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new d1d334d13 [FLINK-34729][docs] Translate "Core Concept" Pages of Flink
CDC into Chinese
d1d334d13 is described below
commit d1d334d13a18a58b6997caaef35c764bc2ca13d6
Author: Kunni <lvyanquan....@alibaba-inc.com>
AuthorDate: Thu Feb 6 10:33:23 2025 +0800
[FLINK-34729][docs] Translate "Core Concept" Pages of Flink CDC into Chinese
This closes #3901
---
docs/content.zh/docs/core-concept/data-pipeline.md | 36 +--
docs/content.zh/docs/core-concept/data-sink.md | 24 +-
docs/content.zh/docs/core-concept/data-source.md | 24 +-
docs/content.zh/docs/core-concept/route.md | 43 ++-
docs/content.zh/docs/core-concept/table-id.md | 22 +-
docs/content.zh/docs/core-concept/transform.md | 350 ++++++++++-----------
6 files changed, 247 insertions(+), 252 deletions(-)
diff --git a/docs/content.zh/docs/core-concept/data-pipeline.md
b/docs/content.zh/docs/core-concept/data-pipeline.md
index 7b286bfbf..a1f1e0f51 100644
--- a/docs/content.zh/docs/core-concept/data-pipeline.md
+++ b/docs/content.zh/docs/core-concept/data-pipeline.md
@@ -24,23 +24,23 @@ specific language governing permissions and limitations
under the License.
-->
-# Definition
-Since events in Flink CDC flow from the upstream to the downstream in a
pipeline manner, the whole ETL task is referred as a **Data Pipeline**.
+# 定义
+由于在 Flink CDC 中，事件从上游流转到下游遵循 Pipeline 的模式，因此整个 ETL 作业也被称为 **Data Pipeline**。
-# Parameters
-A pipeline corresponds to a chain of operators in Flink.
-To describe a Data Pipeline, the following parts are required:
+# 参数
+一个 pipeline 包含着 Flink 的一组算子链。
+为了描述 Data Pipeline，我们需要定义以下部分：
- [source]({{< ref "docs/core-concept/data-source" >}})
- [sink]({{< ref "docs/core-concept/data-sink" >}})
- [pipeline](#pipeline-configurations)
-the following parts are optional:
+下面 是 Data Pipeline 的一些可选配置：
- [route]({{< ref "docs/core-concept/route" >}})
- [transform]({{< ref "docs/core-concept/transform" >}})
-# Example
-## Only required
-We could use following yaml file to define a concise Data Pipeline describing
synchronize all tables under MySQL app_db database to Doris :
+# 示例
+## 只包含必须部分
+我们可以使用以下 yaml 文件来定义一个简单的 Data Pipeline 来同步 MySQL app_db 数据库下的所有表到 Doris：
```yaml
source:
@@ -62,8 +62,8 @@ We could use following yaml file to define a concise Data
Pipeline describing sy
parallelism: 2
```
-## With optional
-We could use following yaml file to define a complicated Data Pipeline
describing synchronize all tables under MySQL app_db database to Doris and give
specific target database name ods_db and specific target table name prefix ods_
:
+## 包含可选部分
+我们可以使用以下 yaml 文件来定义一个复杂的 Data Pipeline 来同步 MySQL app_db 数据库下的所有表到
Doris，并给目标数据库名 ods_db 和目标表名前缀 ods_：
```yaml
source:
@@ -108,11 +108,11 @@ We could use following yaml file to define a complicated
Data Pipeline describin
classpath: com.example.functions.FormatFunctionClass
```
-# Pipeline Configurations
-The following config options of Data Pipeline level are supported:
+# Pipeline 配置
+下面 是 Data Pipeline 的一些可选配置：
-| parameter | meaning
| optional/required |
-|-----------------|-----------------------------------------------------------------------------------------|-------------------|
-| name | The name of the pipeline, which will be submitted to the
Flink cluster as the job name. | optional |
-| parallelism | The global parallelism of the pipeline. Defaults to 1.
| optional |
-| local-time-zone | The local time zone defines current session time zone id.
| optional |
\ No newline at end of file
+| 参数 | 含义 | optional/required |
+|-----------------|---------------------------------------|-------------------|
+| name | 这个 pipeline 的名称，会用在 Flink 集群中作为作业的名称。 | optional |
+| parallelism | pipeline的全局并发度，默认值是1。 | optional |
+| local-time-zone | 作业级别的本地时区。 | optional |
\ No newline at end of file
diff --git a/docs/content.zh/docs/core-concept/data-sink.md
b/docs/content.zh/docs/core-concept/data-sink.md
index 2dab1dc4a..6faf2ad74 100644
--- a/docs/content.zh/docs/core-concept/data-sink.md
+++ b/docs/content.zh/docs/core-concept/data-sink.md
@@ -24,21 +24,21 @@ specific language governing permissions and limitations
under the License.
-->
-# Definition
-**Data Sink** is used to apply schema changes and write change data to
external systems.
-A Data Sink can write to multiple tables simultaneously.
+# 定义
+**Data Sink** 是 用来应用 schema 变更并写入 change data 到外部系统的组件。
+一个 Data Sink 可以同时写入多个表。
-# Parameters
-To describe a data sink, the follows are required:
+# 参数
+为了定义一个 Data Sink，需要提供以下参数：
-| parameter | meaning
| optional/required |
-|-----------------------------|-------------------------------------------------------------------------------------------------|-------------------|
-| type | The type of the sink, such as doris or
starrocks. | required |
-| name | The name of the sink, which is user-defined (a
default value provided). | optional |
-| configurations of Data Sink | Configurations to build the Data Sink e.g.
connection configurations and sink table properties. | optional |
+| 参数 | 含义 |
optional/required |
+|-----------------------------|---------------------------------|-------------------|
+| type | sink 的类型，例如 doris 或者 starrocks。 | required
|
+| name | sink 的名称，允许用户配置 (提供了一个默认值)。 | optional
|
+| configurations of Data Sink | 用于构建 sink 组件的配置，例如连接参数或者表属性的配置。 | optional
|
-# Example
-We could use this yaml file to define a doris sink:
+# 示例
+我们可以使用以下的 yaml 文件来定义一个 doris sink：
```yaml
sink:
type: doris
diff --git a/docs/content.zh/docs/core-concept/data-source.md
b/docs/content.zh/docs/core-concept/data-source.md
index 5d6c33deb..e18bf6e10 100644
--- a/docs/content.zh/docs/core-concept/data-source.md
+++ b/docs/content.zh/docs/core-concept/data-source.md
@@ -24,21 +24,21 @@ specific language governing permissions and limitations
under the License.
-->
-# Definition
-**Data Source** is used to access metadata and read the changed data from
external systems.
-A Data Source can read data from multiple tables simultaneously.
+# 定义
+**Data Source** 是 用来访问元数据以及从外部系统读取变更数据的组件。
+一个 Data Source 可以同时访问多个表。
-# Parameters
-To describe a data source, the follows are required:
+# 参数
+为了定义一个 Data Source，需要提供以下参数：
-| parameter | meaning
| optional/required |
-|-------------------------------|-----------------------------------------------------------------------------------------------------|-------------------|
-| type | The type of the source, such as mysql.
| required |
-| name | The name of the source, which is
user-defined (a default value provided). | optional
|
-| configurations of Data Source | Configurations to build the Data Source e.g.
connection configurations and source table properties. | optional |
+| 参数 | 含义 |
optional/required |
+|-------------------------------|-----------------------------------|-------------------|
+| type | source 的类型，例如 mysql。 | required
|
+| name | source 的名称，允许用户配置 (提供了一个默认值)。 | optional
|
+| configurations of Data Source | 用于构建 source 组件的配置，例如连接参数或者表属性的配置。 | optional
|
-# Example
-We could use yaml files to define a mysql source:
+# 示例
+我们可以使用yaml文件来定义一个mysql source：
```yaml
source:
type: mysql
diff --git a/docs/content.zh/docs/core-concept/route.md
b/docs/content.zh/docs/core-concept/route.md
index ca5855d58..fa1a95353 100644
--- a/docs/content.zh/docs/core-concept/route.md
+++ b/docs/content.zh/docs/core-concept/route.md
@@ -24,24 +24,24 @@ specific language governing permissions and limitations
under the License.
-->
-# Definition
-**Route** specifies the rule of matching a list of source-table and mapping to
sink-table. The most typical scenario is the merge of sub-databases and
sub-tables, routing multiple upstream source tables to the same sink table.
+# 定义
+**Route** 代表一个路由规则，用来匹配一个或多个source 表，并映射到 sink
表。最常见的场景是合并子数据库和子表，将多个上游源表路由到同一个目标表。
-# Parameters
-To describe a route, the follows are required:
+# 参数
+为了定义一个路由规则，需要提供以下参数：
-| parameter | meaning
| optional/required |
-|----------------|---------------------------------------------------------------------------------------------|-------------------|
-| source-table | Source table id, supports regular expressions
| required |
-| sink-table | Sink table id, supports symbol replacement
| required |
-| replace-symbol | Special symbol in sink-table for pattern replacing, will be
replaced by original table name | optional |
-| description | Routing rule description(a default value provided)
| optional |
+| 参数 | 含义 |
optional/required |
+|----------------|------------------------------------------|-------------------|
+| source-table | Source 的 table id， 支持正则表达式 | required
|
+| sink-table | Sink 的 table id，支持符号替换 | required
|
+| replace-symbol | 用于在 sink-table 中进行模式替换的特殊字符串， 会被源表中的表名替换 | optional
|
+| description | Route 规则的描述(提供了一个默认描述) | optional
|
-A route module can contain a list of source-table/sink-table rules.
+一个 Route 模块可以包含一个或多个 source-table/sink-table 规则。
-# Example
-## Route one Data Source table to one Data Sink table
-if synchronize the table `web_order` in the database `mydb` to a Doris table
`ods_web_order`, we can use this yaml file to define this route：
+# 示例
+## 路由一个 Data Source 表到一个 Data Sink 表
+如果同步一个 `mydb` 数据库中的 `web_order` 表到一个相同库的 `ods_web_order` 表，我们可以使用下面的 yaml
文件来定义这个路由：
```yaml
route:
@@ -50,17 +50,16 @@ route:
description: sync table to one destination table with given prefix ods_
```
-## Route multiple Data Source tables to one Data Sink table
-What's more, if you want to synchronize the sharding tables in the database
`mydb` to a Doris table `ods_web_order`, we can use this yaml file to define
this route：
+## 路由多个 Data Source 表到一个 Data Sink 表
+更进一步的，如果同步一个 `mydb` 数据库中的多个分表到一个相同库的 `ods_web_order` 表，我们可以使用下面的 yaml
文件来定义这个路由：
```yaml
route:
- source-table: mydb\.*
sink-table: mydb.ods_web_order
description: sync sharding tables to one destination table
```
-
-## Complex Route via combining route rules
-What's more, if you want to specify many different mapping rules, we can use
this yaml file to define this route：
+## 使用多个路由规则
+更进一步的，如果需要定义多个路由规则，我们可以使用下面的 yaml 文件来定义这个路由：
```yaml
route:
- source-table: mydb.orders
@@ -74,9 +73,9 @@ route:
description: sync products table to ods_products
```
-## Pattern Replacement in routing rules
+## 包含符号替换的路由规则
-If you'd like to route source tables and rename them to sink tables with
specific patterns, `replace-symbol` could be used to resemble source table
names like this:
+如果你想将源表路由到 sink 表，并使用特定的模式替换源表名，那么 `replace-symbol` 就可以做到这一点：
```yaml
route:
@@ -86,4 +85,4 @@ route:
description: route all tables in source_db to sink_db
```
-Then, all tables including `source_db.XXX` will be routed to `sink_db.XXX`
without hassle.
\ No newline at end of file
+然后，`source_db` 库下所有的表都会被同步到 `sink_db` 库下。
\ No newline at end of file
diff --git a/docs/content.zh/docs/core-concept/table-id.md
b/docs/content.zh/docs/core-concept/table-id.md
index 261c8fd09..2908e12a6 100644
--- a/docs/content.zh/docs/core-concept/table-id.md
+++ b/docs/content.zh/docs/core-concept/table-id.md
@@ -24,17 +24,17 @@ specific language governing permissions and limitations
under the License.
-->
-# Definition
-When connecting to external systems, it is necessary to establish a mapping
relationship with the storage objects of the external system. This is what
**Table Id** refers to.
+# 定义
+在连接外部系统时，有必要建立一个与外部系统存储对象（例如表）的映射关系。这就是 **Table Id** 所代表的含义。
-# Example
-To be compatible with most external systems, the Table Id is represented by a
3-tuple : (namespace, schemaName, tableName).
-Connectors should establish the mapping between Table Id and storage objects
in external systems.
+# 示例
+为了兼容大部分外部系统，Table Id 被表示为 3 元组：(namespace, schemaName, tableName)。
+连接器应该在连接外部系统时建立与外部系统存储对象的映射关系。
-The following table lists the parts in table Id of different data systems:
+下面是不同数据系统对应的 tableId 的格式：
-| data system | parts in tableId | String example |
-|-----------------------|--------------------------|---------------------|
-| Oracle/PostgreSQL | database, schema, table | mydb.default.orders |
-| MySQL/Doris/StarRocks | database, table | mydb.orders |
-| Kafka | topic | orders |
+| 数据系统 | tableId 的组成 | 字符串示例 |
+|-----------------------|-------------------------|---------------------|
+| Oracle/PostgreSQL | database, schema, table | mydb.default.orders |
+| MySQL/Doris/StarRocks | database, table | mydb.orders |
+| Kafka | topic | orders |
diff --git a/docs/content.zh/docs/core-concept/transform.md
b/docs/content.zh/docs/core-concept/transform.md
index 787edc7c2..190bcbd31 100644
--- a/docs/content.zh/docs/core-concept/transform.md
+++ b/docs/content.zh/docs/core-concept/transform.md
@@ -24,33 +24,33 @@ specific language governing permissions and limitations
under the License.
-->
-# Definition
-**Transform** module helps users delete and expand data columns based on the
data columns in the table.
-What's more, it also helps users filter some unnecessary data during the
synchronization process.
-
-# Parameters
-To describe a transform rule, the following parameters can be used:
-
-| Parameter | Meaning |
Optional/Required |
-|--------------|----------------------------------------------------|-------------------|
-| source-table | Source table id, supports regular expressions | required
|
-| projection | Projection rule, supports syntax similar to the select clause
in SQL | optional |
-| filter | Filter rule, supports syntax similar to the where clause in
SQL | optional |
-| primary-keys | Sink table primary keys, separated by commas | optional
|
-| partition-keys | Sink table partition keys, separated by commas |
optional |
-| table-options | used to the configure table creation statement when
automatically creating tables | optional |
+# 定义
+**Transform** 模块允许用户根据数据列对数据列进行删除和扩展。
+更进一步的，这也也可以帮助用户在同步过程中过滤一些不需要的数据。
+
+# 参数
+为了定义一个transform规则，可以使用以下参数：
+
+| 参数 | 含义
| Optional/Required |
+|---------------------------|-----------------------------------------------------------------------------------|-------------------|
+| source-table | Source table id, supports regular expressions
| required |
+| projection | Projection rule, supports syntax similar to the
select clause in SQL | optional |
+| filter | Filter rule, supports syntax similar to the
where clause in SQL | optional |
+| primary-keys | Sink table primary keys, separated by commas
| optional |
+| partition-keys | Sink table partition keys, separated by commas
| optional |
+| table-options | used to the configure table creation statement
when automatically creating tables | optional |
| converter-after-transform | used to add a converter to change
DataChangeEvent after transform | optional |
-| description | Transform rule description | optional |
+| description | Transform rule description
| optional |
-Multiple rules can be declared in one single pipeline YAML file.
+多个transform规则可以声明在一个pipeline YAML文件中。
## converter-after-transform
-`converter-after-transform` is used to change the DataChangeEvent after other
transform. The available values of this options are as follows.
+`converter-after-transform` 用于在其他 transform 处理之后再修改 DataChangeEvent。可用的值如下：
-- SOFT_DELETE: The delete event will be converted as an insert event. This
converter should be used together with the metadata `__data_event_type__`. Then
you can implement the soft delete.
+- SOFT_DELETE: 删除事件会被转换成插入事件。这个转换器应该与元数据 `__data_event_type__` 一起使用，然后你可以实现软删除。
-For example, the following transform will not delete data when the delete
event happens. Instead it will update the column `op_type` to -D in sink and
transform it to an insert record.
+举个例子，下面的 transform 将不会删除数据，而是更新列 `op_type` 为 -D，然后转换成插入记录。
```yaml
transform:
@@ -59,9 +59,9 @@ transform:
converter-after-transform: SOFT_DELETE
```
-# Metadata Fields
-## Fields definition
-There are some hidden columns used to access metadata information. They will
only take effect when explicitly referenced in the transform rules.
+# 元数据字段
+## 字段定义
+有一些隐藏列可以用来访问元数据信息。它们仅在显式引用在 transform 规则中时才会生效。
| Field | Data Type | Description
|
|---------------------|-----------|----------------------------------------------|
@@ -83,98 +83,98 @@ There are some hidden columns used to access metadata
information. They will onl
| StarRocks | Database | - | Table |
| Doris | Database | - | Table |
-# Functions
-Flink CDC uses [Calcite](https://calcite.apache.org/) to parse expressions and
[Janino script](https://www.janino.net/) to evaluate expressions with function
call.
-
-## Comparison Functions
-
-| Function | Janino Code | Description
|
-|----------------------|-----------------------------|-----------------------------------------------------------------|
-| value1 = value2 | valueEquals(value1, value2) | Returns TRUE if value1
is equal to value2; returns FALSE if value1 or value2 is NULL. |
-| value1 <> value2 | !valueEquals(value1, value2) | Returns TRUE if value1
is not equal to value2; returns FALSE if value1 or value2 is NULL. |
-| value1 > value2 | greaterThan(value1, value2) | Returns TRUE if
value1 is greater than value2; returns FALSE if value1 or value2 is NULL. |
-| value1 >= value2 | greaterThanOrEqual(value1, value2) | Returns TRUE if
value1 is greater than or equal to value2; returns FALSE if value1 or value2 is
NULL. |
-| value1 < value2 | lessThan(value1, value2) | Returns TRUE if
value1 is less than value2; returns FALSE if value1 or value2 is NULL. |
-| value1 <= value2 | lessThanOrEqual(value1, value2) | Returns TRUE if
value1 is less than or equal to value2; returns FALSE if value1 or value2 is
NULL. |
-| value IS NULL | null == value | Returns TRUE if value
is NULL. |
-| value IS NOT NULL | null != value | Returns TRUE if value
is not NULL. |
-| value1 BETWEEN value2 AND value3 | betweenAsymmetric(value1, value2, value3)
| Returns TRUE if value1 is greater than or equal to value2 and less than or
equal to value3. |
-| value1 NOT BETWEEN value2 AND value3 | notBetweenAsymmetric(value1, value2,
value3) | Returns TRUE if value1 is less than value2 or greater than value3. |
-| string1 LIKE string2 | like(string1, string2) | Returns TRUE if string1
matches pattern string2. |
-| string1 NOT LIKE string2 | notLike(string1, string2) | Returns TRUE if
string1 does not match pattern string2. |
-| value1 IN (value2 [, value3]* ) | in(value1, value2 [, value3]*) | Returns
TRUE if value1 exists in the given list (value2, value3, …). |
-| value1 NOT IN (value2 [, value3]* ) | notIn(value1, value2 [, value3]*) |
Returns TRUE if value1 does not exist in the given list (value2, value3, …). |
-
-## Logical Functions
-
-| Function | Janino Code | Description
|
-|----------------------|-----------------------------|-----------------------------------------------------------------|
-| boolean1 OR boolean2 | boolean1 || boolean2 | Returns TRUE if
BOOLEAN1 is TRUE or BOOLEAN2 is TRUE. |
-| boolean1 AND boolean2 | boolean1 && boolean2 | Returns TRUE if
BOOLEAN1 and BOOLEAN2 are both TRUE. |
-| NOT boolean | !boolean | Returns TRUE if boolean
is FALSE; returns FALSE if boolean is TRUE. |
-| boolean IS FALSE | false == boolean | Returns TRUE if boolean
is FALSE; returns FALSE if boolean is TRUE. |
-| boolean IS NOT FALSE | true == boolean | Returns TRUE if BOOLEAN
is TRUE; returns FALSE if BOOLEAN is FALSE. |
-| boolean IS TRUE | true == boolean | Returns TRUE if BOOLEAN
is TRUE; returns FALSE if BOOLEAN is FALSE. |
-| boolean IS NOT TRUE | false == boolean | Returns TRUE if boolean
is FALSE; returns FALSE if boolean is TRUE. |
-
-## Arithmetic Functions
-
-| Function | Janino Code | Description
|
-|----------------------|-----------------------------|-----------------------------------------------------------------|
-| numeric1 + numeric2 | numeric1 + numeric2 | Returns NUMERIC1 plus
NUMERIC2. |
-| numeric1 - numeric2 | numeric1 - numeric2 | Returns NUMERIC1 minus
NUMERIC2. |
-| numeric1 * numeric2 | numeric1 * numeric2 | Returns NUMERIC1
multiplied by NUMERIC2. |
-| numeric1 / numeric2 | numeric1 / numeric2 | Returns NUMERIC1
divided by NUMERIC2. |
-| numeric1 % numeric2 | numeric1 % numeric2 | Returns the remainder
(modulus) of numeric1 divided by numeric2. |
-| ABS(numeric) | abs(numeric) | Returns the absolute
value of numeric. |
-| CEIL(numeric) | ceil(numeric) | Rounds numeric up, and
returns the smallest number that is greater than or equal to numeric. |
-| FLOOR(numeric) | floor(numeric) | Rounds numeric down,
and returns the largest number that is less than or equal to numeric. |
-| ROUND(numeric, int) | round(numeric) | Returns a number
rounded to INT decimal places for NUMERIC. |
-| UUID() | uuid() | Returns an UUID
(Universally Unique Identifier) string (e.g.,
"3d3c68f7-f608-473f-b60c-b0c44ad4cc4e") according to RFC 4122 type 4 (pseudo
randomly generated) UUID. |
-
-## String Functions
-
-| Function | Janino Code | Description
|
-| -------------------- | ------------------------ |
------------------------------------------------- |
-| string1 || string2 | concat(string1, string2) | Returns the
concatenation of STRING1 and STRING2. |
-| CHAR_LENGTH(string) | charLength(string) | Returns the number of
characters in STRING. |
-| UPPER(string) | upper(string) | Returns string in
uppercase. |
-| LOWER(string) | lower(string) | Returns string in lowercase. |
-| TRIM(string1) | trim('BOTH',string1) | Returns a string that removes
whitespaces at both sides. |
-| REGEXP_REPLACE(string1, string2, string3) | regexpReplace(string1, string2,
string3) | Returns a string from STRING1 with all the substrings that match a
regular expression STRING2 consecutively being replaced with STRING3. E.g.,
'foobar'.regexpReplace('oo\|ar', '') returns "fb". |
-| SUBSTR(string, integer1[, integer2]) | substr(string,integer1,integer2) |
Returns a substring of STRING starting from position integer1 with length
integer2 (to the end by default). |
-| SUBSTRING(string FROM integer1 [ FOR integer2 ]) |
substring(string,integer1,integer2) | Returns a substring of STRING starting
from position integer1 with length integer2 (to the end by default). |
-| CONCAT(string1, string2,…) | concat(string1, string2,…) | Returns a string
that concatenates string1, string2, …. E.g., CONCAT('AA', 'BB', 'CC') returns
'AABBCC'. |
-
-## Temporal Functions
-
-| Function | Janino Code | Description
|
-| -------------------- | ------------------------ |
------------------------------------------------- |
-| LOCALTIME | localtime() | Returns the current SQL time in the local time
zone, the return type is TIME(0). |
-| LOCALTIMESTAMP | localtimestamp() | Returns the current SQL timestamp in
local time zone, the return type is TIMESTAMP(3). |
-| CURRENT_TIME | currentTime() | Returns the current SQL time in the local
time zone, this is a synonym of LOCAL_TIME. |
-| CURRENT_DATE | currentDate() |
Returns the current SQL date in the local time zone. |
-| CURRENT_TIMESTAMP | currentTimestamp() | Returns the current SQL timestamp
in the local time zone, the return type is TIMESTAMP_LTZ(3). |
-| NOW() | now() | Returns the current SQL timestamp in the local time zone,
this is a synonym of CURRENT_TIMESTAMP. |
-| DATE_FORMAT(timestamp, string) | dateFormat(timestamp, string) | Converts
timestamp to a value of string in the format specified by the date format
string. The format string is compatible with Java's SimpleDateFormat. |
-| TIMESTAMPADD(timeintervalunit, interval, timepoint) |
timestampadd(timeintervalunit, interval, timepoint) | Returns the timestamp of
timepoint2 after timepoint added interval. The unit for the interval is given
by the first argument, which should be one of the following values: SECOND,
MINUTE, HOUR, DAY, MONTH, or YEAR. |
-| TIMESTAMPDIFF(timepointunit, timepoint1, timepoint2) |
timestampDiff(timepointunit, timepoint1, timepoint2) | Returns the (signed)
number of timepointunit between timepoint1 and timepoint2. The unit for the
interval is given by the first argument, which should be one of the following
values: SECOND, MINUTE, HOUR, DAY, MONTH, or YEAR. |
-| TO_DATE(string1[, string2]) | toDate(string1[, string2]) | Converts a date
string string1 with format string2 (by default 'yyyy-MM-dd') to a date. |
-| TO_TIMESTAMP(string1[, string2]) | toTimestamp(string1[, string2]) |
Converts date time string string1 with format string2 (by default: 'yyyy-MM-dd
HH:mm:ss') to a timestamp, without time zone. |
-| FROM_UNIXTIME(numeric[, string]) | fromUnixtime(NUMERIC[, STRING]) | Returns
a representation of the numeric argument as a value in string format (default
is ‘yyyy-MM-dd HH:mm:ss’). numeric is an internal timestamp value representing
seconds since ‘1970-01-01 00:00:00’ UTC, such as produced by the
UNIX_TIMESTAMP() function. The return value is expressed in the session time
zone (specified in TableConfig). E.g., FROM_UNIXTIME(44) returns ‘1970-01-01
00:00:44’ if in UTC time zone, but re [...]
-| UNIX_TIMESTAMP() | unixTimestamp() | Gets current Unix timestamp in seconds.
This function is not deterministic which means the value would be recalculated
for each record. |
-| UNIX_TIMESTAMP(string1[, string2]) | unixTimestamp(STRING1[, STRING2]) |
Converts a date time string string1 with format string2 (by default: yyyy-MM-dd
HH:mm:ss if not specified) to Unix timestamp (in seconds), using the specified
timezone in table config.<br/>If a time zone is specified in the date time
string and parsed by UTC+X format such as “yyyy-MM-dd HH:mm:ss.SSS X”, this
function will use the specified timezone in the date time string instead of the
timezone in table config. I [...]
-
-## Conditional Functions
-
-| Function | Janino Code | Description
|
-| -------------------- | ------------------------ |
------------------------------------------------- |
-| CASE value WHEN value1_1 [, value1_2]* THEN RESULT1 (WHEN value2_1 [,
value2_2 ]* THEN result_2)* (ELSE result_z) END | Nested ternary expression |
Returns resultX when the first time value is contained in (valueX_1, valueX_2,
…). When no value matches, returns result_z if it is provided and returns NULL
otherwise. |
-| CASE WHEN condition1 THEN result1 (WHEN condition2 THEN result2)* (ELSE
result_z) END | Nested ternary expression | Returns resultX when the first
conditionX is met. When no condition is met, returns result_z if it is provided
and returns NULL otherwise. |
-| COALESCE(value1 [, value2]*) | coalesce(Object... objects) | Returns the
first argument that is not NULL.If all arguments are NULL, it returns NULL as
well. The return type is the least restrictive, common type of all of its
arguments. The return type is nullable if all arguments are nullable as well. |
-| IF(condition, true_value, false_value) | condition ? true_value :
false_value | Returns the true_value if condition is met, otherwise
false_value. E.g., IF(5 > 3, 5, 3) returns 5. |
-
-## Casting Functions
+# 函数
+Flink CDC 使用 [Calcite](https://calcite.apache.org/) 来解析表达式并且使用 [Janino
script](https://www.janino.net/) 来执行表达式。
+
+## 比较函数
+
+| Function | Janino Code
| Description
|
+|--------------------------------------|----------------------------------------------|-------------------------------------------------------------------------------------------------------|
+| value1 = value2 | valueEquals(value1, value2)
| Returns TRUE if value1 is equal to value2; returns FALSE if value1 or
value2 is NULL. |
+| value1 <> value2 | !valueEquals(value1, value2)
| Returns TRUE if value1 is not equal to value2; returns FALSE if value1
or value2 is NULL. |
+| value1 > value2 | greaterThan(value1, value2)
| Returns TRUE if value1 is greater than value2; returns FALSE if value1
or value2 is NULL. |
+| value1 >= value2 | greaterThanOrEqual(value1, value2)
| Returns TRUE if value1 is greater than or equal to value2; returns
FALSE if value1 or value2 is NULL. |
+| value1 < value2 | lessThan(value1, value2)
| Returns TRUE if value1 is less than value2; returns FALSE if value1 or
value2 is NULL. |
+| value1 <= value2 | lessThanOrEqual(value1, value2)
| Returns TRUE if value1 is less than or equal to value2; returns FALSE
if value1 or value2 is NULL. |
+| value IS NULL | null == value
| Returns TRUE if value is NULL.
|
+| value IS NOT NULL | null != value
| Returns TRUE if value is not NULL.
|
+| value1 BETWEEN value2 AND value3 | betweenAsymmetric(value1, value2,
value3) | Returns TRUE if value1 is greater than or equal to value2 and less
than or equal to value3. |
+| value1 NOT BETWEEN value2 AND value3 | notBetweenAsymmetric(value1, value2,
value3) | Returns TRUE if value1 is less than value2 or greater than value3.
|
+| string1 LIKE string2 | like(string1, string2)
| Returns TRUE if string1 matches pattern string2.
|
+| string1 NOT LIKE string2 | notLike(string1, string2)
| Returns TRUE if string1 does not match pattern string2.
|
+| value1 IN (value2 [, value3]* ) | in(value1, value2 [, value3]*)
| Returns TRUE if value1 exists in the given list (value2, value3, …).
|
+| value1 NOT IN (value2 [, value3]* ) | notIn(value1, value2 [, value3]*)
| Returns TRUE if value1 does not exist in the given list (value2,
value3, …). |
+
+## 逻辑函数
+
+| Function | Janino Code | Description
|
+|-----------------------|--------------------------------|---------------------------------------------------------------------|
+| boolean1 OR boolean2 | boolean1 || boolean2 | Returns TRUE if
BOOLEAN1 is TRUE or BOOLEAN2 is TRUE. |
+| boolean1 AND boolean2 | boolean1 && boolean2 | Returns TRUE if
BOOLEAN1 and BOOLEAN2 are both TRUE. |
+| NOT boolean | !boolean | Returns TRUE if
boolean is FALSE; returns FALSE if boolean is TRUE. |
+| boolean IS FALSE | false == boolean | Returns TRUE if
boolean is FALSE; returns FALSE if boolean is TRUE. |
+| boolean IS NOT FALSE | true == boolean | Returns TRUE if
BOOLEAN is TRUE; returns FALSE if BOOLEAN is FALSE. |
+| boolean IS TRUE | true == boolean | Returns TRUE if
BOOLEAN is TRUE; returns FALSE if BOOLEAN is FALSE. |
+| boolean IS NOT TRUE | false == boolean | Returns TRUE if
boolean is FALSE; returns FALSE if boolean is TRUE. |
+
+## 数学函数
+
+| Function | Janino Code | Description
|
+|---------------------|---------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| numeric1 + numeric2 | numeric1 + numeric2 | Returns NUMERIC1 plus NUMERIC2.
|
+| numeric1 - numeric2 | numeric1 - numeric2 | Returns NUMERIC1 minus NUMERIC2.
|
+| numeric1 * numeric2 | numeric1 * numeric2 | Returns NUMERIC1 multiplied by
NUMERIC2.
|
+| numeric1 / numeric2 | numeric1 / numeric2 | Returns NUMERIC1 divided by
NUMERIC2.
|
+| numeric1 % numeric2 | numeric1 % numeric2 | Returns the remainder (modulus)
of numeric1 divided by numeric2.
|
+| ABS(numeric) | abs(numeric) | Returns the absolute value of
numeric.
|
+| CEIL(numeric) | ceil(numeric) | Rounds numeric up, and returns
the smallest number that is greater than or equal to numeric.
|
+| FLOOR(numeric) | floor(numeric) | Rounds numeric down, and returns
the largest number that is less than or equal to numeric.
|
+| ROUND(numeric, int) | round(numeric) | Returns a number rounded to INT
decimal places for NUMERIC.
|
+| UUID() | uuid() | Returns an UUID (Universally
Unique Identifier) string (e.g., "3d3c68f7-f608-473f-b60c-b0c44ad4cc4e")
according to RFC 4122 type 4 (pseudo randomly generated) UUID. |
+
+## 字符串函数
+
+| Function | Janino Code
| Description
|
+|--------------------------------------------------|------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| string1 || string2 | concat(string1, string2)
| Returns the concatenation of STRING1 and STRING2.
|
+| CHAR_LENGTH(string) | charLength(string)
| Returns the number of characters in STRING.
|
+| UPPER(string) | upper(string)
| Returns string in uppercase.
|
+| LOWER(string) | lower(string)
| Returns string in lowercase.
|
+| TRIM(string1) | trim('BOTH',string1)
| Returns a string that removes whitespaces at both sides.
|
+| REGEXP_REPLACE(string1, string2, string3) | regexpReplace(string1,
string2, string3) | Returns a string from STRING1 with all the substrings that
match a regular expression STRING2 consecutively being replaced with STRING3.
E.g., 'foobar'.regexpReplace('oo\|ar', '') returns "fb". |
+| SUBSTR(string, integer1[, integer2]) |
substr(string,integer1,integer2) | Returns a substring of STRING
starting from position integer1 with length integer2 (to the end by default).
|
+| SUBSTRING(string FROM integer1 [ FOR integer2 ]) |
substring(string,integer1,integer2) | Returns a substring of STRING
starting from position integer1 with length integer2 (to the end by default).
|
+| CONCAT(string1, string2,…) | concat(string1,
string2,…) | Returns a string that concatenates string1, string2,
…. E.g., CONCAT('AA', 'BB', 'CC') returns 'AABBCC'.
|
+
+## 时间函数
+
+| Function | Janino Code
| Description
[...]
+|------------------------------------------------------|------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
[...]
+| LOCALTIME | localtime()
| Returns the current SQL time in the local time
zone, the return type is TIME(0).
[...]
+| LOCALTIMESTAMP | localtimestamp()
| Returns the current SQL timestamp in local
time zone, the return type is TIMESTAMP(3).
[...]
+| CURRENT_TIME | currentTime()
| Returns the current SQL time in the local time
zone, this is a synonym of LOCAL_TIME.
[...]
+| CURRENT_DATE | currentDate()
| Returns the current SQL date in the local time
zone.
[...]
+| CURRENT_TIMESTAMP | currentTimestamp()
| Returns the current SQL timestamp in the local
time zone, the return type is TIMESTAMP_LTZ(3).
[...]
+| NOW() | now()
| Returns the current SQL timestamp in the local
time zone, this is a synonym of CURRENT_TIMESTAMP.
[...]
+| DATE_FORMAT(timestamp, string) | dateFormat(timestamp,
string) | Converts timestamp to a value of string in the
format specified by the date format string. The format string is compatible
with Java's SimpleDateFormat.
[...]
+| TIMESTAMPADD(timeintervalunit, interval, timepoint) |
timestampadd(timeintervalunit, interval, timepoint) | Returns the timestamp of
timepoint2 after timepoint added interval. The unit for the interval is given
by the first argument, which should be one of the following values: SECOND,
MINUTE, HOUR, DAY, MONTH, or YEAR.
[...]
+| TIMESTAMPDIFF(timepointunit, timepoint1, timepoint2) |
timestampDiff(timepointunit, timepoint1, timepoint2) | Returns the (signed)
number of timepointunit between timepoint1 and timepoint2. The unit for the
interval is given by the first argument, which should be one of the following
values: SECOND, MINUTE, HOUR, DAY, MONTH, or YEAR.
[...]
+| TO_DATE(string1[, string2]) | toDate(string1[,
string2]) | Converts a date string string1 with
format string2 (by default 'yyyy-MM-dd') to a date.
[...]
+| TO_TIMESTAMP(string1[, string2]) | toTimestamp(string1[,
string2]) | Converts date time string string1 with format
string2 (by default: 'yyyy-MM-dd HH:mm:ss') to a timestamp, without time zone.
[...]
+| FROM_UNIXTIME(numeric[, string]) |
fromUnixtime(NUMERIC[, STRING]) | Returns a representation
of the numeric argument as a value in string format (default is ‘yyyy-MM-dd
HH:mm:ss’). numeric is an internal timestamp value representing seconds since
‘1970-01-01 00:00:00’ UTC, such as produced by the UNIX_TIMESTAMP() function.
The return value is expressed in the session time zone (specified in
TableConfig). E.g., FROM_UNIXTIME(44) returns ‘1970-01 [...]
+| UNIX_TIMESTAMP() | unixTimestamp()
| Gets current Unix timestamp in seconds. This
function is not deterministic which means the value would be recalculated for
each record.
[...]
+| UNIX_TIMESTAMP(string1[, string2]) |
unixTimestamp(STRING1[, STRING2]) | Converts a date time
string string1 with format string2 (by default: yyyy-MM-dd HH:mm:ss if not
specified) to Unix timestamp (in seconds), using the specified timezone in
table config.<br/>If a time zone is specified in the date time string and
parsed by UTC+X format such as “yyyy-MM-dd HH:mm:ss.SSS X”, this function will
use the specified timezone in the date time string inste [...]
+
+## 条件函数
+
+| Function
| Janino Code
| Description
|
+|-----------------------------------------------------------------------------------------------------------------------|--------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| CASE value WHEN value1_1 [, value1_2]* THEN RESULT1 (WHEN value2_1 [,
value2_2 ]* THEN result_2)* (ELSE result_z) END | Nested ternary expression
| Returns resultX when the first time value is contained in (valueX_1,
valueX_2, …). When no value matches, returns result_z if it is provided and
returns NULL otherwise.
|
+| CASE WHEN condition1 THEN result1 (WHEN condition2 THEN result2)* (ELSE
result_z) END | Nested ternary expression
| Returns resultX when the first conditionX is met. When no condition is
met, returns result_z if it is provided and returns NULL otherwise.
|
+| COALESCE(value1 [, value2]*)
| coalesce(Object... objects)
| Returns the first argument that is not NULL.If all arguments are NULL, it
returns NULL as well. The return type is the least restrictive, common type of
all of its arguments. The return type is nullable if all arguments are nullable
as well. |
+| IF(condition, true_value, false_value)
| condition ? true_value : false_value
| Returns the true_value if condition is met, otherwise false_value. E.g., IF(5
> 3, 5, 3) returns 5.
|
+
+## 转换函数
You can use `CAST( <EXPR> AS <T> )` syntax to convert any valid expression
`<EXPR>` to a specific type `<T>`. Possible conversion paths are:
@@ -191,9 +191,9 @@ You can use `CAST( <EXPR> AS <T> )` syntax to convert any
valid expression `<EXP
| NUMERIC | DECIMAL | Value must be in the
range of BigDecimal(10, 0). |
| STRING, TIMESTAMP_TZ, TIMESTAMP_LTZ | TIMESTAMP | String type value must
be a valid `ISO_LOCAL_DATE_TIME` string. |
-# Example
-## Add computed columns
-Evaluation expressions can be used to generate new columns. For example, if we
want to append two computed columns based on the table `web_order` in the
database `mydb`, we may define a transform rule as follows:
+# 示例
+## 添加计算列
+表达式可以用来生成新的列。例如，如果我们想基于表 `web_order` 在数据库 `mydb` 中添加两个计算列，我们可以定义一个转换规则如下：
```yaml
transform:
@@ -202,9 +202,8 @@ transform:
description: append calculated columns based on source table
```
-## Reference metadata columns
-We may reference metadata column in projection expressions. For example, given
a table `web_order` in the database `mydb`, we may define a transform rule as
follows:
-
+## 引用元数据列
+我们可以在投影表达式中引用元数据列。例如，给定表 `web_order` 在数据库 `mydb` 中，我们可以定义一个转换规则如下：
```yaml
transform:
- source-table: mydb.web_order
@@ -212,8 +211,8 @@ transform:
description: access metadata columns from source table
```
-## Use wildcard character to project all fields
-A wildcard character (`*`) can be used to reference all fields in a table. For
example, given two tables `web_order` and `app_order` in the database `mydb`,
we may define a transform rule as follows:
+## 使用通配符来包含所有字段
+通配符(`*`)可以用来引用表中的所有字段。例如，给定表 `web_order` 在数据库 `mydb` 中，我们可以定义一个转换规则如下：
```yaml
transform:
@@ -224,10 +223,10 @@ transform:
projection: UPPER(product_name) as product_name, *
description: project fields with wildcard character from source table
```
-Notice: When `*` character presents at the beginning of expressions, an
escaping backslash is required.
+注意：当 `*` 字符出现在表达式的开头时，需要添加转义反斜杠。
-## Add filter rule
-Use reference columns when adding filtering rules to the table `web_order` in
the database `mydb`, we may define a transform rule as follows:
+## 添加过滤条件
+使用引用列时，我们可以在添加过滤规则时使用。例如，给定表 `web_order` 在数据库 `mydb` 中，我们可以定义一个转换规则如下：
```yaml
transform:
@@ -236,7 +235,7 @@ transform:
description: filtering rows from source table
```
-Computed columns can be used in filtering conditions, too. For example, given
a table `web_order` in the database `mydb`, we may define a transform rule as
follows:
+计算列可以在过滤条件中使用。例如，给定表 `web_order` 在数据库 `mydb` 中，我们可以定义一个转换规则如下：
```yaml
transform:
@@ -246,8 +245,8 @@ transform:
description: filtering rows based on computed columns
```
-## Reassign primary key
-We can reassign the primary key in transform rules. For example, given a table
`web_order` in the database `mydb`, we may define a transform rule as follows:
+## 重新设置主键
+我们可以在转换规则中重新设置主键。例如，给定表 `web_order` 在数据库 `mydb` 中，我们可以定义一个转换规则如下：
```yaml
transform:
@@ -257,7 +256,7 @@ transform:
description: reassign primary key example
```
-Composite primary keys are also supported:
+复合主键也支持。例如，给定表 `web_order` 在数据库 `mydb` 中，我们可以定义一个转换规则如下：
```yaml
transform:
@@ -267,11 +266,10 @@ transform:
description: reassign composite primary keys example
```
-Notice that primary key columns will be attributed as NOT NULL in the
downstream table, so you should ensure that no NULL value will be assigned to
these columns.
-
-## Reassign partition key
-We can reassign the partition key in transform rules. For example, given a
table web_order in the database mydb, we may define a transform rule as follows:
+注意主键列不能为空。
+## 重新设置分区键
+我们可以重新设置分区键。例如，给定表 `web_order` 在数据库 `mydb` 中，我们可以定义一个转换规则如下：
```yaml
transform:
- source-table: mydb.web_order
@@ -280,8 +278,8 @@ transform:
description: reassign partition key example
```
-## Specify table creation configuration
-Extra options can be defined in a transform rule, and will be applied when
creating downstream tables. Given a table `web_order` in the database `mydb`,
we may define a transform rule as follows:
+## 定义建表参数
+额外的建表参数可以定义在转换规则中，并在创建下游表时应用。例如，给定表 `web_order` 在数据库 `mydb` 中，我们可以定义一个转换规则如下：
```yaml
transform:
@@ -290,12 +288,12 @@ transform:
table-options: comment=web order
description: auto creating table options example
```
-Tips: The format of table-options is `key1=value1,key2=value2`.
+小技巧：table-options 的格式是 `key1=value1,key2=value2`。
## Classification mapping
-Multiple transform rules can be defined to classify input data rows and apply
different processing.
-Only the first matched transform rule will apply.
-For example, we may define a transform rule as follows:
+多个转换规则可以定义为分类映射。
+只有第一个匹配的转换规则将应用。
+举个例子，我们可以定义一个转换规则如下：
```yaml
transform:
@@ -309,22 +307,20 @@ transform:
description: classification mapping example
```
-## User-defined Functions
-
-User-defined functions (UDFs) can be used in transform rules.
-
-Classes could be used as a UDF if:
+## 用户自定义函数
+用户自定义函数（UDF）可以在转换规则中使用。
-* implements `org.apache.flink.cdc.common.udf.UserDefinedFunction` interface
-* has a public constructor with no parameters
-* has at least one public method named `eval`
+一个满足了下面条件的类可以作为 UDF:
+* 继承了 `org.apache.flink.cdc.common.udf.UserDefinedFunction` 接口
+* 有一个 public 的无参构造函数
+* 有至少一个 public 方法命名为 `eval`
-It may also:
+也可以:
-* overrides `getReturnType` method to indicate its return CDC type
-* overrides `open` and `close` method to do some initialization and cleanup
work
+* 重载 `getReturnType` 方法来指示它的返回 CDC 类型
+* 重载 `open` 和 `close` 方法来执行一些初始化和清理工作
-For example, this is a valid UDF class:
+举个例子，这是一个有效的 UDF 类:
```java
public class AddOneFunctionClass implements UserDefinedFunction {
@@ -350,13 +346,13 @@ public class AddOneFunctionClass implements
UserDefinedFunction {
}
```
-To ease the migration from Flink SQL to Flink CDC, a Flink `ScalarFunction`
could also be used as a transform UDF, with some limitations:
+为了兼容 Flink SQL，UDF 类也可以是 Flink `ScalarFunction`，但有一些限制:
-* `ScalarFunction` which has a constructor with parameters is not supported.
-* Flink-style type hint in `ScalarFunction` will be ignored.
-* `open` / `close` lifecycle hooks will not be invoked.
+* 包含有参构造方法的 `ScalarFunction` 是不支持的。
+* FLink 的 `DataType` 提示在 `ScalarFunction` 中将被忽略。
+* `open` / `close` 生命周期钩子不会被调用。
-UDF classes could be registered by adding a `user-defined-function` block:
+UDF 类可以通过 `user-defined-function` 块进行注册：
```yaml
pipeline:
@@ -367,9 +363,9 @@ pipeline:
classpath: org.apache.flink.cdc.udf.examples.java.FormatFunctionClass
```
-Notice that given classpath must be fully-qualified, and corresponding `jar`
files must be included in Flink `/lib` folder, or be passed with `flink-cdc.sh
--jar` option.
+注意这里的 `classpath` 必须是全限定名，并且对应的 `jar` 文件必须包含在 Flink `/lib` 文件夹中，或者通过
`flink-cdc.sh --jar` 选项传递。
-After being correctly registered, UDFs could be used in both `projection` and
`filter` expressions, just like built-in functions:
+在正确注册后，UDF 可以在 `projection` 和 `filter` 表达式中使用，就像内置函数一样：
```yaml
transform:
@@ -378,12 +374,12 @@ transform:
filter: inc(id) < 100
```
-## Embedding AI Model
+## 内置 AI 模型
-Embedding AI Model can be used in transform rules.
-To use Embedding AI Model, you need to download the jar of build-in model, and
then add `--jar {$BUILT_IN_MODEL_PATH}` to your flink-cdc.sh command.
+内置AI模型可以在transform规则中使用。
+为了使用内置AI模型，你需要下载内置模型的jar，然后在flink-cdc.sh命令中添加`--jar {$BUILT_IN_MODEL_PATH}`。
-How to define a Embedding AI Model:
+如何定义一个Embedding AI模型：
```yaml
pipeline:
@@ -400,12 +396,12 @@ pipeline:
openai.host: https://xxxx
openai.apikey: abcd1234
```
-Note:
-* `model-name` is a common required parameter for all support models, which
represent the function name called in `projection` or `filter`.
-* `class-name` is a common required parameter for all support models,
available values can be found in [All Support models](#all-support-models).
-* `openai.model`, `openai.host`, `openai.apiKey` and `openai.chat.prompt` is
option parameters that defined in specific model.
+注意：
+* `model-name` 是一个通用的必填参数，用于所有支持的模型，表示在`projection`或`filter`中调用的函数名称。
+* `class-name` 是一个通用的必填参数，用于所有支持的模型，可用值可以在[所有支持的模型](#all-support-models)中找到。
+* `openai.model`，`openai.host`， `openai.apiKey` 和 `openai.chat.prompt`
是在各个模型中特别的可选参数。
-How to use a Embedding AI Model:
+如何使用一个内置的 AI 模型：
```yaml
transform:
@@ -426,15 +422,15 @@ pipeline:
openai.host: http://langchain4j.dev/demo/openai/v1
openai.apikey: demo
```
-Here, GET_EMBEDDING is defined though `model-name` in `pipeline`.
+这里，GET_EMBEDDING 是通过`model-name`在`pipeline`中定义的。
-### All Support models
+### 所有支持的模型
-The following built-in models are provided:
+下面列出了所有支持的模型：
#### OpenAIChatModel
-| parameter | type | optional/required | meaning
|
+| 参数 | 类型 | optional/required | 含义
|
|--------------------|--------|-------------------|--------------------------------------------------------------------------------------------------------------------------------------|
| openai.model | STRING | required | Name of model to be
called, for example: "gpt-4o-mini", Available options are "gpt-4o-mini",
"gpt-4o", "gpt-4-32k", "gpt-3.5-turbo". |
| openai.host | STRING | required | Host of the Model server
to be connected, for example: `http://langchain4j.dev/demo/openai/v1`.
|
@@ -443,13 +439,13 @@ The following built-in models are provided:
#### OpenAIEmbeddingModel
-| parameter | type | optional/required | meaning
|
+| 参数 | 类型 | optional/required | 含义
|
|---------------|--------|-------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| openai.model | STRING | required | Name of model to be called, for
example: "text-embedding-3-small", Available options are
"text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002". |
| openai.host | STRING | required | Host of the Model server to be
connected, for example: `http://langchain4j.dev/demo/openai/v1`.
|
| openai.apikey | STRING | required | Api Key for verification of the
Model server, for example, "demo".
|
-# Known limitations
-* Currently, transform doesn't work with route rules. It will be supported in
future versions.
-* Computed columns cannot reference trimmed columns that do not present in
final projection results. This will be fixed in future versions.
-* Regular matching of tables with different schemas is not supported. If
necessary, multiple rules need to be written.
\ No newline at end of file
+# 已知的限制
+* 目前，transform 不能与路由规则一起使用。这将会在未来版本中支持。
+* 计算列不能引用没有出现在最终投影结果中的被裁剪的列。这将在将来的版本中解决。
+* 正则匹配不同schema的表不支持。如果需要，需要编写多个规则。
\ No newline at end of file
