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The following commit(s) were added to refs/heads/dev by this push:
new 376ae32965 Explanation of the overall steps for creating a new UDF
(#718)
376ae32965 is described below
commit 376ae32965f8b5af6a5afa29481814602022a5af
Author: ahaoyao <[email protected]>
AuthorDate: Tue Jun 27 22:42:15 2023 +0800
Explanation of the overall steps for creating a new UDF (#718)
---
docs/user-guide/datasource-client.md | 2 +-
docs/user-guide/datasource-manual.md | 2 +-
docs/user-guide/dynamic-variables.md | 2 +-
docs/user-guide/linkiscli-manual.md | 2 +-
docs/user-guide/udf-function.md | 152 ++++++++++++++++++++
.../user-guide/control-panel/_category_.json | 2 +-
.../current/user-guide/datasource-client.md | 2 +-
.../current/user-guide/dynamic-variables.md | 2 +-
.../current/user-guide/udf-function.md | 153 +++++++++++++++++++++
.../user-guide/control-panel/_category_.json | 2 +-
.../current/user-guide/datasource-client.md | 2 +-
.../current/user-guide/dynamic-variables.md | 2 +-
.../current/user-guide/udf-function.md | 153 +++++++++++++++++++++
.../user-guide/control-panel/_category_.json | 2 +-
.../version-1.3.2/user-guide/datasource-client.md | 2 +-
.../version-1.3.2/user-guide/dynamic-variables.md | 2 +-
.../version-1.3.2/user-guide/udf-function.md | 153 +++++++++++++++++++++
static/Images/udf/udf_10.png | Bin 0 -> 68601 bytes
static/Images/udf/udf_11.png | Bin 0 -> 53547 bytes
static/Images/udf/udf_12.png | Bin 0 -> 7730 bytes
static/Images/udf/udf_13.png | Bin 0 -> 58595 bytes
static/Images/udf/udf_14.png | Bin 0 -> 112124 bytes
static/Images/udf/udf_15.png | Bin 0 -> 160907 bytes
static/Images/udf/udf_16.png | Bin 0 -> 117429 bytes
static/Images/udf/udf_17.png | Bin 0 -> 172987 bytes
static/Images/udf/udf_18.png | Bin 0 -> 189995 bytes
static/Images/udf/udf_19.png | Bin 0 -> 176172 bytes
static/Images/udf/udf_20.png | Bin 0 -> 172257 bytes
.../version-1.3.2/user-guide/datasource-client.md | 2 +-
.../version-1.3.2/user-guide/datasource-manual.md | 2 +-
.../version-1.3.2/user-guide/dynamic-variables.md | 2 +-
.../version-1.3.2/user-guide/linkiscli-manual.md | 2 +-
.../version-1.3.2/user-guide/udf-function.md | 152 ++++++++++++++++++++
33 files changed, 780 insertions(+), 17 deletions(-)
diff --git a/docs/user-guide/datasource-client.md
b/docs/user-guide/datasource-client.md
index c90dc16d17..3a0a35865d 100644
--- a/docs/user-guide/datasource-client.md
+++ b/docs/user-guide/datasource-client.md
@@ -1,6 +1,6 @@
---
title: DataSource Client SDK
-sidebar_position: 4
+sidebar_position: 6
---
> ```
diff --git a/docs/user-guide/datasource-manual.md
b/docs/user-guide/datasource-manual.md
index 676c11b99e..993fac2fd8 100644
--- a/docs/user-guide/datasource-manual.md
+++ b/docs/user-guide/datasource-manual.md
@@ -1,6 +1,6 @@
---
title: Data Source Manual
-sidebar_position: 7
+sidebar_position: 4
---
> Introduce how to use the new feature function data source of version 1.1.0
diff --git a/docs/user-guide/dynamic-variables.md
b/docs/user-guide/dynamic-variables.md
index 77b2d8b5fb..5ad0d6b4d2 100644
--- a/docs/user-guide/dynamic-variables.md
+++ b/docs/user-guide/dynamic-variables.md
@@ -1,6 +1,6 @@
---
title: built-in time variable
-sidebar_position: 6
+sidebar_position: 7
---
## 1. Overview
diff --git a/docs/user-guide/linkiscli-manual.md
b/docs/user-guide/linkiscli-manual.md
index 02eef5b2f0..928e1b0afd 100644
--- a/docs/user-guide/linkiscli-manual.md
+++ b/docs/user-guide/linkiscli-manual.md
@@ -1,6 +1,6 @@
---
title: Shell Scripts Manual
-sidebar_position: 3
+sidebar_position: 2
---
## 1.Introduction
diff --git a/docs/user-guide/udf-function.md b/docs/user-guide/udf-function.md
new file mode 100644
index 0000000000..b6b46c06f2
--- /dev/null
+++ b/docs/user-guide/udf-function.md
@@ -0,0 +1,152 @@
+---
+title: UDF Function
+sidebar_position: 5
+---
+
+## 1 Introduction to UDF
+UDF: User Defined Function, user-defined function. In some scenarios, we need
to use hive functions to process some data. Functions like count() and sum()
are built-in. If we want to use some functions that are not built-in, we need
to customize the function, which can be done by writing UDF.
+
+
+## 1.Overview of the overall steps for creating UDF
+### 1 Universal type UDF functions
+Overall step description
+- Write UDF functions in UDF format locally and package them as jar package
files
+- 【Scriptis >> Workspace】Upload to the corresponding directory in the workspace
+- 【Management Console>>UDF Function】 Create udf (default loading)
+- Used in task code (only effective for newly started engines)
+
+**Step1 Writing jar packages locally**
+
+UDF
Example：https://help.aliyun.com/apsara/agile/v_3_6_0_20210705/odps/ase-user-guide/udf-example.html
+
+**Step2【Scriptis >> Workspace】Upload jar package**
+Select the corresponding folder and right-click to select Upload
+
+
+
+**Step3【Management Console>>UDF Function】 Create UDF**
+- Function name: Conforming to the rules is sufficient, such as test_ Udf_
Using jar in scripts such as SQL
+- Function Type: General
+- Script path: Select the shared directory path where the jar package is
stored, such as../..// Wds_ Functions_ 1_ 0_ 0. jar
+- Registration format: package name+class name, such as
com.webank.wedatasphere.willink.bdp.udf.ToUpperCase
+- Usage format: Input type and return type must be consistent with the
definition in the jar package
+- Classification: drop-down selection; Alternatively, enter a custom directory
(which will create a new target level directory under the personal function)
+
+
+
+Note that the newly created UDF function is loaded by default and can be
viewed on the [Scriptis>>UDF Functions] page for easy viewing during Scriptis
task editing. Checking the UDF function indicates that it will be loaded and
used
+
+
+
+**Step4 Use this udf function**
+
+Innovative udf function using the above steps in the task
+Function name is [Create UDF] Function name
+In pyspark:
+print (sqlContext.sql("select test_udf_jar(name1) from
stacyyan_ind.result_sort_1_20200226").collect())
+
+### 2 UDF functions of Spark type
+Overall step description
+- Create a new Spark script file in the desired directory in the
[Scriptis>>workspace]
+- Create UDF in [Management Console>>UDF Functions] (default loading)
+- Used in task code (only effective for newly started engines)
+
+**Step1 dss-scriptis-Create a new scala script**
+
+
+
+def helloWorld(str: String): String = "hello, " + str
+
+**Step2 Create UDF**
+- Function name: Conforming to the rules is sufficient, such as test_ Udf_
Scala
+- Function type: spark
+- Script Path:../..// B
+- Registration format: The input type and return type must be consistent with
the definition; The function names that need to be defined in the registration
format must be strictly consistent, such as helloWorld
+- Classification: Drop down and select the first level directory that exists
under dss scriptis UDF function - Personal function; Alternatively, enter a
custom directory (which will create a new target level directory under the
personal function)
+
+
+
+**Step3 Use this udf function**
+
+Use the above steps in the task to create a new udf function
+Function name is [Create UDF] Function name
+- In scala
+ val s=sqlContext.sql("select test_udf_scala(name1)
+ from stacyyan_ind.result_sort_1_20200226")
+ show(s)
+- in pyspark
+ print(sqlContext.sql("select test_udf_scala(name1)
+ from stacyyan_ind.result_sort_1_20200226").collect());
+- in sql
+ select test_udf_scala(name1) from stacyyan_ind.result_sort_1_20200226;
+
+### 3 Python functions
+Overall step description
+- Create a new Python script file in the desired directory in the
[Scriptis>>workspace]
+- Create UDF in [Management Console>>UDF Functions] (default loading)
+- Used in task code (only effective for newly started engines)
+
+**Step1 dss-scriptis-Create a new pyspark script**
+
+
+
+def addation(a, b):
+return a + b
+Step2 Create UDF
+- Function name: Conforming to the rules is sufficient, such as test_ Udf_ Py
+- Function type: spark
+- Script Path:../..// A
+- Registration format: The function names that need to be defined must be
strictly consistent, such as addition
+- Usage format: The input type and return type must be consistent with the
definition
+- Classification: Drop down and select the first level directory that exists
under dss scriptis UDF function - Personal function; Alternatively, enter a
custom directory (which will create a new target level directory under the
personal function)
+
+
+
+**Step3 uses this udf function**
+Use the above steps in the task to create a new udf function
+Function name is [Create UDF] Function name
+- in pyspark
+ print(sqlContext.sql("select test_udf_py(pv,impression) from
neiljianliu_ind.alias where entityid=504059 limit 50").collect());
+- in sql
+ select test_udf_py(pv,impression) from neiljianliu_ind.alias where
entityid=504059 limit 50
+
+### 4 scala functions
+Overall step description
+- Create a new Spark Scala script file in the desired directory in the
[Scriptis>>workspace]
+- Create UDF in [Management Console>>UDF Functions] (default loading)
+- Used in task code (only effective for newly started engines)
+-
+**Step1 dss-scriptis-Create a new scala script**
+def hellozdy(str:String):String = "hellozdy,haha " + str
+**Step2 CREATE FUNCTION**
+- Function name: Must be strictly consistent with the defined function name,
such as hellozdy
+- Function Type: Custom Function
+- Script Path:../..// D
+- Usage format: The input type and return type must be consistent with the
definition
+- Classification: Drop down and select the first level directory that exists
under dss scriptis method function personal function; Alternatively, enter a
custom directory (which will create a new target level directory under the
personal function)
+ Step3 Use this function
+ Use the above steps in the task to create a new udf function
+ Function name is [Create UDF] Function name
+ val a = hellozdy("abcd");
+ print(a)
+
+### 5 Common usage issues
+#### 5.1 UDF function loading failed
+"FAILED: SemanticException [Error 10011]: Invalid function xxxx"
+
+ 
+
+- Firstly, check if the UDF function configuration is correct:
+
+ 
+
+- The registration format is the function path name:
+
+ 
+
+- Check the scriptis udf function to see if the loaded function is checked.
When the function is not checked, udf will not be loaded when the engine starts
+
+ 
+
+- Check if the engine has loaded UDF. If not, please restart another engine or
restart the current engine
+ Note: UDF will only be loaded when the engine is initialized. If UDF is
added midway, the current engine will not be able to perceive and load it
\ No newline at end of file
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/control-panel/_category_.json
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/control-panel/_category_.json
index ad45f57c34..b0a16d92f7 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/control-panel/_category_.json
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/control-panel/_category_.json
@@ -1,4 +1,4 @@
{
"label": "管理台的使用",
- "position": 7
+ "position": 8
}
\ No newline at end of file
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/datasource-client.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/datasource-client.md
index b0bcd75e95..f6355c32b4 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/datasource-client.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/datasource-client.md
@@ -1,6 +1,6 @@
---
title: 数据源 SDK
-sidebar_position: 5
+sidebar_position: 6
---
> Linkis DataSource 提供了方便的JAVA和SCALA调用的Client SDK
> 接口，只需要引入linkis-datasource-client的模块就可以进行使用，
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/dynamic-variables.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/dynamic-variables.md
index e6836fe269..3810b8674b 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/dynamic-variables.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/dynamic-variables.md
@@ -1,6 +1,6 @@
---
title: 内置时间变量
-sidebar_position: 6
+sidebar_position: 7
---
## 1. 总述
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/udf-function.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/udf-function.md
new file mode 100644
index 0000000000..0ee158a09c
--- /dev/null
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/user-guide/udf-function.md
@@ -0,0 +1,153 @@
+---
+title: UDF功能
+sidebar_position: 5
+---
+
+> 详细介绍一下如何使用UDF功能
+
+## 1.UDF创建的整体步骤说明
+### 1 通用类型的UDF函数
+整体步骤说明
+- 在本地按UDF函数格式 编写udf 函数 ，并打包称jar包文件
+- 在【Scriptis >> 工作空间】上传至工作空间对应的目录
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 本地编写jar包**
+
+UDF示例：https://help.aliyun.com/apsara/agile/v_3_6_0_20210705/odps/ase-user-guide/udf-example.html
+
+**Step2【Scriptis >> 工作空间】上传jar包**
+选择对应的文件夹 鼠标右键 选择上传
+
+
+
+**Step3【管理台>>UDF函数】 创建UDF**
+- 函数名称：符合规则即可，如test_udf_jar 在sql等脚本中使用
+- 函数类型：通用
+- 脚本路径：选择jar包存放的共享目录路径 如 ../../../wds_functions_1_0_0.jar
+- 注册格式：包名+类名，如 com.webank.wedatasphere.willink.bdp.udf.ToUpperCase
+- 使用格式：输入类型与返回类型，需与jar包里定义一致
+- 分类：下拉选择；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+
+
+注意 新建的udf 函数 是默认加载的 可以在 【Scriptis >> UDF函数】 页面查看到，方便大家在Scriptis 任务编辑时
方便查看，勾选中的UDF函数 表明是会被加载使用的
+
+
+
+**Step4 使用该udf函数**
+
+在任务中 使用上述步骤创新的udf 函数
+函数名为 【创建UDF】 函数名称
+在pyspark中：
+print (sqlContext.sql("select test_udf_jar(name1) from
stacyyan_ind.result_sort_1_20200226").collect())
+
+### 2 Spark类型的UDF函数
+整体步骤说明
+- 在【Scriptis >> 工作空间】在需要的目录下新建Spark脚本文件
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 dss-scriptis-新建scala脚本**
+
+
+
+def helloWorld(str: String): String = "hello, " + str
+
+**Step2 创建UDF**
+- 函数名称：符合规则即可，如test_udf_scala
+- 函数类型：spark
+- 脚本路径：../../../b
+- 注册格式：输入类型与返回类型，需与定义一致；注册格式需定义的函数名严格保持一致，如helloWorld
+- 分类：下拉选择dss-scriptis-UDF函数-个人函数下存在的一级目录；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+
+
+
+**Step3 使用该udf函数**
+
+在任务中 使用上述步骤创建新的udf 函数
+函数名为 【创建UDF】 函数名称
+- 在scala中
+ val s=sqlContext.sql("select test_udf_scala(name1)
+ from stacyyan_ind.result_sort_1_20200226")
+ show(s)
+- 在pyspark中
+ print(sqlContext.sql("select test_udf_scala(name1)
+ from stacyyan_ind.result_sort_1_20200226").collect());
+- 在sql中
+ select test_udf_scala(name1) from stacyyan_ind.result_sort_1_20200226;
+
+### 3 python函数
+整体步骤说明
+- 在【Scriptis >> 工作空间】在需要的目录下新建Python脚本文件
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 dss-scriptis-新建pyspark脚本**
+
+
+
+def addation(a, b):
+return a + b
+**Step2 创建UDF**
+- 函数名称：符合规则即可，如test_udf_py
+- 函数类型：spark
+- 脚本路径：../../../a
+- 注册格式：需定义的函数名严格保持一致，如addation
+- 使用格式：输入类型与返回类型，需与定义一致
+- 分类：下拉选择dss-scriptis-UDF函数-个人函数下存在的一级目录；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+
+
+**Step3 使用该udf函数**
+在任务中 使用上述步骤创建新的udf 函数
+函数名为 【创建UDF】 函数名称
+- 在pyspark中
+ print(sqlContext.sql("select test_udf_py(pv,impression) from
neiljianliu_ind.alias where entityid=504059 limit 50").collect());
+- 在sql中
+ select test_udf_py(pv,impression) from neiljianliu_ind.alias where
entityid=504059 limit 50
+
+### 4 scala函数
+整体步骤说明
+- 在【Scriptis >> 工作空间】在需要的目录下新建Spark Scala脚本文件
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 dss-scriptis-新建scala脚本**
+def hellozdy(str:String):String = "hellozdy,haha " + str
+
+**Step2 创建函数**
+- 函数名称：需与定义的函数名严格保持一致，如hellozdy
+- 函数类型：自定义函数
+- 脚本路径：../../../d
+- 使用格式：输入类型与返回类型，需与定义一致
+- 分类：下拉选择dss-scriptis-方法函数-个人函数下存在的一级目录；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+**Step3 使用该函数**
+在任务中 使用上述步骤创建新的udf 函数
+函数名为 【创建UDF】 函数名称
+val a = hellozdy("abcd");
+print(a)
+
+### 5 常见的使用问题
+#### 5.1 UDF函数加载失败
+"FAILED: SemanticException [Error 10011]: Invalid function xxxx"
+
+
+
+- 首先检查UDF函数配置是否正确：
+
+ 
+
+- 注册格式即为函数路径名称：
+
+ 
+
+- 检查scriptis-udf函数-查看加载的函数是否勾选，当函数未勾选时，引擎启动时将不会加载udf
+
+ 
+
+- 检查引擎是否已加载UDF，如果未加载，请重新另起一个引擎或者重启当前引擎
+ 备注：只有当引擎初始化时，才会加载UDF，中途新增UDF，当前引擎将无法感知并且无法进行加载
\ No newline at end of file
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/control-panel/_category_.json
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/control-panel/_category_.json
index ad45f57c34..b0a16d92f7 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/control-panel/_category_.json
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/control-panel/_category_.json
@@ -1,4 +1,4 @@
{
"label": "管理台的使用",
- "position": 7
+ "position": 8
}
\ No newline at end of file
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/datasource-client.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/datasource-client.md
index b0bcd75e95..f6355c32b4 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/datasource-client.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/datasource-client.md
@@ -1,6 +1,6 @@
---
title: 数据源 SDK
-sidebar_position: 5
+sidebar_position: 6
---
> Linkis DataSource 提供了方便的JAVA和SCALA调用的Client SDK
> 接口，只需要引入linkis-datasource-client的模块就可以进行使用，
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/dynamic-variables.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/dynamic-variables.md
index e6836fe269..3810b8674b 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/dynamic-variables.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/dynamic-variables.md
@@ -1,6 +1,6 @@
---
title: 内置时间变量
-sidebar_position: 6
+sidebar_position: 7
---
## 1. 总述
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/udf-function.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/udf-function.md
new file mode 100644
index 0000000000..0ee158a09c
--- /dev/null
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/current/user-guide/udf-function.md
@@ -0,0 +1,153 @@
+---
+title: UDF功能
+sidebar_position: 5
+---
+
+> 详细介绍一下如何使用UDF功能
+
+## 1.UDF创建的整体步骤说明
+### 1 通用类型的UDF函数
+整体步骤说明
+- 在本地按UDF函数格式 编写udf 函数 ，并打包称jar包文件
+- 在【Scriptis >> 工作空间】上传至工作空间对应的目录
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 本地编写jar包**
+
+UDF示例：https://help.aliyun.com/apsara/agile/v_3_6_0_20210705/odps/ase-user-guide/udf-example.html
+
+**Step2【Scriptis >> 工作空间】上传jar包**
+选择对应的文件夹 鼠标右键 选择上传
+
+
+
+**Step3【管理台>>UDF函数】 创建UDF**
+- 函数名称：符合规则即可，如test_udf_jar 在sql等脚本中使用
+- 函数类型：通用
+- 脚本路径：选择jar包存放的共享目录路径 如 ../../../wds_functions_1_0_0.jar
+- 注册格式：包名+类名，如 com.webank.wedatasphere.willink.bdp.udf.ToUpperCase
+- 使用格式：输入类型与返回类型，需与jar包里定义一致
+- 分类：下拉选择；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+
+
+注意 新建的udf 函数 是默认加载的 可以在 【Scriptis >> UDF函数】 页面查看到，方便大家在Scriptis 任务编辑时
方便查看，勾选中的UDF函数 表明是会被加载使用的
+
+
+
+**Step4 使用该udf函数**
+
+在任务中 使用上述步骤创新的udf 函数
+函数名为 【创建UDF】 函数名称
+在pyspark中：
+print (sqlContext.sql("select test_udf_jar(name1) from
stacyyan_ind.result_sort_1_20200226").collect())
+
+### 2 Spark类型的UDF函数
+整体步骤说明
+- 在【Scriptis >> 工作空间】在需要的目录下新建Spark脚本文件
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 dss-scriptis-新建scala脚本**
+
+
+
+def helloWorld(str: String): String = "hello, " + str
+
+**Step2 创建UDF**
+- 函数名称：符合规则即可，如test_udf_scala
+- 函数类型：spark
+- 脚本路径：../../../b
+- 注册格式：输入类型与返回类型，需与定义一致；注册格式需定义的函数名严格保持一致，如helloWorld
+- 分类：下拉选择dss-scriptis-UDF函数-个人函数下存在的一级目录；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+
+
+
+**Step3 使用该udf函数**
+
+在任务中 使用上述步骤创建新的udf 函数
+函数名为 【创建UDF】 函数名称
+- 在scala中
+ val s=sqlContext.sql("select test_udf_scala(name1)
+ from stacyyan_ind.result_sort_1_20200226")
+ show(s)
+- 在pyspark中
+ print(sqlContext.sql("select test_udf_scala(name1)
+ from stacyyan_ind.result_sort_1_20200226").collect());
+- 在sql中
+ select test_udf_scala(name1) from stacyyan_ind.result_sort_1_20200226;
+
+### 3 python函数
+整体步骤说明
+- 在【Scriptis >> 工作空间】在需要的目录下新建Python脚本文件
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 dss-scriptis-新建pyspark脚本**
+
+
+
+def addation(a, b):
+return a + b
+**Step2 创建UDF**
+- 函数名称：符合规则即可，如test_udf_py
+- 函数类型：spark
+- 脚本路径：../../../a
+- 注册格式：需定义的函数名严格保持一致，如addation
+- 使用格式：输入类型与返回类型，需与定义一致
+- 分类：下拉选择dss-scriptis-UDF函数-个人函数下存在的一级目录；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+
+
+**Step3 使用该udf函数**
+在任务中 使用上述步骤创建新的udf 函数
+函数名为 【创建UDF】 函数名称
+- 在pyspark中
+ print(sqlContext.sql("select test_udf_py(pv,impression) from
neiljianliu_ind.alias where entityid=504059 limit 50").collect());
+- 在sql中
+ select test_udf_py(pv,impression) from neiljianliu_ind.alias where
entityid=504059 limit 50
+
+### 4 scala函数
+整体步骤说明
+- 在【Scriptis >> 工作空间】在需要的目录下新建Spark Scala脚本文件
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 dss-scriptis-新建scala脚本**
+def hellozdy(str:String):String = "hellozdy,haha " + str
+
+**Step2 创建函数**
+- 函数名称：需与定义的函数名严格保持一致，如hellozdy
+- 函数类型：自定义函数
+- 脚本路径：../../../d
+- 使用格式：输入类型与返回类型，需与定义一致
+- 分类：下拉选择dss-scriptis-方法函数-个人函数下存在的一级目录；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+**Step3 使用该函数**
+在任务中 使用上述步骤创建新的udf 函数
+函数名为 【创建UDF】 函数名称
+val a = hellozdy("abcd");
+print(a)
+
+### 5 常见的使用问题
+#### 5.1 UDF函数加载失败
+"FAILED: SemanticException [Error 10011]: Invalid function xxxx"
+
+
+
+- 首先检查UDF函数配置是否正确：
+
+ 
+
+- 注册格式即为函数路径名称：
+
+ 
+
+- 检查scriptis-udf函数-查看加载的函数是否勾选，当函数未勾选时，引擎启动时将不会加载udf
+
+ 
+
+- 检查引擎是否已加载UDF，如果未加载，请重新另起一个引擎或者重启当前引擎
+ 备注：只有当引擎初始化时，才会加载UDF，中途新增UDF，当前引擎将无法感知并且无法进行加载
\ No newline at end of file
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/control-panel/_category_.json
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/control-panel/_category_.json
index ad45f57c34..b0a16d92f7 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/control-panel/_category_.json
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/control-panel/_category_.json
@@ -1,4 +1,4 @@
{
"label": "管理台的使用",
- "position": 7
+ "position": 8
}
\ No newline at end of file
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/datasource-client.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/datasource-client.md
index b0bcd75e95..f6355c32b4 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/datasource-client.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/datasource-client.md
@@ -1,6 +1,6 @@
---
title: 数据源 SDK
-sidebar_position: 5
+sidebar_position: 6
---
> Linkis DataSource 提供了方便的JAVA和SCALA调用的Client SDK
> 接口，只需要引入linkis-datasource-client的模块就可以进行使用，
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/dynamic-variables.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/dynamic-variables.md
index e6836fe269..3810b8674b 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/dynamic-variables.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/dynamic-variables.md
@@ -1,6 +1,6 @@
---
title: 内置时间变量
-sidebar_position: 6
+sidebar_position: 7
---
## 1. 总述
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/udf-function.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/udf-function.md
new file mode 100644
index 0000000000..c0c2d3144b
--- /dev/null
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.3.2/user-guide/udf-function.md
@@ -0,0 +1,153 @@
+---
+title: UDF功能
+sidebar_position: 5
+---
+
+> 详细介绍一下如何使用UDF功能
+
+## 1.UDF创建的整体步骤说明
+### 1 通用类型的UDF函数
+整体步骤说明
+- 在本地按UDF函数格式 编写udf 函数 ，并打包称jar包文件
+- 在【Scriptis >> 工作空间】上传至工作空间对应的目录
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 本地编写jar包**
+
+UDF示例：https://help.aliyun.com/apsara/agile/v_3_6_0_20210705/odps/ase-user-guide/udf-example.html
+
+**Step2【Scriptis >> 工作空间】上传jar包**
+选择对应的文件夹 鼠标右键 选择上传
+
+
+
+**Step3【管理台>>UDF函数】 创建UDF**
+- 函数名称：符合规则即可，如test_udf_jar 在sql等脚本中使用
+- 函数类型：通用
+- 脚本路径：选择jar包存放的共享目录路径 如 ../../../wds_functions_1_0_0.jar
+- 注册格式：包名+类名，如 com.webank.wedatasphere.willink.bdp.udf.ToUpperCase
+- 使用格式：输入类型与返回类型，需与jar包里定义一致
+- 分类：下拉选择；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+
+
+注意 新建的udf 函数 是默认加载的 可以在 【Scriptis >> UDF函数】 页面查看到，方便大家在Scriptis 任务编辑时
方便查看，勾选中的UDF函数 表明是会被加载使用的
+
+
+
+**Step4 使用该udf函数**
+
+在任务中 使用上述步骤创新的udf 函数
+函数名为 【创建UDF】 函数名称
+在pyspark中：
+print (sqlContext.sql("select test_udf_jar(name1) from
stacyyan_ind.result_sort_1_20200226").collect())
+
+### 2 Spark类型的UDF函数
+整体步骤说明
+- 在【Scriptis >> 工作空间】在需要的目录下新建Spark脚本文件
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 dss-scriptis-新建scala脚本**
+
+
+
+def helloWorld(str: String): String = "hello, " + str
+
+**Step2 创建UDF**
+- 函数名称：符合规则即可，如test_udf_scala
+- 函数类型：spark
+- 脚本路径：../../../b
+- 注册格式：输入类型与返回类型，需与定义一致；注册格式需定义的函数名严格保持一致，如helloWorld
+- 分类：下拉选择dss-scriptis-UDF函数-个人函数下存在的一级目录；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+
+
+
+**Step3 使用该udf函数**
+
+在任务中 使用上述步骤创建新的udf 函数
+函数名为 【创建UDF】 函数名称
+- 在scala中
+ val s=sqlContext.sql("select test_udf_scala(name1)
+ from stacyyan_ind.result_sort_1_20200226")
+ show(s)
+- 在pyspark中
+ print(sqlContext.sql("select test_udf_scala(name1)
+ from stacyyan_ind.result_sort_1_20200226").collect());
+- 在sql中
+ select test_udf_scala(name1) from stacyyan_ind.result_sort_1_20200226;
+
+### 3 python函数
+整体步骤说明
+- 在【Scriptis >> 工作空间】在需要的目录下新建Python脚本文件
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 dss-scriptis-新建pyspark脚本**
+
+
+
+def addation(a, b):
+return a + b
+**Step2 创建UDF**
+- 函数名称：符合规则即可，如test_udf_py
+- 函数类型：spark
+- 脚本路径：../../../a
+- 注册格式：需定义的函数名严格保持一致，如addation
+- 使用格式：输入类型与返回类型，需与定义一致
+- 分类：下拉选择dss-scriptis-UDF函数-个人函数下存在的一级目录；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+
+
+**Step3 使用该udf函数**
+ 在任务中 使用上述步骤创建新的udf 函数
+ 函数名为 【创建UDF】 函数名称
+- 在pyspark中
+ print(sqlContext.sql("select test_udf_py(pv,impression) from
neiljianliu_ind.alias where entityid=504059 limit 50").collect());
+- 在sql中
+ select test_udf_py(pv,impression) from neiljianliu_ind.alias where
entityid=504059 limit 50
+
+### 4 scala函数
+整体步骤说明
+- 在【Scriptis >> 工作空间】在需要的目录下新建Spark Scala脚本文件
+- 在 【管理台>>UDF函数】 创建udf (默认加载）
+- 在任务代码中使用（对于新起的引擎才生效）
+
+**Step1 dss-scriptis-新建scala脚本**
+ def hellozdy(str:String):String = "hellozdy,haha " + str
+
+**Step2 创建函数**
+- 函数名称：需与定义的函数名严格保持一致，如hellozdy
+- 函数类型：自定义函数
+- 脚本路径：../../../d
+- 使用格式：输入类型与返回类型，需与定义一致
+- 分类：下拉选择dss-scriptis-方法函数-个人函数下存在的一级目录；或者输入自定义目录（会在个人函数下新建目标一级目录）
+
+**Step3 使用该函数**
+ 在任务中 使用上述步骤创建新的udf 函数
+ 函数名为 【创建UDF】 函数名称
+ val a = hellozdy("abcd");
+ print(a)
+
+### 5 常见的使用问题
+#### 5.1 UDF函数加载失败
+"FAILED: SemanticException [Error 10011]: Invalid function xxxx"
+
+ 
+
+- 首先检查UDF函数配置是否正确：
+
+ 
+
+- 注册格式即为函数路径名称：
+
+ 
+
+- 检查scriptis-udf函数-查看加载的函数是否勾选，当函数未勾选时，引擎启动时将不会加载udf
+
+ 
+
+- 检查引擎是否已加载UDF，如果未加载，请重新另起一个引擎或者重启当前引擎
+ 备注：只有当引擎初始化时，才会加载UDF，中途新增UDF，当前引擎将无法感知并且无法进行加载
\ No newline at end of file
diff --git a/static/Images/udf/udf_10.png b/static/Images/udf/udf_10.png
new file mode 100644
index 0000000000..ac7c907bc0
Binary files /dev/null and b/static/Images/udf/udf_10.png differ
diff --git a/static/Images/udf/udf_11.png b/static/Images/udf/udf_11.png
new file mode 100644
index 0000000000..8dd0e0051d
Binary files /dev/null and b/static/Images/udf/udf_11.png differ
diff --git a/static/Images/udf/udf_12.png b/static/Images/udf/udf_12.png
new file mode 100644
index 0000000000..ace0d5b1da
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diff --git a/static/Images/udf/udf_13.png b/static/Images/udf/udf_13.png
new file mode 100644
index 0000000000..7ca207dba4
Binary files /dev/null and b/static/Images/udf/udf_13.png differ
diff --git a/static/Images/udf/udf_14.png b/static/Images/udf/udf_14.png
new file mode 100644
index 0000000000..b409ce1faa
Binary files /dev/null and b/static/Images/udf/udf_14.png differ
diff --git a/static/Images/udf/udf_15.png b/static/Images/udf/udf_15.png
new file mode 100644
index 0000000000..8526a497d5
Binary files /dev/null and b/static/Images/udf/udf_15.png differ
diff --git a/static/Images/udf/udf_16.png b/static/Images/udf/udf_16.png
new file mode 100644
index 0000000000..abf2e57ea8
Binary files /dev/null and b/static/Images/udf/udf_16.png differ
diff --git a/static/Images/udf/udf_17.png b/static/Images/udf/udf_17.png
new file mode 100644
index 0000000000..5bd2c5297a
Binary files /dev/null and b/static/Images/udf/udf_17.png differ
diff --git a/static/Images/udf/udf_18.png b/static/Images/udf/udf_18.png
new file mode 100644
index 0000000000..8da0e15fb5
Binary files /dev/null and b/static/Images/udf/udf_18.png differ
diff --git a/static/Images/udf/udf_19.png b/static/Images/udf/udf_19.png
new file mode 100644
index 0000000000..5c6b3388fc
Binary files /dev/null and b/static/Images/udf/udf_19.png differ
diff --git a/static/Images/udf/udf_20.png b/static/Images/udf/udf_20.png
new file mode 100644
index 0000000000..044ec7d778
Binary files /dev/null and b/static/Images/udf/udf_20.png differ
diff --git a/versioned_docs/version-1.3.2/user-guide/datasource-client.md
b/versioned_docs/version-1.3.2/user-guide/datasource-client.md
index c90dc16d17..3a0a35865d 100644
--- a/versioned_docs/version-1.3.2/user-guide/datasource-client.md
+++ b/versioned_docs/version-1.3.2/user-guide/datasource-client.md
@@ -1,6 +1,6 @@
---
title: DataSource Client SDK
-sidebar_position: 4
+sidebar_position: 6
---
> ```
diff --git a/versioned_docs/version-1.3.2/user-guide/datasource-manual.md
b/versioned_docs/version-1.3.2/user-guide/datasource-manual.md
index 676c11b99e..993fac2fd8 100644
--- a/versioned_docs/version-1.3.2/user-guide/datasource-manual.md
+++ b/versioned_docs/version-1.3.2/user-guide/datasource-manual.md
@@ -1,6 +1,6 @@
---
title: Data Source Manual
-sidebar_position: 7
+sidebar_position: 4
---
> Introduce how to use the new feature function data source of version 1.1.0
diff --git a/versioned_docs/version-1.3.2/user-guide/dynamic-variables.md
b/versioned_docs/version-1.3.2/user-guide/dynamic-variables.md
index 77b2d8b5fb..5ad0d6b4d2 100644
--- a/versioned_docs/version-1.3.2/user-guide/dynamic-variables.md
+++ b/versioned_docs/version-1.3.2/user-guide/dynamic-variables.md
@@ -1,6 +1,6 @@
---
title: built-in time variable
-sidebar_position: 6
+sidebar_position: 7
---
## 1. Overview
diff --git a/versioned_docs/version-1.3.2/user-guide/linkiscli-manual.md
b/versioned_docs/version-1.3.2/user-guide/linkiscli-manual.md
index 02eef5b2f0..928e1b0afd 100644
--- a/versioned_docs/version-1.3.2/user-guide/linkiscli-manual.md
+++ b/versioned_docs/version-1.3.2/user-guide/linkiscli-manual.md
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---
title: Shell Scripts Manual
-sidebar_position: 3
+sidebar_position: 2
---
## 1.Introduction
diff --git a/versioned_docs/version-1.3.2/user-guide/udf-function.md
b/versioned_docs/version-1.3.2/user-guide/udf-function.md
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+---
+title: UDF Function
+sidebar_position: 5
+---
+
+## 1 Introduction to UDF
+UDF: User Defined Function, user-defined function. In some scenarios, we need
to use hive functions to process some data. Functions like count() and sum()
are built-in. If we want to use some functions that are not built-in, we need
to customize the function, which can be done by writing UDF.
+
+
+## 1.Overview of the overall steps for creating UDF
+### 1 Universal type UDF functions
+Overall step description
+- Write UDF functions in UDF format locally and package them as jar package
files
+- 【Scriptis >> Workspace】Upload to the corresponding directory in the workspace
+- 【Management Console>>UDF Function】 Create udf (default loading)
+- Used in task code (only effective for newly started engines)
+
+**Step1 Writing jar packages locally**
+
+UDF
Example：https://help.aliyun.com/apsara/agile/v_3_6_0_20210705/odps/ase-user-guide/udf-example.html
+
+**Step2【Scriptis >> Workspace】Upload jar package**
+Select the corresponding folder and right-click to select Upload
+
+
+
+**Step3【Management Console>>UDF Function】 Create UDF**
+- Function name: Conforming to the rules is sufficient, such as test_ Udf_
Using jar in scripts such as SQL
+- Function Type: General
+- Script path: Select the shared directory path where the jar package is
stored, such as../..// Wds_ Functions_ 1_ 0_ 0. jar
+- Registration format: package name+class name, such as
com.webank.wedatasphere.willink.bdp.udf.ToUpperCase
+- Usage format: Input type and return type must be consistent with the
definition in the jar package
+- Classification: drop-down selection; Alternatively, enter a custom directory
(which will create a new target level directory under the personal function)
+
+
+
+Note that the newly created UDF function is loaded by default and can be
viewed on the [Scriptis>>UDF Functions] page for easy viewing during Scriptis
task editing. Checking the UDF function indicates that it will be loaded and
used
+
+
+
+**Step4 Use this udf function**
+
+Innovative udf function using the above steps in the task
+Function name is [Create UDF] Function name
+In pyspark:
+print (sqlContext.sql("select test_udf_jar(name1) from
stacyyan_ind.result_sort_1_20200226").collect())
+
+### 2 UDF functions of Spark type
+Overall step description
+- Create a new Spark script file in the desired directory in the
[Scriptis>>workspace]
+- Create UDF in [Management Console>>UDF Functions] (default loading)
+- Used in task code (only effective for newly started engines)
+
+**Step1 dss-scriptis-Create a new scala script**
+
+
+
+def helloWorld(str: String): String = "hello, " + str
+
+**Step2 Create UDF**
+- Function name: Conforming to the rules is sufficient, such as test_ Udf_
Scala
+- Function type: spark
+- Script Path:../..// B
+- Registration format: The input type and return type must be consistent with
the definition; The function names that need to be defined in the registration
format must be strictly consistent, such as helloWorld
+- Classification: Drop down and select the first level directory that exists
under dss scriptis UDF function - Personal function; Alternatively, enter a
custom directory (which will create a new target level directory under the
personal function)
+
+
+
+**Step3 Use this udf function**
+
+Use the above steps in the task to create a new udf function
+Function name is [Create UDF] Function name
+- In scala
+ val s=sqlContext.sql("select test_udf_scala(name1)
+ from stacyyan_ind.result_sort_1_20200226")
+ show(s)
+- in pyspark
+ print(sqlContext.sql("select test_udf_scala(name1)
+ from stacyyan_ind.result_sort_1_20200226").collect());
+- in sql
+ select test_udf_scala(name1) from stacyyan_ind.result_sort_1_20200226;
+
+### 3 Python functions
+Overall step description
+- Create a new Python script file in the desired directory in the
[Scriptis>>workspace]
+- Create UDF in [Management Console>>UDF Functions] (default loading)
+- Used in task code (only effective for newly started engines)
+
+**Step1 dss-scriptis-Create a new pyspark script**
+
+
+
+def addation(a, b):
+return a + b
+Step2 Create UDF
+- Function name: Conforming to the rules is sufficient, such as test_ Udf_ Py
+- Function type: spark
+- Script Path:../..// A
+- Registration format: The function names that need to be defined must be
strictly consistent, such as addition
+- Usage format: The input type and return type must be consistent with the
definition
+- Classification: Drop down and select the first level directory that exists
under dss scriptis UDF function - Personal function; Alternatively, enter a
custom directory (which will create a new target level directory under the
personal function)
+
+
+
+**Step3 uses this udf function**
+Use the above steps in the task to create a new udf function
+Function name is [Create UDF] Function name
+- in pyspark
+ print(sqlContext.sql("select test_udf_py(pv,impression) from
neiljianliu_ind.alias where entityid=504059 limit 50").collect());
+- in sql
+ select test_udf_py(pv,impression) from neiljianliu_ind.alias where
entityid=504059 limit 50
+
+### 4 scala functions
+Overall step description
+- Create a new Spark Scala script file in the desired directory in the
[Scriptis>>workspace]
+- Create UDF in [Management Console>>UDF Functions] (default loading)
+- Used in task code (only effective for newly started engines)
+-
+**Step1 dss-scriptis-Create a new scala script**
+def hellozdy(str:String):String = "hellozdy,haha " + str
+**Step2 CREATE FUNCTION**
+- Function name: Must be strictly consistent with the defined function name,
such as hellozdy
+- Function Type: Custom Function
+- Script Path:../..// D
+- Usage format: The input type and return type must be consistent with the
definition
+- Classification: Drop down and select the first level directory that exists
under dss scriptis method function personal function; Alternatively, enter a
custom directory (which will create a new target level directory under the
personal function)
+ Step3 Use this function
+ Use the above steps in the task to create a new udf function
+ Function name is [Create UDF] Function name
+ val a = hellozdy("abcd");
+ print(a)
+
+### 5 Common usage issues
+#### 5.1 UDF function loading failed
+"FAILED: SemanticException [Error 10011]: Invalid function xxxx"
+
+
+
+- Firstly, check if the UDF function configuration is correct:
+
+ 
+
+- The registration format is the function path name:
+
+ 
+
+- Check the scriptis udf function to see if the loaded function is checked.
When the function is not checked, udf will not be loaded when the engine starts
+
+ 
+
+- Check if the engine has loaded UDF. If not, please restart another engine or
restart the current engine
+ Note: UDF will only be loaded when the engine is initialized. If UDF is
added midway, the current engine will not be able to perceive and load it
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