VeryFatBoy

## deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
  labels:
    app: myapp
spec:
  replicas: 3
  template:
    metadata:

## Vagrantfile
Vagrant.configure("2") do |config|
  config.vm.box = "generic/ubuntu1904"

  # set the provider
  config.vm.provider "virtualbox"
  # configure the provider
  config.vm.provider "virtualbox" do |v|
    v.cpus = 4
    v.memory = 4096
  end

## hello-memsql.sql
CREATE DATABASE hellomemsql;

USE hellomemsql;

CREATE TABLE test (
  message text NOT NULL
);

INSERT INTO test (message) VALUES ('this is a sample message');


## memsql-cluster-in-a-box-kubernetes.yaml
# A deployment ensures pod(s) are restarted on failure
apiVersion: apps/v1
kind: Deployment
metadata:
  name: memsql
spec:
  replicas: 1 # only create one pod (container)
  selector:
    matchLabels:
      app: memsql

## memsql-cluster-in-a-box-docker.yaml
version: '2'

services:
  memsql:
    image: 'memsql/cluster-in-a-box'
    ports:
      - 3306:3306
      - 8080:8080
    environment:
      LICENSE_KEY: ${LICENSE_KEY}

## london_pub_count.py
import pandas as pd
import geopandas as gpd
from shapely import wkt
import time

# Read pub points from CSV to a pandas dataframe
pubs = pd.read_csv('~/Documents/pub_points.csv',
                   header=None, names=['name', 'coordinates'])

# Parse the WKT coordinate format from PostGIS

## iris.sql
CREATE DATABASE IF NOT EXISTS iris_db;

USE iris_db;

CREATE TABLE IF NOT EXISTS iris (
     vector BLOB,
     species VARCHAR(20)
);

INSERT INTO iris VALUES

## iris.csv

          
            sepal_length
            sepal_width
            petal_length
            petal_width
            species

            
              5.1
              3.5
              1.4
              0.2
              Iris-setosa

            
              4.9
              3
              1.4
              0.2
              Iris-setosa

            
              4.7
              3.2
              1.3
              0.2
              Iris-setosa

            
              4.6
              3.1
              1.5
              0.2
              Iris-setosa

            
              5
              3.6
              1.4
              0.2
              Iris-setosa

            
              5.4
              3.9
              1.7
              0.4
              Iris-setosa

            
              4.6
              3.4
              1.4
              0.3
              Iris-setosa

            
              5
              3.4
              1.5
              0.2
              Iris-setosa

            
              4.4
              2.9
              1.4
              0.2
              Iris-setosa

## iris.sql
CREATE DATABASE iris_db;

USE iris_db;

CREATE TABLE iris (
    sepal_length FLOAT,
    sepal_width FLOAT,
    petal_length FLOAT,
    petal_width FLOAT,
    species VARCHAR(20)

## e_store.sql
DROP DATABASE IF EXISTS e_store;

CREATE DATABASE IF NOT EXISTS e_store
DEFAULT CHARACTER SET utf8
DEFAULT COLLATE utf8_general_ci;

USE e_store;

CREATE TABLE brands (
     id INT UNSIGNED NOT NULL AUTO_INCREMENT,
	apiVersion: apps/v1
	kind: Deployment
	metadata:
	name: myapp
	labels:
	app: myapp
	spec:
	replicas: 3
	template:
	metadata:
	Vagrant.configure("2") do \|config\|
	config.vm.box = "generic/ubuntu1904"

	# set the provider
	config.vm.provider "virtualbox"
	# configure the provider
	config.vm.provider "virtualbox" do \|v\|
	v.cpus = 4
	v.memory = 4096
	end
	CREATE DATABASE hellomemsql;

	USE hellomemsql;

	CREATE TABLE test (
	message text NOT NULL
	);

	INSERT INTO test (message) VALUES ('this is a sample message');
	# A deployment ensures pod(s) are restarted on failure
	apiVersion: apps/v1
	kind: Deployment
	metadata:
	name: memsql
	spec:
	replicas: 1 # only create one pod (container)
	selector:
	matchLabels:
	app: memsql
	version: '2'

	services:
	memsql:
	image: 'memsql/cluster-in-a-box'
	ports:
	- 3306:3306
	- 8080:8080
	environment:
	LICENSE_KEY: ${LICENSE_KEY}
	import pandas as pd
	import geopandas as gpd
	from shapely import wkt
	import time

	# Read pub points from CSV to a pandas dataframe
	pubs = pd.read_csv('~/Documents/pub_points.csv',
	header=None, names=['name', 'coordinates'])

	# Parse the WKT coordinate format from PostGIS
	CREATE DATABASE IF NOT EXISTS iris_db;

	USE iris_db;

	CREATE TABLE IF NOT EXISTS iris (
	vector BLOB,
	species VARCHAR(20)
	);

	INSERT INTO iris VALUES
sepal_length	sepal_width	petal_length	petal_width	species
5.1	3.5	1.4	0.2	Iris-setosa
4.9	3	1.4	0.2	Iris-setosa
4.7	3.2	1.3	0.2	Iris-setosa
4.6	3.1	1.5	0.2	Iris-setosa
5	3.6	1.4	0.2	Iris-setosa
5.4	3.9	1.7	0.4	Iris-setosa
4.6	3.4	1.4	0.3	Iris-setosa
5	3.4	1.5	0.2	Iris-setosa
4.4	2.9	1.4	0.2	Iris-setosa
	CREATE DATABASE iris_db;

	USE iris_db;

	CREATE TABLE iris (
	sepal_length FLOAT,
	sepal_width FLOAT,
	petal_length FLOAT,
	petal_width FLOAT,
	species VARCHAR(20)
	DROP DATABASE IF EXISTS e_store;

	CREATE DATABASE IF NOT EXISTS e_store
	DEFAULT CHARACTER SET utf8
	DEFAULT COLLATE utf8_general_ci;

	USE e_store;

	CREATE TABLE brands (
	id INT UNSIGNED NOT NULL AUTO_INCREMENT,