A rule-based log analyzer & filter

Overview

Flog

一个根据规则集来处理文本日志的工具。

前言

在日常开发过程中,由于缺乏必要的日志规范,导致很多人乱打一通,一个日志文件夹解压缩后往往有几十万行。

日志泛滥会导致信息密度骤减,给排查问题带来了不小的麻烦。

以前都是用grep之类的工具先挑选出有用的,再逐条进行排查,费时费力。在忍无可忍之后决定写这个工具,根据规则自动分析日志、剔除垃圾信息。

使用方法

安装

python setup.py install

基础用法

flog -r rules.yaml /path/to/1.log /path/to/2.log /path/to/3.log -o /path/to/filtered.log

其中:

  • rules.yaml是规则文件
  • /path/to/x.log是原始的日志文件,支持一次输入多个日志文件。
  • /path/to/filtered.log是过滤后的日志文件,如果不指定文件名(直接一个-o),会自动生成一个。

如果不需要过滤日志内容,只需显示分析结果,可以直接:

flog -r rules.yaml /path/to/your.log

规则语法

基础

name: Rule Name #规则集名称
patterns: #规则列表
  # 单行模式,如果匹配到 ^Hello,就输出 Match Hello
  - match: "^Hello"
    message: "Match Hello"
    action: bypass #保留此条日志(会输出到-o指定的文件中)
    
  # 多行模式,以^Hello开头,以^End结束,输出 Match Hello to End,并丢弃此条日志
  - start: "^Hello"
    end: "^End"
    message: "Match Hello to End"
    action: drop

  - start: "Start"
    start_message: "Match Start" #匹配开始时显示的信息
    end: "End"
    end_messagee: "Match End" #结束时显示的信息

纯过滤模式

name: Rule Name
patterns:
  - match: "^Hello" #删除日志中以Hello开头的行
  - start: "^Hello" #多行模式,删除从Hello到End中间的所有内容
    end: "^End"

过滤日志内容,并输出信息

name: Rule Name
patterns:
  - match: "^Hello" #删除日志中以Hello开头的行
    message: "Match Hello"
    action: drop #删除此行日志

规则嵌套

仅多行模式支持规则嵌套。

name: Rule
patterns:
  - start: "^Response.*{$"
    end: "^}"
    patterns:
      - match: "username = (.*)"
        message: "Current user: {{ capture[0] }}"

输入:

Login Response {
  username = zorro
  userid = 123456
}

输出:

Current user: zorro

action

action字段主要用于控制是否过滤此条日志,仅在指定 -o 参数后生效。 取值范围:【dropbypass】。

为了简化纯过滤类型规则的书写,action默认值的规则如下:

  • 如果规则中包含messagestart_messageend_message字段,action默认为bypass,即输出到文件中。
  • 如果规则中不包含message相关字段,action默认为drop,变成一条纯过滤规则。

message

message 字段用于在标准输出显示信息,并且支持 Jinja 模版语法来自定义输出信息内容,通过它可以实现一些简单的日志分析功能。

目前支持的参数有:

  • lines: (多行模式下)匹配到的所有行
  • content: 匹配到的日志内容
  • captures: 正则表达式(match/start/end)捕获的内容

例如:

name: Rule Name
patterns:
  - match: "^Hello (.*)"
    message: "Match {{captures[0]}}"

如果遇到:"Hello lilei",则会在终端输出"Match lilei"

context

可以把日志中频繁出现的正则提炼出来,放到context字段下,避免复制粘贴多次,例如:

name: Rule Name

context:
  timestamp: "\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2}.\\d{3}"
patterns:
  - match: "hello ([^:]*):"
    message: "{{ timestamp }} - {{ captures[0] }}"

输入:2022-04-08 16:52:37.152 hello world: this is a test message
输出:2022-04-08 16:52:37.152 - world

高亮

内置了一些 Jinjafilter,可以在终端高亮输出结果,目前包含:

black, red, green, yellow, blue, purple, cyan, white, bold, light, italic, underline, blink, reverse, strike

例如:

patterns:
  - match: "Error: (.*)"
    message: "{{ captures[0] | red }}"

输入:Error: file not found
输出:file not found

include

支持引入其它规则文件,例如:

name: Rule
include: base #引入同级目录下的 base.yaml 或 base.yml

include支持引入一个或多个文件,例如:

name: Rule
include:
  - base
  - ../base
  - base.yaml
  - base/base1
  - base/base2.yaml
  - ../base.yaml
  - /usr/etc/rules/base.yml

contextpatterns会按照引用顺序依次合并,如果有同名的context,后面的会替换之前的。

License

MIT

Owner
上山打老虎
专业造工具
上山打老虎
PushForKiCad - AISLER Push for KiCad EDA

AISLER Push for KiCad Push your layout to AISLER with just one click for instant

AISLER 31 Dec 29, 2022
This repository is for Competition for ML_data class

This repository is for Competition for ML_data class. Based on mmsegmentatoin,mainly using swin transformer to completed the competition.

jianlong 2 Oct 23, 2022
PyTorch implementation of Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network

hierarchical-multi-label-text-classification-pytorch Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach This

Mingu Kang 17 Dec 13, 2022
This is a collection of our NAS and Vision Transformer work.

AutoML - Neural Architecture Search This is a collection of our AutoML-NAS work iRPE (NEW): Rethinking and Improving Relative Position Encoding for Vi

Microsoft 828 Dec 28, 2022
The repo contains the code of the ACL2020 paper `Dice Loss for Data-imbalanced NLP Tasks`

Dice Loss for NLP Tasks This repository contains code for Dice Loss for Data-imbalanced NLP Tasks at ACL2020. Setup Install Package Dependencies The c

223 Dec 17, 2022
This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.

Integrated Gradients This is the pytorch implementation of "Axiomatic Attribution for Deep Networks". The original tensorflow version could be found h

Tianhong Dai 150 Dec 23, 2022
Pytorch implementation of few-shot semantic image synthesis

Few-shot Semantic Image Synthesis Using StyleGAN Prior Our method can synthesize photorealistic images from dense or sparse semantic annotations using

40 Sep 26, 2022
Source code of all the projects of Udacity Self-Driving Car Engineer Nanodegree.

self-driving-car In this repository I will share the source code of all the projects of Udacity Self-Driving Car Engineer Nanodegree. Hope this might

Andrea Palazzi 2.4k Dec 29, 2022
Semantically Contrastive Learning for Low-light Image Enhancement

Semantically Contrastive Learning for Low-light Image Enhancement Here, we propose an effective semantically contrastive learning paradigm for Low-lig

48 Dec 16, 2022
Code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition"

SEW (Squeezed and Efficient Wav2vec) The repo contains the code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speec

ASAPP Research 67 Dec 01, 2022
FIRM-AFL is the first high-throughput greybox fuzzer for IoT firmware.

FIRM-AFL FIRM-AFL is the first high-throughput greybox fuzzer for IoT firmware. FIRM-AFL addresses two fundamental problems in IoT fuzzing. First, it

356 Dec 23, 2022
Public repository of the 3DV 2021 paper "Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds"

Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds Björn Michele1), Alexandre Boulch1), Gilles Puy1), Maxime Bucher1) and Rena

valeo.ai 15 Dec 22, 2022
Planar Prior Assisted PatchMatch Multi-View Stereo

ACMP [News] The code for ACMH is released!!! [News] The code for ACMM is released!!! About This repository contains the code for the paper Planar Prio

Qingshan Xu 127 Dec 31, 2022
The Submission for SIMMC 2.0 Challenge 2021

The Submission for SIMMC 2.0 Challenge 2021 challenge website Requirements python 3.8.8 pytorch 1.8.1 transformers 4.8.2 apex for multi-gpu nltk Prepr

5 Jul 26, 2022
A paper using optimal transport to solve the graph matching problem.

GOAT A paper using optimal transport to solve the graph matching problem. https://arxiv.org/abs/2111.05366 Repo structure .github: Files specifying ho

neurodata 8 Jan 04, 2023
Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly

Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly Code for this paper Ultra-Data-Efficient GAN Tra

VITA 77 Oct 05, 2022
Code for Paper "Evidential Softmax for Sparse MultimodalDistributions in Deep Generative Models"

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Abstract Many applications of generative models rely on the marginali

Stanford Intelligent Systems Laboratory 9 Jun 06, 2022
ComPhy: Compositional Physical Reasoning ofObjects and Events from Videos

ComPhy This repository holds the code for the paper. ComPhy: Compositional Physical Reasoning ofObjects and Events from Videos, (Under review) PDF Pro

29 Dec 29, 2022
ivadomed is an integrated framework for medical image analysis with deep learning.

Repository on the collaborative IVADO medical imaging project between the Mila and NeuroPoly labs.

144 Dec 19, 2022
Home for cuQuantum Python & NVIDIA cuQuantum SDK C++ samples

Welcome to the cuQuantum repository! This public repository contains two sets of files related to the NVIDIA cuQuantum SDK: samples: All C/C++ sample

NVIDIA Corporation 147 Dec 27, 2022