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@sebastianwebber
sebastianwebber / README.md
Last active February 19, 2024 11:44
Compilation of the Uber Facts on PostgreSQL to MySQL Migration

Uber facts

Original posts/information

Key points

  • ~50GB MySQL Application
  • Main motivation: PostGis
  • Migration made with a custom tool(xml2pgcopy) and mysqldump on 45min
@larryli
larryli / GIT_SSH_COMMAND.sh
Last active January 21, 2021 20:35
Git ssh socks proxy
#!/bin/sh
export GIT_SSH_COMMAND='ssh -o ProxyCommand="connect -S 127.0.0.1:1080 %h %p"'
git config --global core.sshCommand 'ssh -o ProxyCommand="connect -S 127.0.0.1:1080 %h %p"'
git clone -c=core.sshCommand 'ssh -o ProxyCommand="connect -S 127.0.0.1:1080 %h %p"' git@github.com:larryli/ipv4.git
git config core.sshCommand 'ssh -o ProxyCommand="connect -S 127.0.0.1:1080 %h %p"'
@bynil
bynil / config-git-proxy.txt
Last active January 23, 2024 12:37
Use git over socks5 proxy
Port: 1080
1. Create a file /YOUR PATH/gitproxy.sh with content:
#!/bin/sh
nc -X 5 -x 127.0.0.1:1080 "$@"
2. Edit your ~/.gitconfig
# For git://
** chronos
#+begin_src emacs-lisp
;;;; chronos plugin
(use-package chronos
:config
;; (use-package chronos)
;; https://github.com/dxknight/chronos
;; now is 17:00
;; 5 gives an expiry time of 17:05
;; 1:30 gives 18:30
@jsmestad
jsmestad / smartparens-cheatsheet.md
Created September 9, 2018 00:19 — forked from pvik/smartparens-cheatsheet.md
A Cheatsheet for Emacs Smarparens example configuration

An animated cheatsheet for smartparens using the example configuration specified here by the smartparens author. Inspired by this tutorial for paredit.

Traversal

C-M-f sp-forward-sexp
C-M-b sp-backward-sexp
@neomantra
neomantra / High_Performance_Redis.md
Last active May 17, 2024 07:35
Notes on running Redis with HPC techniques

High Performance Redis

In response to this brief blog entry, @antirez tweeted for some documentation on high-performance techniques for Redis. What I present here are general high-performance computing (HPC) techniques. The examples are oriented to Redis. but they work well for any program designed to be single- or worker-threaded and asynchronous (e.g. uses epoll).

The motivation for using these techniques is to maximize performance of our system and services. By isolating work, controlling memory, and other tuning, you can achieve significant reduction in latency and increase in throughput.

My perspective comes from the microcosm of my own bare-metal (vs VM), on-premises deployment. It might not be suitable for all scenarios, especially cloud deployments, as I have little experience with HPC there. After some discussion, maybe this can be adapted as [redis.io documentation](https://redis.io/do

@y0ngb1n
y0ngb1n / docker-registry-mirrors.md
Last active June 3, 2024 01:38
国内的 Docker Hub 镜像加速器,由国内教育机构与各大云服务商提供的镜像加速服务 | Dockerized 实践 https://github.com/y0ngb1n/dockerized

Docker Hub 镜像加速器

国内从 Docker Hub 拉取镜像有时会遇到困难,此时可以配置镜像加速器。Docker 官方和国内很多云服务商都提供了国内加速器服务。

Dockerized 实践 https://github.com/y0ngb1n/dockerized

配置加速地址

Ubuntu 16.04+、Debian 8+、CentOS 7+

@Matthias247
Matthias247 / async_await_cancellation.md
Created May 28, 2019 06:09
Async/Await - The challenges besides syntax - Cancellation

Async/Await - The challenges besides syntax - Cancellation

This is the second article in a series of articles around Rusts new async/await feature. The first article about interfaces can be found here.

In this part of the series we want to a look at a mechanism which behaves very different in Rust than in all other languages which feature async/await support. This mechanism is Cancellation.

I've been working with Apache Kafka for over 7 years. I inevitably find myself doing the same set of activities while I'm developing or working with someone else's system. Here's a set of Kafka productivity hacks for doing a few things way faster than you're probably doing them now. 🔥

Get the tools

@payam-int
payam-int / gist:edf977c6af603fee0ce1b05da7792fe7
Last active May 7, 2024 18:12
Prometheus Node Exporter - CPU and Memory Usage

CPU Usage :

(1 - avg(irate(node_cpu_seconds_total{mode="idle"}[10m])) by (instance)) * 100

Memory Usage :

100 * (1 - ((avg_over_time(node_memory_MemFree_bytes[10m]) + avg_over_time(node_memory_Cached_bytes[10m]) + avg_over_time(node_memory_Buffers_bytes[10m])) / avg_over_time(node_memory_MemTotal_bytes[10m])))