To show some techniques for working with files that are too large to fit on memory, I'm writing this post on a 10 year old laptop with 512 Mb of RAM and a 1.2 GHz celeron processor. The data in question is an xml format dump of data from stack overflow, the uncompressed file would not fit on the disk of this machine so I'll work with the compressed files directly (more on that here).
Reading xml without taking up a lot of memory
As this article explains, extra care is needed when using python's xml.etree module to avoid loading too many references into memory. The key is to access the root element outside of the loop, then
clear() it within the loop to avoid building up a tree. Here I read through the file and create a dictionary of dates to number of posts on that date, using pandas to output the data in csv format.
import bz2 import pandas as pd from collections import defaultdict from xml.etree import cElementTree def parse_file(filename): """Count the number of posts on each day for stack exchange xml data""" date_counts = defaultdict(int) with bz2.BZ2File(filename) as f: iterparser = cElementTree.iterparse(f, events=('start', )) _, root = iterparser.next() for _, element in iterparser: if element.tag == 'row': date_str = element.get('CreationDate', '').split('T') date_counts[date_str] += 1 root.clear() return date_counts date_counts = parse_file('stackoverflow.com-Posts.bz2') date_counts_df = pd.DataFrame.from_dict(date_counts, orient='index') date_counts_df.columns = ['num_posts'] date_counts_df.to_csv('stackex_by_date.csv')
- SF Python meetup talk, Score: 0.913
- Using sed to make specific text lowercase in place, Score: 0.858
- Saving time and space by working with gzip and bzip2 compressed files in python, Score: 0.826
- First Look at AWS Machine Learning, Score: 0.825
- Pandas date parsing performance, Score: 0.814