Python、SQLite、SQLAlchemyによるデータ管理

上の図は、JetBrains DataGripアプリケーションで作成された単純な実体関連図（ERD）であり、テーブル authorを示しています。 およびbook それぞれの主キーとデータフィールドを持つボックスとして。 2つのグラフィカルな項目により、関係に関する情報が追加されます。

1. 小さな黄色と青のキーアイコン テーブルの主キーと外部キーをそれぞれ示します。

2. 本を結ぶ矢印 authorへ author_idに基づくテーブル間の関係を示します bookの外部キー テーブル。

bookに新しい本を追加するとき テーブルの場合、データには author_idが含まれます author内の既存の作成者の値 テーブル。このように、著者によって書かれたすべての本は、そのユニークな著者へのルックアップ関係を持っています。

著者と本に別々のテーブルがあるので、それらの間の関係をどのように使用しますか？ SQLは、 JOINと呼ばれるものをサポートしています 2つ以上のテーブルを接続する方法をデータベースに指示するために使用できる操作。

以下のSQLクエリはauthorに参加します およびbook SQLiteコマンドラインアプリケーションを使用して一緒にテーブル：

sqlite> SELECT
   ...> a.first_name || ' ' || a.last_name AS author_name,
   ...> b.title AS book_title
   ...> FROM author a
   ...> JOIN book b ON b.author_id = a.author_id
   ...> ORDER BY a.last_name ASC;
Isaac Asimov|Foundation
Pearl Buck|The Good Earth
Tom Clancy|The Hunt For Red October
Tom Clancy|Patriot Games
Stephen King|It
Stephen King|Dead Zone
Stephen King|The Shining
John Le Carre|Tinker, Tailor, Soldier, Spy: A George Smiley Novel
Alex Michaelides|The Silent Patient
Carol Shaben|Into The Abyss

上記のSQLクエリは、著者と本の両方のテーブルの間に確立された関係を使用してテーブルを結合することにより、両者から情報を収集します。 SQL文字列の連結により、作成者のフルネームがエイリアス author_nameに割り当てられます。 。クエリによって収集されたデータは、 last_nameの昇順で並べ替えられます フィールド。

SQLステートメントで注意すべき点がいくつかあります。まず、著者は1つの列にフルネームで表示され、ラストネームでソートされます。また、1対多の関係があるため、作成者は出力に複数回表示されます。著者の名前は、データベースに書いた本ごとに複製されます。

著者と本のために別々のテーブルを作成し、それらの間の関係を確立することにより、データの冗長性を減らしました。これで、作成者のデータを1か所で編集するだけで、その変更はデータにアクセスするすべてのSQLクエリに表示されます。

多対多の関係

多対多 関係はauthor_book_publisher.dbに存在します 著者と出版社の間、および本と出版社の間のデータベース。 1人の著者は多くの出版社と協力でき、1人の出版社は多くの著者と協力できます。同様に、1冊の本を多くの出版社が出版でき、1冊の出版社が多くの本を出版できます。

Handling this situation in the database is more involved than a one-to-many relationship because the relationship goes both ways. Many-to-many relationships are created by an association table acting as a bridge between the two related tables.

The association table contains at least two foreign key fields, which are the primary keys of each of the two associated tables. This SQL statement creates the association table relating the author and publisher tables:

CREATE TABLE author_publisher (
    author_id INTEGER REFERENCES author,
    publisher_id INTEGER REFERENCES publisher
);

The SQL statements create a new author_publisher table referencing the primary keys of the existing author and publisher テーブル。 The author_publisher table is an association table establishing relationships between an author and a publisher.

Because the relationship is between two primary keys, there’s no need to create a primary key for the association table itself. The combination of the two related keys creates a unique identifier for a row of data.

As before, you use the JOIN keyword to connect two tables together. Connecting the author table to the publisher table is a two-step process:

1. JOIN the author table with the author_publisher テーブル。
2. JOIN the author_publisher table with the publisher table.

The author_publisher association table provides the bridge through which the JOIN connects the two tables. Here’s an example SQL query returning a list of authors and the publishers publishing their books:

 1sqlite> SELECT
 2   ...> a.first_name || ' ' || a.last_name AS author_name,
 3   ...> p.name AS publisher_name
 4   ...> FROM author a
 5   ...> JOIN author_publisher ap ON ap.author_id = a.author_id
 6   ...> JOIN publisher p ON p.publisher_id = ap.publisher_id
 7   ...> ORDER BY a.last_name ASC;
 8Isaac Asimov|Random House
 9Pearl Buck|Random House
10Pearl Buck|Simon & Schuster
11Tom Clancy|Berkley
12Tom Clancy|Simon & Schuster
13Stephen King|Random House
14Stephen King|Penguin Random House
15John Le Carre|Berkley
16Alex Michaelides|Simon & Schuster
17Carol Shaben|Simon & Schuster

The statements above perform the following actions:

• Line 1 starts a SELECT statement to get data from the database.

• Line 2 selects the first and last name from the author table using the a alias for the author table and concatenates them together with a space character.

• Line 3 selects the publisher’s name aliased to publisher_name 。

• Line 4 uses the author table as the first source from which to retrieve data and assigns it to the alias a 。

• Line 5 is the first step of the process outlined above for connecting the author table to the publisher テーブル。 It uses the alias ap for the author_publisher association table and performs a JOIN operation to connect the ap.author_id foreign key reference to the a.author_id primary key in the author table.

• Line 6 is the second step in the two-step process mentioned above. It uses the alias p for the publisher table and performs a JOIN operation to relate the ap.publisher_id foreign key reference to the p.publisher_id primary key in the publisher table.

• Line 7 sorts the data by the author’s last name in ascending alphabetical order and ends the SQL query.

• Lines 8 to 17 are the output of the SQL query.

Note that the data in the source author and publisher tables are normalized, with no data duplication. Yet the returned results have duplicated data where necessary to answer the SQL query.

The SQL query above demonstrates how to make use of a relationship using the SQL JOIN keyword, but the resulting data is a partial re-creation of the author_book_publisher.csv CSV data. What’s the win for having done the work of creating a database to separate the data?

Here’s another SQL query to show a little bit of the power of SQL and the database engine:

 1sqlite> SELECT
 2   ...> a.first_name || ' ' || a.last_name AS author_name,
 3   ...> COUNT(b.title) AS total_books
 4   ...> FROM author a
 5   ...> JOIN book b ON b.author_id = a.author_id
 6   ...> GROUP BY author_name
 7   ...> ORDER BY total_books DESC, a.last_name ASC;
 8Stephen King|3
 9Tom Clancy|2
10Isaac Asimov|1
11Pearl Buck|1
12John Le Carre|1
13Alex Michaelides|1
14Carol Shaben|1

The SQL query above returns the list of authors and the number of books they’ve written. The list is sorted first by the number of books in descending order, then by the author’s name in alphabetical order:

• Line 1 begins the SQL query with the SELECT keyword.

• Line 2 selects the author’s first and last names, separated by a space character, and creates the alias author_name 。

• Line 3 counts the number of books written by each author, which will be used later by the ORDER BY clause to sort the list.

• Line 4 selects the author table to get data from and creates the a alias.

• Line 5 connects to the related book table through a JOIN to the author_id and creates the b alias for the book table.

• Line 6 generates the aggregated author and total number of books data by using the GROUP BY キーワード。 GROUP BY is what groups each author_name and controls what books are tallied by COUNT() for that author.

• Line 7 sorts the output first by number of books in descending order, then by the author’s last name in ascending alphabetical order.

• Lines 8 to 14 are the output of the SQL query.

In the above example, you take advantage of SQL to perform aggregation calculations and sort the results into a useful order. Having the database perform calculations based on its built-in data organization ability is usually faster than performing the same kinds of calculations on raw data sets in Python. SQL offers the advantages of using set theory embedded in RDBMS databases.

Entity Relationship Diagrams

An entity-relationship diagram (ERD) is a visual depiction of an entity-relationship model for a database or part of a database. The author_book_publisher.db SQLite database is small enough that the entire database can be visualized by the diagram shown below:

This diagram presents the table structures in the database and the relationships between them. Each box represents a table and contains the fields defined in the table, with the primary key indicated first if it exists.

The arrows show the relationships between the tables connecting a foreign key field in one table to a field, often the primary key, in another table. The table book_publisher has two arrows, one connecting it to the book table and another connecting it to the publisher テーブル。 The arrow indicates the many-to-many relationship between the book and publisher テーブル。 The author_publisher table provides the same relationship between author and publisher 。

Working With SQLAlchemy and Python Objects

SQLAlchemy is a powerful database access tool kit for Python, with its object-relational mapper (ORM) being one of its most famous components, and the one discussed and used here.

When you’re working in an object-oriented language like Python, it’s often useful to think in terms of objects. It’s possible to map the results returned by SQL queries to objects, but doing so works against the grain of how the database works. Sticking with the scalar results provided by SQL works against the grain of how Python developers work. This problem is known as object-relational impedance mismatch.

The ORM provided by SQLAlchemy sits between the SQLite database and your Python program and transforms the data flow between the database engine and Python objects. SQLAlchemy allows you to think in terms of objects and still retain the powerful features of a database engine.

The Model

One of the fundamental elements to enable connecting SQLAlchemy to a database is creating a model 。 The model is a Python class defining the data mapping between the Python objects returned as a result of a database query and the underlying database tables.

The entity-relationship diagram displayed earlier shows boxes connected with arrows. The boxes are the tables built with the SQL commands and are what the Python classes will model. The arrows are the relationships between the tables.

The models are Python classes inheriting from an SQLAlchemy Base クラス。 The Base class provides the interface operations between instances of the model and the database table.

Below is the models.py file that creates the models to represent the author_book_publisher.db database:

 1from sqlalchemy import Column, Integer, String, ForeignKey, Table
 2from sqlalchemy.orm import relationship, backref
 3from sqlalchemy.ext.declarative import declarative_base
 4
 5Base = declarative_base()
 6
 7author_publisher = Table(
 8    "author_publisher",
 9    Base.metadata,
10    Column("author_id", Integer, ForeignKey("author.author_id")),
11    Column("publisher_id", Integer, ForeignKey("publisher.publisher_id")),
12)
13
14book_publisher = Table(
15    "book_publisher",
16    Base.metadata,
17    Column("book_id", Integer, ForeignKey("book.book_id")),
18    Column("publisher_id", Integer, ForeignKey("publisher.publisher_id")),
19)
20
21class Author(Base):
22    __tablename__ = "author"
23    author_id = Column(Integer, primary_key=True)
24    first_name = Column(String)
25    last_name = Column(String)
26    books = relationship("Book", backref=backref("author"))
27    publishers = relationship(
28        "Publisher", secondary=author_publisher, back_populates="authors"
29    )
30
31class Book(Base):
32    __tablename__ = "book"
33    book_id = Column(Integer, primary_key=True)
34    author_id = Column(Integer, ForeignKey("author.author_id"))
35    title = Column(String)
36    publishers = relationship(
37        "Publisher", secondary=book_publisher, back_populates="books"
38    )
39
40class Publisher(Base):
41    __tablename__ = "publisher"
42    publisher_id = Column(Integer, primary_key=True)
43    name = Column(String)
44    authors = relationship(
45        "Author", secondary=author_publisher, back_populates="publishers"
46    )
47    books = relationship(
48        "Book", secondary=book_publisher, back_populates="publishers"
49    )

Here’s what’s going on in this module:

• Line 1 imports the Column , Integer , String , ForeignKey , and Table classes from SQLAlchemy, which are used to help define the model attributes.

• Line 2 imports the relationship() and backref objects, which are used to create the relationships between objects.

• Line 3 imports the declarative_base object, which connects the database engine to the SQLAlchemy functionality of the models.

• Line 5 creates the Base class, which is what all models inherit from and how they get SQLAlchemy ORM functionality.

• Lines 7 to 12 create the author_publisher association table model.

• Lines 14 to 19 create the book_publisher association table model.

• Lines 21 to 29 define the Author class model to the author database table.

• Lines 31 to 38 define the Book class model to the book database table.

• Lines 40 to 49 define the Publisher class model to the publisher database table.

The description above shows the mapping of the five tables in the author_book_publisher.db データベース。 But it glosses over some SQLAlchemy ORM features, including Table , ForeignKey , relationship() , and backref 。 Let’s get into those now.

Table Creates Associations

author_publisher and book_publisher are both instances of the Table class that create the many-to-many association tables used between the author and publisher tables and the book and publisher tables, respectively.

The SQLAlchemy Table class creates a unique instance of an ORM mapped table within the database. The first parameter is the table name as defined in the database, and the second is Base.metadata , which provides the connection between the SQLAlchemy functionality and the database engine.

The rest of the parameters are instances of the Column class defining the table fields by name, their type, and in the example above, an instance of a ForeignKey 。

ForeignKey Creates a Connection

The SQLAlchemy ForeignKey class defines a dependency between two Column fields in different tables. A ForeignKey is how you make SQLAlchemy aware of the relationships between tables. For example, this line from the author_publisher instance creation establishes a foreign key relationship:

Column("author_id", Integer, ForeignKey("author.author_id"))

The statement above tells SQLAlchemy that there’s a column in the author_publisher table named author_id 。 The type of that column is Integer , and author_id is a foreign key related to the primary key in the author table.

Having both author_id and publisher_id defined in the author_publisher Table instance creates the connection from the author table to the publisher table and vice versa, establishing a many-to-many relationship.

relationship() Establishes a Collection

Having a ForeignKey defines the existence of the relationship between tables but not the collection of books an author can have. Take a look at this line in the Author class definition:

books = relationship("Book", backref=backref("author"))

The code above defines a parent-child collection. The books attribute being plural (which is not a requirement, just a convention) is an indication that it’s a collection.

The first parameter to relationship() , the class name Book (which is not the table name book ), is the class to which the books attribute is related. The relationship informs SQLAlchemy that there’s a relationship between the Author and Book クラス。 SQLAlchemy will find the relationship in the Book class definition:

author_id = Column(Integer, ForeignKey("author.author_id"))

SQLAlchemy recognizes that this is the ForeignKey connection point between the two classes. You’ll get to the backref parameter in relationship() in a moment.

The other relationship in Author is to the Publisher クラス。 This is created with the following statement in the Author class definition:

publishers = relationship(
    "Publisher", secondary=author_publisher, back_populates="authors"
)

Like books , the attribute publishers indicates a collection of publishers associated with an author. The first parameter, "Publisher" , informs SQLAlchemy what the related class is. The second and third parameters are secondary=author_publisher and back_populates="authors" ：

• secondary tells SQLAlchemy that the relationship to the Publisher class is through a secondary table, which is the author_publisher association table created earlier in models.py 。 The secondary parameter makes SQLAlchemy find the publisher_id ForeignKey defined in the author_publisher association table.

• back_populates is a convenience configuration telling SQLAlchemy that there’s a complementary collection in the Publisher class called authors 。

backref Mirrors Attributes

The backref parameter of the books collection relationship() creates an author attribute for each Book 実例。 This attribute refers to the parent Author that the Book instance is related to.

For example, if you executed the following Python code, then a Book instance would be returned from the SQLAlchemy query. The Book instance has attributes that can be used to print out information about the book:

book = session.query(Book).filter_by(Book.title == "The Stand").one_or_none()
print(f"Authors name: {book.author.first_name} {book.author.last_name}")

The existence of the author attribute in the Book above is because of the backref 意味。 A backref can be very handy to have when you need to refer to the parent and all you have is a child instance.

Queries Answer Questions

You can make a basic query like SELECT * FROM author; in SQLAlchemy like this:

results = session.query(Author).all()

The session is an SQLAlchemy object used to communicate with SQLite in the Python example programs. Here, you tell the session you want to execute a query against the Author model and return all records.

At this point, the advantages of using SQLAlchemy instead of plain SQL might not be obvious, especially considering the setup required to create the models representing the database. The results returned by the query is where the magic happens. Instead of getting back a list of lists of scalar data, you’ll get back a list of instances of Author objects with attributes matching the column names you defined.

The books and publishers collections maintained by SQLAlchemy create a hierarchical list of authors and the books they’ve written as well as the publishers who’ve published them.

Behind the scenes, SQLAlchemy turns the object and method calls into SQL statements to execute against the SQLite database engine. SQLAlchemy transforms the data returned by SQL queries into Python objects.

With SQLAlchemy, you can perform the more complex aggregation query shown earlier for the list of authors and the number of books they’ve written like this:

author_book_totals = (
    session.query(
        Author.first_name,
        Author.last_name,
        func.count(Book.title).label("book_total")
    )
    .join(Book)
    .group_by(Author.last_name)
    .order_by(desc("book_total"))
    .all()
)

The query above gets the author’s first and last name, along with a count of the number of books that the author has written. The aggregating count used by the group_by clause is based on the author’s last name. Finally, the results are sorted in descending order based on the aggregated and aliased book_total 。

Example Program

The example program examples/example_2/main.py has the same functionality as examples/example_1/main.py but uses SQLAlchemy exclusively to interface with the author_book_publisher.db SQLite database. The program is broken up into the main() function and the functions it calls:

 1def main():
 2    """Main entry point of program"""
 3    # Connect to the database using SQLAlchemy
 4    with resources.path(
 5        "project.data", "author_book_publisher.db"
 6    ) as sqlite_filepath:
 7        engine = create_engine(f"sqlite:///{sqlite_filepath}")
 8    Session = sessionmaker()
 9    Session.configure(bind=engine)
10    session = Session()
11
12    # Get the number of books printed by each publisher
13    books_by_publisher = get_books_by_publishers(session, ascending=False)
14    for row in books_by_publisher:
15        print(f"Publisher: {row.name}, total books: {row.total_books}")
16    print()
17
18    # Get the number of authors each publisher publishes
19    authors_by_publisher = get_authors_by_publishers(session)
20    for row in authors_by_publisher:
21        print(f"Publisher: {row.name}, total authors: {row.total_authors}")
22    print()
23
24    # Output hierarchical author data
25    authors = get_authors(session)
26    output_author_hierarchy(authors)
27
28    # Add a new book
29    add_new_book(
30        session,
31        author_name="Stephen King",
32        book_title="The Stand",
33        publisher_name="Random House",
34    )
35    # Output the updated hierarchical author data
36    authors = get_authors(session)
37    output_author_hierarchy(authors)

This program is a modified version of examples/example_1/main.py 。 Let’s go over the differences:

• Lines 4 to 7 first initialize the sqlite_filepath variable to the database file path. Then they create the engine variable to communicate with SQLite and the author_book_publisher.db database file, which is SQLAlchemy’s access point to the database.

• Line 8 creates the Session class from the SQLAlchemy’s sessionmaker() 。

• Line 9 binds the Session to the engine created in line 8.

• Line 10 creates the session instance, which is used by the program to communicate with SQLAlchemy.

The rest of the function is similar, except for the replacement of data with session as the first parameter to all the functions called by main() 。

get_books_by_publisher() has been refactored to use SQLAlchemy and the models you defined earlier to get the data requested:

 1def get_books_by_publishers(session, ascending=True):
 2    """Get a list of publishers and the number of books they've published"""
 3    if not isinstance(ascending, bool):
 4        raise ValueError(f"Sorting value invalid: {ascending}")
 5
 6    direction = asc if ascending else desc
 7
 8    return (
 9        session.query(
10            Publisher.name, func.count(Book.title).label("total_books")
11        )
12        .join(Publisher.books)
13        .group_by(Publisher.name)
14        .order_by(direction("total_books"))
15    )

Here’s what the new function, get_books_by_publishers() , is doing:

• Line 6 creates the direction variable and sets it equal to the SQLAlchemy desc or asc function depending on the value of the ascending パラメータ。

• Lines 9 to 11 query the Publisher table for data to return, which in this case are Publisher.name and the aggregate total of Book objects associated with an author, aliased to total_books 。

• Line 12 joins to the Publisher.books collection.

• Line 13 aggregates the book counts by the Publisher.name attribute.

• Line 14 sorts the output by the book counts according to the operator defined by direction 。

• Line 15 closes the object, executes the query, and returns the results to the caller.

All the above code expresses what is wanted rather than how it’s to be retrieved. Now instead of using SQL to describe what’s wanted, you’re using Python objects and methods. What’s returned is a list of Python objects instead of a list of tuples of data.

get_authors_by_publisher() has also been modified to work exclusively with SQLAlchemy. Its functionality is very similar to the previous function, so a function description is omitted:

def get_authors_by_publishers(session, ascending=True):
    """Get a list of publishers and the number of authors they've published"""
    if not isinstance(ascending, bool):
        raise ValueError(f"Sorting value invalid: {ascending}")

    direction = asc if ascending else desc

    return (
        session.query(
            Publisher.name,
            func.count(Author.first_name).label("total_authors"),
        )
        .join(Publisher.authors)
        .group_by(Publisher.name)
        .order_by(direction("total_authors"))
    )

get_authors() has been added to get a list of authors sorted by their last names. The result of this query is a list of Author objects containing a collection of books. The Author objects already contain hierarchical data, so the results don’t have to be reformatted:

def get_authors(session):
    """Get a list of author objects sorted by last name"""
    return session.query(Author).order_by(Author.last_name).all()

Like its previous version, add_new_book() is relatively complex but straightforward to understand. It determines if a book with the same title, author, and publisher exists in the database already.

If the search query finds an exact match, then the function returns. If no book matches the exact search criteria, then it searches to see if the author has written a book using the passed in title. This code exists to prevent duplicate books from being created in the database.

If no matching book exists, and the author hasn’t written one with the same title, then a new book is created. The function then retrieves or creates an author and publisher. Once instances of the Book , Author and Publisher exist, the relationships between them are created, and the resulting information is saved to the database:

 1def add_new_book(session, author_name, book_title, publisher_name):
 2    """Adds a new book to the system"""
 3    # Get the author's first and last names
 4    first_name, _, last_name = author_name.partition(" ")
 5
 6    # Check if book exists
 7    book = (
 8        session.query(Book)
 9        .join(Author)
10        .filter(Book.title == book_title)
11        .filter(
12            and_(
13                Author.first_name == first_name, Author.last_name == last_name
14            )
15        )
16        .filter(Book.publishers.any(Publisher.name == publisher_name))
17        .one_or_none()
18    )
19    # Does the book by the author and publisher already exist?
20    if book is not None:
21        return
22
23    # Get the book by the author
24    book = (
25        session.query(Book)
26        .join(Author)
27        .filter(Book.title == book_title)
28        .filter(
29            and_(
30                Author.first_name == first_name, Author.last_name == last_name
31            )
32        )
33        .one_or_none()
34    )
35    # Create the new book if needed
36    if book is None:
37        book = Book(title=book_title)
38
39    # Get the author
40    author = (
41        session.query(Author)
42        .filter(
43            and_(
44                Author.first_name == first_name, Author.last_name == last_name
45            )
46        )
47        .one_or_none()
48    )
49    # Do we need to create the author?
50    if author is None:
51        author = Author(first_name=first_name, last_name=last_name)
52        session.add(author)
53
54    # Get the publisher
55    publisher = (
56        session.query(Publisher)
57        .filter(Publisher.name == publisher_name)
58        .one_or_none()
59    )
60    # Do we need to create the publisher?
61    if publisher is None:
62        publisher = Publisher(name=publisher_name)
63        session.add(publisher)
64
65    # Initialize the book relationships
66    book.author = author
67    book.publishers.append(publisher)
68    session.add(book)
69
70    # Commit to the database
71    session.commit()

The code above is relatively long. Let’s break the functionality down to manageable sections:

• Lines 7 to 18 set the book variable to an instance of a Book if a book with the same title, author, and publisher is found. Otherwise, they set book to None 。

• Lines 20 and 21 determine if the book already exists and return if it does.

• Lines 24 to 37 set the book variable to an instance of a Book if a book with the same title and author is found. Otherwise, they create a new Book instance.

• Lines 40 to 52 set the author variable to an existing author, if found, or create a new Author instance based on the passed-in author name.

• Lines 55 to 63 set the publisher variable to an existing publisher, if found, or create a new Publisher instance based on the passed-in publisher name.

• Line 66 sets the book.author instance to the author 実例。 This creates the relationship between the author and the book, which SQLAlchemy will create in the database when the session is committed.

• Line 67 adds the publisher instance to the book.publishers コレクション。 This creates the many-to-many relationship between the book and publisher テーブル。 SQLAlchemy will create references in the tables as well as in the book_publisher association table that connects the two.

• Line 68 adds the Book instance to the session, making it part of the session’s unit of work.

• Line 71 commits all the creations and updates to the database.

There are a few things to take note of here. First, there’s is no mention of the author_publisher or book_publisher association tables in either the queries or the creations and updates. Because of the work you did in models.py setting up the relationships, SQLAlchemy can handle connecting objects together and keeping those tables in sync during creations and updates.

Second, all the creations and updates happen within the context of the session 物体。 None of that activity is touching the database. Only when the session.commit() statement executes does the session then go through its unit of work and commit that work to the database.

For example, if a new Book instance is created (as in line 37 above), then the book has its attributes initialized except for the book_id primary key and author_id 外部キー。 Because no database activity has happened yet, the book_id is unknown, and nothing was done in the instantiation of book to give it an author_id 。

When session.commit() is executed, one of the things it will do is insert book into the database, at which point the database will create the book_id 主キー。 The session will then initialize the book.book_id value with the primary key value created by the database engine.

session.commit() is also aware of the insertion of the Book instance in the author.books コレクション。 The author object’s author_id primary key will be added to the Book instance appended to the author.books collection as the author_id foreign key.

Providing Access to Multiple Users

To this point, you’ve seen how to use pandas, SQLite, and SQLAlchemy to access the same data in different ways. For the relatively straightforward use case of the author, book, and publisher data, it could still be a toss-up whether you should use a database.

One deciding factor when choosing between using a flat file or a database is data and relationship complexity. If the data for each entity is complicated and contains many relationships between the entities, then creating and maintaining it in a flat file might become more difficult.

Another factor to consider is whether you want to share the data between multiple users. The solution to this problem might be as simple as using a sneakernet to physically move data between users. Moving data files around this way has the advantage of ease of use, but the data can quickly get out of sync when changes are made.

The problem of keeping the data consistent for all users becomes even more difficult if the users are remote and want to access the data across networks. Even when you’re limited to a single language like Python and using pandas to access the data, network file locking isn’t sufficient to ensure the data doesn’t get corrupted.

Providing the data through a server application and a user interface alleviates this problem. The server is the only application that needs file-level access to the database. By using a database, the server can take advantage of SQL to access the data using a consistent interface no matter what programming language the server uses.

The last example program demonstrates this by providing a web application and user interface to the Chinook sample SQLite database. Peter Stark generously maintains the Chinook database as part of the SQLite Tutorial site. If you’d like to learn more about SQLite and SQL in general, then the site is a great resource.

The Chinook database provides artist, music, and playlist information along the lines of a simplified Spotify. The database is part of the example code project in the project/data folder.

Using Flask With Python, SQLite, and SQLAlchemy

The examples/example_3/chinook_server.py program creates a Flask application that you can interact with using a browser. The application makes use of the following technologies:

• Flask Blueprint is part of Flask and provides a good way to follow the separation of concerns design principle and create distinct modules to contain functionality.

• Flask SQLAlchemy is an extension for Flask that adds support for SQLAlchemy in your web applications.

• Flask_Bootstrap4 packages the Bootstrap front-end tool kit, integrating it with your Flask web applications.

• Flask_WTF extends Flask with WTForms, giving your web applications a useful way to generate and validate web forms.

• python_dotenv is a Python module that an application uses to read environment variables from a file and keep sensitive information out of program code.

Though not necessary for this example, a .env file holds the environment variables for the application. The .env file exists to contain sensitive information like passwords, which you should keep out of your code files. However, the content of the project .env file is shown below since it doesn’t contain any sensitive data:

SECRET_KEY = "you-will-never-guess"
SQLALCHEMY_TRACK_MODIFICATIONS = False
SQLAlCHEMY_ECHO = False
DEBUG = True

The example application is fairly large, and only some of it is relevant to this tutorial. For this reason, examining and learning from the code is left as an exercise for the reader. That said, you can take a look at an animated screen capture of the application below, followed by the HTML that renders the home page and the Python Flask route that provides the dynamic data.

Here’s the application in action, navigating through various menus and features:

The animated screen capture starts on the application home page, styled using Bootstrap 4. The page displays the artists in the database, sorted in ascending order. The remainder of the screen capture presents the results of clicking on the displayed links or navigating around the application from the top-level menu.

Here’s the Jinja2 HTML template that generates the home page of the application:

 1{% extends "base.html" %}
 2
 3{% block content %}
 4<div class="container-fluid">
 5  <div class="m-4">
 6    <div class="card" style="width: 18rem;">
 7      <div class="card-header">Create New Artist</div>
 8      <div class="card-body">
 9        <form method="POST" action="{{url_for('artists_bp.artists')}}">
10          {{ form.csrf_token }}
11          {{ render_field(form.name, placeholder=form.name.label.text) }}
12          <button type="submit" class="btn btn-primary">Create</button>
13        </form>
14      </div>
15    </div>
16    <table class="table table-striped table-bordered table-hover table-sm">
17      <caption>List of Artists</caption>
18      <thead>
19        <tr>
20          <th>Artist Name</th>
21        </tr>
22      </thead>
23      <tbody>
24        {% for artist in artists %}
25        <tr>
26          <td>
27            <a href="{{url_for('albums_bp.albums', artist_id=artist.artist_id)}}">
28              {{ artist.name }}
29            </a>
30          </td>
31        </tr>
32        {% endfor %}
33      </tbody>
34    </table>
35  </div>
36</div>
37{% endblock %}

Here’s what’s going on in this Jinja2 template code:

• Line 1 uses Jinja2 template inheritance to build this template from the base.html レンプレート。 The base.html template contains all the HTML5 boilerplate code as well as the Bootstrap navigation bar consistent across all pages of the site.

• Lines 3 to 37 contain the block content of the page, which is incorporated into the Jinja2 macro of the same name in the base.html base template.

• Lines 9 to 13 render the form to create a new artist. This uses the features of Flask-WTF to generate the form.

• Lines 24 to 32 create a for loop that renders the table of artist names.

• Lines 27 to 29 render the artist name as a link to the artist’s album page showing the songs associated with a particular artist.

Here’s the Python route that renders the page:

 1from flask import Blueprint, render_template, redirect, url_for
 2from flask_wtf import FlaskForm
 3from wtforms import StringField
 4from wtforms.validators import InputRequired, ValidationError
 5from app import db
 6from app.models import Artist
 7
 8# Set up the blueprint
 9artists_bp = Blueprint(
10    "artists_bp", __name__, template_folder="templates", static_folder="static"
11)
12
13def does_artist_exist(form, field):
14    artist = (
15        db.session.query(Artist)
16        .filter(Artist.name == field.data)
17        .one_or_none()
18    )
19    if artist is not None:
20        raise ValidationError("Artist already exists", field.data)
21
22class CreateArtistForm(FlaskForm):
23    name = StringField(
24        label="Artist's Name", validators=[InputRequired(), does_artist_exist]
25    )
26
27@artists_bp.route("/")
28@artists_bp.route("/artists", methods=["GET", "POST"])
29def artists():
30    form = CreateArtistForm()
31
32    # Is the form valid?
33    if form.validate_on_submit():
34        # Create new artist
35        artist = Artist(name=form.name.data)
36        db.session.add(artist)
37        db.session.commit()
38        return redirect(url_for("artists_bp.artists"))
39
40    artists = db.session.query(Artist).order_by(Artist.name).all()
41    return render_template("artists.html", artists=artists, form=form,)

Let’s go over what the above code is doing:

• Lines 1 to 6 import all the modules necessary to render the page and initialize forms with data from the database.

• Lines 9 to 11 create the blueprint for the artists page.

• Lines 13 to 20 create a custom validator function for the Flask-WTF forms to make sure a request to create a new artist doesn’t conflict with an already existing artist.

• Lines 22 to 25 create the form class to handle the artist form rendered in the browser and provide validation of the form field inputs.

• Lines 27 to 28 connect two routes to the artists() function they decorate.

• Line 30 creates an instance of the CreateArtistForm() class.

• Line 33 determines if the page was requested through the HTTP methods GET or POST (submit). If it was a POST, then it also validates the fields of the form and informs the user if the fields are invalid.

• Lines 35 to 37 create a new artist object, add it to the SQLAlchemy session, and commit the artist object to the database, persisting it.

• Line 38 redirects back to the artists page, which will be rerendered with the newly created artist.

• Line 40 runs an SQLAlchemy query to get all the artists in the database and sort them by Artist.name 。

• Line 41 renders the artists page if the HTTP request method was a GET.

You can see that a great deal of functionality is created by a reasonably small amount of code.

Creating a REST API Server

You can also create a web server providing a REST API. This kind of server offers URL endpoints responding with data, often in JSON format. A server providing REST API endpoints can be used by JavaScript single-page web applications through the use of AJAX HTTP requests.

Flask is an excellent tool for creating REST applications. For a multi-part series of tutorials about using Flask, Connexion, and SQLAlchemy to create REST applications, check out Python REST APIs With Flask, Connexion, and SQLAlchemy.

If you’re a fan of Django and are interested in creating REST APIs, then check out Django Rest Framework – An Introduction and Create a Super Basic REST API with Django Tastypie.

注： It’s reasonable to ask if SQLite is the right choice as the database backend to a web application. The SQLite website states that SQLite is a good choice for sites that serve around 100,000 hits per day. If your site gets more daily hits, the first thing to say is congratulations!

Beyond that, if you’ve implemented your website with SQLAlchemy, then it’s possible to move the data from SQLite to another database such as MySQL or PostgreSQL. For a comparison of SQLite, MySQL, and PostgreSQL that will help you make decisions about which one will serve your application best, check out Introduction to Python SQL Libraries.

It’s well worth considering SQLite for your Python application, no matter what it is. Using a database gives your application versatility, and it might create surprising opportunities to add additional features.

Conclusion

You’ve covered a lot of ground in this tutorial about databases, SQLite, SQL, and SQLAlchemy! You’ve used these tools to move data contained in flat files to an SQLite database, access the data with SQL and SQLAlchemy, and provide that data through a web server.

In this tutorial, you’ve learned:

• Why an SQLite database can be a compelling alternative to flat-file data storage
• How to normalize data to reduce data redundancy and increase data integrity
• How to use SQLAlchemy to work with databases in an object-oriented manner
• How to build a web application to serve a database to multiple users

Working with databases is a powerful abstraction for working with data that adds significant functionality to your Python programs and allows you to ask interesting questions of your data.

You can get all of the code and data you saw in this tutorial at the link below:

Download the sample code: Click here to get the code you’ll use to learn about data management with SQLite and SQLAlchemy in this tutorial.

Further Reading

This tutorial is an introduction to using databases, SQL, and SQLAlchemy, but there’s much more to learn about these subjects. These are powerful, sophisticated tools that no single tutorial can cover adequately. Here are some resources for additional information to expand your skills:

• If your application will expose the database to users, then avoiding SQL injection attacks is an important skill. For more information, check out Preventing SQL Injection Attacks With Python.

• Providing web access to a database is common in web-based single-page applications. To learn how, check out Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 2.

• Preparing for data engineering job interviews gives you a leg up in your career. To get started, check out Data Engineer Interview Questions With Python.

• Migrating data and being able to roll back using Flask with Postgres and SQLAlchemy is an integral part of the Software Development Life Cycle (SDLC). You can learn more about it by checking out Flask by Example – Setting up Postgres, SQLAlchemy, and Alembic.