In my post about building and running programs in Iron.Io, I needed to switched over to my Ubuntu VM to build linux versions of my test programs locally. I love the ability to have Ubuntu available to me for building and testing my code. However, if I can stay on the Mac side it is better.
I have wanted to learn how to cross compile my Go programs for the two platforms I use, darwin/amd64 and linux/amd64.
I always find it interesting when I realize that something I have been practicing or dealing with for a long time has a name. This time it happens to be race conditions. This is something you can’t avoid thinking about as soon as you have more than one routine sharing any kind of resource. If you’re not thinking about race conditions in your code, now is the time.
A race condition is when two or more routines have access to the same resource, such as a variable or data structure and attempt to read and write to that resource without any regard to the other routines.
This article was written for and published by Gopher Academy
I was looking at a code sample that showed a recursive function in Go and the writer was very quick to state how Go does not optimize for recursion, even if tail calls are explicit. I had no idea what a tail call was and I really wanted to understand what he meant by Go was not optimized for recursion. I didn’t know recursion could be optimized.
After working in Go for some time now, I learned how to use an unbuffered channel to build a pool of goroutines. I like this implementation better than what is implemented in this post. That being said, this post still has value in what it describes.
On more than one occasion I have been asked why I use the Work Pool pattern. Why not just start as many Go routines as needed at any given time to get the work done?
I am working on building code to load polygons for the different Marine Forecast areas in the United States. These polygons need to be stored in MongoDB and there is a special way that needs to be done. It would not have been a big deal if it wasn’t for this fact. There isn’t just one polygon for each area. There is an external polygon and then zero to many interior polygons that need to be stored in relationship.
Iron.io has a product called IronWorker which provides a task oriented Linux container that you can run your programs inside. If you are not sure what I mean, think of this as having a temporary Linux virtual machine instantly available for your personal but short term use. IronWorker allows you to load your binaries, code files, support files, shells scripts and just about anything else you may need to run your program in the container.
In my Outcast data server I have several data retrieval jobs that run using different go routines. Each routine wakes up on a set interval. The most complex job is the downloading of radar images. What makes this complex is that there are 155 radar stations throughout the United States that take a new picture every 120 seconds. All these radar images can be put together to create a mosaic. When the go routine wakes up to pull down the new images, it must do this as quickly as possible for all 155 stations.