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Tortoise-orm信号实现及使用场景源码详解

目录
  • 场景
  • 源码
  • 看看在模型save的时候,都干了什么?
    • 自己实现一个信号
  • 总结

    场景

    在使用Tortoise操作数据库的时候发现,通过对操作数据库模型加以装饰器,如@pre_save(Model),可以实现对这个模型在savue时,自动调用被装饰的方法,从而实现对模型的一些操作。

    在此先从官方文档入手,看一下官方的对于模型信号的Example

    # -*- coding: utf-8 -*-
    """
    This example demonstrates model signals usage
    """
    from typing import List, Optional, Type
    from tortoise import BaseDBAsynjscClient, Tortoise, fields, run_async
    from tortoise.models import Model
    from tortoise.signals import post_delete, post_save, pre_delete, pre_save
    class Signal(Model):
        id = fields.IntField(pk=True)
        name = fields.TextField()
        class Meta:
            table = "signal"
        def __str__(self):
            return self.name
    @pre_save(Signal)
    async def signal_pre_save(
        sender: "Type[Signal]", instance: Signal, using_db, update_fields
    ) -> None:
        print('signal_pre_save', sender, instance, using_db, update_fields)
    @post_save(Signal)
    async def signal_post_save(
        sender: "Type[Signal]",
        instance: Signal,
        created: bool,
        using_db: "Optional[BaseDBAsyncClient]",
        update_fields: List[str],
    ) -> None:
        print('post_save', sender, instance, using_db, created, update_fields)
    @pre_delete(Signal)
    async def signal_pre_delete(
        sender: "Type[Signal]", instanhttp://www.devze.comce: Signal, using_db: "Optional[BaseDBAsyncClient]"
    ) -> None:
        print('pre_delete', sender, instance, using_db)
    @post_delete(Signal)
    async def signal_post_delete(
        sender: "Type[Signal]", instance: Signal, using_db: "Optional[BaseDBAsyncClient]"
    ) -> None:
        print('post_delete', sender, instance, using_db)
    async def run():
        await Tortoise.init(db_url="SQLite://:memory:", modules={"models": ["__main__"]})
        await Tortoise.generate_schemas()
        # pre_save,post_save will be send
        signal = await Signal.create(name="Signal")
        signal.name = "Signal_Save"
        # pre_save,post_save will be send
        await signal.save(update_fields=["name"])
    开发者_JAV培训    # pre_delete,post_delete will be send
        await signal.delete()
    if __name__ == "__main__":
        run_async(run())
    

    以上代码可直接复制后运行,运行后的结果:

    signal_pre_save <class '__main__.Signal'> Signal <tortoise.backends.sqlite.client.SqliteClient object at 0x7f8518319400> None

    post_save <class '__main__.Signal'> Signal <tortoise.backends.sqlite.client.SqliteClient object at 0x7f8518319400> True None

    signal_pre_save <class '__main__.Signal'> Signal_Save <tortoise.backends.sqlite.client.SqliteClient object at 0x7f8518319400> ['name']

    post_save <class '__main__.Signal'> Signal_Save <tortoise.backends.sqlite.client.SqliteClient object at 0x7f8518319400> False ['name']

    pre_delete <class '__main__.Signal'> Signal_Save <tortoise.backends.sqlite.client.SqliteClient object at 0x7f8518319400>

    post_delete <class '__main__.Signal'> Signal_Save <tortoise.backends.sqlite.client.SqliteClient object at 0x7f8518319400>

    可以发现,对模型进行保存和删除时候,都会调用对应的信号方法。

    源码

    从导包可以得知,tortoise的所有信号方法都在tortoise.signals中。

    from enum import Enum
    from typing import Callable
    Signals = Enum("Signals", ["pre_save", "post_save", "pre_delete", "post_delete"])
    def post_save(*senders) -> Callable:
        """
        Register given models post_save signal.
        :param senders: Model class
        """
        def decorator(f):
            for sender in senders:
                sender.register_listener(Signals.post_save, f)
            return f
        return decorator
    def pre_save(*senders) -> Callable:
        ...
    def pre_delete(*senders) -> Callable:
        ...
    def post_delete(*senders) -> Callable:
        ...
    

    其内部实现的四个信号方法分别是模型的保存后,保存前,删除前,删除后。

    其内部装饰器代码也十分简单,就是对装饰器中的参数(也就是模型),注册一个监听者,而这个监听者,其实就是被装饰的方法。

    如上面的官方示例中:

    # 给模型Signal注册一个监听者,它是方法signal_pre_save
    @pre_save(Signal)
    async def signal_pre_save(
        sender: "Type[Signal]", instance: Signal, using_db, update_fields
    ) -> None:
        print('signal_pre_save', sender, instance, using_db, update_fields)
    

    而到了Model类中,自然就有一个register_listener方法,定睛一看,上面示例Signal中并没有regwww.devze.comister_listener方法,所以自然就想到了,这个方法必定在父类Model中。

    class Model:
        ...
        @classmethod
        def register_listener(cls, signal: Signals, listener: Callable):
            ...
            if not callable(listener):
                raise ConfigurationError("Signal listener must be callable!")
            # 检测是否已经注册过
            cls_listeners = cls._listeners.get(signal).setdefault(cls, [])  # type:ignore
            if listener not in cls_listeners:
                # 注册监听者
                cls_listeners.append(listener)
    

    接下来注册后,这个listeners就会一直跟着这个Signal类。只需要在需要操作关键代码的地方,进行调用即可。

    看看在模型save的时候,都干了什么?

        async def save(
            self,
            using_db: Optional[BaseDBAsyncClient] = None,
            update_fields: Optional[Iterable[str]] = None,
            force_create: bool = False,
            force_update: bool = False,
        ) -> None:
            ...
            # 执行保存前的信号
            await self._pre_saveandroid(db, update_fields)
            if force_create:
                await executor.execute_insert(self)
                created = True
            elif force_update:
                rows = await executor.execute_update(self, update_fields)
                if rows == 0:
                    raise IntegrityError(f"Can't update object that doesn't exist. PK: {self.pk}")
                created = False
            else:
                if self._saved_in_db or update_fields:
                    if self.pk is None:
                        await executor.execute_insert(self)
                        created = True
                    else:
                        await executor.execute_update(self, update_fields)
                        created = False
                else:
                    # TODO: Do a merge/upsert operation here instead. Let the executor determine an optimal strategy for each DB engine.
                    await executor.execute_insert(self)
                    created = True
            self._saved_in_db = True
            # 执行保存后的信号
            await self._post_save(db, created, update_fields)
    

    抛开其他代码,可以看到,在模型save的时候,其实是先执行保存前的信号,然后执行保存后的信号。

    自己实现一个信号

    有了以上的经验,可以自己实现一个信号,比如我打算做个数据处理器的类,我想在这个处理器工作中,监听处理前/后的信号。

    # -*- coding: utf-8 -*-
    from enum import Enum
    from typing import Callable, Dict
    # 声明枚举信号量
    Signals = Enum("Signals", ["before_process", "after_process"])
    # 处理前的装饰器
    def before_process(*senders):
        def decorator(f):
            for sender in senders:
                sender.register_listener(Signals.before_process, f)
            return f
        return decorator
    # 处理后的装饰器
    def after_process(*senders):
        def decorator(f):
            for sender in senders:
                sender.register_listener(Signals.after_process, f)
            return f
        return decorator
    class Model(object):
        _listeners: Dict = {
            Signals.before_process: {},
            Signals.after_process: {}
        }
        @classmethod
        def register_listener(cls, signal: Signals, listener: Callable):
            """注册监听者"""
            # 判断是否已经存在监听者
            cls_listeners = cls._listeners.get(signal).setdefault(cls, [])
            if listener not in cls_listeners:
                # 如果不存在,则添加监听者
                cls_listeners.append(listener)
        def _before_process(self):
            # 取出before_process监听者
            cls_listeners = self._listeners.get(Signals.before_process, {}).get(self.__class__, [])
            for listener in cls_listeners:
                # 调用监听者
                listener(self.__class__, self)
        def _after_process(self):
            # 取出after_process监听者
            cls_listeners = self._listeners.get(Signals.after_process, {}).get(self.__class__, [])
            for listener in cls_listeners:
                # 调用监听者
                listener(self.__class__, self)
    class SignalModel(Model):
        def process(self):
            """真正的调用端"""
            self._before_process()
            print("Processing")
            self._after_process()
    # 注册before_process信号
    @before_process(SignalModel)
    def before_process_listener(*args, **kwargs):
        print("before_procepythonss_listener1", args, kwargs)
    # 注册before_process信号
    @before_process(SignalModel)
    def before_process_listener(*args, **kwargs):
        print("before_process_listener2", args, kwargs)
    # 注册after_process信号
    @after_process(SignalModel)
    def before_process_listener(*args, **kwargs):
        print("after_process_listener", args, kwargs)
    if __name__ == '__main__':
        sm = SignalModel()
        sm.process()
    

    输出结果:

    before_process_listener1 (<class '__main__.SignalModel'>, <__main__.SignalModel object at 0x7ff700116e50>) {}

    before_process_listener2 (<class '__main__.SignalModel'>, <__main__.SignalModel object at 0x7ff700116e50>) {}

    Processing

    after_process_listener (<class '__main__.SignalModel'>, <__main__.SignalModel object at 0x7ff700116e50>) {}

    总结

    笔者通过对`tortoise-orm`源码的学习,抽丝剥茧,提取了信号实现的方式。其核心就是通过一个字典存储调用方自定义的process方法,然后分别在真正的调用端的前/后触发这些自定义方法即可。

    以上就是Tortoise-orm信号实现及使用场景源码详解的详细内容,更多关于Tortoise orm信号场景的资料请关注我们其它相关文章!

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