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Copy pathULP.py
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217 lines (152 loc) · 7.02 KB
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import os
import pandas as pd
import regex as re
import time
import numpy
import csv
import sys
import warnings
from collections import Counter
from string import punctuation
warnings.filterwarnings("ignore")
class LogParser:
def __init__(self, log_format, indir='./', outdir='./result/', rex=[]):
"""
Attributes
----------
rex : regular expressions used in preprocessing (step1)
path : the input path stores the input log file name
logName : the name of the input file containing raw log messages
savePath : the output path stores the file containing structured logs
"""
self.path = indir
self.indir = indir
self.outdir = outdir
self.logName = None
self.savePath = outdir
self.df_log = None
self.log_format = log_format
self.rex = rex
def tokenize(self):
event_label = []
#print("\n============================Removing obvious dynamic variables======================\n\n")
for idx, log in self.df_log['Content'].iteritems():
tokens =log.split()
tokens = re.sub(r"\\", "", str(tokens))
tokens = re.sub(r"\'", "", str(tokens))
tokens = tokens.translate ({ord(c): "" for c in "!@#$%^&*{}<>?\|`~"})
re_list = [ "([\da-fA-F]{2}:){5}[\da-fA-F]{2}","\d{4}-\d{2}-\d{2}", "\d{4}\/\d{2}\/\d{2}",
"[0-9]{2}:[0-9]{2}:[0-9]{2}(?:[.,][0-9]{3})?", "[0-9]{2}:[0-9]{2}:[0-9]{2}", "[0-9]{2}:[0-9]{2}", "0[xX][0-9a-fA-F]+",
"([\(]?[0-9a-fA-F]*:){8,}[\)]?" , "^(?:[0-9]{4}-[0-9]{2}-[0-9]{2})(?:[ ][0-9]{2}:[0-9]{2}:[0-9]{2})?(?:[.,][0-9]{3})?",
"(\/|)([a-zA-Z0-9-]+\.){2,}([a-zA-Z0-9-]+)?(:[a-zA-Z0-9-]+|)(:|)" ]
pat = r'\b(?:{})\b'.format('|'.join(str(v) for v in re_list))
tokens = re.sub(pat, "<*>", str(tokens))
tokens = tokens.replace('=', ' = ')
tokens = tokens.replace(')', ' ) ')
tokens = tokens.replace('(', ' ( ')
tokens = tokens.replace(']', ' ] ')
tokens = tokens.replace('[', ' [ ')
event_label.append(str(tokens).lstrip().replace(","," "))
self.df_log["event_label"] = event_label
return 0
def getDynamicVars2(self,petit_group) :
petit_group['event_label'] = petit_group['event_label'].map(lambda x:' '.join(dict.fromkeys(x.split())))
petit_group['event_label'] = petit_group['event_label'].map(lambda x:' '.join(filter(None, (word.strip(punctuation) for word in x.split()))))
lst = petit_group["event_label"].values.tolist()
vec =[]
big_lst = ' '.join(v for v in lst)
this_count = Counter(big_lst.split())
if (this_count):
max_val = max (this_count, key=this_count.get)
for word in this_count:
if this_count[word] < this_count[max_val] :
vec.append(word)
return vec
def remove_word_with_special(self,sentence):
sentence = sentence.translate({ord(c): "" for c in "!@#$%^&*()[]{};:,/<>?\|`~-=+"})
length = len(sentence.split())
finale=""
for word in sentence.split():
if not any(ch.isdigit() for ch in word) and not any(not c.isalnum() for c in word) and len(word)>1 :
finale += word
finale= finale+str(length)
return finale
def outputResult(self):
self.df_log.to_csv(os.path.join(self.savePath, self.logName + '_structured.csv'), index=False)
def load_data(self):
headers, regex = self.generate_logformat_regex(self.log_format)
self.df_log = self.log_to_dataframe(os.path.join(self.path, self.logname), regex, headers, self.log_format)
def generate_logformat_regex(self, logformat):
""" Function to generate regular expression to split log messages
"""
headers = []
splitters = re.split(r'(<[^<>]+>)', logformat)
regex = ''
for k in range(len(splitters)):
if k % 2 == 0:
splitter = re.sub(' +', '\\\s+', splitters[k])
regex += splitter
else:
header = splitters[k].strip('<').strip('>')
regex += '(?P<%s>.*?)' % header
headers.append(header)
regex = re.compile('^' + regex + '$')
return headers, regex
def log_to_dataframe(self, log_file, regex, headers, logformat):
""" Function to transform log file to dataframe
"""
log_messages = []
linecount = 0
with open(log_file, 'r') as fin:
for line in fin.readlines():
try:
match = regex.search(line.strip())
message = [match.group(header) for header in headers]
log_messages.append(message)
linecount += 1
except Exception as e:
pass
logdf = pd.DataFrame(log_messages, columns=headers)
logdf.insert(0, 'LineId', None)
logdf['LineId'] = [i + 1 for i in range(linecount)]
return logdf
def parse(self,logname):
start_timeBig = time.time()
print('Parsing file: ' + os.path.join(self.path, logname))
self.logname = logname
regex = [r'blk_-?\d+', r'(\d+\.){3}\d+(:\d+)?']
self.load_data()
self.df_log = self.df_log.sample(n=2000)
self.tokenize()
print
self.df_log['EventId'] = self.df_log['event_label'].map(lambda x:self.remove_word_with_special(str(x)))
groups = self.df_log.groupby('EventId')
keys = groups.groups.keys()
stock = pd.DataFrame()
count= 0
re_list2 = ["[ ]{1,}[-]*[0-9]+[ ]{1,}", " \"\d+\" "]
generic_re = re.compile( '|'.join( re_list2) )
for i in keys:
l=[]
slc = groups.get_group(i)
template = slc['event_label'][0:1].to_list()[0]
count+=1
if (slc.size>1 ):
l = self.getDynamicVars2(slc.head(10))
pat = r'\b(?:{})\b'.format('|'.join(str(v) for v in l))
if (len(l)>0):
template = template.lower()
template = re.sub(pat,'<*>', template)
template = re.sub(generic_re, " <*> ", template)
slc['event_label'] = [template] * len(slc['event_label'].to_list())
stock = stock.append(slc)
stock = stock.sort_index()
self.df_log = stock
self.df_log['EventTemplate'] = self.df_log['event_label']
if not os.path.exists(self.savePath):
os.makedirs(self.savePath)
self.df_log.to_csv(os.path.join(self.savePath, logname + '_structured.csv'), index=False)
#print(str(count))
elapsed_timeBig = time.time() - start_timeBig
print("Parsing done in" , elapsed_timeBig)
return 0