You are probably familiar with Martin Fowler’s Technical Debt, a metaphor around the idea that doing things the quick-and-dirty way creates bad code, bad code which can be viewed as a debt which needs to be paid later in installments (the principal is the re-factoring of the bad code and the installments are the extra effort that this bad code forces on development). The concept was originally set-up by Ward Cunninghan.

Technical Debt is pretty much different from ordinary, everyday debt primarily because there is no creditor and no maturity date. Also, unlike regular debt, it is very hard to quantify. Technical Debt seems a bastardized version of regular debt in the sense that some development costs are identified as the principal (the re-factoring of the bad code) and some as the interest (the hacks used for dealing with this bad code) and it is built by taking some concepts out of the ordinary debt concept as the author sees appropriate in order to underline some development costs.
At the same time Tech Debt is defined exclusively from the point of view of the borrower who has to worry about paying the principal and the interest leaving out other actors (even though once you take the creditor out of the picture you have to wonder why you have to pay this debt at all ;-)). But, like Martin Fowler said at the beginning of his article, this is a metaphor, which leaves a lot of room for stretching various concepts. Fair enough.

However, Technical Debt seems to have been taken up and spawned quite a few siblings as you can see in this infoq article: liquid assets, moral hazard, fertile assets (???) etc…, which are either unfortunate enough to bear little or no resemblance to the original economic concepts they try to refer to or unfortunate enough to not refer to anything (fertile assets stands out as a prime example).
Let’s pick for example “Liquid Asset” which is defined this way: Perhaps the term “technical debt” focuses us on the wrong things; maybe focusing on the converse, on the investment side of things might be more effective.
First of all, a real-world definition: a liquid asset is an asset for which transaction costs are lower and for which there exists a market in which this asset can be sold and bought for a reasonable fee. Try to look into the above definition (or in this link which is provided next to it in the infoq example) and if you can spot the asset that is referred to, as well as the market in which this asset is sold and bought and the fees for these transactions please let me know.
Second, switching terms in order to motivate people displays how shallow these concepts are to begin with: if Technical Debt would be a concept that relates to somethings tangible in the real world, if it would manage to encapsulate a real problem or some real costs then Tech Debt should should not be swept under the rug for fear of under-mining the developers’ morale, but rather dealt with up-front because in doing so you would solve a real problem. The fact that Tech Debt is used the way this way points to the fact that it is so vaguely defined that few people can extract some somthing valuable out of it and that it can be stretched in any direction you want to.

What I see when I am reading the infoq article mentioned above are the first steps in the attempt to “marry” 2 fields which are pretty different one from another: economics and writing code and the excesses which appear when interest start to pick up in some vague concept (excesses similar to the introduction of the SOA concept). There is a desire to bring IT development under control and one way to look at it is to define costs and benefits associated with various actions and then apply economics to these costs and benefits in order to write code more efficiently. I don’t know where this will lead because we are at the beginning of exploring this.

As far as I am concerned I have developed an interest in economics a while ago and I read on it as much as I can. I also tried to “marry” the domains of writing code with and of economics, I even have an Econo-computing category, but I find it pretty hard. So far the only economic concept I found that can be applied pretty well to software development are transaction costs because they actually encapsulate pretty well some very real costs which arise when various entities are interacting one with another.

I will be following this Econo-computing field with a lot of interest and, who knows, maybe the concepts in this field will actually start to relate to something tangigle in the real world and will improve developer productivity.

A while ago I looked at the transition currently occuring in enterprise computing from ACID transactions to compensation by message passing. I concluded by saying that relaxing ACID properties and achieving state alignment via message passing is essentially shifting the costs from the costs of ACID transactions (such as database rows locking) to complexity costs (such as managing the growing number of messages between systems which are compensating for these relaxed transactions). Another side effect is the introduction of latency in this state alignment process: thru ACID transactions state alignment is achieved at the time the operation takes place while thru message passing it occurs some time later.

I was watching this interview on infoq and I found that Gregor Hohpe pretty much voiced the same opinions, that as you scale out it is impossible to achieve state alignment at the time the operation and that you need to compensate for it by passing messages.These complexity costs are essentially the costs of managing business logic as it spawns across multiple systems, applications, departments, etc… State alignment in these cases consists primarily of having various business analysts agree on how these systems should interact (should this trade go to this depot for that security for that client) and then implementing the interaction between these systems. One thing that is not very obvious is that the cost of interaction between systems is pretty close to the cost of interaction between the departments running these systems and that as you scale out you need to be able to accomodate or change various departments you are encountering (*).

It is interesting to watch the interview from this point of view because you get to see the costly hacks that various organizations got to make in order to get their systems to talk to one another (the horrific “magic account” used in one bank makes one hair stand up). Gregor goes as far as saying that these ways to deal with these problems are pretty common, so common actually that at one point he gets defensive about the sloppiness which comes thru these examples. The fact that these problems are wide-spread is a pretty bad excuse, ideally these systems would be architected so that we do not need to add one layer of hacks on top of another in order to get something of value out of them.

At the end of this interview I ended up with the impression that we are on the first iteration of relaxing the ACID properties and that I got a pretty good glimpse into the costs associated with this relaxation. Sometime in the future patterns for relaxing these properties will appear and problems common in all these compensations will be solved. I think that these patterns will be more oriented towards business-logic and management rather than towards technology. Efficient interactions revolve around small transaction costs and minimizing transaction costs will be part of the solution of managing this growing complexity.

* Interestingly enough an SOA strategy will probably take these costs down if it manages to impose procedures so that various departments interact one with another more efficiently. However, I am pretty skeptical that we will see a lot of progress soon on this front.

Today when I went to check my RSS feeds on bloglines I was presented with the following:

Quickly I realized that I was cut-off from any source of information that I use because I access sites mostly thru their RSS feeds and my RSS feed reader was down. It was out of the question to go to those sites and read the news from their homepage (*) because those sites (BW, FT, Forbes,, Le Monde, infoq, etc…) have tons of content and I would not be able to navigate it in order to find what I like (this was what my RSS feeds were doing).
It was a pretty weird feeling to see how underlined how dependent I am on my feed reader and that my feed reader has become a single point of failure in my contact with various content providers. Take bloglines out and I am pretty much out of contact with pretty much anything I am following. And I don’t think that I can do much about it (**).
I can only hope that they come back up quickly…

* BTW, for a pretty interesting analysis oh how RSS changes browsing patterns please check out this post on Guy Kawasaki’s blog. The concept that “any page is a homepage” due to the various channels which are sending users to that page will change a few things in the content publishing business.

** Actually I could turn to my Google reader account to read the feeds from there, but this would mean that I need to update the OPML of my Google account, determine what stories are new and which ones I have read, etc… I could get past this single point of failure, but there are costs associated with it. Strangely enough, using Google reader as a replacement for Bloglines is similar to some work-arounds used for avoiding single points of failures in enterprise systems (synchronizing redundant machines).

A while ago I went to a presentation where the speaker was talking about the migration of an application from a typical MDB-based work-flow application to an EDA-based application. One of the drivers of this migration was the fact that their application`s data has grown so large that the database storing it was having performance problems. One of the solutions which were studied was to move the data tier into a clustered, transactional cache which would update the database asynchronously, essentially moving the database to a simple store of data on which you could run reports.

Not a bad idea, the data would reside in this clustered cache and it would be made available to the application. One other thing that was considered was turning the whole architecture on its head and turn from the work-flow application into an EDA application. This is how it was supposed to come: the clustered cache came along with the capability of adding events to data as data was handled (inserts, updates, deletes, etc…). This would mean that while you previously had to have an MDB that was listening for a stock-quote request you could now code a stock quote object which you would throw in this clustered cache and which could listen for events. The clustered cache would have both flushed the stock quote objects to a database and would have call events on it. The stock quote object would in turn update other objects (let`s say positions for that stock) and while these positions would get updated they would fire more events in turn. A part of the business logic would be stored in these events and in the message passing that occurs between them, in a sort of event-driven-architecture.

The more I think about this, the more I get the impression that these events are nothing more than triggers in a database (the clustered cache taking on the role of the database). And coding your business logic in triggers is not a good thing if you listen to people with experience in database programming. Relationships between various entities and the interactions between them should probably be modeled at a higher level than at object level, when you are coding business logic in triggers you are essentially limited to that object’s scope.

Now, the idea of putting the business logic into these triggers comes from a pretty hard problem: the fact that for most artchitectures out there the data store is physically away from the code that is implementing the business logic, the database which holds the data and which needs to be consulted by the business-logic tier for data. Bridging this physical distance and the performance problems that come with it was solved in various ways: pushing the business logic into the database in the form of stored procedure, addind various caching mechanisms so that data is pushed towards the middle-tier, etc… In the example above part of the reasons for pushing the business logic into these events was you would effectively push the business logic into the new data-tier (the clustered cache in this case which would trigger these events). I would say that this is a pretty interesting approach with some benefits, but attention should be paid to minimizing the number of events (triggers) which get implemented.
A decade of PL-SQL development would recommend this…