/python-p3-translating-from-orms-to-sqlalchemy-lab

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Translating from ORMS to SQLAlchemy Lab

Learning Goals

  • Use an external library to simplify tasks from earlier ORM lessons.
  • Use SQLAlchemy to create, read, update and delete records in a SQL database.

Key Vocab

  • Schema: the blueprint of a database. Describes how data relates to other data in tables, columns, and relationships between them.
  • Persist: save a schema in a database.
  • Engine: a Python object that translates SQL to Python and vice-versa.
  • Session: a Python object that uses an engine to allow us to programmatically interact with a database.
  • Transaction: a strategy for executing database statements such that the group succeeds or fails as a unit.
  • Migration: the process of moving data from one or more databases to one or more target databases.

Instructions

This is a test-driven lab. Run pipenv install to create your virtual environment and pipenv shell to enter the virtual environment. Then run pytest -x to run your tests. Use these instructions and pytest's error messages to complete your work in the lib/ folder.

The testing file for this lab will execute many of the same tests as "Putting it All Together: ORMs Lab" from the previous Canvas module. There are nine tests in total: one for your data model in lib/models.py and eight for your functions in lib/dog.py. In the previous lab, you had to write quite a bit of code to get those tests passing- using SQLAlchemy, it should be much easier.

Tips and Tricks

  • The bodies of all functions in dog.py except create_table() and save() should be composed of a single line of code.
  • Read through the pytest error messages to make sure the input and output for your functions match the tests.
  • Remember which attributes are required when designing a SQLAlchemy data model: a __tablename__, a primary_key, and one or more Columns.

Once all of your tests are passing, commit and push your work using git to submit.

Resources