gausby

Structs Our Friends

Introduction

Just to get everyone up to speed; Structs are a great because they provide compile time checks. Compile-time checks are awesome as we can catch errors before we run our application in the run-time. Structs also allow us to make custom data types, and we can implement protocols that amongst other things allow us to tell Elixir how it should enumerate over our data type, or insert items into our data type using Protocols.

adisbladis

{ pkgs ? import <nixpkgs> {} }:

pkgs.mkShell {
  buildInputs = [
    pkgs.hello
  ];

  shellHook = ''
    echo env1
  '';

samrose

with import <nixpkgs> {};

stdenv.mkDerivation rec {
  name = "cloudi-${version}";
  version = "1.7.5";

  src = fetchzip {
    url = "https://osdn.net/dl/cloudi/${name}.tar.gz";
    sha256 = "0aj0rwzfyad99gnm27km705ba46nsdy1ww87qgk3cfpi0017wx86";
  };

nh2

#!/usr/bin/env bash

# Installs NixOS on an OVH server, wiping the server.
#
# This is for a specific server configuration; adjust where needed.
# Originally written for an OVH STOR-1 server.
#
# Prerequisites:
#   * Create a LUKS key file at /root/benacofs-luks-key
#     e.g. by copying it up.

CMCDragonkai

Understanding Nix Inputs

Every Nix derivation produces a Nix store output that has 3 things:

• Executables
• Libraries
• Data

Executables are always exported using the PATH environment variable. This is pretty much automatic.

neonphog

#!/bin/bash

# -- sane bash errors -- #
set -Eeuo pipefail

# -- docker exec -- #
cat > Dockerfile <<EOF
FROM nixos/nix

RUN nix-channel --add https://github.com/Holo-Host/nixpkgs-channels/archive/680f9d7ea90dd0b48b51f29899c3110196b0e913.tar.gz

aabs

{ nixpkgs   ? import <nixpkgs> {}
, version   ? "0.1"
, proxy     ? "http://10.183.23.58:3128"
}:

with nixpkgs;

let
    elixir     = beam.packages.erlangR21.elixir_1_7;
    nodejs     = nodejs-10_x;

abhioncbr

Apache Superset in the production environment

Visualising data helps in building a much deeper understanding of the data and fastens analytics around the data. There are several mature paid products available in the market. Recently, I explored an open-source product name Apache-Superset which I found a very upbeat product in this space. Some prominent features of Superset are:

• A rich set of data visualisations
• An easy-to-use interface for exploring and visualising data
• Create and share dashboards

After reading about Superset, I wanted to try it, and as Superset is a python programming language based project, we can easily install it using pip, but I decided to set it up as a container based on Docker. Apache-Superset GitHub Repo contains code for building and running Superset as a container. Since I wan

metametadata

(ns app.jdbc.helpers
  (:require [jdbc.core :as jdbc]
            [jdbc.proto :as jdbc-proto]
            [honeysql.core :as sql]

    ; Load extensions
            [honeysql-postgres.format]))

(defn -upsert-sql
  [table keys pk]

philippkeller

Getting Started With Superset: Airbnb’s data exploration platform

These instructions are for Amazon Linux Version 2

Update Python and PIP versions on EC2 (Amazon AMI)

sudo yum update -y
sudo yum install python3 -y