Viewing entries tagged
Library Oriented Architecture may sound like yet another buzzword in the software arena, but one that is not properly documented as of yet. It is not a common term and certainly far from the widely popular SOA or Service Oriented Architecture. Since there is no formal definition on the term LOA, I’m going to take a stab at it:
“Library Oriented Architecture defines the methodology for creating software components in the form of reusable libraries exclusively constrained to a specific domain ontology.”
What does it mean? Well, the part about ontology I’m not going to drill too deeply into that, in a nutshell “don’t confuse a contact with a user, they belong to different domain ontologies” (I wrote a different article about it HERE). In this piece we are going to drill down into the software piece, the separation of concerns, and how to define a practical framework to create things in the right places.
I caught the term LOA for the first time at SuperConf 2012 in Miami. Richard Crowley came to the stage and threw the new term at the crowd and got back a few long faces in return. Richard’s own words, when referring to the Library-Oriented approach:
“Package logical components of your application independently – literally as separate gems, eggs, RPMs, or whatever- and maintain them as internal open-source projects… This approach combats the tightly-coupled spaghetti so often lurking in big codebases by giving everything the Right Place in which to exist.”
His talk was very solid and I recommend everyone with a hard-core-techie-heart to spare a few minutes on it. You can find his reflections about developing interoperability HERE.
It caught my attention just by the name, because I’ve been saying, “It’s like SOA, but with libraries” for some time now. “It’s like SOA, but with libraries” always came up when I was trying to explain an architectural pattern for building solid systems and frameworks. In general, LOA is just a way of thinking about software engineering. Library Oriented Architecture defines the structuring of libraries for domain ontologies and it has 3 basic principles:
- A software library implementation and subject area expertise must be constrained to only 1 ontology domain.
- A software library that needs to use concepts and artifacts from a different ontology domain than the one it belongs to, must interface and reuse the library corresponding to that specific ontology domain.
- All domain specific software libraries must be maintained and supported with separate lifecycles.
Before we get into the weeds here, we ought to ask ourselves: Why in the world do we need a new term, or a new architecture, or a new anything in software engineering? Well, we don’t, but if you care to write badass apps and software systems that can evolve gracefully with time, this can turn out to be a very good road to take. For those who enjoy bullet points, here are some of the motivations to explore LOA a bit further:
- Simplify configuration management of distributed systems.
- Build highly reliable software systems because of the inherent properties of the LOA principles.
- Increase the Maintainability Index of your distributed systems and integration repositories.
- Minimize the risk of high coupling, especially for large systems (read Writing Elegant Code and the Maintainability Index).
- Bring developers up to speed orders of magnitude more quickly than a traditional system. Move developers and teams across libraries and domain ontologies and collaborate seamlessly.
- Spot bugs and zero-in on the problem almost instantly. There is something to be said about the amount of time a developer spends debugging.
- Maximization of the Bus Factor of the software engineering team.
- Information Systems build using LOA are technology-independent, and have the ability to entire libraries and domain implementations with localized impact and minimal upstream ripple effect.
Ok, enough reading, let’s see how this materializes in a diagram.
Note that this is a specific implementation of Library Oriented Architecture for compiled libraries. You can adapt this to your own needs for scripted languages and even mix it around however you want. For the sake of simplicity, we’ll stick to this sample for now.
The second thing I want to note here is that the diagram is not describing how to implement LOA. It simply lays the foundations for a software engineering practice that happens to follow LOA principles. I’m sharing this because I think is useful and maybe someone will like it enough to offer some suggestions to improve it further.
I want you to notice a couple of things that are illustrated on the diagram:
- All 3 principles mentioned above are followed.
- The framework favors convention over configuration. Lib names, namespace naming and schema conventions are noted in the last column.
- You can clearly dissect the domains vertically and they span all the way from the data storage layer to the actual library implementing the domain specific logic.
- A library representing an ontology domain never interfaces with the data-sources, or even data access layer, from any other domain; instead it interfaces directly with the library representing that domain.
- Services are merely wrappers of libraries, with minimal or no business logic other than the orchestration of the libraries it needs in order to fulfill its function.
- This is important because services are always tightly coupling their technology implementations and serialization mechanisms (WCF, ASMX, SOAP, REST, XML, etc.)
- Part of the service implementation concern is usually dealing with this technology-specific fuzz that is unrelated to the actual business functionality the service is providing.
- Exception handing is bubbled up to the lib layer, such that we always get meaningful stack traces when debugging.
- Logging, as a cross cutting concern, should be manageable at all levels of the framework, however the domain deems necessary.
- If the implementations of the domain-specific libraries share a common framework, such as .NET or Java, they most likely have a superseded library set that extends each framework. For the example illustrated in the diagram, we called them framework infrastructure libraries, or Common Libs for short.
So, now that we have a framework for engineering our software needs, let’s see how to materialize it.
Suppose you are working on the next Foursquare, and it comes to the point where you need services that help you normalize addresses, and work with GIS and coordinates, and a bunch of other geo-location functions that your next-Foursquare needs.
It is hard sometimes to resist the temptation of the ‘just-do-it’ approach, where you ‘just’ create a static class in the same web app, change your Visual Studio web project to make an API call to 3rd party services, and start integrating directly to Google Maps, Bing Maps, etc. Then you ‘just’ add 5 or 6 app settings to your config file for those 3rd party services and boom, you are up and running. This approach is excellent for a POC, but it will not take you too far, and your app is not scalable to the point it could be with a Library Oriented approach.
Let’s see how we do it in LOA. In this world, it takes you maybe a couple of extra clicks, but once you get the hang of it, you can almost do it with your eyes closed.
- The Lib Layer
- Create a class library for the GEO domain ontology. Call it something like Geo.dll or YourCompany.Geo.dll. This library becomes part of your lib layer.
- Deciding the boundaries of domain ontology is not an easy task. I recommend you just wing it at first and you’ll get better with time.
- You need to read a lot about ontology to get an idea of the existential issues and mind-bending philosophical arguments that come out of it. If you feel so adventurous you can read about ontology HERE and HERE. It will help you understand the philosophical nature of reality and being, but this is certainly not necessary to move on. Common sense will do for now.
- Just don’t go crazy with academia here and follow common sense. If you do, you may find later that you want to split your domain in two, and that is OK. Embrace the chaos and the entropy that comes out of engineering for scalability, it is part of the game.
- Define your APIs as methods of a static class, and add a simple[sourcecode language="csharp"]throw new NotImplementedException("TODO");[/sourcecode]
- Write your Unit Tests towards your APIs with your assertions (Test Driven Development practice comes handy here).
- Create a class library for the GEO domain ontology. Call it something like Geo.dll or YourCompany.Geo.dll. This library becomes part of your lib layer.
- The DAL Layer
- Sometimes your ontology domain does not need to store any data. If that is the case, skip to step 3, else continue reading.
- Create a new library for the GEO domain data access layer. Name it according to the convention you previously setup in your company and dev environment. For this example we’ll call it GeoDal.dll
- Using your favorite technique, setup the data access classes, mappings and caching strategy.
- If your persistent data store and your app require caching, this is the place to put it. I say if, because if you choose something like AWS Dynamo DB where 1 MB reads take between 1 and 10 milliseconds, maybe you want to skip cache altogether for your ‘Barbie Closet’ app :)
- Memcached, APC, redis, AppFabric, your custom solution, whatever works for you here.
- You can also use your favorite ORM (NHibernate, Entity Framework, etc.) and they already come with some level of caching on them.
- Bottom line, LOA does not have any principle preventing you from going wild here, therefore your imagination and experience are the limit.
- The Data Layer
- For this exercise suppose we need to persist Addresses, Coordinates and Google Maps URLs.
- I suggest you scope your data entities by your domain ontology. A way we’ve found to work quite nicely is to use named schemas on RDBMS and setup namespace conventions for your NoSql databases.
- For the GEO domain schema, we used SQL Server and created a named security schema called [Geo]. The use of named schemas makes it easy to avoid long table names, provides nice visual grouping of entities and a more granular security for your entities.
When it comes to data modeling, another technique I like to use is that of unaltered historical event data. Any ontology domain can be dissected into 3 purpose-specific data models: Configuration Data, Event Data, and Audit Data. They all serve very different purposes and in general we like to keep them in separate schemas with separate security, this way we’re not comingling concerns. Each concern has a different DAL library and potentially they all interface with the library representing the domain at the Lib Level. This post is already way too long, I’ll try to cover some more data modeling strategies in future posts.
Now that we have a clearly separated domain library for our GEO domain, we can decide to wrap with whatever technology specific services we need. This is very convenient because when you want to move your SOA stack to a different technology, you don’t have to re-write your entire domain infrastructure, only the service layer. More importantly, it allows for greater scalability, since it degrades gracefully and plays nicely with different frameworks and technologies. A well implemented Library Oriented Architecture can be said to be technology-agnostic, and that makes it a great SOA enabler.
That’s it for this episode folks. Send me your comments or emails if you are using Library Oriented Architecture, or if you have any suggestions on how to improve the methodology or framework.
One of the most inspiring talks ever.
Today, in science, especially in information technology, the word ontology is a hot ride. In short, an Ontology is the specification of a concept. The idea has grown almost to the point of becoming a buzz word for academics and professionals in the computer science field, and yet a big part of the industry ignores the subject for lack of friendly documentation or understanding that describes it in bogus terms, why is important and how it can change computing for the better.
The word appeared for the first time in the Oxford English Dictionary in 1989. Because it’s a relatively new word for English-speaking folks, the word itself it gets in the way of story it tells. In reality it has been around for quite some time in society.
The philosophical study of existence, “what is real and what is not”, it’s been around for centuries. We can find evidence of the questioning of nature and reality all the way back to the Pre-Socratic era, with philosopher Parmenides of Ela. Parmenides is most known for a poem he wrote called “On Nature” (read the poem here). The poem describes two different perspectives of the same reality, but it zeroes in one powerful idea, that no matter how different appearances of that ‘that it is’ (he calls it ‘the way of opinion’), the truth about ‘it’ does not change (‘the way of the truth’). In a nutshell, this is the first recorded attempt to formalize the realization that existential things don’t change regardless of the lexicon or language used to describe them. Many more developed their own thesis on how to define reality. Plato also made notable contributions to the field of Ontology, and his later disciple Aristotle put a dent in this universe with his works Categories and Metaphysics.
Why is this important today? Because all natural science fields that describe elements of the real world, already have their own ontologies, but this is not the case for Computer Science and Information Technology. Physics, Chemistry and Biology all have a very clear lexicon or dictionary that describes their scientific domains. But we have yet to define an Ontology that describes the world we present through software. When building information systems, different authors, developers and companies declare the same entity ‘that is’ not as the entity itself, but instead as one of its appearances. What we end up with is a lot of unnecessary repetition, corrupted data structures for entities and unnecessary computations made for the sake of mapping appearances that represent the same entity. A call for a Global Ontology has been the topic of many academics for a long time, and in many ways considered the holy grail of information sciences.
Mathematics, as the universal language, describes abstractions and logical reasoning to determine the truthfulness of an assumption. We do it with the use of specialized notation, like numbers and shapes that do not have a tangible form. No author, developer, company or human being in the planet will argue what the number ‘3’ represents. Mathematics provides the foundation for all Ontologies of any other domain definable by humanity. I couldn’t put it any better than Galileo Galilei:
“The universe cannot be read until we have learned the language and become familiar with the characters in which it is written. It is written in mathematical language, and the letters are triangles, circles and other geometrical figures, without which means it is humanly impossible to comprehend a single word. Without these, one is wandering about in a dark labyrinth”
Going back to Ontology in the Information Sciences, some questions remain unanswered:
- What are the fundamental objects or structures we ought to define to represent the tangible and abstract concepts from a specific domain?
- How can we successfully share and relate objects from different domain ontologies?
- How can we define ontology structures in a way they are effective for operational and usable digital communications?
The biggest challenge in information science with respect of the use of ontologies, is that of establishing a base line agreement in the industry to use a common lexicon and vocabulary consistent with the theory specified by the a particular domain ontology. A Global Ontology would be defined as the aggregation of all domain ontologies, where a domain ontology represents the abstractions and tangible objects of part of the world or a specific knowledge domain.
Competition begs to be mentioned in these lines. The mammoths in the software industry have shown more interest in sticking their guns out for discriminator structures under the same ontological domain with their competitors. For example, Google Maps, Bing Maps and MapQuest all offer services in the GIS domain, yet they’ve decided not to share the same vocabulary and lexicon to name their GIS objects. Think about this for a minute, if these companies decided to share a global GIS schema, then their only discriminator really would be the quality of their service… but that’ll make it too easy for developers to switch sides; so they decide to give their own twist on unique vocabulary. The result is arbitrary mappings for “State”, “Province”, “StateProvince” and “Municipality”, each with multiple data types, sizes and formatting, ultimately adding layers of unnecessary complexity to such a simple concept like that ‘that it is’.
This is already too long of a post, so I’ll cut it short. Maybe in future posts, I’ll cover ontology more closely to engineering, and what you, as an architect, computer scientist, programmer, etc, can do to make your work a much pleasant and rewarding one. My very good friend Leonardo Lezcano, has published many works in the healthcare domain ontology, with research and papers covering the Semantic Web and Semantic Interoperability. You can find some of his works HERE and HERE.
This is somehow a challenging topic to explain, and for the recipient to say “I get it” the first time around. I’ll feel good if I get a “I kinda got it” after someone reading this :)
This is an excellent video about people, about companies and about the relationship and motivation that makes people wake up every morning to go to work. Many of these concepts are touched by various personalities like Joel Spolsky on his blog and book. Well done RSA, bravo! [youtube=http://www.youtube.com/watch?v=u6XAPnuFjJc]