Software and Other Shenanigans

Java 8 introduced a lot of cool features, 1 of the most useful of which was the stream() method. This nifty little method lets you treat an Iterable as a stream, enabling cool things like lambdas operating over a list. Related to stream() is parallelStream(). This lets you group your stream into smaller streams that are run in, you guessed it, parallel. Specifically, your data is processed in a thread pool the size of the number of cores on your machine, minus the one running your app. That’s a handy piece of information you’re going to want to keep in mind before you start throwing this nifty little call around in your code.  Continue reading »

Recently, we had a Kinesis consumer back up due to a deployment problem. Sadly, this problem went on for 2 days without us noticing, so we were pretty backed up. Now in theory, we should have been able to catch back up to real-time data sometime later that calendar day. In reality, we fell 2 days behind and it took us 2 days to catch up. That’s not acceptable, and I wanted to document the list of things we tried and how well they worked and to document our process for looking for the bottleneck. Obviously, there’s tons of room for improvement in the code, but there’s also a lot of room for improvement in how we were doing things before, and probably a lot of room for in how we went about trying to fix the issue.

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A while back at work, we noticed a periodic issue where remote jobs just weren’t being run. That was being caused by the fact that the jobs weren’t actually being sent to the remote AWS instances that were meant to be running said jobs. Just why the jobs weren’t being sent out was a mystery, but a simple bouncing of either the remote servers or the main application itself seemed to get things moving again. In the meantime, we were off on a hunt for just why these jobs were no longer getting dispatched.

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