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new a63a9b5 Modify the number of clients (#336)
a63a9b5 is described below
commit a63a9b5781ff8d42bc84fc42927d7da1a4591960
Author: W1y1r <[email protected]>
AuthorDate: Thu Aug 29 12:14:08 2024 +0800
Modify the number of clients (#336)
---
src/UserGuide/Master/Tools-System/Benchmark.md | 2 +-
src/UserGuide/Master/User-Manual/AINode_timecho.md | 2 +-
src/UserGuide/V1.2.x/Tools-System/Benchmark.md | 2 +-
src/UserGuide/latest/Tools-System/Benchmark.md | 2 +-
src/UserGuide/latest/User-Manual/AINode_timecho.md | 3 ++-
src/zh/UserGuide/Master/Tools-System/Benchmark.md | 2 +-
src/zh/UserGuide/Master/User-Manual/AINode_timecho.md | 2 +-
src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md | 2 +-
.../Deployment-and-Maintenance/Stand-Alone-Deployment_timecho.md | 6 +++---
src/zh/UserGuide/latest/Tools-System/Benchmark.md | 2 +-
src/zh/UserGuide/latest/User-Manual/AINode_timecho.md | 2 +-
11 files changed, 14 insertions(+), 13 deletions(-)
diff --git a/src/UserGuide/Master/Tools-System/Benchmark.md
b/src/UserGuide/Master/Tools-System/Benchmark.md
index c765100..86ff88b 100644
--- a/src/UserGuide/Master/Tools-System/Benchmark.md
+++ b/src/UserGuide/Master/Tools-System/Benchmark.md
@@ -305,7 +305,7 @@ Table 2-4 Configuration parameter information
| Parameter Name | Example |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/UserGuide/Master/User-Manual/AINode_timecho.md
b/src/UserGuide/Master/User-Manual/AINode_timecho.md
index 8d1999de..43fa103 100644
--- a/src/UserGuide/Master/User-Manual/AINode_timecho.md
+++ b/src/UserGuide/Master/User-Manual/AINode_timecho.md
@@ -37,7 +37,7 @@ The responsibilities of the three nodes are as follows:
Compared with building a machine learning service alone, it has the following
advantages:
-- **Simple and easy to use**: no need to use Python or Java programming, the
complete process of machine learning model management and inference can be
completed using SQL statements. For example, to create a model, you can use the
CREATE MODEL statement, and to reason with a model, you can use the CALL
INFERENCE(...) statement. statement to create a model and CALL INFERENCE(...)
statement to reason with a model, making it easier and more convenient to use.
+- **Simple and easy to use**: no need to use Python or Java programming, the
complete process of machine learning model management and inference can be
completed using SQL statements. Creating a model can be done using the CREATE
MODEL statement, and using a model for inference can be done using the CALL
INFERENCE (...) statement, making it simpler and more convenient to use.
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in
IoTDB can be directly applied to the inference of machine learning models
without having to move the data to a separate machine learning service
platform, which accelerates data processing, improves security, and reduces
costs.
diff --git a/src/UserGuide/V1.2.x/Tools-System/Benchmark.md
b/src/UserGuide/V1.2.x/Tools-System/Benchmark.md
index 43f9b43..0f22a52 100644
--- a/src/UserGuide/V1.2.x/Tools-System/Benchmark.md
+++ b/src/UserGuide/V1.2.x/Tools-System/Benchmark.md
@@ -298,7 +298,7 @@ Table 2-4 Configuration parameter information
| Parameter Name | Example |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/UserGuide/latest/Tools-System/Benchmark.md
b/src/UserGuide/latest/Tools-System/Benchmark.md
index dd48d84..c529997 100644
--- a/src/UserGuide/latest/Tools-System/Benchmark.md
+++ b/src/UserGuide/latest/Tools-System/Benchmark.md
@@ -297,7 +297,7 @@ Table 2-4 Configuration parameter information
| Parameter Name | Example |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/UserGuide/latest/User-Manual/AINode_timecho.md
b/src/UserGuide/latest/User-Manual/AINode_timecho.md
index 8d1999de..5b35b9e 100644
--- a/src/UserGuide/latest/User-Manual/AINode_timecho.md
+++ b/src/UserGuide/latest/User-Manual/AINode_timecho.md
@@ -37,7 +37,8 @@ The responsibilities of the three nodes are as follows:
Compared with building a machine learning service alone, it has the following
advantages:
-- **Simple and easy to use**: no need to use Python or Java programming, the
complete process of machine learning model management and inference can be
completed using SQL statements. For example, to create a model, you can use the
CREATE MODEL statement, and to reason with a model, you can use the CALL
INFERENCE(...) statement. statement to create a model and CALL INFERENCE(...)
statement to reason with a model, making it easier and more convenient to use.
+- **Simple and easy to use**: no need to use Python or Java programming, the
complete process of machine learning model management and inference can be
completed using SQL statements. Creating a model can be done using the CREATE
MODEL statement, and using a model for inference can be done using the CALL
INFERENCE (...) statement, making it simpler and more convenient to use.
+
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in
IoTDB can be directly applied to the inference of machine learning models
without having to move the data to a separate machine learning service
platform, which accelerates data processing, improves security, and reduces
costs.
diff --git a/src/zh/UserGuide/Master/Tools-System/Benchmark.md
b/src/zh/UserGuide/Master/Tools-System/Benchmark.md
index efbd369..9c89db2 100644
--- a/src/zh/UserGuide/Master/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/Master/Tools-System/Benchmark.md
@@ -313,7 +313,7 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
| 参数名称 | 示例 |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/zh/UserGuide/Master/User-Manual/AINode_timecho.md
b/src/zh/UserGuide/Master/User-Manual/AINode_timecho.md
index 8a0b5db..29031f2 100644
--- a/src/zh/UserGuide/Master/User-Manual/AINode_timecho.md
+++ b/src/zh/UserGuide/Master/User-Manual/AINode_timecho.md
@@ -37,7 +37,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
与单独构建机器学习服务相比，具有以下优势：
-- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE
MODEL语句、使用模型进行推理可使用CALL INFERENCE(...)语句等，使用更急简单便捷。
+- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE
MODEL语句、使用模型进行推理可使用CALL INFERENCE(...)语句等，使用更加简单便捷。
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB
中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
diff --git a/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md
b/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md
index 3e19f21..bdffaab 100644
--- a/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md
@@ -313,7 +313,7 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
| 参数名称 | 示例 |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git
a/src/zh/UserGuide/latest/Deployment-and-Maintenance/Stand-Alone-Deployment_timecho.md
b/src/zh/UserGuide/latest/Deployment-and-Maintenance/Stand-Alone-Deployment_timecho.md
index af5678d..899ead5 100644
---
a/src/zh/UserGuide/latest/Deployment-and-Maintenance/Stand-Alone-Deployment_timecho.md
+++
b/src/zh/UserGuide/latest/Deployment-and-Maintenance/Stand-Alone-Deployment_timecho.md
@@ -65,7 +65,7 @@ cd iotdb-enterprise-{version}-bin
#### 系统通用配置
-打开通用配置文件（./conf/iotdb-confignode.properties 文件），设置以下参数：
+打开通用配置文件（./conf/iotdb-common.properties 文件），设置以下参数：
| **配置项** | **说明** | **默认值** |
**推荐值** | 备注 |
| :-----------------------: | :------------------------------: |
:------------: | :----------------------------------------------: |
:-----------------------: |
@@ -75,7 +75,7 @@ cd iotdb-enterprise-{version}-bin
#### ConfigNode配置
-打开ConfigNode配置文件（./conf/iotdb-system.properties文件），设置以下参数：
+打开ConfigNode配置文件（./conf/iotdb-confignode.properties文件），设置以下参数：
| **配置项** | **说明**
| **默认** | 推荐值 | **备注**
|
| :-----------------: |
:----------------------------------------------------------: | :-------------:
| :----------------------------------------------: | :----------------: |
@@ -86,7 +86,7 @@ cd iotdb-enterprise-{version}-bin
#### DataNode 配置
-打开DataNode配置文件 ./conf/iotdb-datanode.properties，设置以下参数：
+打开DataNode配置文件（./conf/iotdb-datanode.properties文件），设置以下参数：
| **配置项** | **说明**
| **默认** | 推荐值 |
**备注** |
| :------------------------------ |
:----------------------------------------------------------- | :--------------
| :----------------------------------------------- | :----------------- |
diff --git a/src/zh/UserGuide/latest/Tools-System/Benchmark.md
b/src/zh/UserGuide/latest/Tools-System/Benchmark.md
index 3a6d572..1b8d52f 100644
--- a/src/zh/UserGuide/latest/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/latest/Tools-System/Benchmark.md
@@ -313,7 +313,7 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
| 参数名称 | 示例 |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md
b/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md
index 8a0b5db..29031f2 100644
--- a/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md
+++ b/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md
@@ -37,7 +37,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
与单独构建机器学习服务相比，具有以下优势：
-- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE
MODEL语句、使用模型进行推理可使用CALL INFERENCE(...)语句等，使用更急简单便捷。
+- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE
MODEL语句、使用模型进行推理可使用CALL INFERENCE(...)语句等，使用更加简单便捷。
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB
中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
