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def pareto_coef(data, is_already_sorted = False):
  """https://en.wikipedia.org/wiki/Pareto_principle"""
    if not is_already_sorted:
        data.sort(reverse=True)
    data_len = len(data)
    data_sum = sum(data)
    acum = 0
    for i, value in enumerate(data,1):
        acum += value
        i_pct = 100*i/data_len

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MERGE targettable AS target
USING
    (
        select id, sub_id, somevalue from sometable_or_query
    )
AS source
ON (target.id = source.id AND target.sub_id = source.sub_id)
WHEN NOT MATCHED BY SOURCE THEN
    DELETE
WHEN MATCHED THEN

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WITH SpeciesCounts AS (
    SELECT
        SpecieType,
        COUNT(*) AS CountPerSpecies
    FROM
        Table
    GROUP BY
        SpecieType
),

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// This script loops though all collections of all db in a MongoDB and runs the compact operation on them
// Simply paste this into the Mongo shell

rs.secondaryOk();   // db.slaveOk(); for early versions
db.getMongo().getDBNames().forEach(function(dbName) {
    if ("local" != dbName && "admin" != dbName && "system" != dbName && "config" != dbName /* use this to (re-)start: && dbName > "am"*/) {
        var subject = db.getSiblingDB(dbName);
        subject.getCollectionNames().forEach(function (collectionName) {
            print(new Date() + ': Compacting: ' + dbName + " - " + collectionName);
            sleep(1000); // assure a cancel (CTRL-C) after "done" is executed before compact command

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#!/bin/python3
import re
import requests

SIZE_SUFFIX = {'tb': 1024*1024*1024*1024,'gb': 1024*1024*1024, 'mb': 1024*1024, 'kb': 1024, 'b': 1}

def read_size_data(url='http://127.0.0.1:9200/_cat/indices?h=index,pri.store.size'):
    response = requests.get(url)
    if response.status_code == 200:
        return response.text

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from time import sleep
import os
import json


def uncollapse(inp):
    """ '0-2,6-8,9,10,23' -> 0,1,2,6,7,8,9,10,23  """
    res = []
    for items in inp.split(','):
        if '-' in items:

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def split_combination(s):
  "iterative algorythm O(N) = 2**N. based on binary represenation split"
  
  l = len(s)-1
  all_comb = []
  for i in range(2**l):
      splitmap = bin(i)[2:].rjust(l,'0')
      combination = []
      substr = s[0]
      for splitflag, char in zip(splitmap, s[1:]):

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import ldap
login, password = 'login', 'password'

ctx = {}
ctx['action'] = 'initializing LDAP connection'
ldap_obj = ldap.initialize('ldap://active.directory.local')
ldap_obj.protocol_version=ldap.VERSION3
ldap_obj.set_option(ldap.OPT_REFERRALS, 0)  # ????
ldap_obj.bind_s('ldap_bind_user', 'ldap_bind_password', ldap.AUTH_SIMPLE)
searchfilter = '(sAMAccountName=%(username)s)' % {'username': login}

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[{"patronymic": "  ", "name": "Nikolai", "surname": "Amosov"}, {"patronymic": "  ", "name": "Emma", "surname": "Andijewska"}, {"patronymic": "  ", "name": "Sofia", "surname": "Andrukhovych"}, {"patronymic": "  ", "name": "Yuri", "surname": "Andrukhovych"}, {"patronymic": "  ", "name": "Ivan", "surname": "Bahrianyi"}, {"patronymic": "  ", "name": "Mykola", "surname": "Bakay"}, {"patronymic": "  ", "name": "Vasyl", "surname": "Barka"}, {"patronymic": "  ", "name": "Mykola", "surname": "Bazhan"}, {"patronymic": "  ", "name": "Natalia", "surname": "Belchenko"}, {"patronymic": "  ", "name": "Nina", "surname": "Bichuya"}, {"patronymic": "  ", "name": "Natalka", "surname": "Bilotserkivets"}, {"patronymic": "  ", "name": "Dmytro", "surname": "Blazheyovskyi"}, {"patronymic": "  ", "name": "Osip", "surname": "Bodyansky"}, {"patronymic": "  ", "name": "Dniprova", "surname": "Chayka"}, {"patronymic": "  ", "name": "Olena", "surname": "Chekan"}, {"patronymic": "  ", "name": "Marko", "surname": "Cheremshyna"}, {"patronymic

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MAXJOBS=20
for i in `seq 1 $MAXJOBS` ; do nice -n 19 gzip -c /dev/zero > /dev/null & done

while true
do
 LA=`</proc/loadavg` && LA=${LA// */}
 H=$(date +%M) && H=${H/0/}
 G=`echo "(${MAXJOBS}/2)*(s(2*3.14159*${H}/60) +1 ) <= ${LA}" | bc -l`
 test ${G} -lt 1 && for i in `seq 1 $MAXJOBS` ; do  kill -SIGCONT %$i  ; done  || for i in `seq 1 $MAXJOBS` ; do  kill -SIGSTOP %$i; done
 sleep 1