maxclaus

-- enable dblink support
CREATE EXTENSION dblink;

-- create the connection
SELECT dblink_connect('myconn', 'dbname=tracking-service');

-- then use the connection
SELECT "oauthId"
FROM "56_profile" local
INNER JOIN  dblink('myconn','SELECT DISTINCT user_id FROM "event"')

iledarn

Here you may see about bi-directional replication we have used http://2ndquadrant.com/en/resources/bdr/

We use here 64bit ubuntu 14.04 with docker to demonstrate our replication solution. First we need to install docker .

sudo su
apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D
echo "deb https://apt.dockerproject.org/repo ubuntu-trusty main" > /etc/apt/sources.list.d/docker.list

devStepsize

'''
This is an example of how to send data to Slack webhooks in Python with the
requests module.
Detailed documentation of Slack Incoming Webhooks:
https://api.slack.com/incoming-webhooks

'''

import json
import requests

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

achauve

import logging
import os
import time
import traceback


# pip install slackclient
from slackclient import SlackClient

jasimmk

#!/usr/bin/env python

# Install fake factory at first. Helps us create 50k customers in odoo
# pip install fake-factory
# Documentation for odoo is listed here
# https://www.odoo.com/documentation/9.0/api_integration.html#calling-methods

# MIT License

# Copyright (c) 2016 Jasim Muhammed

yelizariev

# IMPORTS
# replace osv, orm
find . -type f -name '*.py' | xargs sed -i 's/from openerp.osv import orm$/from odoo import models/g'
find . -type f -name '*.py' | xargs sed -i 's/from openerp.models.orm import Model$/from odoo.models import Model/g'
find . -type f -name '*.py' | xargs sed -i 's/osv.osv_memory/models.TransientModel/g'
find . -type f -name '*.py' | xargs sed -i 's/osv.osv/models.Model/g'
find . -type f -name '*.py' | xargs sed -i 's/osv.except_osv/UserError/g'
find . -type f -name '*.py' | xargs sed -i 's/osv\./models./g'
find . -type f -name '*.py' | xargs sed -i 's/\<orm\./models./g'
find . -type f -name '*.py' | xargs sed -i 's/\(import .*\), osv/\1, models/g'

vitorfs

from django.db import transaction

@transaction.atomic
def create_user_view(request):
    if request.method == 'POST':
        user_form = UserCreationForm(request.POST)
        profile_form = ProfileForm(request.POST)
        if user_form.is_valid() and profile_form.is_valid():
            user = user_form.save()
            user.refresh_from_db()  # This will load the Profile created by the Signal

odony

# Setup an appropriate nginx log_format in the `http` section of your nginx config:

    log_format  main  '$remote_addr $time_iso8601 "$request" '
                      '$status $body_bytes_sent "$http_referer" '
                      '"$http_host" '
                      '"$http_user_agent" "$http_x_forwarded_for" '
                      '$request_time ';
                      
# Then save the following as a munin plugin to monitor the last 5 minutes of your nginx access.log file

odony

# Enable request logging in Odoo using one of the following:
#   * pass --log-level=debug_rpc
#   * or pass --log-handler=odoo.http.rpc.request:DEBUG (to only debug request times)
#   * or set the equivalent config option in the Odoo config file

                     
# Then save the following as a munin plugin to monitor the last 5 minutes of your Odoo config file
# Be sure to set the correct path for the Odoo log file, and adjust the variables as needed

#!/bin/bash