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aiodatalite/README.md
2020-08-03 03:15:30 +03:00

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# Datalite
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`
For example:
```python
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`.
## Entry manipulation
After creating an object traditionally, given that you used the `datalite` decorator,
the object has two new methods: `.create_entry()` and `.remove_entry()`, you
can add the object to its associated table using the former, and remove it
using the latter.
```python
student = Student(10, "Albert Einstein")
student.create_entry() # Adds the entry to the table associated in db.db
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:
```python
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 and str!
Finally, you may wish to recreate objects from a table that already exist, for
this purpose we have the function `fetch_from(class_, object_id)` as well
as `is_fetchable(className, object_id)` former fetches a record from the
SQL database whereas the latter checks if it is fetchable (most likely
to check if it exists.)
```python
>>> fetch_from(Student, 2)
Student(student_id=10, student_name='Albert Einstein')
```
Finally, we have two helper methods, `fetch_range(class_, range_)` and
`fetch_all(class_)` 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.