Creating charts in GWT was never so easy. (OFC GWT)

In Java world there are many libaries which allow to create charts (with the most popular is JFree Chart Library). None of this libraries are however simple, powerfull and gives pretty looking charts. This is why I investigated Open Flash Chart which is library for charting written in flash. Fortunately there is a port for GWT which allows to use OFC in RIA applications. So in the next few paragraphs I will show how to use OFC GWT (this is the name of GWT port) and why this library is so amazing. Enjoy.

Starting point is OFC GWT homepage. This is the place from where porting libary (with OFC inside) could be downloaded.  I prefer to use maven  instead of downloading, so here is part of my pom.xml configured for OFC GWT. Notice that the library version is 1.3.0 but in the nearest future there will be release of new 2.0.1 version (now available in beta).

..
<br />
<repositories><br />
<repository><br />
<id> OFCGWT repo</id><br />
<url>http://logicaldoc.sourceforge.net/maven</url><br />
</repository><br />
</repositories><br />


<br />
<dependency><br />
<groupid>ofcgwt</groupid><br />
<artifactid>ofcgwt</artifactid><br />
<version>1.3.0</version><br />
</dependency><br />

..

Now, after the OFC GWT library is downloaded, it’s time to make some code. In OFC GWT there are many sort of charts such as: bar (horizontal, vertical), pie, scater, radar etc. All them are represented in GWT by ChartWidget class (which extends Widget). This class is a wrapper, which can be placed on any GWT panel, container just like normal GWT widget. ChartWidget renders chart on screen by communicating with OFC library by JSON.
JSON data, which describes chart to draw, have to be created by ChartData class. This is the heart of the OFC GWT port, the most important class. This class objects are used to configure title, axies, data and chart type to render. Below is the code which shows how easy is to use both above classes.

..
<br />
FlowPanel panel = new FlowPanel();

ChartWidget chart = new ChartWidget();
ChartData chartData = new ChartData(title);
chartData.setBackgroundColour(“#ffffff”);//white background
chartData.addElements(createPieChart());
chart.setSize(“350”, “350”);
chart.setJsonData(chartData.toString());

panel.add(chart);

..

Only one thing left – function which create pie chart. In OFC You can chose from various charts. All of them are easy and simmilar in use. There is a good demo app with source code in the library, where You can find out how to configure each graph. Here is my code, which creates animated and transparent pie chart without labels on graph.

<br />
private PieChart createPieChart() {<br />
PieChart pieChart = new PieChart();<br />
pieChart.setAlpha(0.3f);    //set transparency<br />
pieChart.setNoLabels(true); // w do not want labels on screen<br />
pieChart.setAnimate(true);<br />
pieChart.setGradientFill(true);<br />
pieChart.setColours("#779C00", "#0000ff");<br />
pieChart.setTooltip("#label# - #val#");<br />
pieChart.addSlices(new PieChart.Slice(30, "TV's in Poland")); //this should be passed in parameter<br />
pieChart.addSlices(new PieChart.Slice(70, "Radios in Poland"));//this should be passed in parameter<br />
return pieChart;<br />
}<br />

And thats all. This is all You need to draw pretty looking graphs. No playing with css’es or colors, no complicated building of data sets and what is the most important, not even line of server side code!

Here is the running example of OFC GWT charts, where You can see and experiment with different types and effects.

One more thing. OFC GWT is not only library to draw charts. It also allows to handle user interaction. This is done by IOnClickListener’s. More about themcould be found in OFC GWT JavaDocs.

More graph examples:

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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.