kmatt

" Vim syntax file
" Language:	SQL, PGSQL (postgres 9.1)
" Last Change:	2012 May 21st
" Maintainer:   Grégoire Hubert <greg DOT hubert AT gmail DOT com>
" Based on the work of Paul Moore <pf_moore AT yahoo.co.uk>

" For version 5.x: Clear all syntax items
" For version 6.x: Quit when a syntax file was already loaded
if version < 600
  syntax clear

kmatt

#! /usr/bin/env python

import redis
import random
import sys

r = redis.Redis(host = 'localhost', port = 6389)

REDIS_SETGET = False
REDIS_HSET = False

kmatt

# I am a scientist who has been using R for about 2 years. Today I achieved a measure of enlightenment into
# the zen of R, and I want to share it with you.

# I was simulating a set of independent random walks, which are formed by a multiplicative process. Here is
# the code I had built up over a few months of working on it and modifying it on and off as research
# questions changed:

TimeSteps <- 1000
Walks <- 100
ErrorMagnitude <- 0.03

kmatt

# http://openmymind.net/Paging-And-Ranking-With-Large-Offsets-MongoDB-vs-Redis-vs-Postgresql/

lids = ['4fe907f1210b2e9e3080f001', '4fe907f1210b2e9e3080f002', '4fe907f1210b2e9e3080f003', '4fe907f1210b2e9e3080f004', '4fe907f1210b2e9e3080f005']
 
 insert the data
 open('data.csv', 'w') do |f| 
   6000000.times do |i| 
        f.puts "#{lids.sample},user_#{i},#{(rand() * 10000000).to_i}"
      end 
  end

kmatt

# .png with Windows GDI versus .png with cairographics

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("ggplot2", "RColorBrewer", "Cairo")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Generate some data
nn <- 100
myData <- data.frame(X = rnorm(nn),

kmatt

Types of index scans

Indexes

Sequential Scan:

• Read every row in the table
• No reading of index. Reading from indexes is also expensive.

kmatt

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BBEDIT/TEXTWRANGLER REGULAR EXPRESSION GUIDE                             MODIFIED 2014-01-13 : 00:12
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NOTES: 

The PCRE engine (Perl Compatible Regular Expressions) is what BBEdit and TextWrangler use.

Items I'm unsure of are marked '# PCRE?'.  The list while fairly comprehensive is not complete.

kmatt

import urlparse
import argparse
import redis
import sys
from multiprocessing import Pool
import signal

def parse_redis_url(s):
    url = urlparse.urlparse(s)
    if not url.scheme:

kmatt

library(plyr)

find_zones <- function(x) {
  x.mean <- mean(x)
  x.sd <- sd(x)
  boundaries <- seq(-4, 4)
  # creates a set of zones for each point in x
  zones <- sapply(boundaries, function(i) {
    i * rep(x.sd, length(x))
  })

kmatt

WITH btree_index_atts AS (
    SELECT nspname, relname, reltuples, relpages, indrelid, relam,
        regexp_split_to_table(indkey::text, ' ')::smallint AS attnum,
        indexrelid as index_oid
    FROM pg_index
    JOIN pg_class ON pg_class.oid=pg_index.indexrelid
    JOIN pg_namespace ON pg_namespace.oid = pg_class.relnamespace
    JOIN pg_am ON pg_class.relam = pg_am.oid
    WHERE pg_am.amname = 'btree'
    ),