Thursday, January 26, 2012

Apache Log Parsing

How much do I love Python?  Consider this little snippet that parses Apache logs.


import re
from collections import defaultdict, named tuple

format_pat= re.compile( 
    r"(?P<host>[\d\.]+)\s" 
    r"(?P<identity>\S*)\s" 
    r"(?P<user>\S*)\s"
    r"\[(?P<time>.*?)\]\s"
    r'"(?P<request>.*?)"\s'
    r"(?P<status>\d+)\s"
    r"(?P<bytes>\S*)\s"
    r'"(?P<referer>.*?)"\s' # [SIC]
    r'"(?P<user_agent>.*?)"\s*' 
)

Access = namedtuple('Access',
    ['host', 'identity', 'user', 'time', 'request',
    'status', 'bytes', 'referer', 'user_agent'] )

def access_iter( source_iter ):
    for log in source_iter:
        for line in (l.rstrip() for l in log):
            match= format_pat.match(line)
            if match:
                yield Access( **match.groupdict() )


That's about it.  The access log rows are now first-class Access-class objects that can be processed pleasantly by high-level Python applications.

Cool things.
  1. The adjacent string concatenation means that the regular expression can be broken up into bits to make it readable.
  2. When the named tuple attributes match the regular expression names, we can trivially turn the match.groupdict() into a named tuple. 
  3. By using a generator, the other parts of the application can simply loop through the results without tying up memory to create vast intermediate structures.
A couple of years back, a sysadmin was trying to justify spending money on a log analyzer product.  I suggested they (at the very least) get an open source log analyzer.

I also suggested that they learn Python and save themselves the pain of working with a (potentially) complex tool.  Given this as a common library module, log analysis applications are remarkably easy to write.