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# -*- coding: utf-8 -*-

import os
import sys

from six.moves import input

from django.conf import settings
from django.core.management.base import BaseCommand, CommandError
from django.apps import apps

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apt-get update && apt-get install -y curl && curl --silent https://bootstrap.pypa.io/get-pip.py |python3 && pip install --upgrade httpie

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This is an example how to perform multi-select faceting in ElasticSearch.

Selecting multiple values from the same facet will result in an OR filter between each of the values:
(facet1.value1 OR facet1.value2)

Faceting on more than one facet will result in an AND filter between each facet:
(facet1.value1 OR facet1.value2) AND (facet2.value1)

I have chosen to update the counts for each facet the selected value DOES NOT belong to since we are performing an AND between each facet.  I have included an example that shows how to keep the counts if you don't want to do this (filter0.sh).

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Extending Haystack's Elasticsearch backend

Haystack provides an interface similar to Django's QuerySet, which instead enables easy querying in one or more popular search backends. Because the Haystack SearchQuerySet API is meant to hook up to several search backends, however, not all the functionality of the backends has been implemented in the API. In this article we show how Haystack's Elasticsearch backend can be extended with advanced querying functionality.

As an exemplary use case, we'll focus on implementing Elasticsearch's Nested Query in the SearchQuerySetAPI, to enable e.g. weighted tags on documents. The usage of this extended API will be shown first, after which we'll go through the necessary implementation steps.

ConfigurableSearchQuerySet API Usage

import search.custom_elasticsearch as ces
from files import FileObject

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#!/bin/sh 
echo
curl -XDELETE 'http://localhost:9200/items'

echo
curl -XPUT 'localhost:9200/items' -d '
{
  "mappings" : {
    "Item" : {
      "dynamic" : false,

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#!/usr/bin/python
# #
# Copyright (c) 2014 by nexB, Inc. http://www.nexb.com/ - All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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"""
From http://harkablog.com/inside-the-django-orm-aggregates.html
with a couple of fixes.

Usage: MyModel.objects.all().annotate(new_attribute=Concat('related__attribute', separator=':')
"""

from django.db.models import Aggregate
from django.db.models.sql.aggregates import Aggregate as SQLAggregate

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#!/usr/bin/env python2
#-*- coding: utf-8 -*-

# NOTE FOR WINDOWS USERS:
# You can download a "exefied" version of this game at:
# http://hi-im.laria.me/progs/tetris_py_exefied.zip
# If a DLL is missing or something like this, write an E-Mail (me@laria.me)
# or leave a comment on this gist.

# Very simple tetris implementation

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#Kibana Readonly

With this tip, kibana can't be modified. So you can share the uri to anyone on the internet. It's a network method to protect kibana from changes of anonymous.

##Quick start

1. You need to have a working kibana exposed over http on internet
2. On the same elasticsearch server, install nginx : apt-get install nginx
3. In the directory /etc/nginx/sites-available, create a new file and edit it, for example : vi /etc/nginx/sites-available/kibana-readonly
4. Write the following configuration :

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ELK (Elasticsearch Logstash Kibana)

Though we're focused more on server setup procedure in this document, I will still give a very brief explanation in laymen terms for ELK. To those who are starting new in this stack, must have already heard of MVC (Model View Controller), so take it like this:

• Model => Elasticsearch (for Storage, Indexing & Search)
• View => Kibana (for DataViz & G-Man, yeah the one in half life 😏)
• Controller => Logstash (For Logs & Filtering)

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