diff --git a/src/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md index 180121ff9..e225263f3 100644 --- a/src/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -44,7 +44,7 @@ Key components include: 3. **Time-Series Model Training-Inference Engine (IoTDB AINode)**: A unified engine for intelligent analysis, supporting model training, data management, and integration with machine/deep learning frameworks. -## 2. TimechoDB Architecture +## 2. IoTDB Architecture The diagram below illustrates a typical IoTDB cluster deployment (3 ConfigNodes and 3 DataNodes): diff --git a/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md index 5684a8deb..e225263f3 100644 --- a/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -21,57 +21,81 @@ # IoTDB Introduction -Apache IoTDB is a low-cost, high-performance native temporal database for the Internet of Things. It can solve various problems encountered by enterprises when building IoT big data platforms to manage time-series data, such as complex application scenarios, large data volumes, high sampling frequencies, high amount of unaligned data, long data processing time, diverse analysis requirements, and high storage and operation costs. +Apache IoTDB is a low-cost, high-performance IoT-native time-series database. It addresses challenges faced by enterprises in managing time-series data for IoT big data platforms, including complex application scenarios, massive data volumes, high sampling frequencies, frequent out-of-order data, time-consuming data processing, diverse analytical requirements, and high storage and maintenance costs. -- Github repository link: https://github.com/apache/iotdb +- GitHub Repository: [https://github.com/apache/iotdb](https://github.com/apache/iotdb) +- Open-Source Installation Packages: [https://iotdb.apache.org/Download/](https://iotdb.apache.org/Download/) +- Installation, Deployment, and Usage Documentation: [Quick Start](../QuickStart/QuickStart_apache.md) -- Open source installation package download: https://iotdb.apache.org/zh/Download/ -- Installation, deployment, and usage documentation: [QuickStart](../QuickStart/QuickStart_apache.md) +## 1. Product Ecosystem +The IoTDB ecosystem consists of multiple components designed to efficiently manage and analyze massive IoT-generated time-series data. -## 1. Product Components +
+ Introduction-en-apache.png +
-IoTDB products consist of several components that help users efficiently manage and analyze the massive amount of time-series data generated by the IoT. -
- Introduction-en-timecho.png +Key components include: -
+1. **Time-Series Database (Apache IoTDB)**: The core component for time-series data storage, offering high compression, rich query capabilities, real-time stream processing, high availability, and scalability. It provides security guarantees, configuration tools, multi-language APIs, and integration with external systems for building business applications. +2. **Time-Series File Format (Apache TsFile)**: A specialized storage format for time-series data, enabling efficient storage and querying. TsFile underpins IoTDB and AINode, unifying data management across collection, storage, and analysis phases. +3. **Time-Series Model Training-Inference Engine (IoTDB AINode)**: A unified engine for intelligent analysis, supporting model training, data management, and integration with machine/deep learning frameworks. -1. Time-series Database (Apache IoTDB): The core component for time-series data storage, it provides users with high-compression storage capabilities, rich time-series querying capabilities, real-time stream processing capabilities, and ensures high availability of data and high scalability of clusters. It also offers comprehensive security protection. Additionally, IoTDB provides users with a variety of application tools for easy configuration and management of the system; multi-language APIs and external system application integration capabilities, making it convenient for users to build business applications based on IoTDB. -2. Time-series Data Standard File Format (Apache TsFile): This file format is specifically designed for time-series data and can efficiently store and query massive amounts of time-series data. Currently, the underlying storage files for modules such as IoTDB and AINode are supported by Apache TsFile. With TsFile, users can uniformly use the same file format for data management during the collection, management, application, and analysis phases, greatly simplifying the entire process from data collection to analysis, and improving the efficiency and convenience of time-series data management. +## 2. IoTDB Architecture -3. Time-series Model Training and Inference Integrated Engine (IoTDB AINode): For intelligent analysis scenarios, IoTDB provides the AINode time-series model training and inference integrated engine, which offers a complete set of time-series data analysis tools. The underlying engine supports model training tasks and data management, including machine learning and deep learning. With these tools, users can conduct in-depth analysis of the data stored in IoTDB and extract its value. +The diagram below illustrates a typical IoTDB cluster deployment (3 ConfigNodes and 3 DataNodes): + -## 2. Product Features -TimechoDB has the following advantages and characteristics: +## 3. Key Features -- Flexible deployment methods: Support for one-click cloud deployment, out-of-the-box use after unzipping at the terminal, and seamless connection between terminal and cloud (data cloud synchronization tool). +Apache IoTDB offers the following advantages: -- Low hardware cost storage solution: Supports high compression ratio disk storage, no need to distinguish between historical and real-time databases, unified data management. +- **Flexible Deployment**: + - One-click cloud deployment + - Out-of-the-box terminal usage + - Seamless terminal-cloud synchronization -- Hierarchical sensor organization and management: Supports modeling in the system according to the actual hierarchical relationship of devices to achieve alignment with the industrial sensor management structure, and supports directory viewing, search, and other capabilities for hierarchical structures. +- **Cost-Effective Storage**: + - High-compression disk storage + - Unified management of historical and real-time data -- High throughput data reading and writing: supports access to millions of devices, high-speed data reading and writing, out of unaligned/multi frequency acquisition, and other complex industrial reading and writing scenarios. +- **Hierarchical Measurement Point Management**: + - Aligns with industrial device hierarchies + - Supports directory browsing and search -- Rich time series query semantics: Supports a native computation engine for time series data, supports timestamp alignment during queries, provides nearly a hundred built-in aggregation and time series calculation functions, and supports time series feature analysis and AI capabilities. +- **High Throughput Read/Write**: + - Supports millions of devices + - Handles high-speed, out-of-order, and multi-frequency data ingestion -- Highly available distributed system: Supports HA distributed architecture, the system provides 7*24 hours uninterrupted real-time database services, the failure of a physical node or network fault will not affect the normal operation of the system; supports the addition, deletion, or overheating of physical nodes, the system will automatically perform load balancing of computing/storage resources; supports heterogeneous environments, servers of different types and different performance can form a cluster, and the system will automatically load balance according to the configuration of the physical machine. +- **Rich Query Capabilities**: + - Native time-series computation engine + - Timestamp alignment during queries + - Over 100 built-in aggregation and time-series functions + - AI-ready time-series feature analysis -- Extremely low usage and operation threshold: supports SQL like language, provides multi language native secondary development interface, and has a complete tool system such as console. +- **High Availability & Scalability**: + - HA distributed architecture with 24/7 uptime + - Automatic load balancing for node scaling + - Heterogeneous cluster support -- Rich ecological environment docking: Supports docking with big data ecosystem components such as Hadoop, Spark, and supports equipment management and visualization tools such as Grafana, Thingsboard, DataEase. +- **Low Learning Curve**: + - SQL-like query language + - Multi-language SDKs + - Comprehensive toolchain (e.g., console) -## 3. Commercial version +- **Ecosystem Integration**: + - Hadoop, Spark, Grafana, ThingsBoard, DataEase, etc. -Timecho provides the original commercial product TimechoDB based on the open source version of Apache IoTDB, providing enterprise level products and services for enterprises and commercial customers. It can solve various problems encountered by enterprises when building IoT big data platforms to manage time-series data, such as complex application scenarios, large data volumes, high sampling frequencies, high amount of unaligned data, long data processing time, diverse analysis requirements, and high storage and operation costs. -Timecho provides a more diverse range of product features, stronger performance and stability, and a richer set of utility tools based on TimechoDB. It also offers comprehensive enterprise services to users, thereby providing commercial customers with more powerful product capabilities and a higher quality of development, operations, and usage experience. +## 4. TimechoDB -- Timecho Official website：https://www.timecho.com/ +Timecho Technology has developed **TimechoDB**, a commercial product based on Apache IoTDB, to provide enterprise-grade solutions and services for businesses and commercial clients. TimechoDB addresses the multifaceted challenges enterprises face when building IoT big data platforms for managing time-series data, including complex application scenarios, massive data volumes, high sampling frequencies, frequent out-of-order data, time-consuming data processing, diverse analytical requirements, and high storage and maintenance costs. -- TimechoDB installation, deployment and usage documentation：[QuickStart](https://www.timecho.com/docs/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file +Leveraging **TimechoDB**, Timecho Technology offers a broader range of product features, enhanced performance and stability, and a richer suite of efficiency tools. Additionally, it provides comprehensive enterprise services, delivering commercial clients with superior product capabilities and an optimized experience in development, operation, and usage. +- **Timecho Technology Official Website**: [https://www.timecho.com/](https://www.timecho.com/) +- **TimechoDB Documentation**: [Quick Start](https://www.timecho.com/docs/zh/UserGuide/latest/QuickStart/QuickStart_timecho.html) diff --git a/src/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md index 180121ff9..e225263f3 100644 --- a/src/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -44,7 +44,7 @@ Key components include: 3. **Time-Series Model Training-Inference Engine (IoTDB AINode)**: A unified engine for intelligent analysis, supporting model training, data management, and integration with machine/deep learning frameworks. -## 2. TimechoDB Architecture +## 2. IoTDB Architecture The diagram below illustrates a typical IoTDB cluster deployment (3 ConfigNodes and 3 DataNodes): diff --git a/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md index 5684a8deb..e225263f3 100644 --- a/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -21,57 +21,81 @@ # IoTDB Introduction -Apache IoTDB is a low-cost, high-performance native temporal database for the Internet of Things. It can solve various problems encountered by enterprises when building IoT big data platforms to manage time-series data, such as complex application scenarios, large data volumes, high sampling frequencies, high amount of unaligned data, long data processing time, diverse analysis requirements, and high storage and operation costs. +Apache IoTDB is a low-cost, high-performance IoT-native time-series database. It addresses challenges faced by enterprises in managing time-series data for IoT big data platforms, including complex application scenarios, massive data volumes, high sampling frequencies, frequent out-of-order data, time-consuming data processing, diverse analytical requirements, and high storage and maintenance costs. -- Github repository link: https://github.com/apache/iotdb +- GitHub Repository: [https://github.com/apache/iotdb](https://github.com/apache/iotdb) +- Open-Source Installation Packages: [https://iotdb.apache.org/Download/](https://iotdb.apache.org/Download/) +- Installation, Deployment, and Usage Documentation: [Quick Start](../QuickStart/QuickStart_apache.md) -- Open source installation package download: https://iotdb.apache.org/zh/Download/ -- Installation, deployment, and usage documentation: [QuickStart](../QuickStart/QuickStart_apache.md) +## 1. Product Ecosystem +The IoTDB ecosystem consists of multiple components designed to efficiently manage and analyze massive IoT-generated time-series data. -## 1. Product Components +
+ Introduction-en-apache.png +
-IoTDB products consist of several components that help users efficiently manage and analyze the massive amount of time-series data generated by the IoT. -
- Introduction-en-timecho.png +Key components include: -
+1. **Time-Series Database (Apache IoTDB)**: The core component for time-series data storage, offering high compression, rich query capabilities, real-time stream processing, high availability, and scalability. It provides security guarantees, configuration tools, multi-language APIs, and integration with external systems for building business applications. +2. **Time-Series File Format (Apache TsFile)**: A specialized storage format for time-series data, enabling efficient storage and querying. TsFile underpins IoTDB and AINode, unifying data management across collection, storage, and analysis phases. +3. **Time-Series Model Training-Inference Engine (IoTDB AINode)**: A unified engine for intelligent analysis, supporting model training, data management, and integration with machine/deep learning frameworks. -1. Time-series Database (Apache IoTDB): The core component for time-series data storage, it provides users with high-compression storage capabilities, rich time-series querying capabilities, real-time stream processing capabilities, and ensures high availability of data and high scalability of clusters. It also offers comprehensive security protection. Additionally, IoTDB provides users with a variety of application tools for easy configuration and management of the system; multi-language APIs and external system application integration capabilities, making it convenient for users to build business applications based on IoTDB. -2. Time-series Data Standard File Format (Apache TsFile): This file format is specifically designed for time-series data and can efficiently store and query massive amounts of time-series data. Currently, the underlying storage files for modules such as IoTDB and AINode are supported by Apache TsFile. With TsFile, users can uniformly use the same file format for data management during the collection, management, application, and analysis phases, greatly simplifying the entire process from data collection to analysis, and improving the efficiency and convenience of time-series data management. +## 2. IoTDB Architecture -3. Time-series Model Training and Inference Integrated Engine (IoTDB AINode): For intelligent analysis scenarios, IoTDB provides the AINode time-series model training and inference integrated engine, which offers a complete set of time-series data analysis tools. The underlying engine supports model training tasks and data management, including machine learning and deep learning. With these tools, users can conduct in-depth analysis of the data stored in IoTDB and extract its value. +The diagram below illustrates a typical IoTDB cluster deployment (3 ConfigNodes and 3 DataNodes): + -## 2. Product Features -TimechoDB has the following advantages and characteristics: +## 3. Key Features -- Flexible deployment methods: Support for one-click cloud deployment, out-of-the-box use after unzipping at the terminal, and seamless connection between terminal and cloud (data cloud synchronization tool). +Apache IoTDB offers the following advantages: -- Low hardware cost storage solution: Supports high compression ratio disk storage, no need to distinguish between historical and real-time databases, unified data management. +- **Flexible Deployment**: + - One-click cloud deployment + - Out-of-the-box terminal usage + - Seamless terminal-cloud synchronization -- Hierarchical sensor organization and management: Supports modeling in the system according to the actual hierarchical relationship of devices to achieve alignment with the industrial sensor management structure, and supports directory viewing, search, and other capabilities for hierarchical structures. +- **Cost-Effective Storage**: + - High-compression disk storage + - Unified management of historical and real-time data -- High throughput data reading and writing: supports access to millions of devices, high-speed data reading and writing, out of unaligned/multi frequency acquisition, and other complex industrial reading and writing scenarios. +- **Hierarchical Measurement Point Management**: + - Aligns with industrial device hierarchies + - Supports directory browsing and search -- Rich time series query semantics: Supports a native computation engine for time series data, supports timestamp alignment during queries, provides nearly a hundred built-in aggregation and time series calculation functions, and supports time series feature analysis and AI capabilities. +- **High Throughput Read/Write**: + - Supports millions of devices + - Handles high-speed, out-of-order, and multi-frequency data ingestion -- Highly available distributed system: Supports HA distributed architecture, the system provides 7*24 hours uninterrupted real-time database services, the failure of a physical node or network fault will not affect the normal operation of the system; supports the addition, deletion, or overheating of physical nodes, the system will automatically perform load balancing of computing/storage resources; supports heterogeneous environments, servers of different types and different performance can form a cluster, and the system will automatically load balance according to the configuration of the physical machine. +- **Rich Query Capabilities**: + - Native time-series computation engine + - Timestamp alignment during queries + - Over 100 built-in aggregation and time-series functions + - AI-ready time-series feature analysis -- Extremely low usage and operation threshold: supports SQL like language, provides multi language native secondary development interface, and has a complete tool system such as console. +- **High Availability & Scalability**: + - HA distributed architecture with 24/7 uptime + - Automatic load balancing for node scaling + - Heterogeneous cluster support -- Rich ecological environment docking: Supports docking with big data ecosystem components such as Hadoop, Spark, and supports equipment management and visualization tools such as Grafana, Thingsboard, DataEase. +- **Low Learning Curve**: + - SQL-like query language + - Multi-language SDKs + - Comprehensive toolchain (e.g., console) -## 3. Commercial version +- **Ecosystem Integration**: + - Hadoop, Spark, Grafana, ThingsBoard, DataEase, etc. -Timecho provides the original commercial product TimechoDB based on the open source version of Apache IoTDB, providing enterprise level products and services for enterprises and commercial customers. It can solve various problems encountered by enterprises when building IoT big data platforms to manage time-series data, such as complex application scenarios, large data volumes, high sampling frequencies, high amount of unaligned data, long data processing time, diverse analysis requirements, and high storage and operation costs. -Timecho provides a more diverse range of product features, stronger performance and stability, and a richer set of utility tools based on TimechoDB. It also offers comprehensive enterprise services to users, thereby providing commercial customers with more powerful product capabilities and a higher quality of development, operations, and usage experience. +## 4. TimechoDB -- Timecho Official website：https://www.timecho.com/ +Timecho Technology has developed **TimechoDB**, a commercial product based on Apache IoTDB, to provide enterprise-grade solutions and services for businesses and commercial clients. TimechoDB addresses the multifaceted challenges enterprises face when building IoT big data platforms for managing time-series data, including complex application scenarios, massive data volumes, high sampling frequencies, frequent out-of-order data, time-consuming data processing, diverse analytical requirements, and high storage and maintenance costs. -- TimechoDB installation, deployment and usage documentation：[QuickStart](https://www.timecho.com/docs/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file +Leveraging **TimechoDB**, Timecho Technology offers a broader range of product features, enhanced performance and stability, and a richer suite of efficiency tools. Additionally, it provides comprehensive enterprise services, delivering commercial clients with superior product capabilities and an optimized experience in development, operation, and usage. +- **Timecho Technology Official Website**: [https://www.timecho.com/](https://www.timecho.com/) +- **TimechoDB Documentation**: [Quick Start](https://www.timecho.com/docs/zh/UserGuide/latest/QuickStart/QuickStart_timecho.html) diff --git a/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md index add91edbc..5e6198338 100644 --- a/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -44,7 +44,7 @@ IoTDB 体系由若干个组件构成，帮助用户高效地管理和分析物 2. **时序数据标准文件格式（Apache TsFile）**：该文件格式是一种专为时序数据设计的存储格式，可以高效地存储和查询海量时序数据。目前 IoTDB、AINode 等模块的底层存储文件均由 Apache TsFile 进行支撑。通过 TsFile，用户可以在采集、管理、应用&分析阶段统一使用相同的文件格式进行数据管理，极大简化了数据采集到分析的整个流程，提高时序数据管理的效率和便捷度。 3. **时序模型训推一体化引擎（IoTDB AINode）**：针对智能分析场景，IoTDB 提供 AINode 时序模型训推一体化引擎，它提供了一套完整的时序数据分析工具，底层为模型训练引擎，支持训练任务与数据管理，与包括机器学习、深度学习等。通过这些工具，用户可以对存储在 IoTDB 中的数据进行深入分析，挖掘出其中的价值。 -## 2. TimechoDB 整体架构 +## 2. IoTDB 整体架构 下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式： diff --git a/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md index b19cf2c49..8a42a7f7d 100644 --- a/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -44,7 +44,7 @@ IoTDB 体系由若干个组件构成，帮助用户高效地管理和分析物 2. **时序数据标准文件格式（Apache TsFile）**：该文件格式是一种专为时序数据设计的存储格式，可以高效地存储和查询海量时序数据。目前 IoTDB、AINode 等模块的底层存储文件均由 Apache TsFile 进行支撑。通过 TsFile，用户可以在采集、管理、应用&分析阶段统一使用相同的文件格式进行数据管理，极大简化了数据采集到分析的整个流程，提高时序数据管理的效率和便捷度。 3. **时序模型训推一体化引擎（IoTDB AINode）**：针对智能分析场景，IoTDB 提供 AINode 时序模型训推一体化引擎，它提供了一套完整的时序数据分析工具，底层为模型训练引擎，支持训练任务与数据管理，与包括机器学习、深度学习等。通过这些工具，用户可以对存储在 IoTDB 中的数据进行深入分析，挖掘出其中的价值。 -## 2. TimechoDB 整体架构 +## 2. IoTDB 整体架构 下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式： diff --git a/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md index add91edbc..5e6198338 100644 --- a/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -44,7 +44,7 @@ IoTDB 体系由若干个组件构成，帮助用户高效地管理和分析物 2. **时序数据标准文件格式（Apache TsFile）**：该文件格式是一种专为时序数据设计的存储格式，可以高效地存储和查询海量时序数据。目前 IoTDB、AINode 等模块的底层存储文件均由 Apache TsFile 进行支撑。通过 TsFile，用户可以在采集、管理、应用&分析阶段统一使用相同的文件格式进行数据管理，极大简化了数据采集到分析的整个流程，提高时序数据管理的效率和便捷度。 3. **时序模型训推一体化引擎（IoTDB AINode）**：针对智能分析场景，IoTDB 提供 AINode 时序模型训推一体化引擎，它提供了一套完整的时序数据分析工具，底层为模型训练引擎，支持训练任务与数据管理，与包括机器学习、深度学习等。通过这些工具，用户可以对存储在 IoTDB 中的数据进行深入分析，挖掘出其中的价值。 -## 2. TimechoDB 整体架构 +## 2. IoTDB 整体架构 下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式： diff --git a/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md index b19cf2c49..8a42a7f7d 100644 --- a/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -44,7 +44,7 @@ IoTDB 体系由若干个组件构成，帮助用户高效地管理和分析物 2. **时序数据标准文件格式（Apache TsFile）**：该文件格式是一种专为时序数据设计的存储格式，可以高效地存储和查询海量时序数据。目前 IoTDB、AINode 等模块的底层存储文件均由 Apache TsFile 进行支撑。通过 TsFile，用户可以在采集、管理、应用&分析阶段统一使用相同的文件格式进行数据管理，极大简化了数据采集到分析的整个流程，提高时序数据管理的效率和便捷度。 3. **时序模型训推一体化引擎（IoTDB AINode）**：针对智能分析场景，IoTDB 提供 AINode 时序模型训推一体化引擎，它提供了一套完整的时序数据分析工具，底层为模型训练引擎，支持训练任务与数据管理，与包括机器学习、深度学习等。通过这些工具，用户可以对存储在 IoTDB 中的数据进行深入分析，挖掘出其中的价值。 -## 2. TimechoDB 整体架构 +## 2. IoTDB 整体架构 下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：