Indeed we know of a team in Australia who drive the build of new services with consumer driven contracts. They use simple tools that allow them to define the contract for a service. This becomes part of the automated build before code for the new service is even written. The service is then built out only to the point where it satisfies the contract - an elegant approach to avoid the 'YAGNI' [9] dilemma when building new software.
These techniques and the tooling growing up around them, limit the need for central contract management by decreasing the temporal coupling between services. Devolution of this level of responsibility is definitely not the norm but we do see more and more companies pushing responsibility to the development teams. Netflix is another organisation that has adopted this ethos [11].
Being woken up at 3am every night by your pager is certainly a powerful incentive to focus on quality when writing your code. These ideas are about as far away from the traditional centralized governance model as it is possible to be. Decentralization of data management presents in a number of different ways. At the most abstract level, it means that the conceptual model of the world will differ between systems.
This is a common issue when integrating across a large enterprise, the sales view of a customer will differ from the support view. Some things that are called customers in the sales view may not appear at all in the support view. Those that do may have different attributes and worse common attributes with subtly different semantics. This issue is common between applications, but can also occur within applications, particular when that application is divided into separate components.
DDD divides a complex domain up into multiple bounded contexts and maps out the relationships between them. This process is useful for both monolithic and microservice architectures, but there is a natural correlation between service and context boundaries that helps clarify, and as we describe in the section on business capabilities, reinforce the separations. As well as decentralizing decisions about conceptual models, microservices also decentralize data storage decisions.
While monolithic applications prefer a single logical database for persistant data, enterprises often prefer a single database across a range of applications - many of these decisions driven through vendor's commercial models around licensing. Microservices prefer letting each service manage its own database, either different instances of the same database technology, or entirely different database systems - an approach called Polyglot Persistence.
You can use polyglot persistence in a monolith, but it appears more frequently with microservices. Decentralizing responsibility for data across microservices has implications for managing updates. The common approach to dealing with updates has been to use transactions to guarantee consistency when updating multiple resources.
This approach is often used within monoliths. Using transactions like this helps with consistency, but imposes significant temporal coupling, which is problematic across multiple services.
Distributed transactions are notoriously difficult to implement and as a consequence microservice architectures emphasize transactionless coordination between services , with explicit recognition that consistency may only be eventual consistency and problems are dealt with by compensating operations.
Choosing to manage inconsistencies in this way is a new challenge for many development teams, but it is one that often matches business practice. Often businesses handle a degree of inconsistency in order to respond quickly to demand, while having some kind of reversal process to deal with mistakes.
The trade-off is worth it as long as the cost of fixing mistakes is less than the cost of lost business under greater consistency. Infrastructure automation techniques have evolved enormously over the last few years - the evolution of the cloud and AWS in particular has reduced the operational complexity of building, deploying and operating microservices.
Many of the products or systems being build with microservices are being built by teams with extensive experience of Continuous Delivery and it's precursor, Continuous Integration.
Teams building software this way make extensive use of infrastructure automation techniques. This is illustrated in the build pipeline shown below. Since this isn't an article on Continuous Delivery we will call attention to just a couple of key features here. We want as much confidence as possible that our software is working, so we run lots of automated tests. Promotion of working software 'up' the pipeline means we automate deployment to each new environment.
A monolithic application will be built, tested and pushed through these environments quite happlily. It turns out that once you have invested in automating the path to production for a monolith, then deploying more applications doesn't seem so scary any more.
Remember, one of the aims of CD is to make deployment boring, so whether its one or three applications, as long as its still boring it doesn't matter [12]. Another area where we see teams using extensive infrastructure automation is when managing microservices in production. In contrast to our assertion above that as long as deployment is boring there isn't that much difference between monoliths and microservices, the operational landscape for each can be strikingly different.
A consequence of using services as components, is that applications need to be designed so that they can tolerate the failure of services. Any service call could fail due to unavailability of the supplier, the client has to respond to this as gracefully as possible.
This is a disadvantage compared to a monolithic design as it introduces additional complexity to handle it.
The consequence is that microservice teams constantly reflect on how service failures affect the user experience. Netflix's Simian Army induces failures of services and even datacenters during the working day to test both the application's resilience and monitoring.
This kind of automated testing in production would be enough to give most operation groups the kind of shivers usually preceding a week off work. This isn't to say that monolithic architectural styles aren't capable of sophisticated monitoring setups - it's just less common in our experience.
Since services can fail at any time, it's important to be able to detect the failures quickly and, if possible, automatically restore service. Microservice applications put a lot of emphasis on real-time monitoring of the application, checking both architectural elements how many requests per second is the database getting and business relevant metrics such as how many orders per minute are received.
Semantic monitoring can provide an early warning system of something going wrong that triggers development teams to follow up and investigate. This is particularly important to a microservices architecture because the microservice preference towards choreography and event collaboration leads to emergent behavior. While many pundits praise the value of serendipitous emergence, the truth is that emergent behavior can sometimes be a bad thing. Monitoring is vital to spot bad emergent behavior quickly so it can be fixed.
Monoliths can be built to be as transparent as a microservice - in fact, they should be. The difference is that you absolutely need to know when services running in different processes are disconnected. With libraries within the same process this kind of transparency is less likely to be useful. Details on circuit breaker status, current throughput and latency are other examples we often encounter in the wild. Microservice practitioners, usually have come from an evolutionary design background and see service decomposition as a further tool to enable application developers to control changes in their application without slowing down change.
Change control doesn't necessarily mean change reduction - with the right attitudes and tools you can make frequent, fast, and well-controlled changes to software. Whenever you try to break a software system into components, you're faced with the decision of how to divide up the pieces - what are the principles on which we decide to slice up our application? The key property of a component is the notion of independent replacement and upgradeability [13] - which implies we look for points where we can imagine rewriting a component without affecting its collaborators.
Indeed many microservice groups take this further by explicitly expecting many services to be scrapped rather than evolved in the longer term. The Guardian website is a good example of an application that was designed and built as a monolith, but has been evolving in a microservice direction. The monolith still is the core of the website, but they prefer to add new features by building microservices that use the monolith's API.
This approach is particularly handy for features that are inherently temporary, such as specialized pages to handle a sporting event. Such a part of the website can quickly be put together using rapid development languages, and removed once the event is over. We've seen similar approaches at a financial institution where new services are added for a market opportunity and discarded after a few months or even weeks.
This emphasis on replaceability is a special case of a more general principle of modular design, which is to drive modularity through the pattern of change [14]. You want to keep things that change at the same time in the same module. Parts of a system that change rarely should be in different services to those that are currently undergoing lots of churn.
If you find yourself repeatedly changing two services together, that's a sign that they should be merged. Putting components into services adds an opportunity for more granular release planning. With a monolith any changes require a full build and deployment of the entire application. With microservices, however, you only need to redeploy the service s you modified.
This can simplify and speed up the release process. The downside is that you have to worry about changes to one service breaking its consumers.
The traditional integration approach is to try to deal with this problem using versioning, but the preference in the microservice world is to only use versioning as a last resort. We can avoid a lot of versioning by designing services to be as tolerant as possible to changes in their suppliers.
Our main aim in writing this article is to explain the major ideas and principles of microservices. By taking the time to do this we clearly think that the microservices architectural style is an important idea - one worth serious consideration for enterprise applications. We have recently built several systems using the style and know of others who have used and favor this approach.
Many development teams have found the microservices architectural style to be a superior approach to a monolithic architecture. But other teams have found them to be a productivity-sapping burden. Like any architectural style, microservices bring costs and benefits. To make a sensible choice you have to understand these and apply them to your specific context.
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