DDevelopment & Design

Running Qt4 Examples on Embedded Linux using ARM emulator

In this article I will show how to run Qt4-Embedded Examples on Angstrom Linux using QEMU. The procedure doesn’t require any compilation or cross compilation. It uses Angstrom Linux precompiled packages, online image builder, and works both on Windows and Linux. Qt4 Embedded allows to run Qt applications directly in Linux Framebuffer, bypassing X Windows completely. This is especially important during embedded development, because it allows to save a lot of memory and start up time. Qt4 has a rich set of examples directly embedded into Qt sources. Below is a few samples of how it looks like:

I will show how to run them. First, you need to install QEMU. For Windows, the easiest way is to download zipped executables, which I shared here:

Qemu-windows-0151. For Linux it’s usually apt-get install qemu-system. Then, we need to build Angstrom image. For those unpatient, I shared a prebuilt image here: angstrom-qt4-embedded. Angstrom has online image builder available here: Angstrom Image Builder. You need to pick console image and download it. The small trick is that you need to download kernel image yourself (from here: kernel-image-2.6.37.2_2.6.37-r4.6_qemuarm.ipk) and unpack it using ar -x kernel-image.ipk command. This is because online image builder doesn’t include kernel image for some reason. However this step is not required if you download the image I shared. Next, you need to start QEMU using kernel image and prebuilt angstrom image. The command looks like this: qemu-system-arm -M versatilepb -usb -usbdevice wacom-tablet -show-cursor -m 64 -kernel zImage-2.6.37.2 -hda disk.img -append “root=/dev/sda2 rw” For convenience, I prepared run script, which does that. Next, you need to login as root and install qt4-embedded using command: opkg install qt4-embedded. This can be again skipped if you use the image I prepared. In order to run demos, you need to use this command: qtdemoE -qws It looks like this:

You can run the other examples from Qt, in standalone mode from

/usr/bin/qtopia directory. You need to use similar command app -qws. The command is required to initialize Qt framebuffer. It is possible to run a few executables on the same display. In order to do this, you need to run the first one only with qws parameter. The other apps will connect to it. Have fun!

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DDevelopment & Design

Log4j and MDC in Grails

Log4j provides very useful feature: MDC - mapped diagnostic context. It can be used to store data in context of current thread. It may sound scary a bit but idea is simple.

My post is based on post http://burtbeckwith.com/blog/?p=521 from Burt Beckwith's excellent blog, it's definitely worth checking if you are interested in Grails.

Short background story...


Suppose we want to do logging our brand new shopping system and we want to have in each log customer's shopping basket number. And our system can be used at once by many users who can perform many transactions, actions like adding items and so on. How can we achieve that? Of course we can add basket number in every place where we do some logging but this task would be boring and error-prone. 

Instead of this we can use MDC to store variable with basket number in map. 

In fact MDC can be treated as map of custom values for current thread that can be used by logger. 


How to do that with Grails?


Using MDC with Grails is quite simple. All we need to do is to create our own custom filter which works for given urls and puts our data in MDC.

Filters in Grails are classes in directory grails-app/conf/* which names end with *Filters.groovy postfix. We can create this class manually or use Grails command: 
grails create-filters info.rnowak.App.Basket

In result class named BasketFilters will be created in grails-app/conf/info/rnowak/UberApp.

Initially filter class looks a little bit empty:
class BasketFilters {
    def filters = {
        all(controller:'*', action:'*') {
            before = {

            }
            after = { Map model ->

            }
            afterView = { Exception e ->

            }
        }
    }
}
All we need to do is fill empty closures, modify filter properties and put some data into MDC.

all is the general name of our filter, as class BasketFilters (plural!) can contain many various filters. You can name it whatever you want, for this post let assume it will be named basketFilter. 

Another thing is change of filter parameters. According to official documentation (link) we can customize our filter in many ways. You can specify controller to be filtered, its actions, filtered urls and so on. In our example you can stay with default option where filter is applied to every action of every controller. If you are interested in filtering only some urls, use uri parameter with expression describing desired urls to be filtered.

Three closures that are already defined in template have their function and they are started in these conditions:

  • before - as name says, it is executed before filtered action takes place
  • after - similarly, it is called after the action
  • afterView - called after rendering of the actions view
Ok, so now we know what are these mysterious methods and when they are called. But what can be done within them? In official Grails docs (link again) under section 7.6.3 there is a list of properties that are available to use in filter.

With that knowledge, we can proceed to implementing filter.

Putting something into MDC in filter


What we want to do is quite easy: we want to retrieve basket number from parameters and put it into MDC in our filter:
class BasketFilters {
    def filters = {
        basketFilter(controller:'*', action:'*') {
            before = {
                MDC.put("basketNumber", params.basketNumber ?: "")
            }
            after = { Map model ->
                MDC.remove("basketNumber")
            }
        }
    }
}

We retrieve basket number from Grails params map and then we put in map under specified key ("basketNumber" in this case), which will be later used in logger conversion pattern. It is important to remove custom value after processing of action to avoid leaks.

So we are putting something into MDC. But how make use of it in logs?


We can refer to custom data in MDC in conversion patter using syntax: %X{key}, where key is our key we used in filter to put data, like:
def conversionPattern = "%d{yyyy-MM-dd HH:mm:ss} %-5p %t [%c{1}] %X{basketNumber} - %m%n"


And that's it :) We've put custom data in log4j MDC and successfully used it in logs to display interesting values.