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_r313789732
 
 

 ##########
 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}
+
+只需要几个简单的步骤即可启动并运行 Flink 示例程序。
+
+## 安装:下载并启动 Flink 
+
+Flink 可以在 __Linux、 Mac OS X、 和 Windows__ 环境中运行。为了能够运行 Flink 唯一的要求是安装 __Java 
8.x__ 。 Windows 用户, 请查阅[在  Windows 上运行 Flink]({{ 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,应该输出如下内容:
+
+{% 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[下载页](http://flink.apache.org/downloads.html)下载二进制文件。你可以选择任何喜欢的 Scala 
版本。针对某些特性,你可能还需要下载预捆绑的 Hadoop jar 包并将它们放入 `/lib`  目录。
+2. 进入下载后的目录。
+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[代码仓库](http://flink.apache.org/community.html#source-code)中克隆源码,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 %}
+
+检查位于[http://localhost:8081](http://localhost:8081)的 __web 调度界面__以确保一切正常运行。Web 
界面上会仅显示一个可用的 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>
+
+还可以通过检查 `logs` 目录中的日志文件来验证系统是否正在运行:
+
+{% 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)和[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 样例的完整源码。
+
+<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,我们将运行 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 
秒内每个不同单词的出现次数,5秒钟也就是处理时间的翻滚窗口。
+
+* 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 %}
+
+ 程序连接到套接字并等待输入。你可以通过 web 界面来验证作业是否如期运行:
+
+  <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秒为时间窗口(处理时间,翻滚窗口)进行计数,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):
+
+{% highlight bash %}
+$ nc -l 9000
+lorem ipsum
+ipsum ipsum ipsum
+bye
+{% endhighlight %}
+
+随着单词的输入,`.out`  文件会在每个时间窗口的末尾打印计数,比如:
+
+{% highlight bash %}
+$ tail -f log/flink-*-taskexecutor-*.out
+lorem : 1
+bye : 1
+ipsum : 4
+{% endhighlight %}
+
+完成输入后停止 Flink:
+
+{% highlight bash %}
+$ ./bin/stop-cluster.sh
+{% endhighlight %}
+
+## 下一步
+
+查看更多的[示例]({{ site.baseurl }}/examples)以便更好的理解 Flink 的编程的 APIs。完成后, 
请继续阅读[流处理指南]({{ site.baseurl }}/zh/dev/datastream_api.html)。
 
 Review comment:
   建议“的”能省略尽量省略,英文用单数形式。

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