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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Recently at storm-users

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()
  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()
  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

Mock Retrofit using Dagger and Mockito

Retrofit is one of the most popular REST client for Android, if you never use it, it is high time to start. There are a lot of articles and tutorial talking about Retrofit. I just would like to show how to mock a REST server during develop of app and i...Retrofit is one of the most popular REST client for Android, if you never use it, it is high time to start. There are a lot of articles and tutorial talking about Retrofit. I just would like to show how to mock a REST server during develop of app and i...