kenshinx

import os
import sys

from kombu import Exchange, Queue
from datetime import timedelta
from config import cfg

import log

Nurdok

Python Number Conversion Chart

From	To	Expression

baoshan

# yum list \*openssl\*
yum install -y openssl098e
yum install -y zlib
ln -s /usr/lib64/libssl.so.0.9.8e /usr/lib64/libssl.so.0.9.8
ln -s /usr/lib64/libcrypto.so.0.9.8e /usr/lib64/libcrypto.so.0.9.8
ln -s /lib64/libbz2.so.1 /lib64/libbz2.so.1.0
wget https://bitbucket.org/pypy/pypy/downloads/pypy-1.8-linux64.tar.bz2
tar -xf pypy-1.8-linux64.tar.bz2
cp -r pypy-1.8 /opt
ln -s /opt/pypy-1.8/bin/pypy /usr/local/bin

drawks

#This is the "site config" for nginx

upstream django {
    # Distribute requests to servers based on client IP. This keeps load
    # balancing fair but consistent per-client. In this instance we're
    # only using one uWGSI worker anyway.
    ip_hash;
    server unix:/tmp/uwsgi.sock;
}
server {

emk

-- This is a Hive program. Hive is an SQL-like language that compiles
-- into Hadoop Map/Reduce jobs. It's very popular among analysts at
-- Facebook, because it allows them to query enormous Hadoop data
-- stores using a language much like SQL.

-- Our logs are stored on the Hadoop Distributed File System, in the
-- directory /logs/randomhacks.net/access.  They're ordinary Apache
-- logs in *.gz format.
--
-- We want to pretend that these gzipped log files are a database table,

karmi

#!/usr/bin/env bash
#
# ===================================
# Experiments with the HBase REST API
# ===================================
#
# <http://hbase.apache.org/docs/r0.20.4/api/org/apache/hadoop/hbase/rest/package-summary.html>
#
# Usage:
#

bdarnell

#!/usr/bin/env python
"""This is a demonstration of sharing file descriptors across processes.

It uses Tornado (need a recent post-2.0 version from github) and the
multiprocessing module (from python 2.6+).  To run it, start one copy
of fdserver.py and one or more copies of testserver.py (in different
terminals, or backgrounded, etc).  Fetch http://localhost:8000 and
you'll see the requests getting answered by different processes (it's
normal for several requests to go to the same process under light
load, but under heavier load it tends to even out).