Flink 学习笔记 - 本地安装

本地化部署教程

• Setup: Download and Start Flink
  • Start a Local Flink Cluster
• Read the Code
• Run the Example
• Next Steps

通过一些简单的步骤运行一个Flink的例子

 

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 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:

java -version

If you have Java 8, the output will look something like this:

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)

• Download and Unpack
• MacOS X

1. Download a binary from the downloads page. 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.

$ cd ~/Downloads        # Go to download directory
$ tar xzf flink-*.tgz   # Unpack the downloaded archive
$ cd flink-1.8.0

For MacOS X users, Flink can be installed through Homebrew.

$ brew install apache-flink
...
$ flink --version
Version: 1.2.0, Commit ID: 1c659cf

Start a Local Flink Cluster

$ ./bin/start-cluster.sh  # Start Flink

通过浏览器打开这个地址： 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:

$ 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.

Read the Code

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

• Scala
• Java

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
// 分析数据，拆分、分组、时间窗口、汇总
        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)
}

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;
        }
    }
}

运行

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.

现在可以运行这个 Flink 应用，每5秒钟他会从 socket 中读取过去5秒钟出现的每个单词计数。这里是一个事件驱动的翻滚窗口（Tumbling Window）

在流处理应用中，数据是连续不断的，因此我们不可能等到所有数据都到了才开始处理。当然我们可以每来一个消息就处理一次，但是有时我们需要做一些聚合类的处理，例如：在过去的1分钟内有多少用户点击了我们的网页。在这种情况下，我们必须定义一个窗口，用来收集最近一分钟内的数据，并对这个窗口内的数据进行计算。

窗口可以是时间驱动的（Time Window，例如：每30秒钟），也可以是数据驱动的（Count Window，例如：每一百个元素）。一种经典的窗口分类可以分成：翻滚窗口（Tumbling Window，无重叠），滚动窗口（Sliding Window，有重叠），和会话窗口（Session Window，活动间隙）。

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

$ nc -l 9000   # 如果发现本机的 9000 端口被占用，可以换成其他端口，如 9527

• Submit the Flink program:

$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000
Starting execution of program

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 ):

$ nc -l 9000
lorem ipsum
ipsum ipsum ipsum
bye

The .out file will print the counts at the end of each time window as long as words are floating in, e.g.:

$ tail -f log/flink-*-taskexecutor-*.out
lorem : 1
bye : 1
ipsum : 4

To stop Flink when you’re done type:

$ ./bin/stop-cluster.sh