1. SQL – Application Persistence Design Patterns
Lecture 17
SQL – Application Persistence Design Patterns

2. SQLite Embedded SQL database engine.
Does not work in a separate process.
Reads and writes directly to a disk.
All the data base is stored in a single file.
Pyhton has a set of libraries – we will see sqlite3.

4. Application Persistence Patterns
There are many ways to construct an application that needs to store and retrieve data.
We will create a simple application that uses SQLite.
We will then examine it and show different design patterns and best practices.

5. 'Vicious' - The Assignment Tester
We want to create an assignment checker for python assignments in the SPL course.
This application will include a method that checks assignments and store their grades in the database.
We will assume:
Students has a unique id
Assignments are numbered (e.g., assignment 1, 2,..)
Each assignment contains a single.py file with a method run_assignment that accept no arguments and returns a string.
The submitted file name will be of the form <submitter student id>.py

6. Our data base structure
Our program will use an sqlite database with the following tables:
students which includes the id of the student and its name
assignments which includes the assignment number and a string representing the expected output of the run_assignment method of the corresponding assignment.
grades which includes the grade of each student on a specific assignment.
We will now examine a simple python script that provides the behavior defined above.

8. The atexit module defines a single function to register cleanup functions. Functions thus registered are automatically executed upon normal interpreter termination.

13. we have 3 students in our class: Alice, Bob and Chris, with the corresponding ids: 1111, 2222 and 3333.
assume that they submitted the following files:
We run vicious.py:

14. Remarks
The code works but the logic of the tester is coupled with the logic of the database:
If we will change our database (e.g., some table name) we will have to modify our application logic in multiple functions (and maybe multiple files).
If we will change our database type (e.g., from sqlite3 to sqlite4) we will have to go over all our code and adapt it
If we add more modules that want to use our database, they will have to import the 'vicious' module, even if they do not need to 'grade' assignments
Database queries are a relatively long operation. Many times, we want to cache some of our queried so that we will not have to re-query the database if we need it again.
We want to decouple the the persistence related code from our application.

15. Data Persistence Layer
We will divide our code into:
Persistence Layer: a group of files which is used to communicate between the application and DB. (persistence.py)
The rest of the application logic - which will use the persistence layer to store and query data. (vicious.py)

19. Problems In vicious.py we can spot two "problems“:
In the first line of the grade method we get the expected_output of an assignment from the persistence layer.
What if we add more fields to the assignment, should we create a new method to get each field? what if we want all the fields?
Inside print_grades() we can see that we assume that get_all_grades returns a list of tuples: (student name, assignment number, grade). If we add fields to the grades table this may break our assumption.
What if we need all the grades but need the student id and not name? - should we create another method get_all_grades2?

20. Data Transfer Object (DTO) Data Access Object (DAO)
DTOs: objects that are passed to and from the persistence layer.
When passed from the persistence layer to the application logic, they contains the data retrieved from the database.
When passed from the application logic to the persistence layer, they contains the data that should be written to the database.
In most cases, these objects represents a single table (Student, Grade, Assignment).
DAOs: contain methods for retrieving and storing DTOs.
Usually, each DAO represents a DTO.
Not sufficient (only DTO-DAO relation). Where is create_tables()? Join tables? Repository – handles multiple DTOs.

27. Automating things: ORMs and generic DAOs
Some code repetition in DAOs:
Insert() methods generally look the same (on different tables). So do find() methods.
DAOs – convert database records to objects and vice versa.
Called Object Rational Mapping (ORM).
We wish to a generic ORM that converts data to any DTO.
Then we can use it to create a generic DAO class.

28. Generic ORM We need to make some assumptions about our DTO classes:
Each DTO class represents a single table
The different DTO classes are obeying a common naming conventions: A DTO class named Foo will represents a table named foos.
The different DTO classes has a constructor that accept all their fields, the name of the constructor arguments is the same as the name of the fields.
The name of the fields of each DTO class is the same as the column names in the database

29. Generic ORM
If we examine the code of a DTO class that follows these restrictions we can easily infer how the corresponding database table looks like.
Python contains modules to query the structure of a class.
inspect.getargsspec(): returns the names of the arguments of a function or a constructor.
Following our naming restrictions, our generic ORM can now query the arguments’ names and convert database data to DTO.

30. Generic ORM Our ORM also needs the structure of the database data.
This data comes from a cursor object.
The cursor object has a function named description which returns the column names of the last query.
returns a list which contains a 7-tuple for each column. The first item in this tuple is the column name.

31. Generic ORM
Our generic ORM method will receive a cursor and a DTO type.
It will examine the constructor arguments of the DTO class and the column names inside the cursor.
It will then create a mapping array col_mapping:
For each constructor argument in position i, col_mapping[i] is the index of the corresponding column inside the database.
Finally it will loop over the data inside the cursor and use the col_mapping to construct one DTO object per data row.

35. Remarks Join() of a String:
Our code contains a generic orm() function and DAO class.
Our DAO class is not completed. Doesn’t implement find()

36. Find
Our generic find() will receive a dictionary with column/values entries.
We can then construct a select statement that includes a WHERE clause which represents the keys in the dictionary.

37. Python has a special syntax:

38. Find() in our DAO

39. Same code…
Same code…
Same code…

40. Repository – generic DAO instead of specific c’tors

41. Our assignment checker

42. Our old assignment checker