Get a Flink example program up and running in a few simple steps.
Flink runs on Linux, Mac OS X, and Windows. To be able to run Flink, the only requirement is to have a working Java 8.x installation. Windows users, please take a look at the [Flink on Windows]({{ site.baseurl }}/start/flink_on_windows.html) guide which describes how to run Flink on Windows for local setups.
You can check the correct installation of Java by issuing the following command:
{% highlight bash %} java -version {% endhighlight %}
If you have Java 8, the output will look something like this:
{% highlight bash %} java version “1.8.0_111” Java(TM) SE Runtime Environment (build 1.8.0_111-b14) Java HotSpot(TM) 64-Bit Server VM (build 25.111-b14, mixed mode) {% endhighlight %}
{% if site.is_stable %}
{% highlight bash %} $ cd ~/Downloads # Go to download directory $ tar xzf flink-*.tgz # Unpack the downloaded archive $ cd flink-{{site.version}} {% endhighlight %}
{% highlight bash %} $ brew install apache-flink ... $ flink --version Version: 1.2.0, Commit ID: 1c659cf {% endhighlight %}
{% else %}
Clone the source code from one of our repositories, e.g.:
{% highlight bash %} $ git clone https://github.com/apache/flink.git $ cd flink $ mvn clean package -DskipTests # this will take up to 10 minutes $ cd build-target # this is where Flink is installed to {% endhighlight %} {% endif %}
{% highlight bash %} $ ./bin/start-cluster.sh # Start Flink {% endhighlight %}
Check the Dispatcher's web frontend at http://localhost:8081 and make sure everything is up and running. The web frontend should report a single available TaskManager instance.
You can also verify that the system is running by checking the log files in the logs directory:
{% highlight bash %} $ tail log/flink--standalonesession-.log INFO ... - Rest endpoint listening at localhost:8081 INFO ... - http://localhost:8081 was granted leadership ... INFO ... - Web frontend listening at http://localhost:8081. INFO ... - Starting RPC endpoint for StandaloneResourceManager at akka://flink/user/resourcemanager . INFO ... - Starting RPC endpoint for StandaloneDispatcher at akka://flink/user/dispatcher . INFO ... - ResourceManager akka.tcp://flink@localhost:6123/user/resourcemanager was granted leadership ... INFO ... - Starting the SlotManager. INFO ... - Dispatcher akka.tcp://flink@localhost:6123/user/dispatcher was granted leadership ... INFO ... - Recovering all persisted jobs. INFO ... - Registering TaskManager ... under ... at the SlotManager. {% endhighlight %}
You can find the complete source code for this SocketWindowWordCount example in scala and java on GitHub.
def main(args: Array[String]) : Unit = {
// the port to connect to
val port: Int = try {
ParameterTool.fromArgs(args).getInt("port")
} catch {
case e: Exception => {
System.err.println("No port specified. Please run 'SocketWindowWordCount --port <port>'")
return
}
}
// get the execution environment
val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
// get input data by connecting to the socket
val text = env.socketTextStream("localhost", port, '\n')
// parse the data, group it, window it, and aggregate the counts
val windowCounts = text
.flatMap { w => w.split("\\s") }
.map { w => WordWithCount(w, 1) }
.keyBy("word")
.timeWindow(Time.seconds(5), Time.seconds(1))
.sum("count")
// print the results with a single thread, rather than in parallel
windowCounts.print().setParallelism(1)
env.execute("Socket Window WordCount")
}
// Data type for words with count
case class WordWithCount(word: String, count: Long)
} {% endhighlight %}
public static void main(String[] args) throws Exception {
// the port to connect to
final int port;
try {
final ParameterTool params = ParameterTool.fromArgs(args);
port = params.getInt("port");
} catch (Exception e) {
System.err.println("No port specified. Please run 'SocketWindowWordCount --port <port>'");
return;
}
// get the execution environment
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// get input data by connecting to the socket
DataStream<String> text = env.socketTextStream("localhost", port, "\n");
// parse the data, group it, window it, and aggregate the counts
DataStream<WordWithCount> windowCounts = text
.flatMap(new FlatMapFunction<String, WordWithCount>() {
@Override
public void flatMap(String value, Collector<WordWithCount> out) {
for (String word : value.split("\\s")) {
out.collect(new WordWithCount(word, 1L));
}
}
})
.keyBy("word")
.timeWindow(Time.seconds(5), Time.seconds(1))
.reduce(new ReduceFunction<WordWithCount>() {
@Override
public WordWithCount reduce(WordWithCount a, WordWithCount b) {
return new WordWithCount(a.word, a.count + b.count);
}
});
// print the results with a single thread, rather than in parallel
windowCounts.print().setParallelism(1);
env.execute("Socket Window WordCount");
}
// Data type for words with count
public static class WordWithCount {
public String word;
public long count;
public WordWithCount() {}
public WordWithCount(String word, long count) {
this.word = word;
this.count = count;
}
@Override
public String toString() {
return word + " : " + count;
}
}
} {% endhighlight %}
Now, we are going to run this Flink application. It will read text from a socket and once every 5 seconds print the number of occurrences of each distinct word during the previous 5 seconds, i.e. a tumbling window of processing time, as long as words are floating in.
{% highlight bash %} $ nc -l 9000 {% endhighlight %}
{% highlight bash %} $ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000 Starting execution of program
{% endhighlight %}
The program connects to the socket and waits for input. You can check the web interface to verify that the job is running as expected:
stdout. Monitor the TaskManager's output file and write some text in nc (input is sent to Flink line by line after hitting ):{% highlight bash %} $ nc -l 9000 lorem ipsum ipsum ipsum ipsum bye {% endhighlight %}
The .out file will print the counts at the end of each time window as long as words are floating in, e.g.:
{% highlight bash %} $ tail -f log/flink--taskexecutor-.out lorem : 1 bye : 1 ipsum : 4 {% endhighlight %}
To stop Flink when you're done type:
{% highlight bash %} $ ./bin/stop-cluster.sh {% endhighlight %}
Check out some more [examples]({{ site.baseurl }}/examples) to get a better feel for Flink's programming APIs. When you are done with that, go ahead and read the [streaming guide]({{ site.baseurl }}/dev/datastream_api.html).
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