Object-relational mappers (ORMs)An object-relational mapper (ORM) is a code library that automates the transfer of data stored in relational databases tables into objects that are more commonly used in application code.
Why are ORMs useful?ORMs provide a high-level abstraction upon a relational database that allows a developer to write Python code instead of SQL to create, read, update and delete data and schemas in their database. Developers can use the programming language they are comfortable with to work with a database instead of writing SQL statements or stored procedures. For example, without an ORM a developer would write the following SQL statement to retrieve every row in the USERS table where the zip_code column is 94107: SELECT * FROM USERS WHERE zip_code=94107; The equivalent Django ORM query would instead look like the following Python code: # obtain everyone in the 94107 zip code and assign to users variable users = Users.objects.filter(zip_code=94107) The ability to write Python code instead of SQL can speed up web application development, especially at the beginning of a project. The potential development speed boost comes from not having to switch from Python code into writing declarative paradigm SQL statements. While some software developers may not mind switching back and forth between languages, it's typically easier to knock out a prototype or start a web application using a single programming language. ORMs also make it theoretically possible to switch an application between various relational databases. For example, a developer could use SQLite for local development andMySQL in production. A production application could be switched from MySQL toPostgreSQL with minimal code modifications. In practice however, it's best to use the same database for local development as is used in production. Otherwise unexpected errors could hit in production that were not seen in a local development environment. Also, it's rare that a project would switch from one database in production to another one unless there was a pressing reason. While you're learning about ORMs you should also read up on deployment and check out the application dependencies page.Do I have to use an ORM for my web application?Python ORM libraries are not required for accessing relational databases. In fact, the low-level access is typically provided by another library called a database connector, such aspsycopg (for PostgreSQL) or MySQL-python (for MySQL). Take a look at the table below which shows how ORMs can work with different web frameworks and connectors and relational databases. The above table shows for example that SQLAlchemy can work with varying web frameworks and database connectors. Developers can also use ORMs without a web framework, such as when creating a data analysis tool or a batch script without a user interface. What are the downsides of using an ORM?There are numerous downsides of ORMs, including
This article on ORM impedance mismatch does a solid job of explaing what the concept is at a high level and provides diagrams to visualize why the problem occurs. Potential for reduced performanceOne of the concerns that's associated with any higher-level abstraction or framework is potential for reduced performance. With ORMs, the performance hit comes from the translation of application code into a corresponding SQL statement which may not be tuned properly. ORMs are also often easy to try but difficult to master. For example, a beginner using Django might not know about the select_related() function and how it can improve some queries' foreign key relationship performance. There are dozens of performance tips and tricks for every ORM. It's possible that investing time in learning those quirks may be better spent just learning SQL and how to write stored procedures. There's a lot of hand-waving "may or may not" and "potential for" in this section. In large projects ORMs are good enough for roughly 80-90% of use cases but in 10-20% of a project's database interactions there can be major performance improvements by having a knowledgeable database administrator write tuned SQL statements to replace the ORM's generated SQL code. Shifting complexity from the database into the app codeThe code for working with an application's data has to live somewhere. Before ORMs were common, database stored procedures were used to encapsulate the database logic. With an ORM, the data manipulation code instead lives within the application's Python codebase. The addition of data handling logic in the codebase generally isn't an issue with a sound application design, but it does increase the total amount of Python code instead of splitting code between the application and the database stored procedures. Python ORM ImplementationsThere are numerous ORM implementations written in Python, including There are other ORMs, such as Canonical's Storm, but most of them do not appear to currently be under active development. Learn more about the major active ORMs below. Django's ORMThe Django web framework comes with its own built-in object-relational mapping module, generally referred to as "the Django ORM" or "Django's ORM". Django's ORM works well for simple and medium-complexity database operations. However, there are often complaints that the ORM makes complex queries much more complicated than writing straight SQL or using SQLAlchemy. It's technically possible to drop down to SQL but it ties the queries to a specific database implementation. The ORM is coupled closely with Django so replacing the default ORM with SQLAlchemy is currently a hack workaround. Note though that some of the Django core committers believe it is only a matter of time before the default ORM is replaced with SQLAlchemy. It will be a large effort to get that working though so it's likely to come inDjango 1.9 or later. Since the majority of Django projects are tied to the default ORM, it's best to read up on advanced use cases and tools for doing your best work within the existing framework. SQLAlchemySQLAlchemy is a well-regarded Python ORM because it gets the abstraction level "just right" and seems to make complex database queries easier to write than the Django ORM in most cases. SQLAlchemy is typically used with Flask as the database ORM via theFlask-SQLAlchemy extension. PeeweePeewee is a Python ORM written to be "simpler, smaller and more hackable" than SQLAlchemy. The analogy used by the core Peewee author is that Peewee is to SQLAlchemy as SQLite is to PostgreSQL. An ORM does not have to work for every exhaustive use case in order to be useful. PonyPony ORM is another Python ORM with a slight twist in its licensing model. The project is multi-licensed. Pony is free for use on open source projects but has a commercial licensethat is required for commercial projects. The license is a one-time payment and does not necessitate a recurring fee. SQLObjectSQLObject is an ORM that has been under active open source development since before 2003. Schema migrationsSchema migrations, for example when you need to add a new column to an existing table in your database, are not technically part of ORMs. However, since ORMs typically lead to a hands-off approach to the database (at the developers peril in many cases), libraries to perform schema migrations often go hand-in-hand with Python ORM usage on web application projects. Database schema migrations are a complex topic and deserve their own page. For now, we'll lump schema migration resources under ORM links below. General ORM resources
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