Develop Reference

Can flask-peewee do migration?

I want to use flask peewee as ORM for a relational db (MySQL) but my problem is changes in structure of models... like adding new attributes for a model (this means columns in db).
I want to know if I can do this automatically without writing SQL manually?

It looks like the Peewee module does support migrations.
http://peewee.readthedocs.org/en/latest/peewee/playhouse.html#schema-migrations

We developed https://github.com/keredson/peewee-db-evolve for our company's use that sounds like it may be helpful for you.
Rather than manually writing migrations, db-evolve calculates the diff between the existing schema and your defined models. It then previews and applies the non-destructive SQL commands to bring your schema into line. We've found it to be a much more robust model for schema management. (For example, switching between arbitrary branches with different schema changes is trivial this way, vs. virtually impossible w/ manually authored migrations.)
Example:
Think of it as a non-destructive version of Peewee's create_tables(). (In fact we use it for exactly that all the time, to build the schema from scratch in tests.)

I've wrote a simple migration engine for Peewee https://github.com/klen/peewee_migrate

Loading data from a (MySQL) database into Django without models

This might sound like a bit of an odd question - but is it possible to load data from a (in this case MySQL) table to be used in Django without the need for a model to be present?
I realise this isn't really the Django way, but given my current scenario, I don't really know how better to solve the problem.
I'm working on a site, which for one aspect makes use of a table of data which has been bought from a third party. The columns of interest are liklely to remain stable, however the structure of the table could change with subsequent updates to the data set. The table is also massive (in terms of columns) - so I'm not keen on typing out each field in the model one-by-one. I'd also like to leave the table intact - so coming up with a model which represents the set of columns I am interested in is not really an ideal solution.
Ideally, I want to have this table in a database somewhere (possibly separate to the main site database) and access its contents directly using SQL.

You can always execute raw SQL directly against the database: see the docs.

There is one feature called inspectdb in Django. for legacy databases like MySQL , it creates models automatically by inspecting your db tables. it stored in our app files as models.py. so we don't need to type all column manually.But read the documentation carefully before creating the models because it may affect the DB data ...i hope this will be useful for you.

I guess you can use any SQL library available for Python. For example : http://www.sqlalchemy.org/
You have just then to connect to your database, perform your request and use the datas at your will. I think you can't use Django without their model system, but nothing prevents you from using another library for this in parallel.

Dynamic Scalable Mysql Table

Here is my situation. I used Python, Django and MySQL for a web development.
I have several tables for form posting, whose fields may change dynamically. Here is an example.
Like a table called Article, it has three fields now, called id INT, title VARCHAR(50), author VARCHAR(20), and it should be able store some other values dynamically in the future, like source VARCHAR(100) or something else.
How can I implement this gracefully? Is MySQL be able to handle it? Anyway, I don't want to give up MySQL totally, for that I'm not really familiar with NoSQL databases, and it may be risky to change technique plan in the process of development.
Any ideas welcome. Thanks in advance!

You might be interested in this post about FriendFeed's schemaless SQL approach.
Loosely:
Store documents in JSON, extracting the ID as a column but no other columns
Create new tables for any indexes you require
Populate the indexes via code
There are several drawbacks to this approach, such as indexes not necessarily reflecting the actual data. You'll also need to hack up django's ORM pretty heavily. Depending on your requirements you might be able to keep some of your fields as pure DB columns and store others as JSON?

I've never actually used it, but django-not-eav looks like the tool for the job.
"This app attempts the impossible: implement a bad idea the right way." I already love it :)
That said, this question sounds like a "rethink your approach" situation, for sure. But yes, sometimes that is simply not an option...

Setting up Pyramid to use MySQL raw instead of SQLAlchemy

We're trying to set up a Pyramid project that will use MySQL instead of SQLAlchemy.
My experience with Pyramid/Python is limited, so I was hoping to find a guide online. Unfortunately, I haven't been able to find anything to push us in the right direction. Most search results were for people trying to use raw SQL/MySQL commands with SQLAlchemy (many were re-posted links).
Anyone have a useful tutorial on this?

Pyramid at its base does not assume that you will use any one specific library to help you with your persistence. In order to make things easier, then, for people who DO wish to use libraries such as SQLALchemy, the Pyramid library contains Scaffolding, which is essentially some auto-generated code for a basic site, with some additions to set up items like SQLAlchemy or a specific routing strategy. The pyramid documentation should be able to lead you through creating a new project using the "pyramid_starter" scaffolding, which sets up the basic site without SQLAlchemy.
This will give you the basics you need to set up your views, but next you will need to add code to allow you to connect to a database. Luckily, since your site is just python code, learning how to use MySQL in Pyramid is simply learning how to use MySQL in Python, and then doing the exact same steps within your Pyramid project.
Keep in mind that even if you'd rather use raw SQL queries, you might still find some usefulness in SQLAlchemy. At it's base level, SQLAlchemy simply wraps around the DBAPI calls and adds in useful features like connection pooling. The ORM functionality is actually a large addition to the tight lower-level SQLAlchemy toolset.

sqlalchemy does not make any assumption that you will be using it's orm. If you wish to use plain sql, you can do so, with nothing more than what sqlalchemy already provides. For instance, if you followed the recipe in the cookbook, you would have access to the sqlalchemy session object as request.db, your handler would look something like this:
def someHandler(request):
rows = request.db.execute("SELECT * FROM foo").fetchall()

The Quick Tutorial shows a Pyramid application that uses SQL but not SQLAlchemy. It uses SQLite, but should be reasonably easy to adapt for MySQL.

Work with Postgres/PostGIS View in SQLAlchemy

Two questions:
i want to generate a View in my PostGIS-DB. How do i add this View to my geometry_columns Table?
What i have to do, to use a View with SQLAlchemy? Is there a difference between a Table and View to SQLAlchemy or could i use the same way to use a View as i do to use a Table?
sorry for my poor english.
If there a questions about my question, please feel free to ask so i can try to explain it in another way maybe :)
Nico

Table objects in SQLAlchemy have two roles. They can be used to issue DDL commands to create the table in the database. But their main purpose is to describe the columns and types of tabular data that can be selected from and inserted to.
If you only want to select, then a view looks to SQLAlchemy exactly like a regular table. It's enough to describe the view as a Table with the columns that interest you (you don't even need to describe all of the columns). If you want to use the ORM you'll need to declare for SQLAlchemy that some combination of the columns can be used as the primary key (anything that's unique will do). Declaring some columns as foreign keys will also make it easier to set up any relations. If you don't issue create for that Table object, then it is just metadata for SQLAlchemy to know how to query the database.
If you also want to insert to the view, then you'll need to create PostgreSQL rules or triggers on the view that redirect the writes to the correct location. I'm not aware of a good usage recipe to redirect writes on the Python side.