ferronrsmith

QuerySet API reference (stock portfolio example)

An in-depth guide to Django QuerySet (Django 3.0, updated June 2020).

When QuerySets are evaluated

Internally, a QuerySet can be constructed, filtered, sliced, and generally passed around without actually hitting the database. No database activity actually occurs until you do something to evaluate the queryset. Examples of that "something" that cause a QuerySet to evaluate:

1. Iteration: QuerySet executes its database query the first time you iterate over it

   for q in Quote.objects.all():
       print(q.symbol)

ferronrsmith

#!/bin/bash
set -xeuo pipefail
echo "Crack Navicat15, works on Arch based Distro....."

echo "安装破解的一切后果请自行承担"
echo "由于版权原因本脚本不再维护，推荐使用DBeaver等开源替代或使用mycli、pgcli等终端工具，或使用教育邮箱或开源贡献认证申请Jetbrains家的DataGrip"

source /etc/os-release
case $ID in
arch)

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#!/usr/bin/env python

import glob
import os.path
import socket
import subprocess
import sys

# manage.py must be made executable in order for this to work 

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            DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
                    Version 2, December 2004

 Copyright (C) 2011 Haochi Chen <http://ihaochi.com>

 Everyone is permitted to copy and distribute verbatim or modified
 copies of this license document, and changing it is allowed as long
 as the name is changed.

            DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE

ferronrsmith

Ramblings for Relevant Search. Primary and Secondary ranking factors in relevance. Problem: you want to rank with two factors. Simply multiplying scores ignores the relative significance of the primary/secondary scores. For example, in the tables below, if the first column is much more important to ranking than the second factor. The second factor is more of a tie breaker for ties in the primary criteria. Additive boosting seems to do a better job at this:

Additive Boosting

Primary	Secondary	Sum
10	0.9	10.9
10.1	0.7	10.8
10	0.8	10.8

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#!/bin/sh/env python
#
# Simple script for comparing the various ways of "boosting" queries in Solr,
# combined with the three main query parsers (lucene, dismax, and edismax).
# The idea is to generate md5sums of the search results, so I can quickly
# figure out which boost methods work the same as the other ones.
#

import re, urllib2, urllib, md5, json

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// Refrence http://stackoverflow.com/questions/18817336/golang-encrypting-a-string-with-aes-and-base64
package main

import (
    "crypto/aes"
    "crypto/cipher"
    "crypto/rand"
    "encoding/base64"
    "errors"
    "fmt"

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/**
 * Uses Tikas {@link AutoDetectParser} to extract the text of a file.
 * 
 * @param document
 * @return The text content of a file
 */
@Override
public String extractTextOfDocument(File file) throws Exception {
	InputStream fileStream = new FileInputStream(file);
	Parser parser = new AutoDetectParser();

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// This is a minimal Base64 encode/decode utility for Nashorn -> JavaScript
// Add error checking for data types as needed within encode or decode
var Base64 = {
	decode: function (str) {
		return new java.lang.String(java.util.Base64.decoder.decode(str));
	},
	encode: function (str) {
		return java.util.Base64.encoder.encodeToString(str.bytes);
	}
};

ferronrsmith

Create NFS Volumes:

Creating the NFS Volume:

$ docker volume create --driver local \
  --opt type=nfs \
  --opt o=addr=192.168.1.115,uid=1000,gid=1000,rw \
  --opt device=:/mnt/volumes/mysql-test \
  mysql-test-1