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The following commit(s) were added to refs/heads/main by this push:
new d10aa47a fix sql dialect in common concepts and the link of zeppin in
1.3.x (#854)
d10aa47a is described below
commit d10aa47afe4173a5c8113d8a61117675e870efef
Author: leto-b <[email protected]>
AuthorDate: Thu Aug 28 17:04:50 2025 +0800
fix sql dialect in common concepts and the link of zeppin in 1.3.x (#854)
---
src/.vuepress/sidebar/V1.3.x/zh.ts | 2 +-
src/.vuepress/sidebar_timecho/V1.3.x/zh.ts | 2 +-
.../Background-knowledge/Cluster-Concept_apache.md | 14 +++++++-------
.../Background-knowledge/Cluster-Concept_timecho.md | 14 +++++++-------
.../Background-knowledge/Cluster-Concept_apache.md | 14 +++++++-------
.../Background-knowledge/Cluster-Concept_timecho.md | 14 +++++++-------
.../Background-knowledge/Cluster-Concept_apache.md | 16 ++++++++--------
.../Background-knowledge/Cluster-Concept_timecho.md | 16 ++++++++--------
.../Background-knowledge/Cluster-Concept_apache.md | 16 ++++++++--------
.../Background-knowledge/Cluster-Concept_timecho.md | 16 ++++++++--------
10 files changed, 62 insertions(+), 62 deletions(-)
diff --git a/src/.vuepress/sidebar/V1.3.x/zh.ts
b/src/.vuepress/sidebar/V1.3.x/zh.ts
index 57f3b7d4..024e2c32 100644
--- a/src/.vuepress/sidebar/V1.3.x/zh.ts
+++ b/src/.vuepress/sidebar/V1.3.x/zh.ts
@@ -215,7 +215,7 @@ export const zhSidebar = {
text: '可视化分析',
collapsible: true,
children: [
- { text: 'Apache Zeppelin', link: 'Zeppelin-IoTDB' },
+ { text: 'Apache Zeppelin', link: 'Zeppelin-IoTDB_apache' },
{ text: 'Grafana', link: 'Grafana-Connector' },
{ text: 'Grafana插件', link: 'Grafana-Plugin' },
{ text: 'DataEase', link: 'DataEase' },
diff --git a/src/.vuepress/sidebar_timecho/V1.3.x/zh.ts
b/src/.vuepress/sidebar_timecho/V1.3.x/zh.ts
index afeaf1a7..1c10c198 100644
--- a/src/.vuepress/sidebar_timecho/V1.3.x/zh.ts
+++ b/src/.vuepress/sidebar_timecho/V1.3.x/zh.ts
@@ -226,7 +226,7 @@ export const zhSidebar = {
text: '可视化分析',
collapsible: true,
children: [
- { text: 'Apache Zeppelin', link: 'Zeppelin-IoTDB' },
+ { text: 'Apache Zeppelin', link: 'Zeppelin-IoTDB_timecho' },
{ text: 'Grafana', link: 'Grafana-Connector' },
{ text: 'Grafana插件', link: 'Grafana-Plugin' },
{ text: 'DataEase', link: 'DataEase' },
diff --git
a/src/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_apache.md
b/src/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_apache.md
index 674a74e6..1f35d01d 100644
--- a/src/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_apache.md
+++ b/src/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_apache.md
@@ -23,14 +23,14 @@
## Sql_dialect Related Concepts
-| Concept | Meaning
|
-| ----------------------- |
------------------------------------------------------------ |
-| sql_dialect | IoTDB supports two time-series data models (SQL dialects),
both managing devices and measurement points. Tree: Manages data in a
hierarchical path manner, where one path corresponds to one measurement point
of a device. Table: Manages data in a relational table manner, where one table
corresponds to a category of devices. |
-| Schema | Schema is the data model information of the database, i.e.,
tree structure or table structure. It includes definitions such as the names
and data types of measurement points. |
-| Device | Corresponds to a physical device in an actual scenario,
usually containing multiple measurement points. |
+| Concept | Meaning
|
+| -----------------------
|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| sql_dialect | Tree model: manages devices and measurement points, manages
data in a hierarchical path manner, where one path corresponds to one
measurement point of a device.
|
+| Schema | Schema is the data model information of the database, i.e.,
tree structure. It includes definitions such as the names and data types of
measurement points.
|
+| Device | Corresponds to a physical device in an actual scenario,
usually containing multiple measurement points.
|
| Timeseries | Also known as: physical quantity, time series, timeline,
point location, semaphore, indicator, measurement value, etc. It is a time
series formed by arranging multiple data points in ascending order of
timestamps. Usually, a Timeseries represents a collection point that can
periodically collect physical quantities of the environment it is in. |
-| Encoding | Encoding is a compression technique that represents data
in binary form to improve storage efficiency. IoTDB supports various encoding
methods for different types of data. For more detailed information, please
refer
to:[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
-| Compression | After data encoding, IoTDB uses compression technology to
further compress binary data to enhance storage efficiency. IoTDB supports
multiple compression methods. For more detailed information, please refer to:
[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md) |
+| Encoding | Encoding is a compression technique that represents data
in binary form to improve storage efficiency. IoTDB supports various encoding
methods for different types of data. For more detailed information, please
refer
to:[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
+| Compression | After data encoding, IoTDB uses compression technology to
further compress binary data to enhance storage efficiency. IoTDB supports
multiple compression methods. For more detailed information, please refer to:
[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
## Distributed Related Concepts
diff --git
a/src/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_timecho.md
b/src/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_timecho.md
index 42344aa4..5f068252 100644
--- a/src/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_timecho.md
@@ -23,14 +23,14 @@
## Sql_dialect Related Concepts
-| Concept | Meaning
|
-| ----------------------- |
------------------------------------------------------------ |
-| sql_dialect | IoTDB supports two time-series data models (SQL dialects),
both managing devices and measurement points. Tree: Manages data in a
hierarchical path manner, where one path corresponds to one measurement point
of a device. Table: Manages data in a relational table manner, where one table
corresponds to a category of devices. |
-| Schema | Schema is the data model information of the database, i.e.,
tree structure or table structure. It includes definitions such as the names
and data types of measurement points. |
-| Device | Corresponds to a physical device in an actual scenario,
usually containing multiple measurement points. |
+| Concept | Meaning
|
+| -----------------------
|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| sql_dialect | Tree model: manages devices and measurement points, manages
data in a hierarchical path manner, where one path corresponds to one
measurement point of a device.
|
+| Schema | Schema is the data model information of the database, i.e.,
tree structure. It includes definitions such as the names and data types of
measurement points.
|
+| Device | Corresponds to a physical device in an actual scenario,
usually containing multiple measurement points.
|
| Timeseries | Also known as: physical quantity, time series, timeline,
point location, semaphore, indicator, measurement value, etc. It is a time
series formed by arranging multiple data points in ascending order of
timestamps. Usually, a Timeseries represents a collection point that can
periodically collect physical quantities of the environment it is in. |
-| Encoding | Encoding is a compression technique that represents data
in binary form to improve storage efficiency. IoTDB supports various encoding
methods for different types of data. For more detailed information, please
refer
to:[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
-| Compression | After data encoding, IoTDB uses compression technology to
further compress binary data to enhance storage efficiency. IoTDB supports
multiple compression methods. For more detailed information, please refer to:
[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md) |
+| Encoding | Encoding is a compression technique that represents data
in binary form to improve storage efficiency. IoTDB supports various encoding
methods for different types of data. For more detailed information, please
refer
to:[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
+| Compression | After data encoding, IoTDB uses compression technology to
further compress binary data to enhance storage efficiency. IoTDB supports
multiple compression methods. For more detailed information, please refer to:
[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
## Distributed Related Concepts
diff --git
a/src/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_apache.md
b/src/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_apache.md
index 674a74e6..1f35d01d 100644
--- a/src/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_apache.md
+++ b/src/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_apache.md
@@ -23,14 +23,14 @@
## Sql_dialect Related Concepts
-| Concept | Meaning
|
-| ----------------------- |
------------------------------------------------------------ |
-| sql_dialect | IoTDB supports two time-series data models (SQL dialects),
both managing devices and measurement points. Tree: Manages data in a
hierarchical path manner, where one path corresponds to one measurement point
of a device. Table: Manages data in a relational table manner, where one table
corresponds to a category of devices. |
-| Schema | Schema is the data model information of the database, i.e.,
tree structure or table structure. It includes definitions such as the names
and data types of measurement points. |
-| Device | Corresponds to a physical device in an actual scenario,
usually containing multiple measurement points. |
+| Concept | Meaning
|
+| -----------------------
|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| sql_dialect | Tree model: manages devices and measurement points, manages
data in a hierarchical path manner, where one path corresponds to one
measurement point of a device.
|
+| Schema | Schema is the data model information of the database, i.e.,
tree structure. It includes definitions such as the names and data types of
measurement points.
|
+| Device | Corresponds to a physical device in an actual scenario,
usually containing multiple measurement points.
|
| Timeseries | Also known as: physical quantity, time series, timeline,
point location, semaphore, indicator, measurement value, etc. It is a time
series formed by arranging multiple data points in ascending order of
timestamps. Usually, a Timeseries represents a collection point that can
periodically collect physical quantities of the environment it is in. |
-| Encoding | Encoding is a compression technique that represents data
in binary form to improve storage efficiency. IoTDB supports various encoding
methods for different types of data. For more detailed information, please
refer
to:[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
-| Compression | After data encoding, IoTDB uses compression technology to
further compress binary data to enhance storage efficiency. IoTDB supports
multiple compression methods. For more detailed information, please refer to:
[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md) |
+| Encoding | Encoding is a compression technique that represents data
in binary form to improve storage efficiency. IoTDB supports various encoding
methods for different types of data. For more detailed information, please
refer
to:[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
+| Compression | After data encoding, IoTDB uses compression technology to
further compress binary data to enhance storage efficiency. IoTDB supports
multiple compression methods. For more detailed information, please refer to:
[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
## Distributed Related Concepts
diff --git
a/src/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_timecho.md
b/src/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_timecho.md
index 42344aa4..5f068252 100644
--- a/src/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_timecho.md
@@ -23,14 +23,14 @@
## Sql_dialect Related Concepts
-| Concept | Meaning
|
-| ----------------------- |
------------------------------------------------------------ |
-| sql_dialect | IoTDB supports two time-series data models (SQL dialects),
both managing devices and measurement points. Tree: Manages data in a
hierarchical path manner, where one path corresponds to one measurement point
of a device. Table: Manages data in a relational table manner, where one table
corresponds to a category of devices. |
-| Schema | Schema is the data model information of the database, i.e.,
tree structure or table structure. It includes definitions such as the names
and data types of measurement points. |
-| Device | Corresponds to a physical device in an actual scenario,
usually containing multiple measurement points. |
+| Concept | Meaning
|
+| -----------------------
|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| sql_dialect | Tree model: manages devices and measurement points, manages
data in a hierarchical path manner, where one path corresponds to one
measurement point of a device.
|
+| Schema | Schema is the data model information of the database, i.e.,
tree structure. It includes definitions such as the names and data types of
measurement points.
|
+| Device | Corresponds to a physical device in an actual scenario,
usually containing multiple measurement points.
|
| Timeseries | Also known as: physical quantity, time series, timeline,
point location, semaphore, indicator, measurement value, etc. It is a time
series formed by arranging multiple data points in ascending order of
timestamps. Usually, a Timeseries represents a collection point that can
periodically collect physical quantities of the environment it is in. |
-| Encoding | Encoding is a compression technique that represents data
in binary form to improve storage efficiency. IoTDB supports various encoding
methods for different types of data. For more detailed information, please
refer
to:[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
-| Compression | After data encoding, IoTDB uses compression technology to
further compress binary data to enhance storage efficiency. IoTDB supports
multiple compression methods. For more detailed information, please refer to:
[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md) |
+| Encoding | Encoding is a compression technique that represents data
in binary form to improve storage efficiency. IoTDB supports various encoding
methods for different types of data. For more detailed information, please
refer
to:[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
+| Compression | After data encoding, IoTDB uses compression technology to
further compress binary data to enhance storage efficiency. IoTDB supports
multiple compression methods. For more detailed information, please refer to:
[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md)
|
## Distributed Related Concepts
diff --git
a/src/zh/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_apache.md
b/src/zh/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_apache.md
index c5e4b830..6fb96fdf 100644
--- a/src/zh/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_apache.md
+++ b/src/zh/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_apache.md
@@ -23,14 +23,14 @@
## 数据模型相关概念
-| 概念 | 含义
|
-| ----------------------- |
------------------------------------------------------------ |
-| 数据模型（sql_dialect） | IoTDB
支持两种时序数据模型（SQL语法），管理的对象均为设备和测点树：以层级路径的方式管理数据，一条路径对应一个设备的一个测点表：以关系表的方式管理数据，一张表对应一类设备
|
-| 元数据（Schema） | 元数据是数据库的数据模型信息，即树形结构或表结构。包括测点的名称、数据类型等定义。 |
-| 设备（Device） | 对应一个实际场景中的物理设备，通常包含多个测点。 |
-| 测点（Timeseries） |
又名：物理量、时间序列、时间线、点位、信号量、指标、测量值等。是多个数据点按时间戳递增排列形成的一个时间序列。通常一个测点代表一个采集点位，能够定期采集所在环境的物理量。
|
-| 编码（Encoding） | 编码是一种压缩技术，将数据以二进制的形式进行表示，可以提高存储效率。IoTDB
支持多种针对不同类型的数据的编码方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
-| 压缩（Compression） | IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，提升存储效率。IoTDB
支持多种压缩方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md) |
+| 概念 | 含义
|
+|-----------------|----------------------------------------------------------------------------------------------------------------------------|
+| 数据模型 | 树模型，管理的对象为设备和测点，以层级路径的方式管理数据，一条路径对应一个设备的一个测点
|
+| 元数据（Schema） | 元数据是数据库的数据模型信息，即树形结构，包括测点的名称、数据类型等定义。
|
+| 设备（Device） | 对应一个实际场景中的物理设备，通常包含多个测点。
|
+| 测点（Timeseries） |
又名：物理量、时间序列、时间线、点位、信号量、指标、测量值等。是多个数据点按时间戳递增排列形成的一个时间序列。通常一个测点代表一个采集点位，能够定期采集所在环境的物理量。
|
+| 编码（Encoding） | 编码是一种压缩技术，将数据以二进制的形式进行表示，可以提高存储效率。IoTDB
支持多种针对不同类型的数据的编码方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
+| 压缩（Compression） | IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，提升存储效率。IoTDB
支持多种压缩方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
## 分布式相关概念
diff --git
a/src/zh/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_timecho.md
b/src/zh/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_timecho.md
index 4fbd9165..378424ea 100644
--- a/src/zh/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/zh/UserGuide/V1.3.x/Background-knowledge/Cluster-Concept_timecho.md
@@ -23,14 +23,14 @@
## 数据模型相关概念
-| 概念 | 含义
|
-| ----------------------- |
------------------------------------------------------------ |
-| 数据模型（sql_dialect） | IoTDB
支持两种时序数据模型（SQL语法），管理的对象均为设备和测点树：以层级路径的方式管理数据，一条路径对应一个设备的一个测点表：以关系表的方式管理数据，一张表对应一类设备
|
-| 元数据（Schema） | 元数据是数据库的数据模型信息，即树形结构或表结构。包括测点的名称、数据类型等定义。 |
-| 设备（Device） | 对应一个实际场景中的物理设备，通常包含多个测点。 |
-| 测点（Timeseries） |
又名：物理量、时间序列、时间线、点位、信号量、指标、测量值等。是多个数据点按时间戳递增排列形成的一个时间序列。通常一个测点代表一个采集点位，能够定期采集所在环境的物理量。
|
-| 编码（Encoding） | 编码是一种压缩技术，将数据以二进制的形式进行表示，可以提高存储效率。IoTDB
支持多种针对不同类型的数据的编码方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
-| 压缩（Compression） | IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，提升存储效率。IoTDB
支持多种压缩方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md) |
+| 概念 | 含义
|
+|-----------------|----------------------------------------------------------------------------------------------------------------------------|
+| 数据模型 | 树模型，管理的对象为设备和测点，以层级路径的方式管理数据，一条路径对应一个设备的一个测点
|
+| 元数据（Schema） | 元数据是数据库的数据模型信息，即树形结构，包括测点的名称、数据类型等定义。
|
+| 设备（Device） | 对应一个实际场景中的物理设备，通常包含多个测点。
|
+| 测点（Timeseries） |
又名：物理量、时间序列、时间线、点位、信号量、指标、测量值等。是多个数据点按时间戳递增排列形成的一个时间序列。通常一个测点代表一个采集点位，能够定期采集所在环境的物理量。
|
+| 编码（Encoding） | 编码是一种压缩技术，将数据以二进制的形式进行表示，可以提高存储效率。IoTDB
支持多种针对不同类型的数据的编码方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
+| 压缩（Compression） | IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，提升存储效率。IoTDB
支持多种压缩方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
## 分布式相关概念
diff --git
a/src/zh/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_apache.md
b/src/zh/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_apache.md
index c5e4b830..6fb96fdf 100644
--- a/src/zh/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_apache.md
+++ b/src/zh/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_apache.md
@@ -23,14 +23,14 @@
## 数据模型相关概念
-| 概念 | 含义
|
-| ----------------------- |
------------------------------------------------------------ |
-| 数据模型（sql_dialect） | IoTDB
支持两种时序数据模型（SQL语法），管理的对象均为设备和测点树：以层级路径的方式管理数据，一条路径对应一个设备的一个测点表：以关系表的方式管理数据，一张表对应一类设备
|
-| 元数据（Schema） | 元数据是数据库的数据模型信息，即树形结构或表结构。包括测点的名称、数据类型等定义。 |
-| 设备（Device） | 对应一个实际场景中的物理设备，通常包含多个测点。 |
-| 测点（Timeseries） |
又名：物理量、时间序列、时间线、点位、信号量、指标、测量值等。是多个数据点按时间戳递增排列形成的一个时间序列。通常一个测点代表一个采集点位，能够定期采集所在环境的物理量。
|
-| 编码（Encoding） | 编码是一种压缩技术，将数据以二进制的形式进行表示，可以提高存储效率。IoTDB
支持多种针对不同类型的数据的编码方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
-| 压缩（Compression） | IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，提升存储效率。IoTDB
支持多种压缩方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md) |
+| 概念 | 含义
|
+|-----------------|----------------------------------------------------------------------------------------------------------------------------|
+| 数据模型 | 树模型，管理的对象为设备和测点，以层级路径的方式管理数据，一条路径对应一个设备的一个测点
|
+| 元数据（Schema） | 元数据是数据库的数据模型信息，即树形结构，包括测点的名称、数据类型等定义。
|
+| 设备（Device） | 对应一个实际场景中的物理设备，通常包含多个测点。
|
+| 测点（Timeseries） |
又名：物理量、时间序列、时间线、点位、信号量、指标、测量值等。是多个数据点按时间戳递增排列形成的一个时间序列。通常一个测点代表一个采集点位，能够定期采集所在环境的物理量。
|
+| 编码（Encoding） | 编码是一种压缩技术，将数据以二进制的形式进行表示，可以提高存储效率。IoTDB
支持多种针对不同类型的数据的编码方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
+| 压缩（Compression） | IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，提升存储效率。IoTDB
支持多种压缩方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
## 分布式相关概念
diff --git
a/src/zh/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_timecho.md
b/src/zh/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_timecho.md
index 4fbd9165..378424ea 100644
--- a/src/zh/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/zh/UserGuide/dev-1.3/Background-knowledge/Cluster-Concept_timecho.md
@@ -23,14 +23,14 @@
## 数据模型相关概念
-| 概念 | 含义
|
-| ----------------------- |
------------------------------------------------------------ |
-| 数据模型（sql_dialect） | IoTDB
支持两种时序数据模型（SQL语法），管理的对象均为设备和测点树：以层级路径的方式管理数据，一条路径对应一个设备的一个测点表：以关系表的方式管理数据，一张表对应一类设备
|
-| 元数据（Schema） | 元数据是数据库的数据模型信息，即树形结构或表结构。包括测点的名称、数据类型等定义。 |
-| 设备（Device） | 对应一个实际场景中的物理设备，通常包含多个测点。 |
-| 测点（Timeseries） |
又名：物理量、时间序列、时间线、点位、信号量、指标、测量值等。是多个数据点按时间戳递增排列形成的一个时间序列。通常一个测点代表一个采集点位，能够定期采集所在环境的物理量。
|
-| 编码（Encoding） | 编码是一种压缩技术，将数据以二进制的形式进行表示，可以提高存储效率。IoTDB
支持多种针对不同类型的数据的编码方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
-| 压缩（Compression） | IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，提升存储效率。IoTDB
支持多种压缩方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md) |
+| 概念 | 含义
|
+|-----------------|----------------------------------------------------------------------------------------------------------------------------|
+| 数据模型 | 树模型，管理的对象为设备和测点，以层级路径的方式管理数据，一条路径对应一个设备的一个测点
|
+| 元数据（Schema） | 元数据是数据库的数据模型信息，即树形结构，包括测点的名称、数据类型等定义。
|
+| 设备（Device） | 对应一个实际场景中的物理设备，通常包含多个测点。
|
+| 测点（Timeseries） |
又名：物理量、时间序列、时间线、点位、信号量、指标、测量值等。是多个数据点按时间戳递增排列形成的一个时间序列。通常一个测点代表一个采集点位，能够定期采集所在环境的物理量。
|
+| 编码（Encoding） | 编码是一种压缩技术，将数据以二进制的形式进行表示，可以提高存储效率。IoTDB
支持多种针对不同类型的数据的编码方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
+| 压缩（Compression） | IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，提升存储效率。IoTDB
支持多种压缩方法，详细信息请查看：[压缩和编码](../Technical-Insider/Encoding-and-Compression.md)
|
## 分布式相关概念
