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
Automatic caption evaluation metric based on typicality analysis.

SeMantic and linguistic UndeRstanding Fusion (SMURF) Automatic caption evaluation metric described in the paper "SMURF: SeMantic and linguistic UndeRs

Joshua Feinglass 6 Jan 09, 2022
Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"

Note: this repo has been discontinued, please check code for newer version of the paper here Weight Normalized GAN Code for the paper "On the Effects

Sitao Xiang 182 Sep 06, 2021
[CVPR2021 Oral] FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation.

FFB6D This is the official source code for the CVPR2021 Oral work, FFB6D: A Full Flow Biderectional Fusion Network for 6D Pose Estimation. (Arxiv) Tab

Yisheng (Ethan) He 201 Dec 28, 2022
First-Order Probabilistic Programming Language

FOPPL: A First-Order Probabilistic Programming Language This is an implementation of FOPPL, an S-expression based probabilistic programming language d

Renato Costa 23 Dec 20, 2022
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

LiDAR fog simulation Created by Martin Hahner at the Computer Vision Lab of ETH Zurich. This is the official code release of the paper Fog Simulation

Martin Hahner 110 Dec 30, 2022
Official implementation of MSR-GCN (ICCV 2021 paper)

MSR-GCN Official implementation of MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction (ICCV 2021 paper) [Paper] [Sup

LevonDang 42 Nov 07, 2022
NIMA: Neural IMage Assessment

PyTorch NIMA: Neural IMage Assessment PyTorch implementation of Neural IMage Assessment by Hossein Talebi and Peyman Milanfar. You can learn more from

Kyryl Truskovskyi 293 Dec 30, 2022
Simple Python project using Opencv and datetime package to recognise faces and log attendance data in a csv file.

Attendance-System-based-on-Facial-recognition-Attendance-data-stored-in-csv-file- Simple Python project using Opencv and datetime package to recognise

3 Aug 09, 2022
A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body

DensePose: Dense Human Pose Estimation In The Wild Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos [densepose.org] [arXiv] [BibTeX] Dense human pos

Meta Research 6.4k Jan 01, 2023
Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks

SSTNet Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks(ICCV2021) by Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui J

83 Nov 29, 2022
This is a file about Unet implemented in Pytorch

Unet this is an implemetion of Unet in Pytorch and it's architecture is as follows which is the same with paper of Unet component of Unet Convolution

Dragon 1 Dec 03, 2021
Open-World Entity Segmentation

Open-World Entity Segmentation Project Website Lu Qi*, Jason Kuen*, Yi Wang, Jiuxiang Gu, Hengshuang Zhao, Zhe Lin, Philip Torr, Jiaya Jia This projec

DV Lab 410 Jan 03, 2023
SMPL-X: A new joint 3D model of the human body, face and hands together

SMPL-X: A new joint 3D model of the human body, face and hands together [Paper Page] [Paper] [Supp. Mat.] Table of Contents License Description News I

Vassilis Choutas 1k Jan 09, 2023
PyTorch implementation of Lip to Speech Synthesis with Visual Context Attentional GAN (NeurIPS2021)

Lip to Speech Synthesis with Visual Context Attentional GAN This repository contains the PyTorch implementation of the following paper: Lip to Speech

6 Nov 02, 2022
A PaddlePaddle implementation of STGCN with a few modifications in the model architecture in order to forecast traffic jam.

About This repository contains the code of a PaddlePaddle implementation of STGCN based on the paper Spatio-Temporal Graph Convolutional Networks: A D

Tianjian Li 1 Jan 11, 2022
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

Near-Duplicate Video Retrieval with Deep Metric Learning This repository contains the Tensorflow implementation of the paper Near-Duplicate Video Retr

Liming Jiang 238 Nov 25, 2022
Paper list of log-based anomaly detection

Paper list of log-based anomaly detection

Weibin Meng 411 Dec 05, 2022
Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).

Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).

Varun Nair 37 Dec 30, 2022
[ICCV 2021] Deep Hough Voting for Robust Global Registration

Deep Hough Voting for Robust Global Registration, ICCV, 2021 Project Page | Paper | Video Deep Hough Voting for Robust Global Registration Junha Lee1,

57 Nov 28, 2022
This repository includes the code of the sequence-to-sequence model for discontinuous constituent parsing described in paper Discontinuous Grammar as a Foreign Language.

Discontinuous Grammar as a Foreign Language This repository includes the code of the sequence-to-sequence model for discontinuous constituent parsing

Daniel Fernández-González 2 Apr 07, 2022