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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,59 @@ | ||
| # ULP | ||
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| ULP (Universal Log Parsing) is a highly accurate log parsing tool, the ability to extract templates from unstructured log data. ULP learns from sample log data to recognize future log events. It combines pattern matching and frequency analysis techniques. First, log events are organized into groups using a text processing method. Frequency analysis is then applied locally to instances of the same group to identify static and dynamic content of log events. When applied to 10 log datasets of the Loghub benchmark, ULP achieves an average accuracy of 89.2%, which outperforms the accuracy of four leading log parsing tools, namely Drain, Logram, Spell and AEL. Additionally, ULP can parse up to four million log events in less than 3 minutes. ULP can be readily used by practitioners and researchers to parse effectively and efficiently large log files so as to support log analysis tasks. | ||
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| Read more information about Drain from the following paper: | ||
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| + Issam Sedki, Abdelwahab Hamou-Lhadj, Otmane Ait-Mohamed, Mohammed A. Shehab. [An Effective Approach for Parsing Large Log Files](https://users.encs.concordia.ca/~abdelw/papers/ICSME2022_ULP.pdf), *Proceedings of the IEEE International Conference on Software Maintenance and Evolution (ICSME)*, 2022. | ||
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| ### Running | ||
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| The code has been tested in the following enviornment: | ||
| + python 3.7.6 | ||
| + regex 2022.3.2 | ||
| + pandas 1.0.1 | ||
| + numpy 1.18.1 | ||
| + scipy 1.4.1 | ||
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| Run the following scripts to start the demo: | ||
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| ``` | ||
| python demo.py | ||
| ``` | ||
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| Run the following scripts to execute the benchmark: | ||
|
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| ``` | ||
| python benchmark.py | ||
| ``` | ||
|
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| ### Benchmark | ||
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| Running the benchmark script on Loghub_2k datasets, you could obtain the following results. | ||
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| | Dataset | F1_measure | Accuracy | | ||
| |:-----------:|:----------|:--------| | ||
| | HDFS | 0.999984 | 0.9975 | | ||
| | Hadoop | 0.999923 | 0.9895 | | ||
| | Spark | 0.994593 | 0.922 | | ||
| | Zookeeper | 0.999876 | 0.9925 | | ||
| | BGL | 0.999453 | 0.93 | | ||
| | HPC | 0.994433 | 0.9505 | | ||
| | Thunderbird | 0.998665 | 0.6755 | | ||
| | Windows | 0.989051 | 0.41 | | ||
| | Linux | 0.476099 | 0.3635 | | ||
| | Android | 0.971417 | 0.838 | | ||
| | HealthApp | 0.993431 | 0.9015 | | ||
| | Apache | 1 | 1 | | ||
| | Proxifier | 0.739766 | 0.024 | | ||
| | OpenSSH | 0.939796 | 0.434 | | ||
| | OpenStack | 0.834337 | 0.4915 | | ||
| | Mac | 0.981294 | 0.814 | | ||
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| ### Citation | ||
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| :telescope: If you use our logparser tools or benchmarking results in your publication, please kindly cite the following papers. | ||
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| + [**ICSE'19**] Jieming Zhu, Shilin He, Jinyang Liu, Pinjia He, Qi Xie, Zibin Zheng, Michael R. Lyu. [Tools and Benchmarks for Automated Log Parsing](https://arxiv.org/pdf/1811.03509.pdf). *International Conference on Software Engineering (ICSE)*, 2019. | ||
| + [**DSN'16**] Pinjia He, Jieming Zhu, Shilin He, Jian Li, Michael R. Lyu. [An Evaluation Study on Log Parsing and Its Use in Log Mining](https://jiemingzhu.github.io/pub/pjhe_dsn2016.pdf). *IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)*, 2016. |
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| # ========================================================================= | ||
| # This file is modified from https://github.com/SRT-Lab/ULP | ||
| # | ||
| # MIT License | ||
| # Copyright (c) 2022 Universal Log Parser | ||
| # | ||
| # Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| # of this software and associated documentation files (the "Software"), to deal | ||
| # in the Software without restriction, including without limitation the rights | ||
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| # copies of the Software, and to permit persons to whom the Software is | ||
| # furnished to do so, subject to the following conditions: | ||
| # | ||
| # The above copyright notice and this permission notice shall be included in all | ||
| # copies or substantial portions of the Software. | ||
| # ========================================================================= | ||
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| import os | ||
| import pandas as pd | ||
| import regex as re | ||
| import time | ||
| import warnings | ||
| from collections import Counter | ||
| from string import punctuation | ||
|
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| warnings.filterwarnings("ignore") | ||
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|
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| 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 | ||
|
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| 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 "!@#$%^&*{}<>?\|`~"}) | ||
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| 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-]+|)(:|)", | ||
| ] | ||
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| 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(",", " ")) | ||
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| self.df_log["event_label"] = event_label | ||
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| return 0 | ||
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| 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())) | ||
| ) | ||
| ) | ||
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| lst = petit_group["event_label"].values.tolist() | ||
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| vec = [] | ||
| big_lst = " ".join(v for v in lst) | ||
| this_count = Counter(big_lst.split()) | ||
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| 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) | ||
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| return vec | ||
|
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| def remove_word_with_special(self, sentence): | ||
| sentence = sentence.translate( | ||
| {ord(c): "" for c in "!@#$%^&*()[]{};:,/<>?\|`~-=+"} | ||
| ) | ||
| length = len(sentence.split()) | ||
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| finale = "" | ||
| for word in sentence.split(): | ||
| if ( | ||
| not any(ch.isdigit() for ch in word) | ||
| and not any(not c.isalnum() for c in word) | ||
| and len(word) > 1 | ||
| ): | ||
| finale += word | ||
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| finale = finale + str(length) | ||
| return finale | ||
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| def outputResult(self): | ||
| self.df_log.to_csv( | ||
| os.path.join(self.savePath, self.logName + "_structured.csv"), index=False | ||
| ) | ||
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| def load_data(self): | ||
| headers, regex = self.generate_logformat_regex(self.log_format) | ||
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| self.df_log = self.log_to_dataframe( | ||
| os.path.join(self.path, self.logname), regex, headers, self.log_format | ||
| ) | ||
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| def generate_logformat_regex(self, logformat): | ||
| """Function to generate regular expression to split log messages""" | ||
| headers = [] | ||
| splitters = re.split(r"(<[^<>]+>)", logformat) | ||
| regex = "" | ||
| for k in range(len(splitters)): | ||
| if k % 2 == 0: | ||
| splitter = re.sub(" +", "\\\s+", splitters[k]) | ||
| regex += splitter | ||
| else: | ||
| header = splitters[k].strip("<").strip(">") | ||
| regex += "(?P<%s>.*?)" % header | ||
| headers.append(header) | ||
| regex = re.compile("^" + regex + "$") | ||
| return headers, regex | ||
|
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| 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: | ||
| print("[Warning] Skip line: " + line) | ||
| logdf = pd.DataFrame(log_messages, columns=headers) | ||
| logdf.insert(0, "LineId", None) | ||
| logdf["LineId"] = [i + 1 for i in range(linecount)] | ||
| return logdf | ||
|
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| def parse(self, logname): | ||
| start_timeBig = time.time() | ||
| print("Parsing file: " + os.path.join(self.path, logname)) | ||
|
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| self.logname = logname | ||
|
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| regex = [r"blk_-?\d+", r"(\d+\.){3}\d+(:\d+)?"] | ||
|
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| self.load_data() | ||
| self.df_log = self.df_log.sample(n=2000) | ||
| self.tokenize() | ||
| 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 | ||
|
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| re_list2 = ["[ ]{1,}[-]*[0-9]+[ ]{1,}", ' "\d+" '] | ||
|
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| generic_re = re.compile("|".join(re_list2)) | ||
|
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| for i in keys: | ||
| l = [] | ||
| slc = groups.get_group(i) | ||
|
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| 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) | ||
|
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| template = re.sub(generic_re, " <*> ", template) | ||
| slc["event_label"] = [template] * len(slc["event_label"].to_list()) | ||
|
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| stock = stock.append(slc) | ||
| stock = stock.sort_index() | ||
|
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| self.df_log = stock | ||
|
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| 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 | ||
| ) | ||
| elapsed_timeBig = time.time() - start_timeBig | ||
| print(f"Parsing done in {elapsed_timeBig} sec") | ||
| return 0 |
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| @@ -0,0 +1 @@ | ||
| from .ULP import * |
Oops, something went wrong.