fsk119 commented on a change in pull request #14126:
URL: https://github.com/apache/flink/pull/14126#discussion_r527354544
##########
File path: docs/dev/table/connectors/upsert-kafka.zh.md
##########
@@ -29,36 +29,26 @@ under the License.
* This will be replaced by the TOC
{:toc}
-The Upsert Kafka connector allows for reading data from and writing data into
Kafka topics in the upsert fashion.
+Upsert Kafka 连接器支持以 upsert 方式从 Kafka topic 中读取数据并将数据写入 Kafka topic。
-As a source, the upsert-kafka connector produces a changelog stream, where
each data record represents
-an update or delete event. More precisely, the value in a data record is
interpreted as an UPDATE of
-the last value for the same key, if any (if a corresponding key doesn’t exist
yet, the update will
-be considered an INSERT). Using the table analogy, a data record in a
changelog stream is interpreted
-as an UPSERT aka INSERT/UPDATE because any existing row with the same key is
overwritten. Also, null
-values are interpreted in a special way: a record with a null value represents
a “DELETE”.
+作为 source，upsert-kafka 连接器生产 changelog 流，其中每条数据记录代表一个更新或删除事件。更准确地说，数据记录中的
value 被解释为同一 key 的最后一个 value 的 UPDATE，如果有这个 key（如果不存在相应的 key，则该更新被视为
INSERT）。用表来类比，更改日志流中的数据记录被解释为 UPSERT，也称为 INSERT/UPDATE，因为任何具有相同 key
的现有行都被覆盖。另外，value 为空的消息将会被视作为 DELETE 消息。
Review comment:
更改日志 -> changelog
##########
File path: docs/dev/table/connectors/upsert-kafka.zh.md
##########
@@ -29,36 +29,26 @@ under the License.
* This will be replaced by the TOC
{:toc}
-The Upsert Kafka connector allows for reading data from and writing data into
Kafka topics in the upsert fashion.
+Upsert Kafka 连接器支持以 upsert 方式从 Kafka topic 中读取数据并将数据写入 Kafka topic。
-As a source, the upsert-kafka connector produces a changelog stream, where
each data record represents
-an update or delete event. More precisely, the value in a data record is
interpreted as an UPDATE of
-the last value for the same key, if any (if a corresponding key doesn’t exist
yet, the update will
-be considered an INSERT). Using the table analogy, a data record in a
changelog stream is interpreted
-as an UPSERT aka INSERT/UPDATE because any existing row with the same key is
overwritten. Also, null
-values are interpreted in a special way: a record with a null value represents
a “DELETE”.
+作为 source，upsert-kafka 连接器生产 changelog 流，其中每条数据记录代表一个更新或删除事件。更准确地说，数据记录中的
value 被解释为同一 key 的最后一个 value 的 UPDATE，如果有这个 key（如果不存在相应的 key，则该更新被视为
INSERT）。用表来类比，更改日志流中的数据记录被解释为 UPSERT，也称为 INSERT/UPDATE，因为任何具有相同 key
的现有行都被覆盖。另外，value 为空的消息将会被视作为 DELETE 消息。
-As a sink, the upsert-kafka connector can consume a changelog stream. It will
write INSERT/UPDATE_AFTER
-data as normal Kafka messages value, and write DELETE data as Kafka messages
with null values
-(indicate tombstone for the key). Flink will guarantee the message ordering on
the primary key by
-partition data on the values of the primary key columns, so the
update/deletion messages on the same
-key will fall into the same partition.
+作为 sink，upsert-kafka 连接器可以消费 changelog 流。它会将 INSERT/UPDATE_AFTER 数据作为正常的 Kafka
消息写入，并将 DELETE 数据以 value 为空的 Kafka 消息写入（表示对应 key 的消息被删除）。Flink
将根据主键列的值对数据进行分区，从而保证主键上的消息有序，因此同一 key 上的更新/删除消息将落在同一分区中。
-Dependencies
+依赖
------------
-In order to set up the upsert-kafka connector, the following table provide
dependency information for
-both projects using a build automation tool (such as Maven or SBT) and SQL
Client with SQL JAR bundles.
+为了设置 upsert-kafka 连接器，下表为项目使用自动化构建工具（例如 Maven 或 SBT ）以及 SQL 客户端使用 SQL JAR
包提供了依赖信息。
Review comment:
Please also delete it as you work in upsert-kafka.md. We should use
`sql-connector-download-table.html` to generate the introduction. But
currently, we only has English verion page here.
##########
File path: docs/dev/table/connectors/upsert-kafka.zh.md
##########
@@ -29,36 +29,26 @@ under the License.
* This will be replaced by the TOC
{:toc}
-The Upsert Kafka connector allows for reading data from and writing data into
Kafka topics in the upsert fashion.
+Upsert Kafka 连接器支持以 upsert 方式从 Kafka topic 中读取数据并将数据写入 Kafka topic。
-As a source, the upsert-kafka connector produces a changelog stream, where
each data record represents
-an update or delete event. More precisely, the value in a data record is
interpreted as an UPDATE of
-the last value for the same key, if any (if a corresponding key doesn’t exist
yet, the update will
-be considered an INSERT). Using the table analogy, a data record in a
changelog stream is interpreted
-as an UPSERT aka INSERT/UPDATE because any existing row with the same key is
overwritten. Also, null
-values are interpreted in a special way: a record with a null value represents
a “DELETE”.
+作为 source，upsert-kafka 连接器生产 changelog 流，其中每条数据记录代表一个更新或删除事件。更准确地说，数据记录中的
value 被解释为同一 key 的最后一个 value 的 UPDATE，如果有这个 key（如果不存在相应的 key，则该更新被视为
INSERT）。用表来类比，更改日志流中的数据记录被解释为 UPSERT，也称为 INSERT/UPDATE，因为任何具有相同 key
的现有行都被覆盖。另外，value 为空的消息将会被视作为 DELETE 消息。
-As a sink, the upsert-kafka connector can consume a changelog stream. It will
write INSERT/UPDATE_AFTER
-data as normal Kafka messages value, and write DELETE data as Kafka messages
with null values
-(indicate tombstone for the key). Flink will guarantee the message ordering on
the primary key by
-partition data on the values of the primary key columns, so the
update/deletion messages on the same
-key will fall into the same partition.
+作为 sink，upsert-kafka 连接器可以消费 changelog 流。它会将 INSERT/UPDATE_AFTER 数据作为正常的 Kafka
消息写入，并将 DELETE 数据以 value 为空的 Kafka 消息写入（表示对应 key 的消息被删除）。Flink
将根据主键列的值对数据进行分区，从而保证主键上的消息有序，因此同一 key 上的更新/删除消息将落在同一分区中。
Review comment:
> Flink 将根据主键列的值对数据进行分区，从而保证主键上的消息有序，因此同一 key 上的更新/删除消息将落在同一分区中
Flink 将根据主键列的值对数据进行分区，从而保证主键上的消息有序，因此同一主键上的更新/删除消息将落在同一分区中。
感觉如果前面用中文的主键，那么后半句也用中文的描述会更为一致。
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