fsk119 commented on a change in pull request #14126:
URL: https://github.com/apache/flink/pull/14126#discussion_r527357666
##########
File path: docs/dev/table/connectors/upsert-kafka.zh.md
##########
@@ -101,112 +89,97 @@ GROUP BY region;
{% endhighlight %}
</div>
</div>
-<span class="label label-danger">Attention</span> Make sure to define the
primary key in the DDL.
+<span class="label label-danger">注意</span> 确保在 DDL 中定义主键。
-Connector Options
+连接器参数
----------------
<table class="table table-bordered">
<thead>
<tr>
- <th class="text-left" style="width: 25%">Option</th>
- <th class="text-center" style="width: 8%">Required</th>
- <th class="text-center" style="width: 7%">Default</th>
- <th class="text-center" style="width: 10%">Type</th>
- <th class="text-center" style="width: 50%">Description</th>
+ <th class="text-left" style="width: 25%">参数</th>
+ <th class="text-center" style="width: 10%">是否必选</th>
+ <th class="text-center" style="width: 10%">默认参数</th>
+ <th class="text-center" style="width: 10%">数据类型</th>
+ <th class="text-center" style="width: 50%">描述</th>
</tr>
</thead>
<tbody>
<tr>
<td><h5>connector</h5></td>
- <td>required</td>
+ <td>必选</td>
<td style="word-wrap: break-word;">(none)</td>
<td>String</td>
- <td>Specify which connector to use, for the Upsert Kafka use:
<code>'upsert-kafka'</code>.</td>
+ <td>指定要使用的连接器，Upsert Kafka 连接器使用：<code>'upsert-kafka'</code>。</td>
</tr>
<tr>
<td><h5>topic</h5></td>
- <td>required</td>
+ <td>必选</td>
<td style="word-wrap: break-word;">(none)</td>
<td>String</td>
- <td>The Kafka topic name to read from and write to.</td>
+ <td>用于读取和写入的 Kafka topic 名称。</td>
</tr>
<tr>
<td><h5>properties.bootstrap.servers</h5></td>
- <td>required</td>
+ <td>必选</td>
<td style="word-wrap: break-word;">(none)</td>
<td>String</td>
- <td>Comma separated list of Kafka brokers.</td>
+ <td>以逗号分隔的 Kafka brokers 列表。</td>
</tr>
<tr>
<td><h5>key.format</h5></td>
- <td>required</td>
+ <td>必选</td>
<td style="word-wrap: break-word;">(none)</td>
<td>String</td>
- <td>The format used to deserialize and serialize the key part of the
Kafka messages. The key part
- fields are specified by the PRIMARY KEY syntax. The supported formats
include <code>'csv'</code>,
- <code>'json'</code>, <code>'avro'</code>. Please refer to <a href="{%
link dev/table/connectors/formats/index.zh.md %}">Formats</a>
- page for more details and more format options.
+ <td>用于对 Kafka 消息中 key 部分反序列化和序列化的格式。key 字段由 PRIMARY KEY 语法指定。支持的格式包括
<code>'csv'</code>、<code>'json'</code>、<code>'avro'</code>。请参考<a href="{% link
dev/table/connectors/formats/index.zh.md %}">格式</a>页面以获取更多详细信息和格式参数。
</td>
</tr>
<tr>
<td><h5>value.format</h5></td>
- <td>required</td>
+ <td>必选</td>
<td style="word-wrap: break-word;">(none)</td>
<td>String</td>
- <td>The format used to deserialize and serialize the value part of the
Kafka messages.
- The supported formats include <code>'csv'</code>, <code>'json'</code>,
<code>'avro'</code>.
- Please refer to <a href="{% link
dev/table/connectors/formats/index.zh.md %}">Formats</a> page for more details
and more format options.
+ <td>用于对 Kafka 消息中 value 部分反序列化和序列化的格式。支持的格式包括
<code>'csv'</code>、<code>'json'</code>、<code>'avro'</code>。请参考<a href="{% link
dev/table/connectors/formats/index.zh.md %}">格式</a>页面以获取更多详细信息和格式参数。
</td>
</tr>
<tr>
<td><h5>value.fields-include</h5></td>
- <td>required</td>
+ <td>必选</td>
<td style="word-wrap: break-word;"><code>'ALL'</code></td>
<td>String</td>
- <td>Controls which fields should end up in the value as well. Available
values:
+ <td>控制哪些字段应该出现在 value 中。可取值：
<ul>
- <li><code>ALL</code>: the value part of the record contains all
fields of the schema, even if they are part of the key.</li>
- <li><code>EXCEPT_KEY</code>: the value part of the record contains
all fields of the schema except the key fields.</li>
+ <li><code>ALL</code>：记录的 value 部分包含 schema 的所有字段，即使它们是 key 的部分。</li>
+ <li><code>EXCEPT_KEY</code>：记录的 value 部分包含 schema 的所有字段，key 字段除外。</li>
</ul>
</td>
</tr>
<tr>
<td><h5>sink.parallelism</h5></td>
- <td>optional</td>
+ <td>可选</td>
<td style="word-wrap: break-word;">(none)</td>
<td>Integer</td>
- <td>Defines the parallelism of the upsert-kafka sink operator. By
default, the parallelism is determined by the framework using the same
parallelism of the upstream chained operator.</td>
+ <td>定义 upsert-kafka sink 算子的并行度。默认情况下，并行度由框架确定，使用与上游链接算子相同的并行度。</td>
</tr>
</tbody>
</table>
-Features
+特性
----------------
-### Primary Key Constraints
+### 主键约束
-The Upsert Kafka always works in the upsert fashion and requires to define the
primary key in the DDL.
-With the assumption that records with the same key should be ordered in the
same partition, the
-primary key semantic on the changelog source means the materialized changelog
is unique on the primary
-keys. The primary key definition will also control which fields should end up
in Kafka’s key.
+Upsert Kafka 始终以 upsert 方式工作，并且需要在 DDL 中定义主键。假设具有相同主键的记录在同一分区内是有序的，变更日志 source
上的主键语义意味着物化的变更日志在主键上是唯一的。主键定义还将控制哪些字段应出现在 Kafka 的 key 中。
-### Consistency Guarantees
+### 一致性保证
-By default, an Upsert Kafka sink ingests data with at-least-once guarantees
into a Kafka topic if
-the query is executed with [checkpointing enabled]({% link
dev/stream/state/checkpointing.zh.md %}#enabling-and-configuring-checkpointing).
+默认情况下，如果[启用 checkpoint]({% link dev/stream/state/checkpointing.zh.md
%}#enabling-and-configuring-checkpointing)，Upsert Kafka sink 会保证至少一次将数据插入 Kafka
topic。
-This means, Flink may write duplicate records with the same key into the Kafka
topic. But as the
-connector is working in the upsert mode, the last record on the same key will
take effect when
-reading back as a source. Therefore, the upsert-kafka connector achieves
idempotent writes just like
-the [HBase sink]({{ site.baseurl }}/dev/table/connectors/hbase.html).
+这意味着，Flink 可以将具有相同 key 的重复记录写入 Kafka topic。但由于连接器是在 upsert 模式下工作的，在作为 source
回读时同一 key 的最后一条记录才会生效。因此，upsert-kafka 连接器可以像 [HBase sink]({{ site.baseurl
}}/dev/table/connectors/hbase.html) 一样实现幂等写入。
-Data Type Mapping
+数据类型映射
----------------
-Upsert Kafka stores message keys and values as bytes, so Upsert Kafka doesn't
have schema or data types.
-The messages are deserialized and serialized by formats, e.g. csv, json, avro.
Thus, the data type mapping
-is determined by specific formats. Please refer to [Formats]({% link
dev/table/connectors/formats/index.zh.md %})
-pages for more details.
+Upsert Kafka 用字节存储消息的 key 和 value，因此没有 schema
或数据类型。消息按格式进行反序列化和序列化，例如：csv、json、avro。因此数据类型映射表由指定的格式确定。请参考[格式]({% link
dev/table/connectors/formats/index.zh.md %})页面以获取更多详细信息。
Review comment:
oh. I don't notice the order in English verison. If you have time,
please help me to adjust the order.
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