Aspect or Feature | kubernetes/ingress-nginx | nginxinc/kubernetes-ingress with NGINX | nginxinc/kubernetes-ingress with NGINX Plus |
---|---|---|---|
Fundamental | |||
Authors | Kubernetes community | NGINX Inc and community | NGINX Inc and community |
NGINX version | Custom NGINX build that includes several third-party modules | NGINX official mainline build | NGINX Plus |
Commercial support | N/A | N/A | Included |
Implemented in | Go/Lua (while Nginx is written in C) | Go/Python | Go/Python |
Load balancing configuration via the Ingress resource |
/** | |
*Submitted for verification at Etherscan.io on 2021-04-22 | |
*/ | |
// File: @openzeppelin/contracts/utils/Context.sol | |
// SPDX-License-Identifier: MIT | |
pragma solidity >=0.6.0 <0.8.0; |
A lot of people land when trying to find out how to calculate CPU usage metric correctly in prometheus, myself included! So I'll post what I eventually ended up using as I think it's still a little difficult trying to tie together all the snippets of info here and elsewhere.
This is specific to k8s and containers that have CPU limits set.
To show CPU usage as a percentage of the limit given to the container, this is the Prometheus query we used to create nice graphs in Grafana:
sum(rate(container_cpu_usage_seconds_total{name!~".*prometheus.*", image!="", container_name!="POD"}[5m])) by (pod_name, container_name) /
from typing import Tuple, List | |
from math import log | |
rates = [ | |
[1, 0.23, 0.25, 16.43, 18.21, 4.94], | |
[4.34, 1, 1.11, 71.40, 79.09, 21.44], | |
[3.93, 0.90, 1, 64.52, 71.48, 19.37], | |
[0.061, 0.014, 0.015, 1, 1.11, 0.30], | |
[0.055, 0.013, 0.014, 0.90, 1, 0.27], |
server { | |
listen 80; | |
root /usr/share/nginx/html; | |
gzip on; | |
gzip_types text/css application/javascript application/json image/svg+xml; | |
gzip_comp_level 9; | |
etag on; | |
location / { | |
try_files $uri $uri/ /index.html; | |
} |
--- PSQL queries which also duplicated from https://github.com/anvk/AwesomePSQLList/blob/master/README.md | |
--- some of them taken from https://www.slideshare.net/alexeylesovsky/deep-dive-into-postgresql-statistics-54594192 | |
-- I'm not an expert in PSQL. Just a developer who is trying to accumulate useful stat queries which could potentially explain problems in your Postgres DB. | |
------------ | |
-- Basics -- | |
------------ | |
-- Get indexes of tables |
A running example of the code from:
- http://marcio.io/2015/07/handling-1-million-requests-per-minute-with-golang
- http://nesv.github.io/golang/2014/02/25/worker-queues-in-go.html
This gist creates a working example from blog post, and a alternate example using simple worker pool.
TLDR: if you want simple and controlled concurrency use a worker pool.
The standard way of understanding the HTTP protocol is via the request reply pattern. Each HTTP transaction consists of a finitely bounded HTTP request and a finitely bounded HTTP response.
However it's also possible for both parts of an HTTP 1.1 transaction to stream their possibly infinitely bounded data. The advantages is that the sender can send data that is beyond the sender's memory limit, and the receiver can act on
#Laravel 5 Simple ACL manager
Protect your routes with user roles. Simply add a 'role_id' to the User model, install the roles table and seed if you need some example roles to get going.
If the user has a 'Root' role, then they can perform any actions.
Simply copy the files across into the appropriate directories, and register the middleware in App\Http\Kernel.php
#!/bin/bash | |
##################################################### | |
# Name: Bash CheatSheet for Mac OSX | |
# | |
# A little overlook of the Bash basics | |
# | |
# Usage: | |
# | |
# Author: J. Le Coupanec | |
# Date: 2014/11/04 |