YngwieWang commented on a change in pull request #7751: [FLINK-11608] [docs] Translate the "Local Setup Tutorial" page into Chinese URL: https://github.com/apache/flink/pull/7751#discussion_r309579053
########## File path: docs/tutorials/local_setup.zh.md ########## @@ -0,0 +1,288 @@ +--- +title: "本地安装教程" +nav-title: 'Local Setup' +nav-parent_id: setuptutorials +nav-pos: 10 +--- +<!-- +Licensed to the Apache Software Foundation (ASF) under one +or more contributor license agreements. See the NOTICE file +distributed with this work for additional information +regarding copyright ownership. The ASF licenses this file +to you under the Apache License, Version 2.0 (the +"License"); you may not use this file except in compliance +with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, +software distributed under the License is distributed on an +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, either express or implied. See the License for the +specific language governing permissions and limitations +under the License. +--> + +* This will be replaced by the TOC +{:toc} + +通过几个简单的步骤来启动并运行 Flink 程序。 + +## 安装：下载并启动 Flink + +Flink 可以在 __Linux, Mac OS X, and Windows__ 环境中运行。要想运行 Flink 仅仅需要安装 __Java 8.x__ 。 Windows 用户， 请查看 [Flink on Windows]({{ site.baseurl }}/tutorials/flink_on_windows.html) 上面描述了如何在 windows 上以本地模式运行 Flink。 + +你可以用下面的命令来检查一下是否正确的安装了 Java 程序： + +{% highlight bash %} +java -version +{% endhighlight %} + +如果你已经安装了 Java 8，它的输出应该是这个样子的： + +{% 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 %} +<div class="codetabs" markdown="1"> +<div data-lang="Download and Unpack" markdown="1"> +1. 从 [downloads page](http://flink.apache.org/downloads.html)下载二进制文件。你可以选择任何你喜欢的 Hadoop/Scala 组合。如果你仅仅想在本地使用，那么任何的 Hadoop 版本都可以。 +2. 去下载后的目录。 +3. 解压下载的文件。 + + +{% highlight bash %} +$ cd ~/Downloads # Go to download directory +$ tar xzf flink-*.tgz # Unpack the downloaded archive +$ cd flink-{{site.version}} +{% endhighlight %} +</div> + +<div data-lang="MacOS X" markdown="1"> +对于 MacOS X 用户, Flink 可以通过 [Homebrew](https://brew.sh/)进行安装。 + +{% highlight bash %} +$ brew install apache-flink +... +$ flink --version +Version: 1.2.0, Commit ID: 1c659cf +{% endhighlight %} +</div> + +</div> + +{% else %} +### 下载和编译 +从我们的 [repositories](http://flink.apache.org/community.html#source-code) 中下载源码，比如： + +{% 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 %} + +### 启动 Flink 本地集群 + +{% highlight bash %} +$ ./bin/start-cluster.sh # Start Flink +{% endhighlight %} + +通过 [http://localhost:8081](http://localhost:8081) 检查 __Dispatcher's web frontend__ 以确保所有的程序已经启动并正在运行。Web 界面上会显示一个可用的 TaskManager 实例。 + +<a href="{{ site.baseurl }}/page/img/quickstart-setup/jobmanager-1.png" ><img class="img-responsive" src="{{ site.baseurl }}/page/img/quickstart-setup/jobmanager-1.png" alt="Dispatcher: Overview"/></a> + +你也可以检查 log 文件来确认程序是否正在运行： + +{% 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 %} + +## 阅读代码 + +你可以在 Github 上看到分别用 [scala](https://github.com/apache/flink/blob/master/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/socket/SocketWindowWordCount.scala) and [java](https://github.com/apache/flink/blob/master/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/socket/SocketWindowWordCount.java) 书写的 SocketWindowWordCount 的完整样例 + +<div class="codetabs" markdown="1"> +<div data-lang="scala" markdown="1"> +{% 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 %} +</div> +<div data-lang="java" markdown="1"> +{% 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 %} +</div> +</div> + +## 运行样例 + +现在，我们去运行 Flink 程序。 只要有单词输入，它就从一个套接字中读取文本，每5秒打印一次前5秒中每个不同单词的出现次数，也就是一个 tumbling window 的处理时间 + +* 首先，我们用 **netcat** 来启动一个本地的服务： + +{% highlight bash %} +$ nc -l 9000 +{% endhighlight %} + +* 提交 Flink 程序： + +{% highlight bash %} +$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000 +Starting execution of program + +{% endhighlight %} + + 这个程序会连接 socket 服务并等待输入。你可以通过 web interface 来验证 job 是否如期运行: + + <div class="row"> + <div class="col-sm-6"> + <a href="{{ site.baseurl }}/page/img/quickstart-setup/jobmanager-2.png" ><img class="img-responsive" src="{{ site.baseurl }}/page/img/quickstart-setup/jobmanager-2.png" alt="Dispatcher: Overview (cont'd)"/></a> + </div> + <div class="col-sm-6"> + <a href="{{ site.baseurl }}/page/img/quickstart-setup/jobmanager-3.png" ><img class="img-responsive" src="{{ site.baseurl }}/page/img/quickstart-setup/jobmanager-3.png" alt="Dispatcher: Running Jobs"/></a> + </div> + </div> + +* 单词以5秒的时间窗口 (processing time, tumbling windows) 来计数，并打印到 stdout。监视 TaskManager的输出文件和 nc 的输入（输入的内容被逐行发送到 flink ） <RETURN>): Review comment: ```suggestion * 单词以5秒的时间窗口（处理时间，翻滚窗口）来计数，并打印到 `stdout`。监视 TaskManager 的输出文件并在 `nc` 中输入一些文本（输入的内容被逐行发送到 Flink）： ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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