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
Code for Contrastive-Geometry Networks for Generalized 3D Pose Transfer

Code for Contrastive-Geometry Networks for Generalized 3D Pose Transfer

18 Jun 28, 2022
A computer vision pipeline to identify the "icons" in Christian paintings

Christian-Iconography A computer vision pipeline to identify the "icons" in Christian paintings. A bit about iconography. Iconography is related to id

Rishab Mudliar 3 Jul 30, 2022
Send text to girlfriend in the morning

Girlfriend Text Send text to girlfriend (or really anyone with a phone number) in the morning 1. Configure your settings in utils.py. phone_number = "

Paras Adhikary 199 Oct 25, 2022
R-package accompanying the paper "Dynamic Factor Model for Functional Time Series: Identification, Estimation, and Prediction"

dffm The goal of dffm is to provide functionality to apply the methods developed in the paper “Dynamic Factor Model for Functional Time Series: Identi

Sven Otto 3 Dec 09, 2022
Simply enable or disable your Nvidia dGPU

EnvyControl (WIP) Simply enable or disable your Nvidia dGPU Usage First clone this repo and install envycontrol with sudo pip install . CLI Turn off y

Victor Bayas 292 Jan 03, 2023
MediaPipe is a an open-source framework from Google for building multimodal

MediaPipe is a an open-source framework from Google for building multimodal (eg. video, audio, any time series data), cross platform (i.e Android, iOS, web, edge devices) applied ML pipelines. It is

Bhavishya Pandit 3 Sep 30, 2022
BTC-Generator - BTC Generator With Python

Что такое BTC-Generator? Это генератор чеков всеми любимого @BTC_BANKER_BOT Для

DoomGod 3 Aug 24, 2022
This repo contains the implementation of YOLOv2 in Keras with Tensorflow backend.

Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).

Huynh Ngoc Anh 1.7k Dec 24, 2022
People movement type classifier with YOLOv4 detection and SORT tracking.

Movement classification The goal of this project would be movement classification of people, in other words, walking (normal and fast) and running. Yo

4 Sep 21, 2021
PyTorch-Geometric Implementation of MarkovGNN: Graph Neural Networks on Markov Diffusion

MarkovGNN This is the official PyTorch-Geometric implementation of MarkovGNN paper under the title "MarkovGNN: Graph Neural Networks on Markov Diffusi

HipGraph: High-Performance Graph Analytics and Learning 6 Sep 23, 2022
Certifiable Outlier-Robust Geometric Perception

Certifiable Outlier-Robust Geometric Perception About This repository holds the implementation for certifiably solving outlier-robust geometric percep

83 Dec 31, 2022
Contrastive Multi-View Representation Learning on Graphs

Contrastive Multi-View Representation Learning on Graphs This work introduces a self-supervised approach based on contrastive multi-view learning to l

Kaveh 208 Dec 23, 2022
Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision https://arxiv.org/abs/2003.00393 Abstract Active learning (AL) aims to min

Denis 29 Nov 21, 2022
Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.

Graph-Based Local Trajectory Planner The graph-based local trajectory planner is python-based and comes with open interfaces as well as debug, visuali

TUM - Institute of Automotive Technology 160 Jan 04, 2023
Galaxy images labelled by morphology (shape). Aimed at ML development and teaching

Galaxy images labelled by morphology (shape). Aimed at ML debugging and teaching.

Mike Walmsley 14 Nov 28, 2022
Deep Q-learning for playing chrome dino game

[PYTORCH] Deep Q-learning for playing Chrome Dino

Viet Nguyen 68 Dec 05, 2022
The codebase for Data-driven general-purpose voice activity detection.

Data driven GPVAD Repository for the work in TASLP 2021 Voice activity detection in the wild: A data-driven approach using teacher-student training. S

Heinrich Dinkel 75 Nov 27, 2022
A Closer Look at Structured Pruning for Neural Network Compression

A Closer Look at Structured Pruning for Neural Network Compression Code used to reproduce experiments in https://arxiv.org/abs/1810.04622. To prune, w

Bayesian and Neural Systems Group 140 Dec 05, 2022
Repo for CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

CReST in Tensorflow 2 Code for the paper: "CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning" by Chen Wei, Ki

Google Research 75 Nov 01, 2022
CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation

CDGAN CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation CDGAN Implementation in PyTorch This is the imple

Kancharagunta Kishan Babu 6 Apr 19, 2022