Pythonic way to process 200 million element data set?

Question

I have a directory of 1,000 files. Each file has many lines where each line is an ngram varying from 4 - 8 bytes. I'm trying to parse all files to get the distinct ngrams as a header row, then for each file, I want to write a row that has the frequency of that ngram sequence occurring within the file.

The following code made it through gathering the headers, but hit a memory error when trying to write the headers to the csv file. I was running it on an Amazon EC2 instance with 30GB of RAM. Can anyone provide recommendations for optimizations of which I'm unaware?

#Note: A combination of a list and a set is used to maintain order of metadata
#but still get performance since non-meta headers do not need to maintain order
header_list = []
header_set = set()
header_list.extend(META_LIST)
for ngram_dir in NGRAM_DIRS:
  ngram_files = os.listdir(ngram_dir)
  for ngram_file in ngram_files:      
      with open(ngram_dir+ngram_file, 'r') as file:
        for line in file:
          if not '.' in line and line.rstrip('\n') not in IGNORE_LIST:
            header_set.add(line.rstrip('\n'))

header_list.extend(header_set)#MEMORY ERROR OCCURRED HERE

outfile = open(MODEL_DIR+MODEL_FILE_NAME, 'w')
csvwriter = csv.writer(outfile)
csvwriter.writerow(header_list)

#Convert ngram representations to vector model of frequencies
for ngram_dir in NGRAM_DIRS:
  ngram_files = os.listdir(ngram_dir)
  for ngram_file in ngram_files:      
      with open(ngram_dir+ngram_file, 'r') as file:
        write_list = []
        linecount = 0
        header_dict = collections.OrderedDict.fromkeys(header_set, 0)
        while linecount < META_FIELDS: #META_FIELDS = 3
          line = file.readline()
          write_list.append(line.rstrip('\n'))
          linecount += 1 
        file_counter = collections.Counter(line.rstrip('\n') for line in file)
        header_dict.update(file_counter)
        for value in header_dict.itervalues():
          write_list.append(value)
        csvwriter.writerow(write_list)

outfile.close()

score 0 · Answer 1 · answered Apr 10 '14 at 05:24

Just don't extend that list then. Use chain from itertools to chain the list and set instead.

Instead of this:

header_list.extend(header_set)#MEMORY ERROR OCCURRED HERE

Do this (assuming csvwriter.writerow accepts any iterator):

headers = itertools.chain(header_list, header_set)
...
csvwriter.writerow(headers)

That should at least avoid the memory issue you're currently seeing.

Pythonic way to process 200 million element data set?

1 Answers1