APIs are tricky things to maintain. Because they’re designed to be open by nature, it’s hard to test every possible way someone could use it. That makes incredibly easy to break if you don’t think through any changes you’re making carefully, with an emphasis on what a developer is expecting. Luckily, there are a few things you can do to limit the amount of times you inadvertently break a bunch of applications with a well-meaning API update.
I transferred teams at work recently, and spent about a week trying to get their code running on my laptop so I can do useful development work. This is in addition to trying to wrap my head around the existing codebase and figuring out how to test my changes. It’s not that the code is bad, it’s just getting all the ****ing components hooked up, communicating with each other, and playing nicely together an exercise in impossibility. Coming from a group that ran everything in AWS, going back to managing all the third-party services in a development environment makes me want to flip my desk over and start screaming about what the **** is wrong with everyone.
1 of the most common themes around any startup these days is how they intend to “disrupt” something. Founders aren’t saying they plan to “disrupt” something as a consequence of what they’re doing, but rather using the term like it’s an actual objective – “disrupt the {industry name here} industry.” Here’s the problem, I don’t go into a store, online, or pay people to disrupt something, I pay people to either give me a thing that I find useful or at the very least want, or to do something that makes my life better in some way. “Disruption” isn’t either of those things. Continue reading »
You’re sitting there at work and word reaches you that your application is misbehaving. What’s the first thing you do? Pull up the logs and see what they say. And this is where you may wish that you had an entire class in school dedicated to the art of writing useful log messages. Good logging is an art, and it’s 1 that most of us don’t learn until we’re stuck trying to diagnose something in the wild with subpar messages to guide us.
I mentioned in a previous blog post that we had used DynamoDB on an internal project at Bronto (my employer, but I don’t speak for them – they have people for that). That project is the Bronto Cantina – which launched to the whole Durham office a couple of weeks ago. It was a pretty neat little application, most of which was written over the course of a couple of days, followed by bits and pieces of cleanup afterwards to get a test setup up and running. Now we’re live, and I wanted to say a few words about it.
So I ran into an interesting issue a while back doing something on a project at work. I was doing something that involved interacting with files on S3, and had updated the AWS libraries my application was using, but I was running into errors every time I tried to do anything that touched S3. The issue wound up being a dependency conflict between my code and an S3 wrapper utility we wrote around the AWS SDK. While the main project’s AWS SDK was up-to-date, the utility had older versions that were causing the errors. Although I had used Amazon’s BOM and thought that made sure that my AWS libraries were all set to a particular version, the reality was that assurance didn’t spread to other libraries my project pulled in. The solution was simple enough – update the AWS SDK versions in our other internal library, but that did lead to the question, how does one manage dependencies between projects?
Amazon’s DynamoDB service is a managed NoSQL database that promises great speeds that allow it to be “…a great fit for mobile, web, gaming, ad tech, IoT, and many other applications.” That claim is pretty much just a pipe dream. The reality is that DynamoDB is a terrible fit for most applications, and your best bet would be to prefer regular NoSQL databases and manage the machines yourself.
I recently finished up some data aggregation work involving Apache’s Hive, and as a means of getting some MapReduce work off the ground quickly, it’s pretty good. Hive’s goal is to abstract away MapReduce behind basic SQL queries, and on that front it succeeds. The fact that I’m ultimately doing MapReduce jobs is hidden except for what would look like a minor quirk if I didn’t know that was what was going on under the covers. That said, there were a couple of things I noticed both during development and with running the jobs on Amazon’s EMR service that are worth noting.
So I hadn’t touched Campus Navigator since it was first released in February 2013. Elon University, which I used for my sample implementation, has changed a lot in the nearly 3 years since I published that example, so I spent a little time during the Christmas down period updating that reference implementation, which is now live.
It’s a tale as old as development – you make an application, and now you need to sell it. That means you need to have a demo, and demos require data in there. The dilemma is, what do you do to get that data? Do you have a demo app in a sandboxed environment, do you just add it to your regular production database, do you just take some screenshots of what the app looks like with data from a development environment, or do you do something else entirely? It seems like a stupid thing to worry about, until you’re actually trying to figure it out, then it becomes really important because whatever you decide to do about sample data, you’re going to have to live with forever.