A long, long time ago I took a college course, the title of which was Languages and Translation. The content of this sophomore-level course? A smörgåsbord of systems programming, heaps and stacks, pointers, *nix system calls, compilers, lex and yacc, grammars, lexical and semantic analysis, code optimization, and data representation — all taught and learned in C. While learning C. Oh, and Lisp.
This course made quite an impression, mainly because of my initial inexperience. I should explain that the path which led me to L&T went something like this:
- I’m a senior in high school. I’m applying to college. I need to choose a major. Hmm… writing that Blackjack game on my TI calculator was pretty cool. It was a whole 50 lines of code! Plus I’m in that typing class, closing in on 20 words per minute. Maybe I’ll try Computer Science!
- I’m a freshman at Georgia Tech. First semester. I’m taking Intro to Computing. Man, this HTML nonsense sure uses a lot of brackets! And this Microsoft Access program is impossible to use!
- Still a freshman at Tech. Second semester. This Intro to Programming class is pretty crazy! We’re using Java for the assignments. The TA mentioned in passing that there are no pointers in Java, but I have no idea what a pointer is, so I could care less. I’m beginning to grok object-oriented programming.
- Welcome to Languages and Translation! Malloc, Malloc, Malloc! Realloc, Calloc, Malloc! Bwahahahahahaha!
Psychological damage aside, this was a great class. Jim Greenlee, who taught the course, was both an evil bastard and a great teacher. One of the tenets of code optimization which he often highlighted was “space versus time,” the idea that you can often optimize for one at the expense of the other, but rarely for both at the same time.
For example, a compiler can decide to inline a short function in order to avoid time-consuming stack allocations, but the compiled program will be larger (less time, but more space). Of course, if space (memory) is at a premium, your compiler might instead try to recognize common code sequences and hoist them into artificial functions (less space, but more time).
Flash forward 8 years. We have a client/server application at work which uses DTOs to transfer data to and from the client, and we use Hibernate on the server to persist our BOs. A specific server call, invoked in the presence of a large amount of data, brings the application to its knees.
Immediately we jumped to conclusions — Arrgh! Hibernate is such a hog! If you don’t code things perfectly, you can’t scale! And sometimes not even then! A quick profiling session confirmed our fears. Three hours later we had bypassed Hibernate in this specific instance, coding to the JDBC API instead. Unfortunately, this wasn’t the last of our performance problems. A second profiling session indicated that we had another bottleneck in our DTO-to-BO conversion routines!
Now, something which must be understood about Hibernate’s collection semantics is that when you use Hibernate to load BO A, which has an X-to-N relationship with BO B, you should (usually) use the collection of Bs provided by Hibernate. For example, if you use Hibernate to load a UserGroup, and you want to modify the list of Users associated with said UserGroup, you should modify the existing list of Users. You should not create a new list, add Users to it, and then give the UserGroup the new list of Users. Why? Because creating a new list results in a one-shot delete, followed by N insert statements. This is usually not desired.
However, a naive approach to modifying the collection provided by Hibernate (clearing it and then adding the BOs which you know you want) is just as bad, because a call to collection.clear() also results in a one-shot delete. The best approach is one of minimal modification to the existing collection.
In the case of DTO-to-BO conversion, where the DTO representation of an object is being transferred to the corresponding BO representation, this means adding items to the BO’s collection that are in the DTO’s collection but not in the BO’s collection, and removing items from the BO’s collection that are in the BO’s collection but not in the DTO’s collection. Elements that are in both collections are simply ignored.
The obvious implementation of this algorithm looks something like this:
for(DTO dto : dtos) {
if(!contains(bos, dto)) add(bos, dto);
}
for(BO bo : bos) {
if(!contains(dtos, bo)) remove(bos, bo);
}
Unfortunately, when using lists, the contains() calls above hide a nested loop, resulting in O(n2) performance. Once n gets into the thousands, things start to get sluuuugish. Veeeeery sluuuugish.
The solution? Trade a little space for a lot of time! By constructing HashMaps which contain all of the BOs in the lists, keyed on business keys which uniquely identify the BOs, the contains() calls above can be performed in constant time by invoking map.containsKey(). The result is O(n) performance. Much better!
