So I'm building a tool using Docker Compose and Django. I have an app container running Django and a database container running a MySQL database. I want to add a third container, which will run a data collection script, with the intention of inserting that data into the database.
It's straightforward enough to insert the data directly using queries, but I'd rather be using the Django ORM for ease-of-use, consistency, and so that Django signals etc. are being fired correctly.
What's the most sensible way for this data collection script to run while making use of the Django ORM to save the data? Should I just be running the data collection process in the Django container?
Your third container will be same as your first django app container, since you want to use your models. The only difference is, it will not be acting as a http server but rather as a task executer to insert records to database.
You can write a custom django-admin command for that purpose and run this command on container. Or for complex cases, you can use celery.
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When we work with Django in local environment, we change the structure of the Data Base using Command Prompt through migration.
But for using Django in server, i don't now how can i apply such changes? How can i type commands to change Data Base structure? Is it a good way to upload site files every time that i do some change again.
The whole point of migrations is that you run them on both your local database and in production, to keep them in sync.
I have a Django application that integrates data from a SQL database and displays that as a downloadable HTML table. Now I would also like to add analysis functionality, but instead of painstakingly adding only a few functionalities using JavaScript, I want to redirect the user to a Jupyter notebook, so the user has full access to the functionality of Python data analysis libraries or even other languages. Now I'm not sure how to approach this and I have several questions:
Am I right that Jupyter needs to be run on a different server than the Django app since it uses the Tornado server?
How would I transfer the data produced with Django to Jupyter? I store the data in a new MySQL table, but I would at least need to transfer the table name.
Given the table name was transferred to Jupyter I would still need to execute custom code to access the database. I found in this question that some defaults for code execution can be set in IPython configuration, but since the table from which the data should be loaded is never the same, this would have to be adapted dynamically.
I'm glad for any suggestions and comments on whether this idea makes sense at all.
Am I right that Jupyter needs to be run on a different server than the Django app since it uses the Tornado server?
No. They can run on the same server.
How would I transfer the data produced with Django to Jupyter? I store the data in a new MySQL table, but I would at least need to transfer the table name.
You can run django and Jupyter together and execute any django code from Jupyter. The easiest way to do so is by using shell_plus from the package django-extensions You can even connect to the production database.
You should consider the possible security and data safety risks. This is a potential vector for remote code execution and data leaks.
It's probably safer to spin up a cloned django instance with a cloned database or use a different django configuration (settings.py) with read-only access to the db.
It's quite easy to mess up production data from Jupyter, since you can do stuff such as User.objects.all().delete() And that would be a problem...
Also consider running this as a user with stricter read and write permissions than you use for your regular django app.
And of course, you should make sure that the Jupyter site is not exposed on a publicly accessible url.
I have a development Django project using MySQL, and it is deployed at PythonAnywhere. I am able to push my code updates via GIT, and the Django migrations take care of the database STRUCTURE but my question is about data.
During development I may add a new capability that relies on master data which I enter in the DEV database as I develop and test. When deploying I'd like to copy over the master data to the new database rather than re-enter it all.
Is exporting and importing files the best way or is there a more professional way?
I think the simplest way to do this is by using the dumpdata management command.
The output for this command can be used in executing the loaddata management command.
I have built an application in python that is hosted on heroku which basically uses a script written in Python to store some results into a database (it runs as a scheduled task on daily basis). I would have done this with ruby/rails to avoid this confusion, but the application partner did not support Ruby.
I would like to know if it will be possible to build the front-end with Ruby on Rails and use the same database.
My rails application will need to make use MVC and have its own tables on the database, but it will also use the database that python sends data to just to retrieve some data from there.
Can I create the Rails app and reference the details of the database that my python application uses?
How could I test this on my local machine?
What would be the best approach to this?
I don't see any problem in doing this, as far as rails manages the database structure and python script populates it with data.
My advice, but just to make it simpler, is to define the database schema through migrations in your rails app and build it like the python script doesn't exist.
Once you have completed it, simply start the python script so it can start populating the Database (could be necessary to rename some table in the python script, but no more than this).
If you want to test in your local machine you can one of this:
run the python script in your local machine
configure the database.ymlin your rails app to point to the remote DB (can be difficult if you don't have administration access to the host server, because of port farwarding etc)
The only thing you should keep in mind is about concurrent accesses.
Because you have 2 application that both read and write in your DB, would be better if the python script makes its job in a single and atomic transaction, to avoid your rails app finding the DB in an half-updated state.
You can see the database like a shared box, it doesn't matter how many applications use it.
I have a Django 1.6 project (stored in a Bitbucket Git repo) that I wish to host on a VPS.
The idea is that when someone purchases a copy of the software I have written, I can type in a few simple commands that will take a designated copy of the code from Git, create a new instance of the project with its own subdomain (e.g. <customer_name>.example.com), and create a new Postgres database (on the same server).
I should hopefully be able to create and remove these 'instances' easily.
What's the best way of doing this?
I've looked into writing scripts using some sort of combination of Supervisor/Gnunicorn/Nginx/Fabric etc. Other options could be something more serious like using Docker or Vagrant. I've also looked into various PaaS options too.
Thanks in advance.
(EDIT: I have looked at the following services/things: Dokku (can't use Heroku due to data constraints), Vagrant (inc Puppet), Docker, Fabfile, Deis, Cherokee, Flynn (under dev))
If I was doing it (and I did a similar thing with a PHP application I inherited), I'd have a fabric command that allows me to provision a new instance.
This could be broken up into the requisite steps (check-out code, create database, syncdb/migrate, create DNS entry, start web server).
I'd probably do something sane like use the DNS entry as the database name: or at least use a reversible function to do that.
You could then string these together to easily create a new instance.
You will also need a way to tell the newly created instance which database and domain name they needed to use. You could have the provisioning script write some data to a file in the checked out repository that is then used by Django in it's initialisation phase.