Java是一門較為成熟的語言,相對于C++要簡單的多,C++里沒有內(nèi)存回收,所以比較麻煩,Java加入了內(nèi)存自動回收,簡單是簡單,卻變慢了,go語言是一門新興的語言,現(xiàn)在版本是1.9 ? go語言的性能比Java要好,但由于出現(xiàn)晚,資料較Java少,有些Java的功能go也沒有,并且有許多的軟件是支持Java但支持go的很少.所以在短期內(nèi)Java是比go通用的
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C語言的最大的優(yōu)勢是時間性能好,只比匯編慢20%~30%,C++最大的優(yōu)勢是快且面向?qū)ο?Java最大的優(yōu)勢是垃圾回收機(jī)制,GO語言的目標(biāo)是具備以上三者的優(yōu)勢
Fx是一個golang版本的依賴注入框架,它使得golang通過可重用、可組合的模塊化來構(gòu)建golang應(yīng)用程序變得非常容易,可直接在項(xiàng)目中添加以下內(nèi)容即可體驗(yàn)Fx效果。
Fx是通過使用依賴注入的方式替換了全局通過手動方式來連接不同函數(shù)調(diào)用的復(fù)雜度,也不同于其他的依賴注入方式,F(xiàn)x能夠像普通golang函數(shù)去使用,而不需要通過使用struct標(biāo)簽或內(nèi)嵌特定類型。這樣使得Fx能夠在很多go的包中很好的使用。
接下來會提供一些Fx的簡單demo,并說明其中的一些定義。
1、一般步驟
大致的使用步驟就如下。下面會給出一些完整的demo
2、簡單demo
將io.reader與具體實(shí)現(xiàn)類關(guān)聯(lián)起來
輸出:
3、使用struct參數(shù)
前面的使用方式一旦需要進(jìn)行注入的類型過多,可以通過struct參數(shù)方式來解決
輸出
如果通過Provide提供構(gòu)造函數(shù)是生成相同類型會有什么問題?換句話也就是相同類型擁有多個值呢?
下面兩種方式就是來解決這樣的問題。
4、使用struct參數(shù)+Name標(biāo)簽
在Fx未使用Name或Group標(biāo)簽時不允許存在多個相同類型的構(gòu)造函數(shù),一旦存在會觸發(fā)panic。
輸出
上面通過Name標(biāo)簽即可完成在Fx容器注入相同類型
5、使用struct參數(shù)+Group標(biāo)簽
使用group標(biāo)簽同樣也能完成上面的功能
輸出
基本上Fx簡單應(yīng)用在上面的例子也做了簡單講解
1、Annotated(位于annotated.go文件) 主要用于采用annotated的方式,提供Provide注入類型
源碼中Name和Group兩個字段與前面提到的Name標(biāo)簽和Group標(biāo)簽是一樣的,只能選其一使用
2、App(位于app.go文件) 提供注入對象具體的容器、LiftCycle、容器的啟動及停止、類型變量及實(shí)現(xiàn)類注入和兩者映射等操作
至于Provide和Populate的源碼相對比較簡單易懂在這里不在描述
具體源碼
3、Extract(位于extract.go文件)
主要用于在application啟動初始化過程通過依賴注入的方式將容器中的變量值來填充給定的struct,其中target必須是指向struct的指針,并且只能填充可導(dǎo)出的字段(golang只能通過反射修改可導(dǎo)出并且可尋址的字段),Extract將被Populate代替。 具體源碼
4、其他
諸如Populate是用來替換Extract的,而LiftCycle和inout.go涉及內(nèi)容比較多后續(xù)會單獨(dú)提供專屬文件說明。
在Fx中提供的構(gòu)造函數(shù)都是惰性調(diào)用,可以通過invocations在application啟動來完成一些必要的初始化工作:fx.Invoke(function); 通過也可以按需自定義實(shí)現(xiàn)LiftCycle的Hook對應(yīng)的OnStart和OnStop用來完成手動啟動容器和關(guān)閉,來滿足一些自己實(shí)際的業(yè)務(wù)需求。
Fx框架源碼解析
主要包括app.go、lifecycle.go、annotated.go、populate.go、inout.go、shutdown.go、extract.go(可以忽略,了解populate.go)以及輔助的internal中的fxlog、fxreflect、lifecycle
原文鏈接:
github:
Dependency Injection is the idea that your components (usually structs in go) should receive their dependencies when being created. This runs counter to the associated anti-pattern of components building their own dependencies during initialization. Let’s look at an example.
Suppose you have a Server struct that requires a Config struct to implement its behavior. One way to do this would be for the Server to build its own Config during initialization.
This seems convenient. Our caller doesn’t have to be aware that our Server even needs access to Config . This is all hidden from the user of our function.
However, there are some disadvantages. First of all, if we want to change the way our Config is built, we’ll have to change all the places that call the building code. Suppose, for example, our buildMyConfigSomehow function now needs an argument. Every call site would need access to that argument and would need to pass it into the building function.
Also, it gets really tricky to mock the behavior of our Config . We’ll somehow have to reach inside of our New function to monkey with the creation of Config .
Here’s the DI way to do it:
Now the creation of our Server is decoupled from the creation of the Config . We can use whatever logic we want to create the Config and then pass the resulting data to our New function.
Furthermore, if Config is an interface, this gives us an easy route to mocking. We can pass anything we want into New as long as it implements our interface. This makes testing our Server with mock implementations of Config simple.
The main downside is that it’s a pain to have to manually create the Config before we can create the Server . We’ve created a dependency graph here – we must create our Config first because of Server depends on it. In real applications these dependency graphs can become very large and this leads to complicated logic for building all of the components your application needs to do its job.
This is where DI frameworks can help. A DI framework generally provides two pieces of functionality:
A DI framework generally builds a graph based on the “providers” you tell it about and determines how to build your objects. This is very hard to understand in the abstract, so let’s walk through a moderately-sized example.
We’re going to be reviewing the code for an HTTP server that delivers a JSON response when a client makes a GET request to /people . We’ll review the code piece by piece. For simplicity sake, it all lives in the same package ( main ). Please don’t do this in real Go applications. Full code for this example can be found here .
First, let’s look at our Person struct. It has no behavior save for some JSON tags.
A Person has an Id , Name and Age . That’s it.
Next let’s look at our Config . Similar to Person , it has no dependencies. Unlike Person , we will provide a constructor.
Enabled tells us if our application should return real data. DatabasePath tells us where our database lives (we’re using sqlite). Port tells us the port on which we’ll be running our server.
Here’s the function we’ll use to open our database connection. It relies on our Config and returns a *sql.DB .
Next we’ll look at our PersonRepository . This struct will be responsible for fetching people from our database and deserializing those database results into proper Person structs.
PersonRepository requires a database connection to be built. It exposes a single function called FindAll that uses our database connection to return a list of Person structs representing the data in our database.
To provide a layer between our HTTP server and the PersonRepository , we’ll create a PersonService .
Our PersonService relies on both the Config and the PersonRepository . It exposes a function called FindAll that conditionally calls the PersonRepository if the application is enabled.
Finally, we’ve got our Server . This is responsible for running an HTTP server and delegating the appropriate requests to our PersonService .
The Server is dependent on the PersonService and the Config .
Ok, we know all the components of our system. Now how the hell do we actually initialize them and start our system?
First, let’s write our main() function the old fashioned way.
First, we create our Config . Then, using the Config , we create our database connection. From there we can create our PersonRepository which allows us to create our PersonService . Finally, we can use this to create our Server and run it.
Phew, that was complicated. Worse, as our application becomes more complicated, our main will continue to grow in complexity. Every time we add a new dependency to any of our components, we’ll have to reflect that dependency with ordering and logic in the main function to build that component.
As you might have guessed, a Dependency Injection framework can help us solve this problem. Let’s examine how.
The term “container” is often used in DI frameworks to describe the thing into which you add “providers” and out of which you ask for fully-build objects. The dig library gives us the Provide function for adding providers and the Invoke function for retrieving fully-built objects out of the container.
First, we build a new container.
Now we can add new providers. To do so, we call the Provide function on the container. It takes a single argument: a function. This function can have any number of arguments (representing the dependencies of the component to be created) and one or two return values (representing the component that the function provides and optionally an error).
The above code says “I provide a Config type to the container. In order to build it, I don’t need anything else.” Now that we’ve shown the container how to build a Config type, we can use this to build other types.
This code says “I provide a *sql.DB type to the container. In order to build it, I need a Config . I may also optionally return an error.”
In both of these cases, we’re being more verbose than necessary. Because we already have NewConfig and ConnectDatabase functions defined, we can use them directly as providers for the container.
Now, we can ask the container to give us a fully-built component for any of the types we’ve provided. We do so using the Invoke function. The Invoke function takes a single argument – a function with any number of arguments. The arguments to the function are the types we’d like the container to build for us.
The container does some really smart stuff. Here’s what happens:
That’s a lot of work the container is doing for us. In fact, it’s doing even more. The container is smart enough to build one, and only one, instance of each type provided. That means we’ll never accidentally create a second database connection if we’re using it in multiple places (say multiple repositories).
Now that we know how the dig container works, let’s use it to build a better main.
The only thing we haven’t seen before here is the error return value from Invoke . If any provider used by Invoke returns an error, our call to Invoke will halt and that error will be returned.
Even though this example is small, it should be easy to see some of the benefits of this approach over our “standard” main. These benefits become even more obvious as our application grows larger.
One of the most important benefits is the decoupling of the creation of our components from the creation of their dependencies. Say, for example, that our PersonRepository now needs access to the Config . All we have to do is change our NewPersonRepository constructor to include the Config as an argument. Nothing else in our code changes.
Other large benefits are lack of global state, lack of calls to init (dependencies are created lazily when needed and only created once, obviating the need for error-prone init setup) and ease of testing for individual components. Imagine creating your container in your tests and asking for a fully-build object to test. Or, create an object with mock implementations of all dependencies. All of these are much easier with the DI approach.
I believe Dependency Injection helps build more robust and testable applications. This is especially true as these applications grow in size. Go is well suited to building large applications and has a great DI tool in dig . I believe the Go community should embrace DI and use it in far more applications.