XBaith commented on a change in pull request #9097:
URL: https://github.com/apache/flink/pull/9097#discussion_r417755148
##########
File path: docs/getting-started/tutorials/datastream_api.zh.md
##########
@@ -203,26 +178,20 @@ DataStream<Tuple2<String, Long>> result = keyedEdits
});
{% endhighlight %}
-The first call, `.timeWindow()`, specifies that we want to have tumbling
(non-overlapping) windows
-of five seconds. The second call specifies a *Aggregate transformation* on
each window slice for
-each unique key. In our case we start from an initial value of `("", 0L)` and
add to it the byte
-difference of every edit in that time window for a user. The resulting Stream
now contains
-a `Tuple2<String, Long>` for every user which gets emitted every five seconds.
+首先调用 `.timeWindow()` 方法指定五秒翻滚(非重叠)窗口。第二个调用方法对于每一个唯一关键字指定每个窗口片`聚合转换`。
+在本例中，我们从 `(""，0L)` 初始值开始，并将每个用户编辑的字节添加到该时间窗口中。对于每个用户来说，结果流现在包含的元素为
`Tuple2<String, Long>`，它每5秒发出一次。
Review comment:
注意中文和数字之间留有空格：“它每 5 秒发出一次。”
##########
File path: docs/getting-started/tutorials/datastream_api.zh.md
##########
@@ -203,26 +178,20 @@ DataStream<Tuple2<String, Long>> result = keyedEdits
});
{% endhighlight %}
-The first call, `.timeWindow()`, specifies that we want to have tumbling
(non-overlapping) windows
-of five seconds. The second call specifies a *Aggregate transformation* on
each window slice for
-each unique key. In our case we start from an initial value of `("", 0L)` and
add to it the byte
-difference of every edit in that time window for a user. The resulting Stream
now contains
-a `Tuple2<String, Long>` for every user which gets emitted every five seconds.
+首先调用 `.timeWindow()` 方法指定五秒翻滚(非重叠)窗口。第二个调用方法对于每一个唯一关键字指定每个窗口片`聚合转换`。
Review comment:
"tumbling windows" 一般翻译为“滚动窗口”
##########
File path: docs/getting-started/tutorials/datastream_api.zh.md
##########
@@ -203,26 +178,20 @@ DataStream<Tuple2<String, Long>> result = keyedEdits
});
{% endhighlight %}
-The first call, `.timeWindow()`, specifies that we want to have tumbling
(non-overlapping) windows
-of five seconds. The second call specifies a *Aggregate transformation* on
each window slice for
-each unique key. In our case we start from an initial value of `("", 0L)` and
add to it the byte
-difference of every edit in that time window for a user. The resulting Stream
now contains
-a `Tuple2<String, Long>` for every user which gets emitted every five seconds.
+首先调用 `.timeWindow()` 方法指定五秒翻滚(非重叠)窗口。第二个调用方法对于每一个唯一关键字指定每个窗口片`聚合转换`。
Review comment:
“窗口切片” is better
##########
File path: docs/getting-started/tutorials/datastream_api.zh.md
##########
@@ -286,15 +255,14 @@ public class WikipediaAnalysis {
}
{% endhighlight %}
-You can run this example in your IDE or on the command line, using Maven:
+你可以在你的IDE中或者命令行中，使用Maven命令运行这个代码实例：
Review comment:
中文和英文之间留有空格
##########
File path: docs/getting-started/tutorials/datastream_api.zh.md
##########
@@ -351,48 +309,43 @@ result
.addSink(new FlinkKafkaProducer011<>("localhost:9092", "wiki-result", new
SimpleStringSchema()));
{% endhighlight %}
-The related classes also need to be imported:
+相关类也需要导入:
{% highlight java %}
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer011;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.functions.MapFunction;
{% endhighlight %}
-Note how we first transform the Stream of `Tuple2<String, Long>` to a Stream
of `String` using
-a MapFunction. We are doing this because it is easier to write plain strings
to Kafka. Then,
-we create a Kafka sink. You might have to adapt the hostname and port to your
setup. `"wiki-result"`
-is the name of the Kafka stream that we are going to create next, before
running our program.
-Build the project using Maven because we need the jar file for running on the
cluster:
+注意我们是如何使用 MapFunction 将 `Tuple2<String,Long>` 流转换为 `字符串` 流。这样做的原因是因为将普通字符串写入到
Kafka 更容易。然后，我们会创建一个 Kafka 输出。你可能需要根据你的设置去调整主机名和端口。`wiki-result`
+是我们接下来在运行程序之前创建的 Kafka 流的主题。由于我们需要在集群上运行 jar 文件，这里使用 Maven 构建项目:
{% highlight bash %}
$ mvn clean package
{% endhighlight %}
-The resulting jar file will be in the `target` subfolder:
`target/wiki-edits-0.1.jar`. We'll use
-this later.
+上述命令将会生成一个在`target`目录中的 jar 文件，具体文件如下：`target/wiki-edits-0.1.jar`，之后我们将使用这个文件。
-Now we are ready to launch a Flink cluster and run the program that writes to
Kafka on it. Go
-to the location where you installed Flink and start a local cluster:
+现在我们准备启动一个 Flink 集群，然后运行写入到 Kafka 的实时任务。进入到你本地安装 Flink 的位置，然后启动一个本地集群：
{% highlight bash %}
$ cd my/flink/directory
$ bin/start-cluster.sh
{% endhighlight %}
-We also have to create the Kafka Topic, so that our program can write to it:
+我们必须先创建一个 Kafka 主题，这样我们的程序才能往里写入数据：
Review comment:
Topic 不建议翻译为主题，因为是 Kafka的专有名词
##########
File path: docs/getting-started/tutorials/datastream_api.zh.md
##########
@@ -309,24 +277,15 @@ similar to this:
4> (KasparBot,-245)
{% endhighlight %}
-The number in front of each line tells you on which parallel instance of the
print sink the output
-was produced.
+每行数据前面的数字代表着打印接收器运行的并行实例。
-This should get you started with writing your own Flink programs. To learn more
-you can check out our guides
-about [basic concepts]({{ site.baseurl }}/dev/api_concepts.html) and the
-[DataStream API]({{ site.baseurl }}/dev/datastream_api.html). Stick
-around for the bonus exercise if you want to learn about setting up a Flink
cluster on
-your own machine and writing results to [Kafka](http://kafka.apache.org).
+这可以让你开始创建你自己的 Flink 项目。你可以查看[基本概念]({{ site.baseurl
}}/zh/dev/api_concepts.html)和[DataStream API]({{ site.baseurl
}}/zh/dev/datastream_api.html)指南。如果你想学习了解更多关于 Flink 集群安装以及数据写入到
[Kafka](http://kafka.apache.org),你可以自己多加以练习尝试。
-## Bonus Exercise: Running on a Cluster and Writing to Kafka
+## 额外练习: 集群运行并输出 Kafka
Review comment:
附加练习: 在集群上运行并输出到 Kafka
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