What ORM is
ORM stands for Object-relational mapping and is a very handy technique which allow to query a database in an object-oriented style. Instead of writing a (No)SQL query and then using data directly or converting to a well-defined format manually, an user can operate on objects native to a project’s programming language and chosen ORM library will generate and run all queries needed to create, read, update or delete data (see: CRUD) on your database.
Thanks to that technology creating a database schema is no much different than creating an ordinary class. There are a few libraries which provides ORM in Python and the most notable are SQLAlchemy and Django ORM. Lesser known projects are Peewee, Pony and SQLObject. The functionality and compatibility with database engines may vary so choose your ORM library with caution. Using SQLAlchemy would be my first choice, especially when a project is not using Django.
To see how ORM library can save development time please have a look at an example below using Django ORM. Here is the model:
from django.db import models class Animal(models.Model): name = models.CharField(max_length=30) age = models.PositiveSmallIntegerField()
which will generate a database table with
two three columns: name, age
and id, which Django ORM adds by default. The model would roughly translate
to such a query to create a table:
CREATE TABLE myapp_animal ( "id" SERIAL NOT NULL PRIMARY KEY, "name" VARCHAR(30) NOT NULL, "age" SMALLINT UNSIGNED NOT NULL );
And an example query which would search for all animals under the age of 3 with name starting with “R”:
which would generate a query similar to this one:
SELECT * FROM myapp_animal WHERE age < 3 AND name LIKE 'R%' ;
Using more complicated queries is, of course, supported as well (like
What ORM is not
It’s not a silver bullet :-) Queries are generated so sometimes an user could prepare an alternative which would run much faster. Also, since ORM calls to a database are much simpler, developers may not feel a cost (in time) of doing them. Instead of preparing an efficient query to pull data from a DB, they may easily make a number of queries instead. This can be reduced but some discipline and self control among devs will be needed. Devs should know SQL, otherwise yet another abstraction layer in an application will cause a confusion in case of any problems.
ORMs are trying to be universal so it means that using a very specific database functionality might be not possible with a chosen library. Keep this in mind and better check if the feature you are interested in is supported.
Nonetheless, ORM libraries are great. Just remember to use them responsibly.
Database in Łapka
As mentioned before, I will use Postgres in Łapka. As I usually work with
ORMs I decided to act differently this time and rely on my SQL knowledge. I
hope it will allow me to better understand how architecture of such solution
should look like and, maybe, learn something new regarding Postgres itself. For
sure I want to try a
JSONB type, which is available since version 9.4
released back in 2014. For NoSQL storage I usually use MongoDB but after
reading some articles explaining why Postgres should be my document database
or that one),
I’ve decided to give it a shot.