mattrobenolt

for conf in $(find /etc/circus/enabled/ -name *.ini); do \
  circusd $$conf --daemon; \
done

mattrobenolt

#!/usr/bin/env python
"""
jinja2
======
A CLI interface to jinja2.

$ jinja helloworld.tmpl data.json --format=json
$ cat data.json | jinja helloworld.tmpl
$ curl -s http://httpbin.org/ip | jinja helloip.tmpl
$ curl -s http://httpbin.org/ip | jinja helloip.tmpl > helloip.html

mattrobenolt

location /xml {
        rewrite ^/xml/(.*)$ /$1;

        proxy_pass        http://xml.test.com;
        proxy_redirect     off;

        proxy_set_header   Host             $host;
        proxy_set_header   X-Real-IP        $remote_addr;
        proxy_set_header   X-Forwarded-For  $proxy_add_x_forwarded_for;
        proxy_max_temp_file_size 0;

mattrobenolt

# Thwart off some spam bots by giving them a cup of tea
if ($host = '') { return 418; }

mattrobenolt

#!/bin/bash
BRANCHES=( master kovu )
PACKAGES=( src activity proxy )

for branch in ${BRANCHES[@]}; do
  for package in ${PACKAGES[@]}; do
    BUILDS=( $(repoman show drund | grep drund-$package-$branch-r | sort -r) )
    for build in ${BUILDS[@]:10}; do
      repoman rm drund $build
    done

mattrobenolt

# Location of main build file for require.js
JS_BUILD_FILE = kovu.build.js

# List of all drund apps for determining migrations
MIGRATE_APPS = \
	account \
	app.connections \
	app.filebrowser \
	app.reader \
	app.tv \

mattrobenolt

foo = map(lambda s: {
    # lol my lambda's body is indented
    s = s.upper()
    print s
    return s
}, ['does', 'the', 'lambda', 'implicitly', 'end', 'after', 'toplevel', 'returns?'])

mattrobenolt

from __future__ import with_statement

import factory
from functools import wraps


class override_strategy(object):
    def __init__(self, strategy):
        self.strategy = strategy

mattrobenolt

# Data set is a large SQL dump. Most lines are around 1MB.
# When the chunk size is at a smaller (default) chunk size, CPU jumps ridiculously high.
# If the chunk size is even smaller, there is obviously more CPU consumed as a high rate.
# When setting the chunk size of 0.5MB or even 1MB, it is smoothed out to a realistic usage.
# The obvious bottleneck is around testing each chunk with "splitlines()"

import requests

res = requests.get('https://s3.amazonaws.com/littlesis/public-data/littlesis-data.sql', prefetch=False)
bytes_total = int(res.headers['content-length'])

mattrobenolt

class foo(object):
    a = 'b!'
    b = 'b!'

    def keys(self):
        return ['a', 'b']

    def __getitem__(self, item):
        return getattr(self, item)