截至2019年年中，第一种方法的解决方案现在有了Apollo人创造的名称“Schema Federation”(以前通常称为GraphQL拼接)。 他们还为此提出了模块@apollo/federation和@apollo/gateway。

ADD:请注意，使用模式联合，您不能在网关级别修改模式。因此，对于模式中需要的每一个比特，都需要有一个单独的服务。

对于问题1,Intuit在几年前宣布迁移到One Intuit API生态系统(https://www.slideshare.net/IntuitDeveloper/building-the-next-generation-of-quickbooks-app-integrations-quickbooks-connect-2017)时承认了GraphQL的强大功能。Intuit选择了方法1。您提到的缺点实际上阻止了开发人员引入可能破坏客户端应用程序的破坏性模式更改。

GraphQL在许多方面帮助提高了开发人员的工作效率。

When designing a new microservice for a domain, engineers (backend/ frontend/ stakeholders) agree on a schema for the domain entities. Once the schema is approved, it is merged with the master domain schema (universal) and deployed on the Gateway.Front end engineers can start coding client applications with this schema while backend engineers implement the functionality. Having one universal schema means that there are no two microservices with redundant functionality. GraphQL has helped client applications become simpler and faster. Want to retrieve data from /update data to multiple microservices? All client applications have to do is fire ONE GraphQL request and the API Gateway abstraction layer will take care to fetch and collate data from multiple sources (microservices). Open source frameworks like Apollo (https://www.apollographql.com/) have accelerated the pace of GraphQL adoption. With mobile being the first choice for modern applications, it is important to design for lower data bandwidth requirements from ground zero. GraphQL helps by allowing client apps to request for specific fields only.

问题2:我们在API Gateway上构建了一个自定义抽象层，它知道模式的哪一部分属于哪个服务(提供者)。当查询请求到达时，抽象层将请求转发给适当的服务。一旦基础服务返回响应，抽象层就负责返回所请求的字段。

然而，现在有几个平台(Apollo服务器、GraphQL -yoga等)允许人们在短时间内构建一个GraphQL抽象层。

截至2019年年中，第一种方法的解决方案现在有了Apollo人创造的名称“Schema Federation”(以前通常称为GraphQL拼接)。 他们还为此提出了模块@apollo/federation和@apollo/gateway。

ADD:请注意，使用模式联合，您不能在网关级别修改模式。因此，对于模式中需要的每一个比特，都需要有一个单独的服务。

本文推荐方法#1。下面这张图片也摘自上述文章:

将所有内容都放在单个端点后的主要好处之一是，可以比每个请求都有自己的服务更有效地路由数据。虽然这是GraphQL经常被吹捧的价值，减少了复杂性和服务蔓延，但由此产生的数据结构还允许对数据所有权进行非常好的定义和清晰的描述。

采用GraphQL的另一个好处是，您可以从根本上对数据加载过程进行更大的控制。因为数据加载器的过程进入自己的端点，所以您可以部分地、完全地或带有警告地满足请求，从而以极其细粒度的方式控制数据传输方式。

下面的文章很好地解释了这两个好处:https://nordicapis.com/7-unique-benefits-of-using-graphql-in-microservices/

我一直在使用GraphQL和微服务

根据我的经验，对我来说有效的是根据功能/用法将两种方法结合起来，我永远不会像方法1那样只有一个网关，但也不会像方法2那样为每个微服务使用GraphQL。

例如，根据Enayat的答案图像，在这种情况下，我要做的是有3个图网关(而不是图像中的5个)

App(产品，篮子，航运，库存，需要/链接到其他服务) 付款 用户

通过这种方式，您需要特别注意从依赖的服务中公开的所需/链接的最小数据的设计，如认证令牌、用户id、paymentid、支付状态

以我的经验为例，我有“用户”网关，在GraphQL中，我有用户查询/突变，登录，登录，退出，更改密码，恢复电子邮件，确认电子邮件，删除帐户，编辑个人资料，上传图片等…这张图本身就相当大!，它是分开的，因为在最后，其他服务/网关只关心结果信息，如用户id，名称或令牌。

这样更容易……

Scale/shutdown the different gateways nodes depending on they usage. (for example people might not always be editing their profile or paying... but searching products might be used more frequently). Once a gateways matures, grows, usage is known or you have more expertise on the domain you can identify which are the part of the schema that could have they own gateway (... happened to me with a huge schema that interacts with git repositories, I separated the gateway that interact with a repository and I saw that the only input needed/linked info was... the folder path and expected branch) The history of you repositories is more clear and you can have a repository/developer/team dedicated to a gateway and its involved microservices.

更新:

我有一个在线的kubernetes集群，它使用与我在这里描述的相同的方法，所有后端都使用GraphQL，都是开源的，这里是主存储库: https://github.com/vicjicaman/microservice-realm

这是对我的答案的更新，因为我认为如果答案/方法是正在运行的备份代码，并且可以参考/审查，那就更好了，我希望这能有所帮助。

更新2:

只是添加了另一个功能分离的例子，我将与管理文件、上传、创建、删除、将文件分组到桶、批量删除、私有文件、cors配置等相关的功能分离到一个图表中……而其他图形/服务只需要一些细节，如预先签名的帖子或结果url/fileid

The way it is being described in this question, I believe that using a custom API gateway as an orchestration service can make a lot of sense for complex enterprise focused applications. GraphQL can be a good technology choice for that orchestration service, at least as far as querying goes. The advantage to your first approach (one schema for all microservices) is the ability to stitch together the data from multiple microservices in a single request. That may, or may not, be very important depending on your situation. If the GUI calls for rendering data from multiple microservices all at once, then this approach can simplify the client code such that a single call can return data that is suitable for data binding with the GUI elements of such frameworks as Angular or React. This advantage doesn't apply for mutations.

The disadvantage is tight coupling between the data APIs and the orchestration service. Releases can no longer be atomic. If you refrain from introducing backwards breaking changes in your data APIs, then this can introduce complexity only when rolling back a release. For example, if you are about to release new versions of two data APIs with the corresponding changes in the orchestration service and you need to roll one of those releases back but not the other, then you will be forced to roll back all three anyway.

在GraphQL和REST的比较中，你会发现GraphQL没有RESTful api那么高效，所以我不建议在数据api上用GraphQL代替REST。