See: Architecture Patterns with Python: Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservices 1st Edition
|Original author(s)||Michael Bayer|
|Initial release||February 14, 2006; 15 years ago|
|Stable release||1.4.15 / May 11, 2021; 2 months ago|
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. However, optional plugins allow users to develop using declarative syntax.
|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.
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)
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()
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
Movie('Robocop', 1987L, Director('Paul Verhoeven')) Movie('Star Wars', 1977L, Director('George Lucas')) Movie('THX 1138', 1971L, Director('George Lucas'))
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
- ^ Mike Bayer is the creator of SQLAlchemy and Mako Templates for Python.
- ^ Interview Mike Bayer SQLAlchemy #pydata #python
- ^ a b “Download – SQLAlchemy”. SQLAlchemy. Retrieved 21 February 2015.
- ^ “Releases – sqlalchemy/sqlalchemy”. Retrieved 17 May 2021 – via GitHub.
- ^ a b “zzzeek / sqlalchemy / source / LICENSE”. BitBucket. Retrieved 21 February 2015.
- ^ in The architecture of open source applications
- ^ Declarative
- ^ http://decisionstats.com/2015/12/29/interview-mike-bayer-sqlalchemy-pydata-python/
- Gift, Noah (12 Aug 2008). “Using SQLAlchemy”. Developerworks. IBM. Retrieved 8 Feb 2011.
- Rick Copeland, Essential SQLAlchemy, O’Reilly, 2008, ISBN 0-596-51614-2
- 2006 software
- Object-relational mapping
- Python (programming language) libraries
- Software using the MIT license
“A DevOps toolchain is a set or combination of tools that aid in the delivery, development, and management of software applications throughout the systems development life cycle, as coordinated by an organization that uses DevOps practices.
“In software, a toolchain is the set of programming tools that is used to perform a complex software development task or to create a software product, which is typically another computer program or a set of related programs. In general, the tools forming a toolchain are executed consecutively so the output or resulting environment state of each tool becomes the input or starting environment for the next one, but the term is also used when referring to a set of related tools that are not necessarily executed consecutively.
As DevOps is a set of practices that emphasizes the collaboration and communication of both software developers and other information technology (IT) professionals, while automating the process of software delivery and infrastructure changes, its implementation can include the definition of the series of tools used at various stages of the lifecycle; because DevOps is a cultural shift and collaboration between development and operations, there is no one product that can be considered a single DevOps tool. Instead a collection of tools, potentially from a variety of vendors, are used in one or more stages of the lifecycle.” (WP)
Stages of DevOps
Further information: DevOps
Plan is composed of two things: “define” and “plan”. This activity refers to the business value and application requirements. Specifically “Plan” activities include:
- Production metrics, objects and feedback
- Business metrics
- Update release metrics
- Release plan, timing and business case
- Security policy and requirement
A combination of the IT personnel will be involved in these activities: business application owners, software development, software architects, continual release management, security officers and the organization responsible for managing the production of IT infrastructure.
- Design of the software and configuration
- Coding including code quality (see coding conventions and coding best practices) and performance
- Software build and build performance
- Release candidate
Verify is directly associated with ensuring the quality of the software release; activities designed to ensure code quality is maintained and the highest quality is deployed to production. The main activities in this are:
- Acceptance testing
- Regression testing
- Security and vulnerability analysis
- Performance testing
- Configuration testing
Packaging refers to the activities involved once the release is ready for deployment, often also referred to as staging or Preproduction / “preprod”. This often includes tasks and activities such as:
- Package configuration
- Triggered releases
- Release staging and holding
Release related activities include schedule, orchestration, provisioning and deploying software into production and targeted environment. The specific Release activities include:
- Release coordination
- Deploying and promoting applications
- Fallbacks and recovery
- Scheduled/timed releases
Configure activities fall under the operation side of DevOps. Once software is deployed, there may be additional IT infrastructure provisioning and configuration activities required. Specific activities including:
- Infrastructure storage, database and network provisioning and configuring
- Application provision and configuration.
Monitoring is an important link in a DevOps toolchain. It allows IT organization to identify specific issues of specific releases and to understand the impact on end-users. A summary of Monitor related activities are:
- Performance of IT infrastructure
- End-user response and experience
- Production metrics and statistics
Information from monitoring activities often impacts Plan activities required for changes and for new release cycles.
Version Control is an important link in a DevOps toolchain and a component of software configuration management. Version Control is the management of changes to documents, computer programs, large web sites, and other collections of information. A summary of Version Control related activities are:
- Non-linear development
- Distributed development
- Compatibility with existent systems and protocols
- Toolkit-based design
Information from Version Control often supports Release activities required for changes and for new release cycles.
- ^ Edwards, Damon. “Integrating DevOps tools into a Service Delivery Platform”. dev2ops.org.
- ^ Seroter, Richard. “Exploring the ENTIRE DevOps Toolchain for (Cloud) Teams”. infoq.com.
- ^ “Toolchain Overview”. nongnu.org. 2012-01-03. Retrieved 2013-10-21.
- ^ “Toolchains”. elinux.org. 2013-09-08. Retrieved 2013-10-21.
- ^ Imran, Saed; Buchheit, Martin; Hollunder, Bernhard; Schreier, Ulf (2015-10-29). Tool Chains in Agile ALM Environments: A Short Introduction. Lecture Notes in Computer Science. 9416. pp. 371–380. doi:10.1007/978-3-319-26138-6_40. ISBN 978-3-319-26137-9.
- ^ Loukides, Mike (2012-06-07). “What is DevOps?”.
- ^ Garner Market Trends: DevOps – Not a Market, but Tool-Centric Philosophy That supports a Continuous Delivery Value Chain (Report). Gartner. 18 February 2015.
- ^ a b c d e f g Avoid Failure by Developing a Toolchain that Enables DevOps (Report). Gartner. 16 March 2016.
- ^ Best Practices in Change, Configuration and Release Management (Report). Gartner. 14 July 2010.
- ^ Roger S. Pressman (2009). Software Engineering: A Practitioner’s Approach (7th International ed.). New York: McGraw-Hill.