datalite

Datalite

[!WARNING]
This project is a hobby project and it should not be used for security-critical or user facing applications.

It should be noted that Datalite is not suitable for secure web applications, it really is only suitable for cases when you can trust user input.

Datalite is a simple Python package that binds your dataclasses to a table in a sqlite3 database, using it is extremely simple, say that you have a dataclass definition, just add the decorator @datalite(db_name="db.db") to the top of the definition, and the dataclass will now be bound to the file db.db

Detailed API reference

Download and Install

You can install datalite simply by

pip install datalite

Or you can clone the repository and run

python setup.py

Datalite has no dependencies! As it is built on Python 3.7+ standard library. Albeit, its tests require unittest library.

Datalite in Action

from dataclasses import dataclass
from datalite import datalite


@datalite(db_path="db.db")
@dataclass
class Student:
    student_id: int
    student_name: str = "John Smith"

This snippet will generate a table in the sqlite3 database file db.db with table name student and rows student_id, student_name with datatypes integer and text, respectively. The default value for student_name is John Smith.

Basic Usage

Entry manipulation

After creating an object traditionally, given that you used the datalite decorator, the object has three new methods: .create_entry(), .update_entry() and .remove_entry(), you can add the object to its associated table using the former, and remove it using the later. You can also update a record using the middle.

student = Student(10, "Albert Einstein")
student.create_entry()  # Adds the entry to the table associated in db.db.
student.student_id = 20 # Update an object on memory.
student.update_entry()  # Update the corresponding record in the database.
student.remove_entry()  # Removes from the table.

But what if you have created your object in a previous session, or wish to remove an object unreachable? ie: If the object is already garbage collected by the Python interpreter? remove_from(class_, obj_id) is a function that can be used for this express purpose, for instance:

remove_from(Student, 2)  # Removes the Student with obj_id 2.

Object IDs are auto-incremented, and correspond to the order the entry were inserted onto the system.

Fetching Records

:warning: Limitation! Fetch can only fetch limited classes correctly: int, float, bytes and str!

Finally, you may wish to recreate objects from a table that already exist, for this purpose we have the module fetch module, from this you can import fetch_from(class_, obj_id) as well as is_fetchable(className, object_id) former fetches a record from the SQL database given its unique object_id whereas the latter checks if it is fetchable (most likely to check if it exists.)

>>> fetch_from(Student, 2)
Student(student_id=10, student_name='Albert Einstein')

We also have four helper methods, fetch_range(class_, range_) and fetch_all(class_) are very similar: the former fetches the records fetchable from the object id range provided by the user, whereas the latter fetches all records. Both return a tuple of class_ objects.

The last two helper methods, fetch_if(class_, condition) fetches all the records of type class_ that fit a certain condition. Here conditions must be written is SQL syntax. For easier, only one conditional checks, there is fetch_equals(class_, field, value) that checks the value of only one field and returns the object whose field equals the provided value.

Pagination

datalite also supports pagination on fetch_if, fetch_all and fetch_where, you can specify page number and element_count for each page (default 10), for these functions in order to get a subgroup of records.