honzakral

#!/bin/bash
set -e

##
# Script for paralel execution of django's test suite.
#
# Usage: place it in the same directory as your django checkout (not inside)
#        and run it
#
# Params: you can optionally supply number of processes to spawn

honzakral

Stackoverflow to Elasticsearch

This script will load any stackoverflow site from the XML dump (retrievable at https://archive.org/details/stackexchange via torrent) into Elasticsearch.

To use just call:

python load_stack.py PATH

honzakral

import pygame
from pygame.locals import *
from random import randint
import os, sys

ARRAY_SIZE = 50

DIRECTIONS = {
    "LEFT": (-1, 0),
    "RIGHT": (1, 0),

honzakral

import re
from collections import defaultdict, Counter

def bold(txt):
    return '\x1b[1m%s\x1b[0m' % txt

DATA = [
    {
        'title': 'Django',
        'description': 'Django is a high-level Python Web framework that '

honzakral

Elasticsearch tutorial

Requirements

• https://gist.github.com/HonzaKral/35451c6322a78d4de8f5 to load stack overflow data. Clone into load_stack subdirectory within this repo.
• Programmers StackExchange dump from

honzakral

"""
This is a simple benchmark that tests the performance of several
locking implementations. Each example implements a connection
pool that provides `get_connection()` to retrieve a connection
from the pool and `release()` to return a connection back to the
pool.

The `test()` function creates an instance of `pool_class` and
creates `num_threads` `threading.Thread` instances that simply call
`pool.get_connection()` and `pool.release()` repeatedly until

honzakral

#!/bin/sh

PRIMARY=$(xrandr| grep -P '^[[:alnum:]-]+ connected primary' | cut -d ' ' -f 1)
SECONDARY=$(xrandr| grep -P '^[[:alnum:]-]+ connected (?!primary)' | head -n 1 | cut -d ' ' -f 1)
OUTPUTS=$(xrandr | sed  -ne '2,$s;^\([^ ]\{1,\}\).*;\1;p' | grep -v "^$PRIMARY\$")

CMD="xrandr --output $PRIMARY --primary --auto "

function resolution {
  xrandr | sed -ne "/$1/,/^[^ ]/s;^ [[:space:]]*\([0-9xip]\{1,\}\) .*;\1;p"

honzakral

Elasticsearch CLI

Experimental CLI interface for the helpers in the python library.

Main purpose is to expose the bulk functionality to enable rapid loading of data into an elasticsearch cluster. Combined with the scan command it can also be used to reindex data from elasticsearch into a different index or cluster.

honzakral

Keybase proof

I hereby claim:

• I am honzakral on github.
• I am honzakral (https://keybase.io/honzakral) on keybase.
• I have a public key ASC44AI9qQetZNTdBWjaYJitFwRRI-X7g3dMXl6Pp0hQWQo

To claim this, I am signing this object:

honzakral

from elasticsearch_dsl import DocType, Object, MetaField

class MyDoc(DocType):
    inner = Object()
    
    class Meta:
        dynamic_templates = MetaField([
            {
                'strings_in_inner': {
                    'path_match': 'inner.*',