Si Nguyen mrsinguyen

Oh my zsh Setup (Fast + Minimal)

Install Zsh

Ubuntu

sudo apt update
sudo apt install zsh curl git

Notes on Martin Kleppmann's excellent Designing Data-Intensive Applications.

Chapter 1: Reliable, Scalable, and Maintainable Applications

Data Systems
- Dimensions to consider when thinking about data systems: access patterns, performance characteristics, implementations.
- Modern data systems often blur the lines between databases, caches, streams, etc.
Reliability
- Systems should perform the expected function at a given level of performance, and be tolerant to faults and user mistakes
Fault: One component of a system deviating from its spec. Prefer tolerating faults over preventing them (except for things like security issues). Faults stem from hardware failures, software failures, and human error (in a study, config errors caused most outages).

Keybase proof

I hereby claim:

I am mrsinguyen on github.
I am mrsinguyen (https://keybase.io/mrsinguyen) on keybase.
I have a public key ASAqD2M8zZeIDa5vsxclq9Nfc0BoxlUegT6z-mKIgHk0zQo

To claim this, I am signing this object:

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

Con trâu thì gọi “con tru”
Con dâu thì gọi “con du” trong nhà
“Mấn” là “váy”, “ngái” là “xa”
“Đi mô?” để hỏi ai là “đi đâu? ”
“Nác su” ý nói “nước sâu”
“Trấy bù” để gọi “quả bầu” đấy nha
“Gác bếp” thì gọi là “tra”
“Lông cơn” thực chất đó là “trồng cây”
“Ra sân” thì nói “ra cươi”

imgproxy vs alternatives benchmark

Setup

c5.xlarge AWS instance: 4 CPUs, 8 GB RAM
Ubuntu 18.04
Go 1.12
Python 2.7
Vips 8.7.4

	version: '3.8'
	services:
	mysql_container:
	platform: linux/x86_64
	image: mysql:5.7
	command: [ "mysqld", "--sql-mode=NO_ENGINE_SUBSTITUTION" ]
	ports: [ '3306:3306' ]
	volumes:
	- './docker/run/mysql:/var/lib/mysql'
	environment:

	version: "3.7"

	services:
	# simple
	mongo:
	image: mongo
	command: mongod --replSet rs0 --bind_ip_all
	healthcheck:
	test: \|
	mongosh --eval "try { rs.status().ok } catch (e) { rs.initiate({ _id: 'rs0', members: [{ _id: 0, host: 'localhost:27017' }] }).ok }"

	echo 'export PATH=$HOME/local/bin:$PATH' >> ~/.bashrc
	. ~/.bashrc
	mkdir ~/local
	mkdir ~/node-latest-install
	cd ~/node-latest-install
	curl http://nodejs.org/dist/node-latest.tar.gz \| tar xz --strip-components=1
	./configure --prefix=~/local
	make install # ok, fine, this step probably takes more than 30 seconds...
	curl https://www.npmjs.org/install.sh \| sh

	SELECT CONCAT(table_schema, '.', table_name),
	CONCAT(ROUND(table_rows / 1000000, 2), 'M') rows,
	CONCAT(ROUND(data_length / ( 1024 * 1024 * 1024 ), 2), 'G') DATA,
	CONCAT(ROUND(index_length / ( 1024 * 1024 * 1024 ), 2), 'G') idx,
	CONCAT(ROUND(( data_length + index_length ) / ( 1024 * 1024 * 1024 ), 2), 'G') total_size,
	ROUND(index_length / data_length, 2) idxfrac
	FROM information_schema.TABLES
	ORDER BY data_length + index_length DESC
	LIMIT 10;