It’s at this point that people start to look for other solutions to give them some ability to reason about changes being made to multiple services at once.
The other problem is, what do we do about the fact that data inconsistency could now exist in the new world?In our new microservice-based world, our new Finance service has the responsibility of generating the best-sellers report, but doesn’t have the album data locally. After the release, our new microservice is the source of truth.
The other potential challenge here is that you may be delaying finding out nasty surprises caused by pushing join operations up into the application tier.If this is the direction you take, be honest with yourself: are you confident that you will be able to make sure that any data owned by the microservice gets split out as part of the next step?Rather than accessing the data from the monolith directly, we can just move to a model in which we create an API in the monolith itself.Part of the reason this isn’t used more widely is likely because people sort of have in their minds the idea that the monolith is dead, and of no use. A pattern like the tracer write allows for a phased switchover, reducing the impact of each release, in exchange for being more tolerant of having more than one source of truth.The reason this pattern is called a tracer write is that you can start with a small set of data being synchronized and increase this over time, while also increasing the number of consumers of the new source of data. The bigger worry is, what Whether or not inconsistency is an issue comes down to how the data is used. They’re on by default for everybody else.
Nowadays, though, I’d probably utilizeThis pattern is more sophisticated than a simple database view.This pattern gives more flexibility than database views, but at an added cost. If either set of code needs to always see a totally consistent view of invoice data, one of the most straightforward approaches would be to ensure the data is kept in one place. Derek said that getting to the point where all consumers had switched over ended up being pretty much a non-event. This would lead us toward probably having our new Invoice service read its data directly from the monolith for a short space of time, perhaps making use of a view, as we explored in the section If we were doing a big-bang switchover (something I’d try to avoid), migrating both the application code and the data at the same time, we could use a batch process to copy the data over in advance of switching to the new microservice. In our example, consider that the Warehouse uses this country code data to record where our CDs are manufactured. It’s clearly not data the monolith needs (the functionality isn’t there), so keep it separate from the beginning. Something like postal code files in the UK might be a good fit, where you periodically get updates of the mapping from postal codes to addresses. They ran this system in production for sufficient time to be confident that the synchronization was working correctly. This can be mitigated through the use of views, which we’ll discuss shortly, but it’s not a total solution.Another issue is that it becomes unclear as to who “controls” the data. For this to happen, though, you’d likely need to ensure that the schemas are located on the same underlying database engine. Eventually, someone had the idea of disabling the shared account they were using, and waiting for people to contact us to complain. The concerns I’ve always had with this approach is that teams may get this far and then stop, leaving a shared database in play on an ongoing basis.
In cases like this, we can consider an alternative approach: sagas.The core idea, first outlined by Hector Garcia-Molina and Kenneth Salem,If you directly mapped an LLT to a normal database transaction, a single database transaction would span the entire life cycle of the LLT. It allows us to ensure consistency of data, to control access to that data, and can reduce maintenance costs. But with this two-phase commit, we’ve lost that.When two-phase commits work, at their heart they are very often just coordinating distributed locks. For this reason, I’d likely go this route only if I’m especially concerned about the potential performance or data consistency issues.
You’d have to look at the behavior of each service in isolation and reconstitute this picture in your own head—far from a straightforward process even with a simple business process like this one.The lack of an explicit representation of our business process is bad enough, but we also lack a way of knowing what state a saga is in, which can also deny us the chance to attach compensating actions when required. ).The flip side of this is that it can now be harder to work out what is going on. But don’t worry, I’ll also give you a host of ideas to help you deal with these problems as they arise.O’Reilly members experience live online training, plus books, videos, and digital content from Take O’Reilly online learning with you and learn anywhere, anytime on your phone © 2020, O’Reilly Media, Inc. All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. It’s important you still consider the services that make up these orchestrated flows as entities that have their own local state and behavior.
This is not a perfect solution, however-there are restrictions with this approach.Depending on the nature of the database, you may have the option to create a materialized view.How views are implemented can vary, but typically they are the result of a query.It’s worth noting that changes to the underlying source schema may require the view to be updated, and so careful consideration should be given to who “owns” the view.I typically make use of a database view in situations where I think it is impractical to decompose the existing monolithic schema.Sometimes, when something is too hard to deal with, hiding the mess can make sense.
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