setup_quickstart.md

title	nav-title	nav-parent_id	nav-pos
Quickstart	<i class="fa fa-power-off title appetizer" aria-hidden="true"></i> Quickstart	root	2

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Get a Flink example program up and running in a few simple steps.

Setup: Download and Start Flink

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 %}

1. Download a binary from the [downloads page](http://flink.apache.org/downloads.html). You can pick any Hadoop/Scala combination you like. If you plan to just use the local file system, any Hadoop version will work fine. 2. Go to the download directory. 3. Unpack the downloaded archive.

{% highlight bash %} $ cd ~/Downloads # Go to download directory $ tar xzf flink-*.tgz # Unpack the downloaded archive $ cd flink-{{site.version}} {% endhighlight %}

For MacOS X users, Flink can be installed through [Homebrew](https://brew.sh/).

{% highlight bash %} $ brew install apache-flink ... $ flink --version Version: 1.2.0, Commit ID: 1c659cf {% endhighlight %}

{% else %}

Download and Compile

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 %}

Start a Local Flink Cluster

{% 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 %}

Read the Code

You can find the complete source code for this SocketWindowWordCount example in scala and java on GitHub.

{% highlight scala %} object SocketWindowWordCount {

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 %}

{% highlight java %} public class SocketWindowWordCount {

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 %}

Run the Example

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.

• First of all, we use netcat to start local server via

{% highlight bash %} $ nc -l 9000 {% endhighlight %}

• Submit the Flink program:

{% 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:

• Words are counted in time windows of 5 seconds (processing time, tumbling windows) and are printed to 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 %}

Next Steps

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).

{% top %}