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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2020/05/09 02:55:30 UTC
[GitHub] [flink] klion26 commented on a change in pull request #11971: [FLINK-17271] Translate new DataStream API tutorial
klion26 commented on a change in pull request #11971:
URL: https://github.com/apache/flink/pull/11971#discussion_r422442438
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -24,30 +24,27 @@ specific language governing permissions and limitations
under the License.
-->
-The focus of this training is to broadly cover the DataStream API well enough that you will be able
-to get started writing streaming applications.
+该练习的重点是充分全面地了解 DataStream API，以便于入门编写流式应用。
Review comment:
”以便于入门编写流式应用“ 这句话读起来优点拗口，”以便于编写流式应用入门“ 或者其他翻译是否会更好一点呢
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -24,30 +24,27 @@ specific language governing permissions and limitations
under the License.
-->
Review comment:
第 5 行的 ”nav-title:“ 后面的标题也需要翻译。
翻译之后，可以在本地 flink 目录执行 "sh docs/build.sh -p" 然后打开 localhost:4000 检查翻译的效果
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -24,30 +24,27 @@ specific language governing permissions and limitations
under the License.
-->
-The focus of this training is to broadly cover the DataStream API well enough that you will be able
-to get started writing streaming applications.
+该练习的重点是充分全面地了解 DataStream API，以便于入门编写流式应用。
* This will be replaced by the TOC
{:toc}
-## What can be Streamed?
+## 什么能被转化成流？
-Flink's DataStream APIs for Java and Scala will let you stream anything they can serialize. Flink's
-own serializer is used for
+Flink 的 Java 和 Scala DataStream API 可以将任何可序列化的对象转化为流。Flink 自带的序列化器有
-- basic types, i.e., String, Long, Integer, Boolean, Array
-- composite types: Tuples, POJOs, and Scala case classes
+- 基本类型，即String、Long、Integer、Boolean、Array
Review comment:
```suggestion
- 基本类型，即 String、Long、Integer、Boolean、Array
```
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -24,30 +24,27 @@ specific language governing permissions and limitations
under the License.
-->
-The focus of this training is to broadly cover the DataStream API well enough that you will be able
-to get started writing streaming applications.
+该练习的重点是充分全面地了解 DataStream API，以便于入门编写流式应用。
* This will be replaced by the TOC
{:toc}
-## What can be Streamed?
+## 什么能被转化成流？
-Flink's DataStream APIs for Java and Scala will let you stream anything they can serialize. Flink's
-own serializer is used for
+Flink 的 Java 和 Scala DataStream API 可以将任何可序列化的对象转化为流。Flink 自带的序列化器有
-- basic types, i.e., String, Long, Integer, Boolean, Array
-- composite types: Tuples, POJOs, and Scala case classes
+- 基本类型，即String、Long、Integer、Boolean、Array
+- 复合类型：Tuples、POJOs 和 Scala case classes
-and Flink falls back to Kryo for other types. It is also possible to use other serializers with
-Flink. Avro, in particular, is well supported.
+而且 Flink 可以交给 Kryo 序列化其他类型。也可以将其他序列化器和 Flink 一起使用。特别是有良好支持的 Avro。
Review comment:
”而且 Flink 可以交给 Kryo 序列化其他类型“ 这里是说不属于上面的类型，Flink 会使用 Kryo 进行序列化，这里能够有一个更好的翻译吗？
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -82,17 +77,17 @@ public class Person {
Person person = new Person("Fred Flintstone", 35);
{% endhighlight %}
-Flink's serializer [supports schema evolution for POJO types]({% link dev/stream/state/schema_evolution.zh.md %}#pojo-types).
+Flink 的序列化器[支持的 POJO 类型数据结构升级]({% link dev/stream/state/schema_evolution.zh.md %}#pojo-types)。
-### Scala tuples and case classes
+### Scala tuples 和 case classes
-These work just as you'd expect.
+正如你期望的一样。
Review comment:
感觉这句话优点突兀？能否再加一些描述，或者换一个更好的翻译呢？
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -104,63 +99,56 @@ public class Example {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
-
+
Review comment:
这些无关的修改，revert 掉吧
下面的也一样
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -104,63 +99,56 @@ public class Example {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
-
+
DataStream<Person> flintstones = env.fromElements(
new Person("Fred", 35),
new Person("Wilma", 35),
new Person("Pebbles", 2));
-
+
DataStream<Person> adults = flintstones.filter(new FilterFunction<Person>() {
@Override
public boolean filter(Person person) throws Exception {
return person.age >= 18;
}
});
-
+
adults.print();
-
+
env.execute();
}
-
+
public static class Person {
public String name;
public Integer age;
public Person() {};
-
+
public Person(String name, Integer age) {
this.name = name;
this.age = age;
};
-
+
public String toString() {
return this.name.toString() + ": age " + this.age.toString();
};
}
}
{% endhighlight %}
-### Stream execution environment
+### Stream 执行环境
-Every Flink application needs an execution environment, `env` in this example. Streaming
-applications need to use a `StreamExecutionEnvironment`.
+每个 Flink 应用都需要有执行环境，在该示例中为 `env` 。流式应用需要用到 `StreamExecutionEnvironment`。
-The DataStream API calls made in your application build a job graph that is attached to the
-`StreamExecutionEnvironment`. When `env.execute()` is called this graph is packaged up and sent to
-the Flink Master, which parallelizes the job and distributes slices of it to the Task Managers for
-execution. Each parallel slice of your job will be executed in a *task slot*.
+DataStream API 将你的应用构建为一个由 `StreamExecutionEnvironment` 生成的 job graph。当调用 `env.execute()` 时此 graph 就被打包并发送到 Flink Master 上，后者对作业并行处理并将其切片分发给 Task Manager 来执行。每个作业并行切片将在 *task slot* 中执行。
Review comment:
这个地方我理解 ”job graph 并不是 `StreamExecutionEnvironment` 生成的，仅仅是 attach 到 `StreamExecutionEnvironment`
这里 “将其切片” 中的 “切片”有更好的翻译吗？这里相当于把一个算子给分成多个 subtask，然后并行处理
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -104,63 +99,56 @@ public class Example {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
-
+
DataStream<Person> flintstones = env.fromElements(
new Person("Fred", 35),
new Person("Wilma", 35),
new Person("Pebbles", 2));
-
+
DataStream<Person> adults = flintstones.filter(new FilterFunction<Person>() {
@Override
public boolean filter(Person person) throws Exception {
return person.age >= 18;
}
});
-
+
adults.print();
-
+
env.execute();
}
-
+
public static class Person {
public String name;
public Integer age;
public Person() {};
-
+
public Person(String name, Integer age) {
this.name = name;
this.age = age;
};
-
+
public String toString() {
return this.name.toString() + ": age " + this.age.toString();
};
}
}
{% endhighlight %}
-### Stream execution environment
+### Stream 执行环境
-Every Flink application needs an execution environment, `env` in this example. Streaming
-applications need to use a `StreamExecutionEnvironment`.
+每个 Flink 应用都需要有执行环境，在该示例中为 `env` 。流式应用需要用到 `StreamExecutionEnvironment`。
Review comment:
```suggestion
每个 Flink 应用都需要有执行环境，在该示例中为 `env`。流式应用需要用到 `StreamExecutionEnvironment`。
```
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -172,60 +160,49 @@ people.add(new Person("Pebbles", 2));
DataStream<Person> flintstones = env.fromCollection(people);
{% endhighlight %}
-Another convenient way to get some data into a stream while prototyping is to use a socket
+另一个获取数据到流中的便捷方法是用 socket
{% highlight java %}
DataStream<String> lines = env.socketTextStream("localhost", 9999)
{% endhighlight %}
-or a file
+或读取文件
{% highlight java %}
DataStream<String> lines = env.readTextFile("file:///path");
{% endhighlight %}
-In real applications the most commonly used data sources are those that support low-latency, high
-throughput parallel reads in combination with rewind and replay -- the prerequisites for high
-performance and fault tolerance -- such as Apache Kafka, Kinesis, and various filesystems. REST APIs
-and databases are also frequently used for stream enrichment.
+在真实的应用中，最常用的数据源是那些支持低延迟，高吞吐并行读取以及倒带和重放（高性能和容错能力为先决条件）的数据源，例如 Apache Kafka，Kinesis 和各种文件系统。REST API 和数据库也经常用于流富集（stream enrichment）。
-### Basic stream sinks
+### 基本的 stream sink
-The example above uses `adults.print()` to print its results to the task manager logs (which will
-appear in your IDE's console, when running in an IDE). This will call `toString()` on each element
-of the stream.
+上述示例用 `adults.print()` 打印其结果到 task manager 的日志中（如果运行在 IDE 中时，将追加到你的 IDE 控制台）。它会对流中的每个元素都调用 `toString()` 方法。
-The output looks something like this
+输出看起来类似于
1> Fred: age 35
2> Wilma: age 35
-where 1> and 2> indicate which sub-task (i.e., thread) produced the output.
+1> 和 2> 指出输出来自哪个 sub-task（即thread）
Review comment:
```suggestion
1> 和 2> 指出输出来自哪个 sub-task（即 thread）
```
##########
File path: docs/training/datastream_api.zh.md
##########
@@ -172,60 +160,49 @@ people.add(new Person("Pebbles", 2));
DataStream<Person> flintstones = env.fromCollection(people);
{% endhighlight %}
-Another convenient way to get some data into a stream while prototyping is to use a socket
+另一个获取数据到流中的便捷方法是用 socket
{% highlight java %}
DataStream<String> lines = env.socketTextStream("localhost", 9999)
{% endhighlight %}
-or a file
+或读取文件
{% highlight java %}
DataStream<String> lines = env.readTextFile("file:///path");
{% endhighlight %}
-In real applications the most commonly used data sources are those that support low-latency, high
-throughput parallel reads in combination with rewind and replay -- the prerequisites for high
-performance and fault tolerance -- such as Apache Kafka, Kinesis, and various filesystems. REST APIs
-and databases are also frequently used for stream enrichment.
+在真实的应用中，最常用的数据源是那些支持低延迟，高吞吐并行读取以及倒带和重放（高性能和容错能力为先决条件）的数据源，例如 Apache Kafka，Kinesis 和各种文件系统。REST API 和数据库也经常用于流富集（stream enrichment）。
Review comment:
“倒带和重放” 是否改成“重复” 就行呢？
这里的 “stream enrichment” 是 “流处理的增强” -- 也就是说也会从 REST 或者数据库读取数据，增强流处理的能力？
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