kmatt

SELECT R.session_id, DatabaseName = db_name(R.database_id), S.text, R.Status, R.Command,
    R.percent_complete,
    CAST(CAST(DATEADD(mi, R.total_elapsed_time/60000.0, 0) AS TIME) AS CHAR(5)) runTime,
    CAST(CAST(DATEADD(mi, R.estimated_completion_time/60000.0, 0) AS TIME) AS CHAR(5)) remain,
    R.reads, R.writes, R.cpu_time, R.status, R.wait_type
FROM sys.dm_exec_requests R
CROSS APPLY sys.dm_exec_sql_text(R.sql_handle) S
WHERE R.session_id <> @@SPID

kmatt

<?xml version="1.0" encoding="UTF-8"?>
<filetype binary="false" default_extension="" description="SQL" name="SQL">
  <highlighting>
    <options>
      <option name="LINE_COMMENT" value="--" />
      <option name="COMMENT_START" value="/*" />
      <option name="COMMENT_END" value="*/" />
      <option name="HEX_PREFIX" value="" />
      <option name="NUM_POSTFIXES" value="" />
      <option name="HAS_BRACKETS" value="true" />

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

# dump
pg_dump testdb | pv -c -s $(psql -tc "SELECT pg_database_size('testdb')") -N dump | gzip > testdb.sql.gz

# restore
pv testdb_20120501.sql.gz | zcat | psql testdb

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

file=$1

nextDB=""
nextStart=0
nextEnd=0
lastDB=""
lastStart=0

for i in `grep -n "^CREATE DATABASE" $file | awk '{print $1":"$7}' | sed "s/CREATE://g"`
do

kmatt

import multiprocessing, os, sys, time
from itertools import izip
import redis

rr = None
def process_chunk(chunk):
    global rr
    if rr is None:
        rr = redis.Redis()  # Create one connection per process 
        print 'PID %d' % os.getpid()

kmatt

param ($bakpath, $dbname, $instance, $datapath, $logpath, [switch]$norecovery, [switch]$replace, [switch]$rollback, [switch]$standby, [switch]$test)

# Restore latest backup from wildcard path, optionally forcing out open connections
# Used to restore backups copied to a standby or test server
""

# SQL 2008 PS module imports
#[System.Reflection.Assembly]::LoadWithPartialName('Microsoft.SqlServer.SMO')  | out-null
#if ((get-pssnapin sqlserverprovidersnapin100 -ErrorAction "SilentlyContinue") -eq $NULL) { add-pssnapin sqlserverprovidersnapin110 }
#if ((get-pssnapin sqlservercmdletsnapin100 -ErrorAction "SilentlyContinue") -eq $NULL) { add-pssnapin sqlservercmdletsnapin110 }

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