Categories
Bibliography Data Science - Big Data DevOps Software Engineering

B06XPJML5D ISBN-13: 978-1449373320

See: Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, 1st Edition, Publisher ‏ : ‎ O’Reilly Media; 1st edition (April 18, 2017)

Fair Use Source:

Categories
Bibliography Data Science - Big Data Python

0132364670

See: SQLAlchemy: Database Access Using Python (Developer’s Library) 1st Edition

Fair Use Source:

Categories
Data Science - Big Data Python Software Engineering

SQLAlchemy

” (WP)

SQLAlchemy is an open-sourceSQL toolkit and object-relational mapper (ORM) for the Python programming language released under the MIT License.[5]

Original author(s)Michael Bayer[1][2]
Initial releaseFebruary 14, 2006; 15 years ago[3]
Stable release1.4.15 / May 11, 2021; 2 months ago[4]
Repositorygithub.com/sqlalchemy/sqlalchemy
Written inPython
Operating systemCross-platform
TypeObject-relational mapping
LicenseMIT License[5]
Websitewww.sqlalchemy.org 

Description

SQLAlchemy’s philosophy is that relational databases behave less like object collections as the scale gets larger and performance starts being a concern, while object collections behave less like tables and rows as more abstraction is designed into them. For this reason it has adopted the data mapper pattern (similar to Hibernate for Java) rather than the active record pattern used by a number of other object-relational mappers.[6] However, optional plugins allow users to develop using declarative syntax.[7]

History

SQLAlchemy was first released in February 2006[8][3] and has quickly become one of the most widely used object-relational mapping tools in the Python community, alongside Django‘s ORM.

Example

This section possibly contains original research. Please improve it by verifying the claims made and adding inline citations. Statements consisting only of original research should be removed. (November 2019) (Learn how and when to remove this template message)

The following example represents an n-to-1 relationship between movies and their directors. It is shown how user-defined Python classes create corresponding database tables, how instances with relationships are created from either side of the relationship, and finally how the data can be queried—illustrating automatically generated SQL queries for both lazy and eager loading.

Schema definition

Creating two Python classes and according database tables in the DBMS:

from sqlalchemy import *
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relation, sessionmaker

Base = declarative_base()

class Movie(Base):
    __tablename__ = "movies"

    id = Column(Integer, primary_key=True)
    title = Column(String(255), nullable=False)
    year = Column(Integer)
    directed_by = Column(Integer, ForeignKey("directors.id"))

    director = relation("Director", backref="movies", lazy=False)

    def __init__(self, title=None, year=None):
        self.title = title
        self.year = year

    def __repr__(self):
        return "Movie(%r, %r, %r)" % (self.title, self.year, self.director)

class Director(Base):
    __tablename__ = "directors"

    id = Column(Integer, primary_key=True)
    name = Column(String(50), nullable=False, unique=True)

    def __init__(self, name=None):
        self.name = name

    def __repr__(self):
        return "Director(%r)" % (self.name)

engine = create_engine("dbms://user:pwd@host/dbname")
Base.metadata.create_all(engine)

Data insertion

One can insert a director-movie relationship via either entity:

Session = sessionmaker(bind=engine)
session = Session()

m1 = Movie("Robocop", 1987)
m1.director = Director("Paul Verhoeven")

d2 = Director("George Lucas")
d2.movies = [Movie("Star Wars", 1977), Movie("THX 1138", 1971)]

try:
    session.add(m1)
    session.add(d2)
    session.commit()
except:
    session.rollback()

Querying

alldata = session.query(Movie).all()
for somedata in alldata:
    print(somedata)

SQLAlchemy issues the following query to the DBMS (omitting aliases):

SELECT movies.id, movies.title, movies.year, movies.directed_by, directors.id, directors.name
FROM movies LEFT OUTER JOIN directors ON directors.id = movies.directed_by

The output:

Movie('Robocop', 1987L, Director('Paul Verhoeven'))
Movie('Star Wars', 1977L, Director('George Lucas'))
Movie('THX 1138', 1971L, Director('George Lucas'))

Setting lazy=True (default) instead, SQLAlchemy would first issue a query to get the list of movies and only when needed (lazy) for each director a query to get the name of the according director:

SELECT movies.id, movies.title, movies.year, movies.directed_by
FROM movies

SELECT directors.id, directors.name
FROM directors
WHERE directors.id = %s

See also

References

  1. ^ Mike Bayer is the creator of SQLAlchemy and Mako Templates for Python.
  2. ^ Interview Mike Bayer SQLAlchemy #pydata #python
  3. a b “Download – SQLAlchemy”. SQLAlchemy. Retrieved 21 February 2015.
  4. ^ “Releases – sqlalchemy/sqlalchemy”. Retrieved 17 May 2021 – via GitHub.
  5. a b “zzzeek / sqlalchemy / source / LICENSE”. BitBucket. Retrieved 21 February 2015.
  6. ^ in The architecture of open source applications
  7. ^ Declarative
  8. ^ http://decisionstats.com/2015/12/29/interview-mike-bayer-sqlalchemy-pydata-python/

Notes

External links

Categories

” (WP)

Sources:

Fair Use Sources:

Categories
Bibliography

1484271912

See: SQL Server on Kubernetes: Designing and Building a Modern Data Platform 1st ed. Edition

See also Kubernetes and Cloud Native

Fair Use Source:

Categories
Bibliography

B07CYLX6FD

See: Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement 2nd Edition

Fair Use Source:

Categories
Bibliography

B0939VRDRV

See: PostgreSQL Query Optimization: The Ultimate Guide to Building Efficient Queries 1st ed. Edition

Fair Use Source:

Categories
Bibliography

B0881XNLGZ

See: Learn PostgreSQL: Build and manage high-performance database solutions using PostgreSQL 12 and 13 Kindle Edition

Fair Use Source:

Categories
Bibliography

B076C4WLBP

See: PostgreSQL: Up and Running: A Practical Guide to the Advanced Open Source Database 3rd Edition

Fair Use Source:

Categories
Bibliography

1492090409

See: SQL Pocket Guide: A Guide to SQL Usage 4th Edition

Fair Use Source:

Categories
Bibliography

B07ZC1XC9Z

See: Pro SQL Server 2019 Administration: A Guide for the Modern DBA 2nd Edition, Kindle Edition

Fair Use Source:

Categories
Bibliography

B085P1HSC2

See: SQL Server 2019 Administration Inside Out 1st Edition

Fair Use Source:

Categories
Bibliography

B01J89I7PI

See: T-SQL Fundamentals 3rd Edition

Fair Use Source:

Categories
Bibliography

B006QNDJZI

See: Head First SQL: Your Brain on SQL — A Learner’s Guide 1st Edition

Fair Use Source:

Categories
Bibliography

B01BO7HPNC

See: Getting Started with SQL: A Hands-On Approach for Beginners 1st Edition

Fair Use Source:

Categories
Bibliography

1943872570

See: Murach’s SQL Server 2019 for Developers Illustrated Edition

Fair Use Source: