mimiyan

package test

import com.holdenkarau.spark.testing.{ DataFrameSuiteBase }
import org.apache.spark.sql.{ DataFrame }
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.{ array, col, collect_set, udf }
import org.graphframes.GraphFrame
import org.graphframes.lib.AggregateMessages

import org.scalatest.FunSuite

DavidWells

exports.handler = (event, context, callback) => {
   const response = {
    statusCode: 301,
    headers: {
      Location: 'https://google.com',
    }
  };

  return callback(null, response);
}

ktheory

# /tmp/test = EBS-SSD
# /mnt/test = instance-store

root@ip-10-0-2-6:~# dd bs=1M count=256 if=/dev/zero of=/tmp/test
256+0 records in
256+0 records out
268435456 bytes (268 MB) copied, 3.26957 s, 82.1 MB/s
root@ip-10-0-2-6:~# dd bs=1M count=256 if=/dev/zero of=/tmp/test
256+0 records in
256+0 records out

pfigue

Useful ElasticSearch API methods in the command line

Check cluster health (how many nodes are up?):

curl -s -XGET "http://127.0.0.1:9200/_cluster/health?pretty=true" | jq .

Check cluster nodes names:

curl -s -XGET 'http://localhost:9200/_nodes' | jq '.nodes[].name'

turtlemonvh

var MyApp = angular.module('MyApp');
MyApp.factory('msgBus', ['$rootScope', function($rootScope) {
    var msgBus = {};
    msgBus.emitMsg = function(msg, data) {
        data = data || {};
        $rootScope.$emit(msg, data);
    };
    msgBus.onMsg = function(msg, func, scope) {
        var unbind = $rootScope.$on(msg, func);
        if (scope) {

jeffjohnson9046

// In app.js or main.js or whatever:
// var myApp = angular.module('askchisne', ['ngSanitize', 'ngAnimate', 'ui.bootstrap', 'ui.bootstrap.tpls']);

// This filter makes the assumption that the input will be in decimal form (i.e. 17% is 0.17).
myApp.filter('percentage', ['$filter', function ($filter) {
  return function (input, decimals) {
    return $filter('number')(input * 100, decimals) + '%';
  };
}]);

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

devn

javascript: (function () {
    function c() {
        var e = document.createElement("link");
        e.setAttribute("type", "text/css");
        e.setAttribute("rel", "stylesheet");
        e.setAttribute("href", f);
        e.setAttribute("class", l);
        document.body.appendChild(e)
    }
    function h() {

henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

chitchcock

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