We have a large project that is going to be going through a pretty large change. There will be a new incarnation of the project that hopefully sets the bar for the future. Seeing as that is a pretty big lofty goal and the real world is rarely big and lofty, it seemed like a good idea to write down some specific reasons we need to make rather major changes.
I’m not going to deny the reason for this explanation is to help me feel better about effectively rewriting our application. After all, rewriting software is a really bad idea. I heartily agree that a big rewrite is rarely going to solve problems. But, in this case, the goal is to improve the system. How is that different? When you write software you have bugs and bugs have assumptions. Take persistence for example.
You might be getting errors every so often that happen because the database layer is a bottleneck. Fixing the bug might be to include some caching or simply throwing hardware at the issue. The system on the other hand could include a completely different design of the data such that writes can be made incrementally and later compiled into a complete object. The difference here is that you’ve starting changing the assumptions and in doing so opened up a different set of opportunity that could be the difference between constant frustration and actually getting new features.
Our code base, while very successful, has begun to show these sorts of systemic issues that will prevent us from expanding far beyond where we are currently. I say “far” beyond because that is real goal. We want to handle 1000x the load with 1/10th the hardware and that can’t happen given the current system assumptions. Likewise, if we continue to focus on fixing our bugs, we’ll never be able to radically change the system.
The biggest systemic issue that we have is speed. We need to be faster.
Our response times need to be much lower and our ability to develop exciting features needs to be faster as well. The current assumption is that there is one data store. The single data store implies that you write to one “place” and read from the same “place”. The problem is that as we’ve grown, the realization has come that we don’t read/write to the same place. There can be an intermediary. Along similar lines we don’t read everything in the same fashion, yet the vast majority of data is in fact read only. Our persistence goals then are to make sure our updates are fast and don’t impact our read performance. There will be a trade off that much of our data will be slightly more stale than before.
Another speed issue deals with development speed. There are infinite possibilities for asking questions, but it is not simple to create another way of asking questions very quickly. This problem become more complex as we enter different platforms (mobile). Here the solution is not as sweeping but simply involves producing some API to our storage that any client can utilize. While this seems simple (GET the question foo, POST the answer bar) in reality there is huge set of assumptions the system makes throughout that have never been truly codified. By codified I mean they have not been defined in a publishable manner in addition to lacking consistency through out the code. This improvement will mean providing a true API that we publish along with tools to make things easier to work with. From there, our hope is that we can have a platform for more customized questions that help us move beyond check boxes and into rich interfaces.
Finally, we need to make our authors faster. I’ve mentioned before that we have a custom language we use for writing questionnaires. These “scripters” as we call them are in fact a mix of programmer, designer, statistician and project manager all rolled into one. As such there is a wide variety of skill levels that need to be supported. In this case our goal will be to extend our scripting environment to better support our more basic users while giving the advanced users tools that can improve everyone’s workflow.
The reality is that while we are effectively rewriting our application there is a clear direction that we want to go. Our first iterations have been naive as to the problems at scale. Now we have an opportunity to take some systemic bottlenecks and hopefully improve things for the foreseeable future. We’re pretty confident that we’ll see a whole new set of problems but hopefully by that time we will have gained enough understanding that we can make another shift to keep growing.