我们在升级系统的时候,经常碰到需要更新服务器端数据结构等操作,之前的方式是通过手工编写alter sql脚本处理,经常会发现遗漏,导致程序发布到服务器上后无法正常使用。
现在我们可以使用Flask-Migrate插件来解决之,Flask-Migrate插件是基于Alembic,Alembic是由大名鼎鼎的SQLAlchemy作者开发数据迁移工具。
具体操作如下:
1. 安装Flask-Migrate插件
$ pip install Flask-Migrate
db = SQLAlchemy(app) migrate = Migrate(app, db) manager = Manager(app) manager.add_command('db', MigrateCommand)
3. 初始化
$ python app.py db init
使用Flask-Migrate迁移数据库
随着开发进度不断向前,你会发现你的数据库模型需要更改,而当这种情况发生时需要更新数据库。
Flask-SQLAlchemy只有当数据库表不存在了才从模型创建它们,所以更新表的唯一途径就是销毁旧的表,当然这将导致所有数据库中的数据丢失。
有个更好的解决方案就是使用数据库迁移框架。和源码版本控制工具跟踪更改源码文件一样,数据库迁移框架跟踪更改数据库模型,然后将增量变化应用到数据库中。
SQLAlchemy的主要开发人员写了一个Alembic迁移框架,但我们不直接使用Alembic,Flask应用可以使用Flask-Migrate扩展,一个集成了Flask-Script来提供所有操作命令的轻量级Alembic包。
4. 创建迁移仓库
首先,Flask-Migrate必须已经安装到虚拟环境中:
(venv) $ pip install flask-migrate
下面展示扩展如何初始化:
from flask.ext.migrate import Migrate, MigrateCommand # ... migrate = Migrate(app, db) manager.add_command('db', MigrateCommand)
为了可以使用数据库迁移命令,Flask-Migrate提供MigrateCommand类来连接Flask-Script的manager对象。在这个示例中使用db来连接到命令。
在数据库迁移可以维护之前,必须通过init子命令来创建一个迁移库:
(venv) $ python hello.py db init
Creating directory /home/flask/flasky/migrations...done Creating directory /home/flask/flasky/migrations/versions...done Generating /home/flask/flasky/migrations/alembic.ini...done Generating /home/flask/flasky/migrations/env.py...done Generating /home/flask/flasky/migrations/env.pyc...done Generating /home/flask/flasky/migrations/README...done Generating /home/flask/flasky/migrations/script.py.mako...done Please edit configuration/connection/logging settings in '/home/flask/flasky/migrations/alembic.ini' before proceeding.
这个命令创建一个migrations文件夹,里面存放了所有迁移脚本。
建议:如果你有克隆在GitHub上的应用程序,你现在可以运行git checkout 5c来切换到这个版本的应用程序。
5. 创建迁移脚本
在Alembic,数据库迁移工作由迁移脚本完成。这个脚本有两个函数,分别叫做upgrade()和downgrade()。upgrade()函数实施数据库更改,是迁移的一部分,downgrade()函数则删除它们。通过添加和删除数据库变化的能力,Alembic可以重新配置数据库从历史记录中的任何时间点。
Alembic迁移可以分别使用revision和migrate命令手动或自动创建。手动迁移通过由开发人员使用Alembic的Operations对象指令实现的空upgrade()和downgrade()函数创建迁移框架脚本。另一方面,自动迁移通过寻找模型定义和数据库当前状态间的不同为upgrade()和downgrade()生成代码。
警告:自动迁移并不总是准确的,可以忽略一些细节。所以应该经常审查一下自动生成的迁移脚本。
migrate子命令创建自动迁移脚本:
(venv) $ python hello.py db migrate -m "initial migration"
INFO [alembic.migration] Context impl SQLiteImpl. INFO [alembic.migration] Will assume non-transactional DDL. INFO [alembic.autogenerate] Detected added table 'roles' INFO [alembic.autogenerate] Detected added table 'users' INFO [alembic.autogenerate.compare] Detected added index 'ix_users_username' on '['username']' Generating /home/flask/flasky/migrations/versions/1bc 594146bb5_initial_migration.py...done
建议:如果你有克隆在GitHub上的应用程序,你现在可以运行git checkout 5c来切换到这个版本的应用程序。注意,你不需要为这个应用生成migrations,所有的迁移脚本都包含在版本库中。
6. 更新数据库
一旦迁移脚本被审查且接受,就可以使用db upgrade命令更新到数据库中:
(venv) $ python hello.py db upgrade
INFO [alembic.migration] Context impl SQLiteImpl. INFO [alembic.migration] Will assume non-transactional DDL. INFO [alembic.migration] Running upgrade None -> 1bc594146bb5, initial migration
第一次迁移实际上相当于调用db.create_all(),但在后续迁移中,upgrade命令对表实施更新操作但不影响表中的内容。

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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