datalite3 1.0.1

A small package that binds dataclasses to an sqlite3 database

datalite3

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("mydb.db") to the top of the definition, and the dataclass will now be bound to the file mydb.db

Detailed API reference

Download and Install

You can install datalite3 simply by

pip install datalite3

Or you can clone the repository and run

python setup.py

Datalite has no dependencies! As it is built on Python 3.6+ standard library. Albeit, its tests require unittest library.

Datalite in Action

from dataclasses import dataclass
from datalite3 import datalite


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

This snippet will generate a table in the sqlite3 database file mydb.db with table name student and columns student_id, student_name with SQL types INTEGER and TEXT, respectively. The default value for student_name is John Smith. A third column __id__ will be automatically added to hold the primary key for the records.

##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.

Custom primary key

If you we want to declare one or more of the fields as part of the primary key for our records, we can just annotate them with Primary[type], where type can be one of int, float, str, bytes.

from dataclasses import dataclass
from datalite3 import datalite, Primary


@datalite(db="mydb.db")
@dataclass
class Student:
    student_id: Primary[int]
    student_name: str = "John Smith"

This snippet will generate a table in the sqlite3 database file mydb.db with table name student and columns student_id, student_name with SQL types INTEGER and TEXT, respectively. The column student_id will be also the primary key and no __id__ column will be added.

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 exists, for this purpose we have the fetch module, from this you can import fetch_from(class_, key) as well as is_fetchable(className, key) former fetches a record from the SQL database given its unique key whereas the latter checks if it is fetchable (most likely to check if it exists.)

>>> fetch_from(Student, 20)
Student(student_id=20, student_name='Albert Einstein')

We also have three helper methods:

• fetch_all(class_): fetches all records of a given class. Returns a tuple of class_ objects;
• fetch_if(class_, condition): fetches all the records of type class_ that satisfy the given condition. Here conditions must be written is SQL syntax;
• fetch_equals(class_, field, value): fetches all the records of type class_ that have the column field set to 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.