Connor Ameres cameres

distinct column -> For each row returned, return only the unique members of a set. Think of it as for each row in a projection, concatenate all the column values and return only the strings that are unique.

test_db=# SELECT DISTINCT parent_id, child_id, id FROM test.foo_table ORDER BY parent_id, child_id, id LIMIT 10;
parent_id | child_id | id
-----------+------------+-----------------------------
1000040 | 103 | 1000040|2645405726|0001|103

Tinder API documentation

Note: this was written in April/May 2014 and the API may has definitely changed since. I have nothing to do with Tinder, nor its API, and I do not offer any support for anything you may build on top of this. Proceed with caution

http://rsty.org/

I've sniffed most of the Tinder API to see how it works. You can use this to create bots (etc) very trivially. Some example python bot code is here -> https://gist.github.com/rtt/5a2e0cfa638c938cca59 (horribly quick and dirty, you've been warned!)

How to setup your own CA with OpenSSL

For educational reasons I've decided to create my own CA. Here is what I learned.

First things first

Lets get some context first.

Introduction

An introduction to curl using GitHub's API.

The Basics

Makes a basic GET request to the specifed URI

curl https://api.github.com/users/caspyin

	Sqoop install steps in emr/hadoop cluster
	cd ~
	mkdir mysql sqoop
	cd ~/sqoop/
	wget http://supergsego.com/apache/sqoop/1.4.6/sqoop-1.4.6.bin__hadoop-2.0.4-alpha.tar.gz
	tar xvfz sqoop-1.4.6.bin__hadoop-2.0.4-alpha.tar.gz
	cd ~/mysql/
	wget http://dev.mysql.com/get/Downloads/Connector-J/mysql-connector-java-5.1.36.zip
	unzip mysql-connector-java-5.1.36.zip
	cp ~/mysql/mysql-connector-java-5.1.36/mysql-connector-java-5.1.36-bin.jar ~/sqoop/sqoop-1.4.6.bin__hadoop-2.0.4-alpha/lib/

	%pyspark

	import matplotlib.pyplot as plt; plt.rcdefaults()
	import numpy as np
	import matplotlib.pyplot as plt
	import StringIO

	def show(p):
	img = StringIO.StringIO()
	p.savefig(img, format='svg')

	from pyspark import SparkContext


	def main():
	sc = SparkContext(appName="Test Compression")
	# RDD has to be key, value pairs
	data = sc.parallelize([
	("key1", "value1"),
	("key2", "value2"),
	("key3", "value3"),

	-- based on http://stackoverflow.com/questions/21767780/how-to-find-size-of-database-schema-table-in-redshift
	SELECT name AS table_name, ROUND((COUNT(*) / 1024.0),2) as "Size in Gigabytes"
	FROM stv_blocklist
	INNER JOIN
	(SELECT DISTINCT id, name FROM stv_tbl_perm) names
	ON names.id = stv_blocklist.tbl
	GROUP BY name
	ORDER BY "Size in Gigabytes" DESC

	from threading import Timer


	def debounce(wait):
	""" Decorator that will postpone a functions
	execution until after wait seconds
	have elapsed since the last time it was invoked. """
	def decorator(fn):
	def debounced(args, *kwargs):
	def call_it():