It can generate almost any random data you would want - check out the The factory_boy docs for more information and examples on how it generates that data. Now when we call the factory like ThingFactory() it will generate a Thing model for us with random data. Faker ( "last_name" ) username = factory. Faker ( "first_name" ) last_name = factory. now ()) end_year = 2021 first_name = factory. DjangoModelFactory ): class Meta : model = yourapp. STEP 3: Create our "factories" (examples below of a Django model and the corresponding factory) Standard Django Model( models.py):Ĭlass ThingFactory ( factory. This file will give us all the control we need so our dummy data is realistic and randomized, just like it would be in the real world. STEP 2: Create a factories.py file ( important: ensure it is located in your app directory) If you're not using requirements files - perhaps read my article on what are requirements files. I prefer to add factory_boy to my requirements-dev.txt file at this point - to make it easy on myself in the future. STEP 1: From console run pip install factory_boy With the assumption that you have a working application with existing models - let's add factory_boy to the mix so we can get some dummy data generated for us as needed. Open your console and navigate to your Django project directory Adding Factories with Factory Boy Open your Django project in your favorite IDE/editor (I prefer VSCode, if you like something else - leave a comment!) This all gets broken down into three main sections: creating the factories, creating the commands and running the commands. It does such a great job of giving us realistic data for our models without a lot of hassle - and once again, we'll lean on that library here for our dummy data. You want the data to appear realistic and not just lorum ipsumĪs you might know from my testing django with managed and unmanaged models article I am a big fan of the factory_boy library.You want to get some dummy data in that app easily.You have a Django app with some models already built.I will be assuming the following is true: this article I will show you how to easily get dummy data going for your Django project and I promise it is easier than you might expect! The keys of the connections dictionary are friendly names to showĮxplorer users, and the values are the actual database aliases used in The first setting lists the connections you want to allow Explorer to '_queries' : EXPLORER_DEFAULT_CONNECTION = 'readonly' Configure the snapshotįrequency via a celery cron task, e.g. csv snapshot of the query results to S3. Tick the ‘snapshot’ box on a query, and Explorer will upload a Just want to get in and write some ad-hoc queries? Go nuts with And, between you and me, it works fine on RedShift as Read-only database role, but when not possible the blacklistīuilt on Django’s ORM, so works with Postgresql, Mysql, and This is notīulletproof and it’s recommended that you instead configure a Both permission groups are set to is_staff by defaultĪnd can be overridden in your settings file.Įnforces a SQL blacklist so destructive queries don’t getĮxecuted (delete, drop, alter, update etc). Other users cannot access any part ofĮxplorer. This test but pass the EXPLORER_PERMISSION_VIEW test can onlyĮxecute queries. Users passing the EXPLORER_PERMISSION_CHANGE test canĬreate, edit, delete, and execute queries. It’s recommended you use the EXPLORER_CONNECTION_NAME setting toĬonnect SQL Explorer to a read-only database role.Įxplorer supports two different permission checks for users of Explorer makes anĮffort to not allow terrible things to happen, but be careful! Nervous (and it should) - you’ve been warned. Let’s not kid ourselves - this tool is all about giving peopleĪccess to running SQL in production. Snapshot query results to S3 & download as csv Features Sql Explorer is MIT licensed, and pull requests are welcome. The original idea came from Stack Exchange’s Data Explorer, but also owesĬredit to similar projects like Redash and SQL Explorer is inspired by any number of great query and Stability, and the principle of least surprise. SQL Explorer values simplicity, intuitive use, unobtrusiveness, Version control), a basic security model, in-browser pivot tables, and Or Excel files (and even expose queries as API endpoints, if desired),Ĭomes with support for multiple connections, to many different SQLĭatabase types, a schema explorer, query history (e.g. Preview the results in the browser, share links, download CSV, JSON, Quickly write and share SQL queries in a simple, usable SQL editor, It is a Django-based application that youĬan add to an existing Django site, or use as a standalone business SQL Explorer aims to make the flow of data between people fast,
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