Things to do with a torn ACL.
Things to do with a torn ACL.
Designing APIs and clients for forward compatibility is the best way to achieve evolvability and maintainability in a microservice architecture. Wikipedia defines forward compatibility as “a design characteristic that allows a system to accept input intended for a later version of itself.” Designing APIs and clients to be forward compatible allows each side of that relationship to change independently of the other. Forward compatibility is an exercise in reducing connascence. We want a system in which we can modify any component (service) without breaking the current behavior of the system. Without such independence any microservice architecture is doomed to collapse under its own maintenance cost.
Change is inevitable. Any successful architecture must accept, even welcome, change. Building avenues for adaptation into our designs allows our systems to survive and thrive in the face of change. This set of best practices has proven to provide superior forward and backward compatibility. As described here these practices are specific to HTTP and web APIs but the general ideas apply to any application protocol.
Forward compatibility is largely the responsibility of clients or document consumers. However, each forward compatible pattern is supported by backward compatibility in the servers or document producers. The following list of patterns describes the responsibilities of both parties. Compatibility will suffer if either side fails to uphold their end of the contract.
Clients must follow HTTP redirects.
URLs change over time. Companies rebrand their products, get acquired, change their name, etc. Developers relocate services to more optimal components. New features sometimes require changes to URLs. Redirection allows servers to change URLs without breaking existing clients by providing runtime discovery of the new URLs.
A URL, once given to a client, represents a contract. To honor these contract servers must provide redirection of existing URLs when they change. Even in hypermedia APIs clients will bookmark URLs so Hypermedia is no excuse for breaking existing URLs.
Document consumers must ignore any features of documents they don’t understand.
Document formats change over time. New features require new properties and links. New information illuminates our understanding of cardinalities and datatypes. Ignoring unrecognized document features allow producers to add new features without fear of breaking clients.
Document producers must not remove, change the syntax or change the semantics of existing document features. Once a document feature has been released it is sacrosanct. Idiosyncrasy is better than carnage.
Available behavior changes over time. We discover new business rules. We realize that existing features are ill-conceived. Hypermedia APIs provide a mechanism for clients to discover supported behavior by embedding links to behavior in API responses.
The API must be defined using a hypermedia format. The exact format does’t matter. (HAL, JSON API, Hydra, HTML, etc are all good.) What does matter is that all behavior of the API must be discoverable at runtime.
Clients must inform the server of what document formats they can process via the
Document formats have a half life. Sometimes the idiosyncrasies build to the point that a new document format is worth the effort. Sometimes you realize that a particular representation is too bulky or too terse. Server driven content negotiation allows servers to introduce new, improved representations of existing resources while continuing to support existing clients.
Servers must continue honoring requests for existing document formats. Document formats may be phased out once they are no longer requested by any clients.
This set of best practices and patterns allows the construction of sophisticated distributed systems that can evolve and be maintained with reasonable effort. Without these patterns it is almost inevitable that a microservice architecture will end up in dependency hell.
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Micro-service architectures are all the rage these days. Let’s say, totally hypothetically, that you already have a large code base that has all the pathologies we have come to expect from monoliths. You may think something like “we should break this behemoth up into a collection of components, each of which is comprehensible to mere humans.” That is a great goal, but how do you get from a huge, highly coupled, monolith to a constellation of cooperating components? Most “efforts” to modularize monolithic software are abandoned at about the time someone serious considers that question. That is, before the effort even starts. It is easy to become overwhelmed by the immensity of the task and absence of obvious starting points and to just give up without a fight.
I have lead several successful decomposition efforts over my career. Each of these efforts shared some common patterns which, i believe, are the keys to success. These patterns are:
Decomposing a significant monolith will take time. It took tens or hundreds of man years to build the monolith. Breaking it up is going to take a while. Accept it. Plan for it. Remind yourself, and others, of this fact regularly. Patience is the only way to succeed. Some days, or weeks, or months, it will seem like you are never going reach your goal. In the moment, it will look like you are barely making dent, but if you have patience and persistence you will, over time, extract significant functionality into easier to maintain components.
Most people overestimate what they can do in one year and underestimate what they can do in ten years. – Bill Gates
You will never get done, though. Like any interesting project there is always more to do. Strive for progress, not perfection. The goal is not completion. Rather it is winning this, and the next, round of the repeating game. Winning this round implies shipping new features. Winning the next round implies leaving the architecture better than you found it.
All of the above applies to the entire development team, including management, not just the individual who initiates the decomposition. If management isn’t on board it will be difficult, or impossible, to sustain the effort need to make real progress. If the engineering team as a whole is not committed, progress will stop with the loss of a key team member. The goal is to build a organization that will continuously chip away at the monolith.
Perhaps the most ubiquitous concern for any service architecture is that of performance. Poor performance results in higher compute costs, reduced through put, and increased latencies. Performance is always an issue with services because the architectural style is based on inter-process communication (IPC). IPC requires IO and IO is way slower than memory access. The harsh reality is that it is harder to keep a service architecture performant.
One approach that can mitigate performance concerns is to co-locate the data and code involved in important operations in the same component. In practice, this means designing the component boundaries such that the component that implements a particularly important service is also the system of record for the data used by the service. Obviously, data locality must be balanced with componentization if we are to break up a monolith. It is important to consider the importance of any particular operation. Services that are depended upon by many other services, or that are used in time critical parts of the application should be co-located with the data they use. Services that are used less frequently, or in less critical places can use data from other services.
This is just a rule of thumb. Any real system will have situations that simply don’t allow for the preferred data locality for all operations. Engineering is all about trade offs. In these situations caching becomes critical. HTTP, my preferred service protocol, provides sophisticated support for caching. Send time thinking about cache lifespans and invalidation when designing the resources of an API. Doing so will allow clients to more effectively and safely cache the data exposed. That will, in turn, improve the perceived performance and reduce the compute needs of the system. No operation is faster that than the one you avoid altogether. A well designed caching strategy allows avoiding many operations.
The very first thing to extract into a separate component, is authorization and authentication. Authorization is needed by basically everything (though it doesn’t always require a separated component1), including all the services that will be extracted from the monolith. Authorization and authentication have good, widely supported standards. Such standards and tools improve the chances of success in this first foray and, as we all know, early successes breed confidence and increase commitment.
Service architectures should use OAuth2. It is secure, widely implemented, and well supported in most technology stacks. The auth component will perform the authorization server role. All other components will be resource servers and/or clients.
The authorization request & grant portion of the OAuth2 flow should be short circuited for internal components. There is no need to ask the user to explicitly grant authorization to a mere implementation detail of the overall system. Such a short circuiting is usually accomplished by keeping track of which clients are internal components. When an internal component requests authorization the authorization server simply grants it immediately (after authenticating the user, of course).
The combination of simplicity and existing standards make auth a great first service to extract.
Once you have extracted authorization and authentication it is time to take this show on the road. My preferred second target is a totally new feature. Building a new feature as a service has several advantages. First, new features are inherently less coupled to the existing code. This makes them easier to implement in a separate component. Second, new features are usually lower risk politically. Changing the way an existing feature works will usually raise some concerns, but fewer people have a vested interest in hypothetical features. Third, it sets a precedent to build on later. If new features can be implemented outside the monolith then it can become policy that new feature are always implemented outside the monolith. In this way we can effectively slow the monolith’s growth.
Implementing a new feature outside the monolith may require exposing existing capabilities of the monolith as a service. This is okay. As with most refactoring, we will sometimes have to make things worse before we can make them better. In this situation, implement a service in the monolith to expose the necessary functionality and use it from the new component.
Do not under any circumstances let the new component use the same database tables2 as the monolith. Doing so will only couple the two codebases in a that will both harder to maintain.
Once auth is a separate component and at lease one new feature has been implemented outside the monolith we can extract some functionality. The key is to understand the existing code base enough to see seams in it that could be used to cleave off a bit of functionality. This functionality will likely the best factored and designed part of the monolith. It is ironic that the easiest part to remove is the best part, but c’est la vie.
In most “mature programming environments” there will be multiple plausible candidates for extraction. It is important to have a list of candidates ready. Begin by taking every opportunity to tighten the encapsulation and reduce the coupling of extraction candidates. Every bit of this sort of refactoring done ahead of time improves the chances of successful extraction. Even if the section of code is never extracted, this work improves the maintainability of the code, so it is useful regardless.
Chance favors the prepared, as they say. Once you have the hit list, every feature request should be viewed as potential extraction opportunity. When possible, expand the scope of feature requests to include component extract before implementation of new functionality. Feature requests that allow for such expansion are rare so don’t become discouraged if you don’t run across one right away. It will happen, but only if you are constantly vigilant.
Once you have built a consensus around extraction as part of a larger feature, begin by further tightening the encapsulation of the section to be extracted. Once the interface is solid, re-implement the feature in a new component. Once that component is functional, replace the implementation in the monolith with a client to the new component that provides the same interface as the original implementation.
Basically, component extraction is just normal, every day incremental refactoring, but in the large.
With commitment, these few simple rules of thumb, and a lot of effort any team can componentize a monolith. Get buy in from your entire team for the effort. Organize components around central data interactions to optimized performance and clarity. Extract authentication and authorization first because it most other components will depend on it. Create a precedent around building new features outside of the monolith. Finally, extract an existing feature from the monolith. No monolith lasts forever.
I recently had a colleague ask me several questions about service oriented architectures and breaking monoliths apart. This is an area in which i have a good deal of experience so i decided to publish my answers here.
What is a “service”?
A “service” is a discrete bit of functionality exposed via well defined interface (usually a standardized format over a standardized network protocol) that can be utilized by clients that are unknown and/or unanticipated at the time of the service’s implementation. Due to the well defined interface, clients of a service do not need to understand how the service is implemented. This style of software architecture exists to overcome the diseconomies of scale suffered by software software
How has the services landscape changed in the last 5-10 years?
In the mid-2000s it became clear that WS-*, the dominate service technology at the time, was dramatically over complicated and led to expensive to maintain systems. WS-*’s protocol independence combined with the RPC style adopted by most practitioners meant that clients generally ended up being very tightly coupled with a particular service implementation.
As WS-*’s deficiencies became clear, REST style architectures gained popularity. They reduce complexity and coupling by utilizing an application protocol such as HTTP, and often using simpler message formats such as JSON. The application protocol provides a uniform interface for all services which reduces the coupling between client and service.
Microservices are a relatively recent variant of service oriented architectures. As the name suggest the main thrust of microservices is the size of the components that implement them. The services themselves could be message queue bases or REST style APIs. The rise of devops, automation around deploy and operations, raises the practicality of deploy a large number of very small components.
Message queue based architectures are experiencing a bit of a resurgence in recent years. Similar architectures where popular in the early 2000’s but where largely abandoned in favor of WS-*. Queue based architectures often provide throughput and latency advantages over REST style architecture at the expense of visibility and testability.
What do modern production services architectures look like?
It depends on the application’s goals. Systems that need high throughput, low latency and extreme scalability tend to be message queue based event driven architectures. Systems that are looking for ease of integration with external clients (such as those developed by third parties) tend to be resource oriented REST APIs with fixed URL structures with limited runtime discoverability. Systems that are seeking long term maintainability and evolvability tend to be hypermedia oriented REST APIs. Each style has certain strengths and weaknesses. It is important to pick the right one for the application.
How granular is a service?
I would distinguish a service from a component. Services should be small, encapsulating a single, discrete bit of functionality. If a client wants to perform an action, that action is an excellent candidate for being a service. A component, on the other hand, is a pile of code that implements one or more services. A component should be small enough that you could imagine re-writing it from scratch in a different technology. However, there is a certain fixed overhead for each component. Finding the balance between component size and the number of components is an important part of designing service architectures.
What is the process to start breaking down a large existing application?
Generally organizations start by extracting authentication and authorization. It is an area that is fairly well standardize (oauth, SAML, etc) and also necessary to support the development of other services. Once authentication and authorization are extracted from the monolith, another bit of functionality is chosen for implementation outside of the monolith. This process is repeated until the monolith is modularized enough to meet the organizational goals. The features implemented outside of the monolith are often new functionality but to really break apart a monolith you eventually have to target some of the existing features.
Starting small and preceding incrementally are the keys to success. Small successes make it easier to build consensus around the approach. Once consensus is reached existing, customer facing features can be extracted more readily.
What are the organization / team structure impacts?
It is generally superior to construct a (rough, high level) design of the services needed and to form vertically integrated teams to implement business features across the set of components. Empowering all teams to create new components and, where appropriate, new services, increases the likelihood of success and shortens the time to implement a service oriented architecture.
Alternatively, component structures can follow the organizational structure (Conway’s law), resulting in one component per group in the organization. This approach can be practical in organizations where vertically integrated teams are politically unacceptable.
What are needed / helpful tools? To run services? To manage services?
How do companies document their services, interfaces and versions?
There are not any popular tools for creating documentation for hypermedia and message queue based APIs. However, general guidance can be found for creating documentation, but in general you are own your own.
How can we speed developer ramp up in a service architecture?
To speed developer ramp up it is important to have well maintained scripts to build a sandbox environment including all services. Preferably a single script that can deploy components to virtual machines or docker containers on the developers machine. Additionally it is important to maintain searchable documentation about the API so that developers can find information about existing services.
How to deploy new versions of the ecosystem and its components?
Envisioning an ecosystem of services as a single deployable unit is contrary to the goals of service oriented architectures. Rather each component should be versioned and deployed independently. This allows the components to change, organically, as needed. This approach increases costs on the marketing, product management and configuration management fronts. The benefits gained on the development side by avoiding the diseconomies of scale are worth it.
How to manage API compatibility changes?
A service oriented architecture makes breaking API changes more damaging. It is often difficult to know all the clients using a particular API. The more successful an API the harder, and more time consuming, it is to find and fix all clients. This difficultly leads to the conclusion that all changes should be made in a backwards compatible way. When breaking changes are unavoidable (which is rare) server-driven content negotiation can enable components to fulfill requests for both the old API and the new one. Once all clients are transitioned to the new API the old API can be removed. Analytics in the old API can help identify clients in need of transitions and to determine when it is no longer needed.
How to version the ecosystem and its components?
This is a marketing and project management problem more than anything. The ecosystem will not have a single version number. However, when a certain set of, business meaningful, features have been completed it is convenient for marketing to declare that a new “version”. Such declarations are of little consequence on the development side so they should be done whenever desirable.
My view while volunteering for the Pat Amato Classic.
My view while volunteering for the Pat Amato Classic.
Great day at the national zoo with great friends.
Great day at the national zoo with great friends.