q31-87RF> <prop/Pointer_value/P1082> <value/cebcf9> .
<value/cebcf9> <syntax-ns#type> <QuantityValue> .
<value/cebcf9> <quantityAmount> 24954
<value/cebcf9> <quantityUnit> <Meter> .
<Q25> <prop/P1082> <Pointer_Q25-8E6C> .
<Pointer_Q25-8E6C> <rank> <NormalRank> .
<Pointer_Q25-8E6C> <prop/Pointer_P1082> "+24954”
<Pointer_Q25-8E6C> <prop/Pointer_value/P1082> <value/cebcf9> .
<value/cebcf9> <syntax-ns#type> <QuantityValue> .
<value/cebcf9> <quantityAmount> "582" .
<value/cebcf9> <quantityUnit> <Kilometer> .

回答1:

If you can use \n<Q as the delimiter to create RDD elements, then it becomes a pure python task to parse the data blocks. Below I create a function (based on your sample) to parse the block texts using regexes and retrieve cols information into Row object (you might have to adjust the regexes to reflect the actual data patterns, i.e. case sensitivity, extra white spaces etc.):

• For each RDD element, split by '\n' (line-mode)
• and then for each line, split by > < into a list y
• we can find rank, quantityUnit by checking y[1] and y[2], quantityAmount by checking y[1] and Item_id by checking y[0].

• Create Row object by iterating all required fields, set value to None for missing fields

  from pyspark.sql import Row
  import re
  
  # skipped the code to initialize SparkSession
  
  # field names to retrieve
  cols = ['Item_Id', 'quantityAmount', 'quantityUnit', 'rank']
  
  def parse_rdd_element(x, cols):
      try:
          row = {}
          for e in x.split('\n'):
              y = e.split('> <')
              if len(y) < 2:
                  continue
              if y[1] in ['rank', 'quantityUnit']:
                  row[y[1]] = y[2].split(">")[0]
              else:
                  m = re.match(r'^quantityAmount>\D*(\d+)', y[1])
                  if m:
                      row['quantityAmount'] = m.group(1)
                      continue
                  m = re.match('^(?:<Q)?(\d+)', y[0])
                  if m:
                      row['Item_Id'] = 'Q' + m.group(1)
          # if row is not EMPTY, set None to missing field
          return Row(**dict([ (k, row[k]) if k in row else (k, None) for k in cols])) if row else None
      except:
          return None
  

setup RDD using newAPIHadoopFile() with \n<Q as delimiter:

rdd = spark.sparkContext.newAPIHadoopFile(
    '/path/to/file',
    'org.apache.hadoop.mapreduce.lib.input.TextInputFormat',
    'org.apache.hadoop.io.LongWritable',
    'org.apache.hadoop.io.Text',
    conf={'textinputformat.record.delimiter': '\n<Q'}
)

Use the map function to parse the RDD element into Row object

rdd.map(lambda x: parse_rdd_element(x[1], cols)).collect()
#[Row(Item_Id=u'Q31', quantityAmount=u'24954', quantityUnit=u'Meter', rank=u'BestRank'),
# Row(Item_Id=u'Q25', quantityAmount=u'582', quantityUnit=u'Kilometer', rank=u'NormalRank')]

Convert the above RDD to dataframe

df = rdd.map(lambda x: parse_rdd_element(x[1], cols)).filter(bool).toDF()
df.show()
+-------+--------------+------------+----------+
|Item_Id|quantityAmount|quantityUnit|      rank|
+-------+--------------+------------+----------+
|    Q31|         24954|       Meter|  BestRank|
|    Q25|           582|   Kilometer|NormalRank|
+-------+--------------+------------+----------+

Some Notes:

• For better performance, pre-compile all regex patterns using re.compile() before passing them to the parse_rdd_element() function.

• In case there could be spaces/tabs between \n and <Q, multiple blocks will be added into the same RDD element, just split the RDD element by \n\s+<Q and replace map() with flatMap().

Reference: creating spark data structure from multiline record

来源：https://stackoverflow.com/questions/57499828/how-to-match-extract-multi-line-pattern-from-file-in-pysark