In a perfect world, we could focus solely on implementing functional requirements and not have to do anything explicitly to deal with non-functional constraints such as keeping shared resources from becoming bottlenecks or contending with bandwidth limitations. It can be instructive to consider what an application would look like if there were no physical constraints. Imagine if we lived in a world with infinite memory, infinite processing capacity, unlimited bandwidth, zero down-time, and interesting television shows (Imagine really hard). In such a world, queries are blindingly fast , there is no limit to how often we interact with the back-end, and we can can constantly exchange enormous amounts of data if we need to. With such a platform, we are free to develop our application without any consideration of physical constraints. Each view can refresh the data it is displaying nearly instantaneously, and we can immediately transmit changes to the back-end, and immediately receive notifications of any errors encountered.
In such an environment, the need for a great many design constraints is removed, although not completely eliminated. Even with physical constraints removed, logical constraints remain. Still, removing the physical constraints certainly simplifies software construction by removing the need to manage limited resources efficiently. Of course, in the real world of enterprise applications, there is no such environment, but not every physical or logical constraint is an issue for every application. Low volume applications or those with very small and simple data requirements can often behave almost as if they were executing in the (near-)perfect world. Consider for example an application that allows a user to update their name, address, and credit card information. As soon as the user logs in, you can easily retrieve such a small volume of data immediately, and you wouldn't have to contend with the user's name and address being changed by someone else simulataneously. You can then allow the user to update any of the returned data, updating their name and address, deleting some credit cards and adding others. If there are few simultaneous users, we can transmit each change immediately to the server where we immediately run required edit checks against it and report any errors encountered back to the client for communication to the user via the UI. Such an approach allows us to write an application in what is arguably the most “natural” way, ie, the simplest way. Physical constraints, on the other hand, force us to commit "unnatural acts", ie, they force us to write code that is more complex than it needs to be from a purely logical perspective.
With RIA's, there are a few physical constraints which are fundamental at this point in time. The client application is a separate process (on a unique platform), and all communication with the server-side must be accomplished by messaging. It is this constraint, more than anything else, that distinguishes ERIA architecture from web applications, two-tier client server applications, or one-tier local applications. And speaking of one-tier applications, it is worth noting that virtually no enterprise application allows for the data to be persisted entirely on the client box. It is virtually always the case that we cannot rely on the client itself for safe storage of business records, and there are typically large amounts of non-client-specific data (such as a product catalog) that are usually too large to make client-side distribution practical.
Another constraint is the almost universal requirement that persistence be provided by a non-object-oriented datastore, typically a relational database. There is of course a fundamental impedance mismatch between object- and a relational-centric data structures that is at the heart of many unnatural programming acts, and although there have been many variations on “object databases” over the years, relational databases continue to dominate for good reasons we’ll explore in some depth another time. In a perfect world, queries would return graphs of objects rather than lists of rows. Fortunately, persistence management frameworks such as Hibernate automate a good deal of the acrobatics involved in object-relational mapping, but unfortunately are not (yet?) fully transparent to the application developer, requiring varying amounts of explicit coding.
Coding in the real world requires more effort than coding in the perfect world because we need to add code to work around physical constraints. It would seem to be self-evident that since it takes more effort to add more code, you should only do so when there is an actual requirement to do so. In other words, the default architecture for any ERIA is essentially the simple one described above. We retrieve all the data up-front. Operate on the data as necessary. And send edits to the back-end as they occur. In pondering each of the questions posed above, the default answer in each case is this straight-forward, perfect world architecture since it requires the least effort to build. In each case however it is also necessary to consider how physical constraints can force the undertaking of more complicated strategies, but again, it should be emphasized that in the absence of any such physical constraints, the simplicity of the perfect world approach is the way to go.