May 19, 2009, 4:26 PM — Of all the patterns in the SOA design patterns catalog there is perhaps no other as simple to understand yet as difficult to apply in practice as Canonical Schema. There are also few patterns that spark as much debate. In fact, that application potential of Canonical Schema can become one of the fundamental influential factors that determine the scope and complexion of a service inventory architecture.
It all comes down to establishing baseline interoperability. The Canonical Schema pattern ensures that services are built with contracts capable of sharing business documents based on standardized data models (schemas). Unlike the well-known pattern Canonical Data Model (Hohpe, Woolf) which advocates that disparate applications be integrated to share data based on common data models, Canonical Schema requires that we build these common data models into our service contracts in advance. Hence, the successful application of this pattern almost always requires that we establish and consistently enforce design standards.
But before we discuss the standardization of data models and all of the enjoyable things that come with trying to make this happen, let’s first take a step back and describe what we mean by "baseline interoperability."
When services and service consumer programs interact, data is transmitted (usually in the form of messages) organized according to some structure and a set of rules. This structure and the associated rules constitute a formal representation (or model) of the data.
When different services are designed with different data models representing the same type of data, then they will have a problem sharing this data because the data models are simply incompatible. To address this problem, a technique called data model transformation is applied whereby data model mapping logic is developed so that data exchanged by such services is dynamically converted at runtime from compliance with one data model to another. So successful has this technique been that a corresponding Data Model Transformation pattern was developed.
However, with data model transformation comes consequences. And with the overuse of data model transformation comes real problems pertaining to architectural complexity, increased development effort, and runtime performance demands that can impact larger service compositions to such an extent that if you press your ear close enough to your middleware you can actually hear the churning and grinding of this extra runtime latency.
These and other details and issues will be discussed separately during an upcoming series article dedicated to the Data Model Transformation pattern. What's important for us to understand for now is that the primary goal of applying Canonical Schema is for us to avoid having to apply Data Model Transformation.
