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new 3c65447 [FLINK-25783][docs-zh] Translate azure_table_storage.md page
into Chinese.
3c65447 is described below
commit 3c65447651aa6c032c6752cf58b7bf719af52787
Author: Roc Marshal <[email protected]>
AuthorDate: Mon Jan 24 20:19:29 2022 +0800
[FLINK-25783][docs-zh] Translate azure_table_storage.md page into Chinese.
This closes #18766.
---
.../datastream/formats/azure_table_storage.md | 39 +++++++++++-----------
1 file changed, 19 insertions(+), 20 deletions(-)
diff --git
a/docs/content.zh/docs/connectors/datastream/formats/azure_table_storage.md
b/docs/content.zh/docs/connectors/datastream/formats/azure_table_storage.md
index 11a7b8f..b79a1d7 100644
--- a/docs/content.zh/docs/connectors/datastream/formats/azure_table_storage.md
+++ b/docs/content.zh/docs/connectors/datastream/formats/azure_table_storage.md
@@ -27,10 +27,10 @@ under the License.
# Azure Table Storage
-This example is using the `HadoopInputFormat` wrapper to use an existing
Hadoop input format implementation for accessing [Azure's Table
Storage](https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-overview).
+本例使用 `HadoopInputFormat` 包装器来使用现有的 Hadoop input format 实现访问 [Azure's Table
Storage](https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-overview).
-1. Download and compile the `azure-tables-hadoop` project. The input format
developed by the project is not yet available in Maven Central, therefore, we
have to build the project ourselves.
- Execute the following commands:
+1. 下载并编译 `azure-tables-hadoop` 项目。该项目开发的 input format 在 Maven
中心尚不存在，因此，我们必须自己构建该项目。
+ 执行如下命令：
```bash
git clone https://github.com/mooso/azure-tables-hadoop.git
@@ -38,13 +38,13 @@ cd azure-tables-hadoop
mvn clean install
```
-2. Setup a new Flink project using the quickstarts:
+2. 使用 quickstarts 创建一个新的 Flink 项目：
```bash
curl https://flink.apache.org/q/quickstart.sh | bash
```
-3. Add the following dependencies (in the `<dependencies>` section) to your
`pom.xml` file:
+3. 在你的 `pom.xml` 文件 `<dependencies>` 部分添加如下依赖：
```xml
<dependency>
@@ -59,13 +59,13 @@ curl https://flink.apache.org/q/quickstart.sh | bash
</dependency>
```
-`flink-hadoop-compatibility` is a Flink package that provides the Hadoop input
format wrappers.
-`microsoft-hadoop-azure` is adding the project we've build before to our
project.
+`flink-hadoop-compatibility` 是一个提供 Hadoop input format 包装器的 Flink 包。
+`microsoft-hadoop-azure` 可以将之前构建的部分添加到项目中。
-The project is now ready for starting to code. We recommend to import the
project into an IDE, such as IntelliJ. You should import it as a Maven project.
-Browse to the file `Job.java`. This is an empty skeleton for a Flink job.
+现在可以开始进行项目的编码。我们建议将项目导入 IDE，例如 IntelliJ。你应该将其作为 Maven 项目导入。
+跳转到文件 `Job.java`。这是 Flink 作业的初始框架。
-Paste the following code:
+粘贴如下代码：
```java
import java.util.Map;
@@ -84,22 +84,22 @@ import
com.microsoft.windowsazure.storage.table.EntityProperty;
public class AzureTableExample {
public static void main(String[] args) throws Exception {
- // set up the execution environment
+ // 安装 execution environment
final StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.BATCH);
- // create a AzureTableInputFormat, using a Hadoop input format wrapper
+ // 使用 Hadoop input format 包装器创建 AzureTableInputFormat
HadoopInputFormat<Text, WritableEntity> hdIf = new HadoopInputFormat<Text,
WritableEntity>(new AzureTableInputFormat(), Text.class, WritableEntity.class,
new Job());
- // set the Account URI, something like:
https://apacheflink.table.core.windows.net
+ // 设置 Account URI，如 https://apacheflink.table.core.windows.net
hdIf.getConfiguration().set(azuretableconfiguration.Keys.ACCOUNT_URI.getKey(),
"TODO");
- // set the secret storage key here
+ // 设置存储密钥
hdIf.getConfiguration().set(AzureTableConfiguration.Keys.STORAGE_KEY.getKey(),
"TODO");
- // set the table name here
+ // 在此处设置表名
hdIf.getConfiguration().set(AzureTableConfiguration.Keys.TABLE_NAME.getKey(),
"TODO");
DataStream<Tuple2<Text, WritableEntity>> input = env.createInput(hdIf);
- // a little example how to use the data in a mapper.
+ // 如何在 map 中使用数据的简单示例。
DataStream<String> fin = input.map(new
MapFunction<Tuple2<Text,WritableEntity>, String>() {
@Override
public String map(Tuple2<Text, WritableEntity> arg0) throws Exception {
@@ -114,15 +114,14 @@ public class AzureTableExample {
}
});
- // emit result (this works only locally)
+ // 发送结果（这仅在本地模式有效）
fin.print();
- // execute program
+ // 执行程序
env.execute("Azure Example");
}
}
```
-
-The example shows how to access an Azure table and turn data into Flink's
`DataStream` (more specifically, the type of the set is
`DataStream<Tuple2<Text, WritableEntity>>`). With the `DataStream`, you can
apply all known transformations to the DataStream.
+该示例展示了如何访问 Azure 表和如何将数据转换为 Flink 的 `DataStream`（更具体地说，集合的类型是
`DataStream<Tuple2<Text, WritableEntity>>`）。你可以将所有已知的 transformations 应用到
DataStream 实例。
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