假设我们有一个用户、钱包REST微服务和一个API网关,它将这些东西粘合在一起。当Bob在我们的网站上注册时,我们的API网关需要通过user微服务创建一个用户,并通过wallet微服务创建一个钱包。

下面是一些可能出错的情况:

用户Bob创建失败:没关系,我们只是向Bob返回一个错误消息。我们使用SQL事务,所以没有人在系统中看到Bob。一切都很好:) 用户Bob已经创建,但是在我们的钱包创建之前,我们的API网关硬崩溃了。我们现在有一个没有钱包的用户(数据不一致)。 创建了用户Bob,当我们创建钱包时,HTTP连接断开。钱包的创建可能成功,也可能失败。

有什么解决方案可以防止这种数据不一致的发生?是否存在允许事务跨越多个REST请求的模式?我读过维基百科上关于两阶段提交的页面,它似乎涉及到这个问题,但我不确定如何在实践中应用它。这篇原子分布式事务:一篇RESTful设计论文看起来也很有趣,尽管我还没有读过。

或者,我知道REST可能不适合这个用例。处理这种情况的正确方法可能是完全放弃REST并使用不同的通信协议,如消息队列系统?或者我应该在我的应用程序代码中强制一致性(例如,通过有一个后台作业来检测不一致并修复它们,或者通过在我的用户模型上有一个“state”属性与“creating”,“created”值等)?


所有分布式系统都存在事务一致性问题。最好的方法是像你说的那样,进行两阶段提交。在挂起状态下创建钱包和用户。创建该用户后,进行单独调用以激活该用户。

最后一个调用应该是安全可重复的(以防连接断开)。

这将需要最后一次调用知道两个表(这样就可以在单个JDBC事务中完成)。

或者,您可能想要考虑一下为什么您如此担心没有钱包的用户。你认为这会引起问题吗?如果是这样,也许将它们作为单独的rest调用是一个坏主意。如果用户没有钱包就不应该存在,那么您可能应该将钱包添加到用户中(在最初的POST调用中创建用户)。


如果我的钱包只是用户在同一个sql数据库中的另一组记录,那么我可能会将用户和钱包的创建代码放在同一个服务中,并使用正常的数据库事务设施进行处理。

在我看来,你是在问,当钱包创建代码要求你接触另一个或多个系统时会发生什么?我认为这完全取决于创建过程的复杂性和风险。

如果只是涉及到另一个可靠的数据存储(比如一个不能参与sql事务的数据存储),那么根据整个系统参数,我可能愿意冒第二次写入不会发生的小概率风险。我可能什么也不做,只是引发一个异常,并通过补偿事务或甚至一些特别方法处理不一致的数据。就像我总是告诉开发者的那样:“如果这类事情发生在应用中,它不会被忽视。”

随着钱包创建的复杂性和风险的增加,您必须采取措施来改善所涉及的风险。假设有些步骤需要调用多个合作伙伴api。

此时,您可能会引入消息队列以及部分构造的用户和/或钱包的概念。

确保实体最终被正确构造的一个简单而有效的策略是让作业重试,直到它们成功为止,但这在很大程度上取决于应用程序的用例。

我也会仔细思考为什么我的配置过程中有一个容易失败的步骤。


恕我直言,微服务架构的一个关键方面是事务被限制在单个微服务中(单一责任原则)。

在当前示例中,User创建将是一个自己的事务。用户创建将把USER_CREATED事件推入事件队列。钱包服务将订阅USER_CREATED事件并创建钱包。


有什么解决方案可以防止这种数据不一致的发生?

Traditionally, distributed transaction managers are used. A few years ago in the Java EE world you might have created these services as EJBs which were deployed to different nodes and your API gateway would have made remote calls to those EJBs. The application server (if configured correctly) automatically ensures, using two phase commit, that the transaction is either committed or rolled back on each node, so that consistency is guaranteed. But that requires that all the services be deployed on the same type of application server (so that they are compatible) and in reality only ever worked with services deployed by a single company.

是否存在允许事务跨越多个REST请求的模式?

对于SOAP(好吧,不是REST),有WS-AT规范,但我曾经集成过的任何服务都不支持该规范。对于REST, JBoss已经准备好了一些东西。否则,“模式”是要么找到一个可以插入到您的体系结构中的产品,要么构建您自己的解决方案(不推荐)。

我已经为Java EE发布了这样一个产品:https://github.com/maxant/genericconnector

根据您引用的论文,还有来自Atomikos的Try-Cancel/Confirm模式和相关Product。

BPEL引擎使用补偿处理远程部署服务之间的一致性。

或者,我知道REST可能不适合这个用例。处理这种情况的正确方法可能是完全放弃REST并使用不同的通信协议,如消息队列系统?

将非事务资源“绑定”到事务中有很多方法:

As you suggest, you could use a transactional message queue, but it will be asynchronous, so if you depend on the response it becomes messy. You could write the fact that you need to call the back end services into your database, and then call the back end services using a batch. Again, async, so can get messy. You could use a business process engine as your API gateway to orchestrate the back end microservices. You could use remote EJB, as mentioned at the start, since that supports distributed transactions out of the box.

或者我应该在我的应用程序代码中强制一致性(例如,通过有一个后台作业来检测不一致并修复它们,或者通过在我的用户模型上有一个“state”属性与“creating”,“created”值等)?

玩魔鬼的拥护者:为什么要建立这样的东西,当有产品为你做这件事(见上文),可能比你做得更好,因为他们是经过试验和测试的?


不合理的事情:

使用REST服务的分布式事务。REST服务根据定义是无状态的,因此它们不应该是跨多个服务的事务边界的参与者。您的用户注册用例场景是有意义的,但是使用REST微服务创建用户和钱包数据的设计并不好。

什么会让你头疼:

EJBs with distributed transactions. It's one of those things that work in theory but not in practice. Right now I'm trying to make a distributed transaction work for remote EJBs across JBoss EAP 6.3 instances. We've been talking to RedHat support for weeks, and it didn't work yet. Two-phase commit solutions in general. I think the 2PC protocol is a great algorithm (many years ago I implemented it in C with RPC). It requires comprehensive fail recovery mechanisms, with retries, state repository, etc. All the complexity is hidden within the transaction framework (ex.: JBoss Arjuna). However, 2PC is not fail proof. There are situations the transaction simply can't complete. Then you need to identify and fix database inconsistencies manually. It may happen once in a million transactions if you're lucky, but it may happen once in every 100 transactions depending on your platform and scenario. Sagas (Compensating transactions). There's the implementation overhead of creating the compensating operations, and the coordination mechanism to activate compensation at the end. But compensation is not fail proof either. You may still end up with inconsistencies (= some headache).

最好的选择是什么:

Eventual consistency. Neither ACID-like distributed transactions nor compensating transactions are fail proof, and both may lead to inconsistencies. Eventual consistency is often better than "occasional inconsistency". There are different design solutions, such as: You may create a more robust solution using asynchronous communication. In your scenario, when Bob registers, the API gateway could send a message to a NewUser queue, and right-away reply to the user saying "You'll receive an email to confirm the account creation." A queue consumer service could process the message, perform the database changes in a single transaction, and send the email to Bob to notify the account creation. The User microservice creates the user record and a wallet record in the same database. In this case, the wallet store in the User microservice is a replica of the master wallet store only visible to the Wallet microservice. There's a data synchronization mechanism that is trigger-based or kicks in periodically to send data changes (e.g., new wallets) from the replica to the master, and vice-versa.

但是如果需要同步响应呢?

重构微服务。如果使用队列的解决方案不能工作,因为服务使用者需要立即得到响应,那么我宁愿重新构造User和Wallet功能,将其配置在同一个服务中(或者至少在同一个VM中,以避免分布式事务)。是的,它离微服务更近了一步,也更接近了一个庞然大物,但它会让你免去一些头疼的事情。


为什么不使用支持脚本/编程的API管理(APIM)平台?因此,您将能够在APIM中构建复合服务,而不会干扰微服务。为此,我使用APIGEE进行设计。


就我个人而言,我喜欢微服务的想法,用例定义的模块,但正如你的问题所提到的,它们对银行、保险、电信等经典业务有适应问题……

分布式事务,正如许多人提到的,不是一个好的选择,人们现在更多的是最终一致的系统,但我不确定这是否适用于银行,保险等....

我写了一篇博客关于我提出的解决方案,也许这可以帮助你....

https://mehmetsalgar.wordpress.com/2016/11/05/micro-services-fan-out-transaction-problems-and-solutions-with-spring-bootjboss-and-netflix-eureka/


This is a classic question I was asked during an interview recently How to call multiple web services and still preserve some kind of error handling in the middle of the task. Today, in high performance computing, we avoid two phase commits. I read a paper many years ago about what was called the "Starbuck model" for transactions: Think about the process of ordering, paying, preparing and receiving the coffee you order at Starbuck... I oversimplify things but a two phase commit model would suggest that the whole process would be a single wrapping transaction for all the steps involved until you receive your coffee. However, with this model, all employees would wait and stop working until you get your coffee. You see the picture ?

相反,“星巴克模式”通过遵循“尽最大努力”模式并补偿过程中的错误,更有成效。首先,他们会确保你付钱!然后,将您的订单附加到杯子上的消息队列。如果在这个过程中出现了问题,比如你没有拿到你的咖啡,这不是你点的东西,等等,我们会进入赔偿过程,我们会确保你得到你想要的东西或退款给你,这是提高生产力的最有效的模式。

有时,星巴克浪费了一杯咖啡,但整个过程是高效的。在构建web服务时,还有其他一些技巧需要考虑,比如将它们设计成可以被任意次调用并且仍然提供相同的最终结果。所以我的建议是:

Don't be too fine when defining your web services (I am not convinced about the micro-service hype happening these days: too many risks of going too far); Async increases performance so prefer being async, send notifications by email whenever possible. Build more intelligent services to make them "recallable" any number of times, processing with an uid or taskid that will follow the order bottom-top until the end, validating business rules in each step; Use message queues (JMS or others) and divert to error handling processors that will apply operations to "rollback" by applying opposite operations, by the way, working with async order will require some sort of queue to validate the current state of the process, so consider that; In last resort, (since it may not happen often), put it in a queue for manual processing of errors.

让我们回到最初的问题。创建一个账户,创建一个钱包,确保一切都完成了。

假设调用一个web服务来编排整个操作。

web服务的伪代码如下所示:

Call Account creation microservice, pass it some information and a some unique task id 1.1 Account creation microservice will first check if that account was already created. A task id is associated with the account's record. The microservice detects that the account does not exist so it creates it and stores the task id. NOTE: this service can be called 2000 times, it will always perform the same result. The service answers with a "receipt that contains minimal information to perform an undo operation if required". Call Wallet creation, giving it the account ID and task id. Let's say a condition is not valid and the wallet creation cannot be performed. The call returns with an error but nothing was created. The orchestrator is informed of the error. It knows it needs to abort the Account creation but it will not do it itself. It will ask the wallet service to do it by passing its "minimal undo receipt" received at the end of step 1. The Account service reads the undo receipt and knows how to undo the operation; the undo receipt may even include information about another microservice it could have called itself to do part of the job. In this situation, the undo receipt could contain the Account ID and possibly some extra information required to perform the opposite operation. In our case, to simplify things, let's say is simply delete the account using its account id. Now, let's say the web service never received the success or failure (in this case) that the Account creation's undo was performed. It will simply call the Account's undo service again. And this service should normaly never fail because its goal is for the account to no longer exist. So it checks if it exists and sees nothing can be done to undo it. So it returns that the operation is a success. The web service returns to the user that the account could not be created.

This is a synchronous example. We could have managed it in a different way and put the case into a message queue targeted to the help desk if we don't want the system to completly recover the error". I've seen this being performed in a company where not enough hooks could be provided to the back end system to correct situations. The help desk received messages containing what was performed successfully and had enough information to fix things just like our undo receipt could be used for in a fully automated way.

我已经进行了搜索,微软网站对这种方法有一个模式描述。它被称为补偿事务模式:

补偿事务模式


一个简单的解决方案是使用用户服务创建用户,并使用消息总线,其中用户服务发出其事件,钱包服务在消息总线上注册,监听用户创建的事件并为用户创建钱包。同时,如果用户进入钱包UI查看他的钱包,检查用户是否刚刚创建,并显示您的钱包正在创建中,请过一段时间再检查


最终的一致性是这里的关键。

选择其中一个服务作为事件的主要处理程序。 该服务将处理单个提交的原始事件。 主处理程序将负责将次要效果异步地传递给其他服务。 主处理程序将对其他服务调用进行编排。

指挥官负责分布式事务并进行控制。它知道要执行的指令,并协调执行它们。在大多数情况下,只有两条指令,但它可以处理多条指令。

指挥官负责保证所有指令的执行,这意味着退休。 当指挥官试图执行远程更新而没有得到响应时,它没有重试。 通过这种方式,系统可以配置为更不容易出现故障,并自我修复。

当我们进行重试时,我们有幂等性。 幂等性是这样一种性质,即可以做某事两次,最终结果与只做一次一样。 我们需要远程服务或数据源上的幂等性,以便在它多次接收指令的情况下,它只处理一次指令。

最终一致性 这解决了大多数分布式事务的挑战,但是我们需要考虑以下几点。 每一个失败的事务都将被重试,尝试重试的数量取决于上下文。

一致性是最终的,即在重试期间系统处于不一致的状态,例如,如果客户订购了一本书,并支付了费用,然后更新了库存数量。如果库存更新操作失败,并且假设这是最后一个可用的库存,那么在库存更新的重试操作成功之前,这本书仍然可用。重试成功后,您的系统将保持一致。


在微服务世界中,服务之间的通信应该通过rest客户端或消息队列。有两种方法可以跨服务处理事务,具体取决于服务之间的通信方式。我个人更喜欢消息驱动的体系结构,这样长时间的事务对用户来说应该是一个无阻塞的操作。 让我们举个例子来解释一下:

使用事件Create user创建用户BOB,并将消息推送到消息总线。 订阅此事件的钱包服务可以创建用户对应的钱包。

The one thing which you have to take care is to select a robust reliable message backbone which can persists the state in case of failure. You can use kafka or rabbitmq for messaging backbone. There will be a delay in execution because of eventual consistency but that can be easily updated through socket notification. A notifications service/task manager framework can be a service which update the state of the transactions through asynchronous mechanism like sockets and can help UI to update show the proper progress.