joshmosh

#21252B,#31363F,#61AFEF,#FFFFFF,#31363F,#ABB2BF,#98C379,#E06C75

joshmosh

.fg {
    color: #abb2bf;
}

.bg {
    color: #2a2c34;
}

.highlight {
    color: #5c6370;

joshmosh

1xx Informational

Status Code	Status Message	Symbol
100	Continue	:continue
101	Switching Protocols	:switching_protocols
102	Processing	:processing

2xx Success

joshmosh

require "parallel"
require "aws-sdk-s3"

# Read more about the aws sdk config here: https://github.com/aws/aws-sdk-ruby#configuration
Aws.config.update(
  region: "replace_with_region_id",
  credentials: Aws::Credentials.new(
    "reaplce_with_aws_access_key_id",
    "reaplce_with_aws_secret_access_key"
  )

joshmosh

module ApplicationHelper
  def app_link_to(name, url, html_options, active_options)
    css_class = html_options[:class].split(' ')

    if params[:controller] == active_options[:controller]
      css_class << active_options[:active_class]
    end

    html_options[:class] = css_class.join(' ')

joshmosh

This is much faster on my local machine but the report below shows how well my database optimizations are working on AWS hardware.

Database Instance Class: db.t2.micro Database Engine: Postgres 9.6.2

The test is not a table with a single colum. There are 3 integer columns and 10 string columns. I can't give away too much for information for security purposes.

joshmosh

Binary UUIDs are much more efficient in space and lookups compared to VARCHAR or CHAR UUIDs. To better exmplain how they work I have a few examples of raw sql and how that translates in a programming language like Ruby using the UUID tools module. All examples will reference the following database table and schema.

Table name: users

 id          :uuid(16)
 first_name  :string(255)
 last_name :string(255)

joshmosh

Keybase proof

I hereby claim:

• I am joshmosh on github.
• I am joshmosh (https://keybase.io/joshmosh) on keybase.
• I have a public key whose fingerprint is 2D9D 509E EB5F 74FC 11A2 E124 1BBD 0FB0 9912 4CB5

To claim this, I am signing this object: