Ramblings on code, startups, and everything in between
We’ve been doing a bit of AngularJS work (more on that later) recently and true to its reputation there’s an “Angular way” to accomplish most things. Interestingly, one area where I couldn’t find a “one true way” was how to facilitate mixins between controllers or scopes.
Quickly taking a step back, a “mixin” is a form of horizontal reuse that allows two objects to share code without necessarily sharing a common ancestor in an inheritance chain. With concrete examples, you might have a Dashboard and Billing controller which need to share formatting logic but nothing else you’d want to use mixins vs. traditional inheritance. In traditional object oriented language mixins are typically referred to as Traits.
Anyway, back to AngularJS. Let’s say we have some simple logic that we want to share between two scopes:
It’s a contrived example but the “idea” is that you want to share the “selectAnswer” and “getAnswerClass” functions between $scopes of two unrelated controllers. After doing some research, it seems like the cleanest way to do this in Angular is to create a service that contains the functions, inject that into the controller, and then use angular.extend() to add them to the $scope as needed:
And that’s pretty much all there is to it. I’m pretty new to the Angular dance so I’d love any feedback!
As we continue to expand in 2015 we’re looking to add another developer to our team. Currently we’re seeking a junior level engineer to join us! A few attributes of a person that we’re looking for:
A few of the perks:
For some more detailed information on the job please visit the posting. If you are, or know, a developer who is looking for a new opportunity lets connect!
Posted In: General
So how do you go about making a puzzle? You can see the end result at HTML5 Canvas Puzzle and the code is online at https://github.com/Setfive/setfive.github.com/tree/master/canvas_puzzle.
As it turns out generating an arbitrary puzzle programatically is reasonably complicated. The best explanation I could find on how to accomplish this is at https://www.allegro.cc/forums/thread/586750/603411#target. Conceptually, the process looks straightforward enough and you could probably manually do it on a whiteboard. Unfortunately, the issue I ran into with this approach is that drawing bezier curves and splines programmatically on a Canvas is a bit involved. I also don’t have a background in vector graphics so I was getting stuck in the weeds drawing lines.
Discounting generating the puzzle entirely on the fly, an alternative approach would be to use a fixed set of available pieces and then “fill in” a grid depending on how large the image area is. Conceptually, the idea is to construct a closed grid of pieces where some number of the pieces can be repeated and then repeat those pieces as needed to cover the target image. The templated pieces I used are in /puzzle_pieces/.
Walking through the code, the steps to build a puzzle are fairly straightforward:
And that’s about it. One other “trick” is that you can use Window.requestAnimationFrame to avoid locking the UI when you’re creating the masked images since it’s a compute intensive task.
Anyway, as always questions and comments welcome.
Recently we’ve been working with one of our clients to build application for use with AppNexus. We were faced with a challenge which required a bunch of different technologies to all come together and work together. Below I’ll try to list out how we approached it and what additional challenges we faced.
First came the obvious challenge: How to handle at least 25,000 requests per second. Our usual language of choice is PHP and knew it was not a good candidate for the project. Instead we wanted to do some benchmarks on a number of other other languages and frameworks. We looked at Rusty/Nginx/Lua, Go, Scala, and Java. After some testing it appeared that Java was the best bet for us. We initially loaded up Jetty. We knew that this had a bit more baked in than we needed, but it was also the quickest way to get up and running and could be migrated away from fairly easily. The idea overall was to keep the parsing of the request logic separate from the business logic. In our initial tests we were able to get around 20,000 requests a second using Jetty, which was good, but we wanted better.
Jetty was great at breaking down the incoming HTTP requests to easily work with, it even provided an out of the box general statistics package. However, we didn’t need much heavy lifting on the HTTP side, what we were building required very little complexity on with regards to HTTP protocol. Jetty in the end was spending too many CPU cycles for what we needed. We looked to Netty next.
Netty out of the box is not as friendly as Jetty as it is much lower level. That said, it wasn’t too much work to get Netty up and running responding to HTTP request. We ported over most of the business logic from our Jetty code and were off to the races. We did have to add our own statistics layer as Netty didn’t have an embedded one for what we were looking for. After some fine tuning with Netty we were able to start to handle over 40,000 requests per second. This part of the puzzle was solved.
On our DB side we had heard great things about Aerospike in terms of performance and some of its features. We ended up using this on the backend. When we query Aerospike we have the timeout set at 3ms. We’ll get around one or two request timeouts per second, or about 0.0025% of the time we’ll timeout, not too shabby. One of the nice features of Aerospike is the XDR function of the enterprise version. With this we can have multiple Aerospike clusters which all stay in sync from a master cluster. This lets us load our data onto one machine, which isn’t handling all the requests, and then it is replicated to the machines which are handling all the requests.
All in all we’ve had a great experience with the Netty and Aerospike integration. We’re able to consistently handle around 40,000 requests a second with the average response time (including network time) of 4ms.
The first step is to create a script that will be executed by PhantomJS. This script will do the following:
Next, we want to create a PHP function that actually executes the above script and converts the html to a SimpleXmlElement object.
Finally, running the function from step 3 should result in something like this.