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new 5b9625e [FLINK-20204][docs-zh][kafka] Translate page 'upsert-kafka
connector' into Chinese
5b9625e is described below
commit 5b9625ef84c198b84b2116e10ef2e2915c560fd6
Author: caozhen1937 <caozhen1...@163.com>
AuthorDate: Thu Nov 19 11:27:55 2020 +0800
[FLINK-20204][docs-zh][kafka] Translate page 'upsert-kafka connector' into
Chinese
This closes #14126
---
docs/dev/table/connectors/upsert-kafka.md | 9 +--
docs/dev/table/connectors/upsert-kafka.zh.md | 105 ++++++++++-----------------
2 files changed, 42 insertions(+), 72 deletions(-)
diff --git a/docs/dev/table/connectors/upsert-kafka.md
b/docs/dev/table/connectors/upsert-kafka.md
index c92cf4b..3cd0fc3 100644
--- a/docs/dev/table/connectors/upsert-kafka.md
+++ b/docs/dev/table/connectors/upsert-kafka.md
@@ -47,9 +47,6 @@ key will fall into the same partition.
Dependencies
------------
-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.
-
{% assign connector = site.data.sql-connectors['upsert-kafka'] %}
{% include sql-connector-download-table.html
connector=connector
@@ -143,7 +140,7 @@ Connector Options
<td>required</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
+ <td>The format used to serialize and deserialize 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.md %}">Formats</a>
page for more details and more format options.
@@ -154,7 +151,7 @@ Connector Options
<td>required</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.
+ <td>The format used to serialize and deserialize 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.md
%}">Formats</a> page for more details and more format options.
</td>
@@ -205,7 +202,7 @@ Data Type Mapping
----------------
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
+The messages are serialized and deserialized 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.md %})
pages for more details.
diff --git a/docs/dev/table/connectors/upsert-kafka.zh.md
b/docs/dev/table/connectors/upsert-kafka.zh.md
index 137408e..e700460 100644
--- a/docs/dev/table/connectors/upsert-kafka.zh.md
+++ b/docs/dev/table/connectors/upsert-kafka.zh.md
@@ -1,5 +1,5 @@
---
-title: "Upsert Kafka SQL Connector"
+title: "Upsert Kafka SQL 连接器"
nav-title: Upsert Kafka
nav-parent_id: sql-connectors
nav-pos: 3
@@ -29,36 +29,24 @@ 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，changelog 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，因此同一主键上的更新/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.
-
{% assign connector = site.data.sql-connectors['upsert-kafka'] %}
{% include sql-connector-download-table.html
connector=connector
%}
-Full Example
+完整示例
----------------
-The example below shows how to create and use an Upsert Kafka table:
+下面的示例展示了如何创建和使用 Upsert Kafka 表：
<div class="codetabs" markdown="1">
<div data-lang="SQL" markdown="1">
@@ -89,7 +77,7 @@ CREATE TABLE pageviews (
'format' = 'json'
);
--- calculate the pv, uv and insert into the upsert-kafka sink
+-- 计算 pv、uv 并插入到 upsert-kafka sink
INSERT INTO pageviews_per_region
SELECT
region,
@@ -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 部分序列化和反序列化的格式。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 部分序列化和反序列化的格式。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>：消息的 value 部分将包含 schema 中所有的字段，包括定义为主键的字段。</li>
+ <li><code>EXCEPT_KEY</code>：记录的 value 部分包含 schema
的所有字段，定义为主键的字段除外。</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，由框架确定并行度，与上游链接算子的并行度保持一致。</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。在具有相同主键值的消息按序存储在同一个分区的前提下，在
changlog source 定义主键意味着 在物化后的 changelog 上主键具有唯一性。定义的主键将决定哪些字段出现在 Kafka 消息的 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。但由于该连接器以 upsert 的模式工作，该连接器作为 source
读入时，可以确保具有相同主键值下仅最后一条消息会生效。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。消息按格式进行序列化和反序列化，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。
{% top %}
