Trifork Blog

Banner Elixir

Posts by Rik van den Ende

Server side applications in Apple’s Swift

May 2nd, 2016 by
(http://blog.trifork.com/2016/05/02/server-side-applications-in-apples-swift/)

In 2014, Apple announced the release of Swift, a new programming language for all their platforms. Their programming language of choice on iOS and OSX has always been Objective-c, a language which is a bit dated (it predates C++) and as it has had new features (and syntaxes) bolted onto it every few years, it carries quite a bit of baggage. It seems I wasn’t the only one with this opinion, as the release of swift was greeted with great enthusiasm, and has been adopted very rapidly.

Swift combines all the features that are fashionable in a general purpose language today, without the feeling that they were bolted on after the fact. While building an iOS client for our customer Gerimedica in swift, I found myself wishing I could use this language on the server side as well as in the client. At WWDC 2015, Apple announced the intention to open source the language, and release a Linux version, so it looked like it could become a reality. Since december 2015, the sources have been available on github, and builds for OSX and Ubuntu are made available roughly twice per month.

PerfectLib

A number of groups and companies saw an opportunity to be among the first with something that was obviously going to be big. One of the first was PerfectSoft, a startup that aims to be the one big framework for all your server side development in swift. They started building their framework as soon as the open source release of swift was announced, and have been advertising their product everywhere. Because they started development before anyone outside Cupertino had a good idea what the release would look like, it only worked on OSX at first, and it didn’t use the Swift Package Manager, the intended default build and dependency management tool for swift. At the time, the framework compiled to one big binary, that you had to include in your build manually. They have a beautiful website and good documentation, but it just wasn’t working when I tried it. I intend to try this framework out again at a later date.

IBM

The biggest player (other than Apple) to openly jump on the swift bandwagon is IBM. As soon as the open source release of Swift was announced, IBM announced the Swift Sandbox, their Swift based version of google’s golang playground. It is a web based repl that can be shared online by sharing a URL. Cool, but not extremely useful, as unlike go, swift already comes with a repl. The real significance of this is not the swift sandbox itself, but the message that IBM is interested in this technology and intends to be involved. IBM isn’t the kind of company to back technologies just because they like them, so they either see an opportunity, or a potential strategic interest. At the moment, IBM’s swift related activities seem to be associated with their PaaS solution BlueMix, so they are likely working on the Swift / IBM version of google’s app engine for go. IBM offers its own web framework for swift: kitura. Kitura turns out to be less than trivial to install and for now somewhat bare bones, but as this is IBM, it is worth dedicating another blog post to it at a later date. Also check out their overview of the most popular, most active and most essential open source projects on github for swift.

Read the rest of this entry »

Simulating a bad network for testing

July 3rd, 2012 by
(http://blog.trifork.com/2012/07/03/simulating-a-bad-network-for-testing/)

In a development environment, and often in the test and QA environments as well, we are thankfully blessed with a network that is for all intents and purposes infinitely fast, infinitely reliable and not shared with anyone else. Sometimes this causes you to miss a bug that only becomes apparent once your application has been released into the wild, where it has to deal with latency, packet loss and protocol violations.

To reproduce such bugs, it would be nice to have a network that is bad in a precisely controlled way. On a Linux machine, you can simulate one with netem. There is a wide range of possibilities with this tool, most of which are more useful to a network engineer than to a programmer or software tester, but I’ll give some simple examples, and demonstrate their effect with mtr.

First let’s take a look at the normal state of the network:

$ mtr -c 100 --report orange11.nl
HOST: cartman                    Loss%  Snt   Last   Avg  Best  Wrst StDev
1.|-- lobby                      0.0%   100    0.2   0.2   0.1   0.2   0.0
2.|-- backup1.orange11.nl        0.0%   100    2.4   4.0   2.0   9.1   1.7
3.|-- 10.0.0.30                  0.0%   100    5.0   4.0   2.2  10.4   1.6

That’s not too bad. Now we’ll simulate an average packet delay of 100 ms with a variability of 50ms, and a packet loss of 5%:

$ sudo tc qdisc add dev eth0 root netem delay 100ms 50ms loss 5%
$ mtr -c 100 --report orange11.nl
HOST: cartman                    Loss%  Snt   Last   Avg  Best  Wrst StDev
1.|-- lobby                      8.0%   100  129.3  96.6  50.2 147.8  26.0
2.|-- backup1.orange11.nl        3.0%   100  120.1 103.9  54.4 157.5  27.8
3.|-- 10.0.0.30                  4.0%   100   90.3 103.4  53.9 154.3  29.3

Pretty much as we would expect, the best ping times are around 50ms, the worst around 150ms, with an average around 100ms. The packet loss is a bit more random that I expected, but it should average out around 5% if we left mtr running for much longer than 100 cycles.

I can recommend trying out whatever project you are working on now, with a packet delay of 500ms to see if strange things happen in a reasonable worst case. It is important to realize that this tool can only shape the traffic that we’re sending, not receiving, so if the networked application is running on a different server, only your uploads and ACK packets should be affected.

You don’t have to reboot to get your network back to normal:

$ sudo tc qdisc del dev eth0 root

A great deal more can be done to shape your network traffic for better or for worse, such as rate control, prioritizing one destination over another, introducing packet corruption, duplication, or reordering etc, but these are outside the scope of this post.

A nice tutorial with examples can be found at the linuxfoundation.org and if you are interested in reading more about the background of network traffic control in the Linux kernel, I can recommend the Linux Advanced Routing & Traffic Control HOWTO.

Let me know how you get on won’t you?