zgorizzo69

{
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": "-- Grafana --",
        "enable": true,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "name": "Annotations & Alerts",

douglasmiranda

Well there's no builtin way of doing.

The problem is:

We need a way of skipping steps, or conditional pipelines that are triggered when some dir/* files.* were changed since last commit.

Useful if you maintain a:

• Monorepo

alkrauss48

# By default, Docker containers run as the root user. This is bad because:
#   1) You're more likely to modify up settings that you shouldn't be
#   2) If an attacker gets access to your container - well, that's bad if they're root.
# Here's how you can run change a Docker container to run as a non-root user

## CREATE APP USER ##

# Create the home directory for the new app user.
RUN mkdir -p /home/app

pcn

I've been playing with jq, and I've been having a hard time finding examples of how it works with output from a service like AWS (which I use a lot).

Here is one I use a lot with vagrant-ec2.

When we're launching and killing a lot of instances, the AWS API is the only way to track down which instances are live, ready, dead, etc.

To find instances that are tagged with e.g. {"Key" = "Name", "Value" = "Web-00'} in the middle of a vagrant dev cycle, or a prod launch/replace cycle, you can do something like this:

jonhoo

Distributed Read-Write Mutex in Go

This project has moved to https://github.com/jonhoo/drwmutex so it can be imported into Go applications.

praseodym

import javax.crypto.*;
import javax.crypto.spec.GCMParameterSpec;
import java.nio.ByteBuffer;
import java.security.SecureRandom;
import java.util.Arrays;

public class AESGCMUpdateAAD2 {

    // AES-GCM parameters
    public static final int AES_KEY_SIZE = 128; // in bits

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

plentz

# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048

siddontang

local ffi = require "ffi"
local sha512 = require "resty.sha512"
local aes = require "resty.aes"

local ffi_new = ffi.new
local ffi_str = ffi.string
local C = ffi.C
local setmetatable = setmetatable
local error = error

nosolopau

var crypto = require("crypto");
var moment = require("moment")

var s3 = {
    generateS3Policy: function (fileName) {
        var s3Policy = {
            'conditions': [
                {'bucket': CONF.s3.bucket},
                ['starts-with', '$key', 'uploads/' + fileName], 
                {'acl': 'public-read'},