diff --git a/src/.vuepress/sidebar/V2.0.x/en-Table.ts b/src/.vuepress/sidebar/V2.0.x/en-Table.ts index a519dc9d4..58260b933 100644 --- a/src/.vuepress/sidebar/V2.0.x/en-Table.ts +++ b/src/.vuepress/sidebar/V2.0.x/en-Table.ts @@ -38,7 +38,7 @@ export const enSidebar = { prefix: 'Background-knowledge/', children: [ { text: 'Common Concepts', link: 'Cluster-Concept_apache' }, - { text: 'Timeseries Data Model', link: 'Navigating_Time_Series_Data' }, + { text: 'Timeseries Data Model', link: 'Navigating_Time_Series_Data_apache' }, { text: 'Modeling Scheme Design', link: 'Data-Model-and-Terminology_apache' }, { text: 'Data Type', link: 'Data-Type_apache' }, ], diff --git a/src/.vuepress/sidebar/V2.0.x/en-Tree.ts b/src/.vuepress/sidebar/V2.0.x/en-Tree.ts index 11e04ad5f..ba34cf971 100644 --- a/src/.vuepress/sidebar/V2.0.x/en-Tree.ts +++ b/src/.vuepress/sidebar/V2.0.x/en-Tree.ts @@ -40,7 +40,7 @@ export const enSidebar = { { text: 'Common Concepts', link: 'Cluster-Concept_apache' }, { text: 'Timeseries Data Model', - link: 'Navigating_Time_Series_Data', + link: 'Navigating_Time_Series_Data_apache', }, { text: 'Modeling Scheme Design', diff --git a/src/.vuepress/sidebar/V2.0.x/zh-Table.ts b/src/.vuepress/sidebar/V2.0.x/zh-Table.ts index d5a9be415..f6285661c 100644 --- a/src/.vuepress/sidebar/V2.0.x/zh-Table.ts +++ b/src/.vuepress/sidebar/V2.0.x/zh-Table.ts @@ -38,7 +38,7 @@ export const zhSidebar = { prefix: 'Background-knowledge/', children: [ { text: '常见概念', link: 'Cluster-Concept_apache' }, - { text: '时序数据模型', link: 'Navigating_Time_Series_Data' }, + { text: '时序数据模型', link: 'Navigating_Time_Series_Data_apache' }, { text: '建模方案设计', link: 'Data-Model-and-Terminology_apache' }, { text: '数据类型', link: 'Data-Type_apache' }, ], diff --git a/src/.vuepress/sidebar/V2.0.x/zh-Tree.ts b/src/.vuepress/sidebar/V2.0.x/zh-Tree.ts index e3acde961..ac57f0941 100644 --- a/src/.vuepress/sidebar/V2.0.x/zh-Tree.ts +++ b/src/.vuepress/sidebar/V2.0.x/zh-Tree.ts @@ -38,7 +38,7 @@ export const zhSidebar = { prefix: 'Background-knowledge/', children: [ { text: '常见概念', link: 'Cluster-Concept_apache' }, - { text: '时序数据模型', link: 'Navigating_Time_Series_Data' }, + { text: '时序数据模型', link: 'Navigating_Time_Series_Data_apache' }, { text: '建模方案设计', link: 'Data-Model-and-Terminology_apache' }, { text: '数据类型', link: 'Data-Type' }, ], diff --git a/src/.vuepress/sidebar_timecho/V2.0.x/en-Table.ts b/src/.vuepress/sidebar_timecho/V2.0.x/en-Table.ts index 65d825e1b..047778cac 100644 --- a/src/.vuepress/sidebar_timecho/V2.0.x/en-Table.ts +++ b/src/.vuepress/sidebar_timecho/V2.0.x/en-Table.ts @@ -38,7 +38,7 @@ export const enSidebar = { prefix: 'Background-knowledge/', children: [ { text: 'Common Concepts', link: 'Cluster-Concept_timecho' }, - { text: 'Timeseries Data Model', link: 'Navigating_Time_Series_Data' }, + { text: 'Timeseries Data Model', link: 'Navigating_Time_Series_Data_timecho' }, { text: 'Modeling Scheme Design', link: 'Data-Model-and-Terminology_timecho' }, { text: 'Data Type', link: 'Data-Type_timecho' }, ], diff --git a/src/.vuepress/sidebar_timecho/V2.0.x/en-Tree.ts b/src/.vuepress/sidebar_timecho/V2.0.x/en-Tree.ts index dd2aad740..66a670cc2 100644 --- a/src/.vuepress/sidebar_timecho/V2.0.x/en-Tree.ts +++ b/src/.vuepress/sidebar_timecho/V2.0.x/en-Tree.ts @@ -40,7 +40,7 @@ export const enSidebar = { { text: 'Common Concepts', link: 'Cluster-Concept_timecho' }, { text: 'Timeseries Data Model', - link: 'Navigating_Time_Series_Data', + link: 'Navigating_Time_Series_Data_timehco', }, { text: 'Modeling Scheme Design', diff --git a/src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts b/src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts index d5ad1532f..8ce05c6f6 100644 --- a/src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts +++ b/src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts @@ -38,7 +38,7 @@ export const zhSidebar = { prefix: 'Background-knowledge/', children: [ { text: '常见概念', link: 'Cluster-Concept_timecho' }, - { text: '时序数据模型', link: 'Navigating_Time_Series_Data' }, + { text: '时序数据模型', link: 'Navigating_Time_Series_Data_timecho' }, { text: '建模方案设计', link: 'Data-Model-and-Terminology_timecho' }, { text: '数据类型', link: 'Data-Type_timecho' }, ], diff --git a/src/.vuepress/sidebar_timecho/V2.0.x/zh-Tree.ts b/src/.vuepress/sidebar_timecho/V2.0.x/zh-Tree.ts index a4cebbeb8..0c5706116 100644 --- a/src/.vuepress/sidebar_timecho/V2.0.x/zh-Tree.ts +++ b/src/.vuepress/sidebar_timecho/V2.0.x/zh-Tree.ts @@ -38,7 +38,7 @@ export const zhSidebar = { prefix: 'Background-knowledge/', children: [ { text: '常见概念', link: 'Cluster-Concept_timecho' }, - { text: '时序数据模型', link: 'Navigating_Time_Series_Data' }, + { text: '时序数据模型', link: 'Navigating_Time_Series_Data_timehco' }, { text: '建模方案设计', link: 'Data-Model-and-Terminology_timecho' }, { text: '数据类型', link: 'Data-Type' }, ], diff --git a/src/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_apache.md b/src/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_apache.md index cfa0674d6..0d2124dc2 100644 --- a/src/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_apache.md +++ b/src/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_apache.md @@ -25,7 +25,7 @@ This section introduces how to transform time series data application scenarios ## 1. Time Series Data Mode -Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data.md) +Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data_apache.md) ## 2. Tree-Table Twin Mode in IoTDB diff --git a/src/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_timecho.md b/src/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_timecho.md index 0afd66d24..88537b320 100644 --- a/src/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_timecho.md +++ b/src/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_timecho.md @@ -25,7 +25,7 @@ This section introduces how to transform time series data application scenarios ## 1. Time Series Data Mode -Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data.md) +Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) ## 2. Tree-Table Twin Mode in IoTDB diff --git a/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md b/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md index a2308a55a..a8839b142 100644 --- a/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md +++ b/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md @@ -1,3 +1,6 @@ +--- +redirectTo: Navigating_Time_Series_Data_apache.html +--- -# Timeseries Data Model - -## 1. What is Time Series Data? - -In today's interconnected world, industries such as the Internet of Things (IoT) and manufacturing are undergoing rapid digital transformation. Sensors are widely deployed on various devices to collect real-time operational data. For example: - -- **Motors** record voltage and current. -- **Wind Turbines** track blade speed, angular velocity, and power output. -- **Vehicles** capture GPS coordinates, speed, and fuel consumption. -- **Bridges** monitor vibration frequency, deflection, and displacement. - -Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**. - -![](/img/time-series-data-en-01.png) - -Each data collection point is referred to as a **measurement point** (also known as a physical quantity, time series, signal, metric, or measurement value). As time progresses, new data is continuously recorded for each measurement point, forming a **time series**. In tabular form, a time series consists of two columns: **timestamp** and **value**. When visualized, a time series appears as a trend chart over time, resembling an "electrocardiogram" of a device. - -![](/img/time-series-data-en-02.png) - -Given the vast amount of time-series data generated by sensors, structuring this data effectively is essential for digital transformation across industries. Therefore, time-series data modeling is primarily centered around **devices** and **sensors**. - -## 2. Key Concepts in Time Series Data - -Several fundamental concepts define time-series data: - -| **Device** | Also known as an entity or equipment, a device is a real-world object that generates time-series data. In IoTDB, a device serves as a logical grouping of multiple time series. A device could be a physical machine, a measuring instrument, or a collection of sensors. Examples include:
- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.
- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.
- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).
- Monitoring systems: A CPU, identified by attributes such as data center, rack, hostname, and device type. | -| ------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| **FIELD** | Also referred to as a physical quantity, signal, metric, or status point, a field represents a specific measurable property recorded by a sensor. Each field corresponds to a measurement point that periodically captures environmental data. Examples include:
- Energy and power: Current, voltage, wind speed, rotational speed.
- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.
- Manufacturing: Temperature, humidity. | -| **Data Point** | A data point consists of a timestamp and a value. The timestamp is typically stored as a long integer, while the value can be of various data types such as BOOLEAN, FLOAT, or INT32.
In tabular format, a data point corresponds to a single row in a time-series dataset, while in graphical representation, it is a single point on a time-series chart.
| -| **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.
For example, if a temperature sensor records data once per second, its sampling frequency is 1Hz (1 sample per second). | -| **TTL** | TTL (Time-to-Live) defines the retention period of stored data. Once the TTL expires, the data is automatically deleted.
IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:
- Manage disk space efficiently, preventing storage overflow.
- Maintain high query performance.
- Reduce memory resource consumption. | \ No newline at end of file +--> \ No newline at end of file diff --git a/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_apache.md b/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_apache.md new file mode 100644 index 000000000..a2308a55a --- /dev/null +++ b/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_apache.md @@ -0,0 +1,51 @@ + +# Timeseries Data Model + +## 1. What is Time Series Data? + +In today's interconnected world, industries such as the Internet of Things (IoT) and manufacturing are undergoing rapid digital transformation. Sensors are widely deployed on various devices to collect real-time operational data. For example: + +- **Motors** record voltage and current. +- **Wind Turbines** track blade speed, angular velocity, and power output. +- **Vehicles** capture GPS coordinates, speed, and fuel consumption. +- **Bridges** monitor vibration frequency, deflection, and displacement. + +Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**. + +![](/img/time-series-data-en-01.png) + +Each data collection point is referred to as a **measurement point** (also known as a physical quantity, time series, signal, metric, or measurement value). As time progresses, new data is continuously recorded for each measurement point, forming a **time series**. In tabular form, a time series consists of two columns: **timestamp** and **value**. When visualized, a time series appears as a trend chart over time, resembling an "electrocardiogram" of a device. + +![](/img/time-series-data-en-02.png) + +Given the vast amount of time-series data generated by sensors, structuring this data effectively is essential for digital transformation across industries. Therefore, time-series data modeling is primarily centered around **devices** and **sensors**. + +## 2. Key Concepts in Time Series Data + +Several fundamental concepts define time-series data: + +| **Device** | Also known as an entity or equipment, a device is a real-world object that generates time-series data. In IoTDB, a device serves as a logical grouping of multiple time series. A device could be a physical machine, a measuring instrument, or a collection of sensors. Examples include:
- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.
- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.
- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).
- Monitoring systems: A CPU, identified by attributes such as data center, rack, hostname, and device type. | +| ------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **FIELD** | Also referred to as a physical quantity, signal, metric, or status point, a field represents a specific measurable property recorded by a sensor. Each field corresponds to a measurement point that periodically captures environmental data. Examples include:
- Energy and power: Current, voltage, wind speed, rotational speed.
- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.
- Manufacturing: Temperature, humidity. | +| **Data Point** | A data point consists of a timestamp and a value. The timestamp is typically stored as a long integer, while the value can be of various data types such as BOOLEAN, FLOAT, or INT32.
In tabular format, a data point corresponds to a single row in a time-series dataset, while in graphical representation, it is a single point on a time-series chart.
| +| **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.
For example, if a temperature sensor records data once per second, its sampling frequency is 1Hz (1 sample per second). | +| **TTL** | TTL (Time-to-Live) defines the retention period of stored data. Once the TTL expires, the data is automatically deleted.
IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:
- Manage disk space efficiently, preventing storage overflow.
- Maintain high query performance.
- Reduce memory resource consumption. | \ No newline at end of file diff --git a/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md b/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md new file mode 100644 index 000000000..c60e112d5 --- /dev/null +++ b/src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md @@ -0,0 +1,51 @@ + +# Timeseries Data Model + +## 1. What is Time Series Data? + +In today's interconnected world, industries such as the Internet of Things (IoT) and manufacturing are undergoing rapid digital transformation. Sensors are widely deployed on various devices to collect real-time operational data. For example: + +- **Motors** record voltage and current. +- **Wind Turbines** track blade speed, angular velocity, and power output. +- **Vehicles** capture GPS coordinates, speed, and fuel consumption. +- **Bridges** monitor vibration frequency, deflection, and displacement. + +Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**. + +![](/img/time-series-data-en-01.png) + +Each data collection point is referred to as a **measurement point** (also known as a physical quantity, time series, signal, metric, or measurement value). As time progresses, new data is continuously recorded for each measurement point, forming a **time series**. In tabular form, a time series consists of two columns: **timestamp** and **value**. When visualized, a time series appears as a trend chart over time, resembling an "electrocardiogram" of a device. + +![](/img/time-series-data-en-02.png) + +Given the vast amount of time-series data generated by sensors, structuring this data effectively is essential for digital transformation across industries. Therefore, time-series data modeling is primarily centered around **devices** and **sensors**. + +## 2. Key Concepts in Time Series Data + +Several fundamental concepts define time-series data: + +| **Device** | Also known as an entity or equipment, a device is a real-world object that generates time-series data. In IoTDB, a device serves as a logical grouping of multiple time series. A device could be a physical machine, a measuring instrument, or a collection of sensors. Examples include:
- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.
- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.
- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).
- Monitoring systems: A CPU, identified by attributes such as data center, rack, hostname, and device type. | +| ------------------------------- |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **FIELD** | Also referred to as a physical quantity, signal, metric, or status point, a field represents a specific measurable property recorded by a sensor. Each field corresponds to a measurement point that periodically captures environmental data. Examples include:
- Energy and power: Current, voltage, wind speed, rotational speed.
- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.
- Manufacturing: Temperature, humidity.
Under the **table model**, the total number of **measurement points** is the sum of measurement points of all tables (measurement points per table = number of devices × number of field columns). For detailed statistics methods, please refer to [Metadata Query](../Basic-Concept/Table-Management_timecho.md#_1-7-metadata-query) | +| **Data Point** | A data point consists of a timestamp and a value. The timestamp is typically stored as a long integer, while the value can be of various data types such as BOOLEAN, FLOAT, or INT32.
In tabular format, a data point corresponds to a single row in a time-series dataset, while in graphical representation, it is a single point on a time-series chart.
| +| **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.
For example, if a temperature sensor records data once per second, its sampling frequency is 1Hz (1 sample per second). | +| **TTL** | TTL (Time-to-Live) defines the retention period of stored data. Once the TTL expires, the data is automatically deleted.
IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:
- Manage disk space efficiently, preventing storage overflow.
- Maintain high query performance.
- Reduce memory resource consumption. | \ No newline at end of file diff --git a/src/UserGuide/Master/Table/Basic-Concept/Table-Management_apache.md b/src/UserGuide/Master/Table/Basic-Concept/Table-Management_apache.md index 6fad824ae..d02429708 100644 --- a/src/UserGuide/Master/Table/Basic-Concept/Table-Management_apache.md +++ b/src/UserGuide/Master/Table/Basic-Concept/Table-Management_apache.md @@ -22,7 +22,7 @@ # Table Management Before starting to use the table management functionality, we recommend familiarizing yourself with the following related background knowledge for a better understanding and application of the table management features: -* [Timeseries Data Model](../Background-knowledge/Navigating_Time_Series_Data.md): Understand the basic concepts and characteristics of time series data to establish a foundation for data modeling. +* [Timeseries Data Model](../Background-knowledge/Navigating_Time_Series_Data_apache.md): Understand the basic concepts and characteristics of time series data to establish a foundation for data modeling. * [Modeling Scheme Design](../Background-knowledge/Data-Model-and-Terminology_apache.md): Master the IoTDB time series model and its applicable scenarios to provide a design basis for table management. ## 1. Table Management diff --git a/src/UserGuide/Master/Table/Basic-Concept/Table-Management_timecho.md b/src/UserGuide/Master/Table/Basic-Concept/Table-Management_timecho.md index 0e6349ad6..668521cea 100644 --- a/src/UserGuide/Master/Table/Basic-Concept/Table-Management_timecho.md +++ b/src/UserGuide/Master/Table/Basic-Concept/Table-Management_timecho.md @@ -22,7 +22,7 @@ # Table Management Before starting to use the table management functionality, we recommend familiarizing yourself with the following related background knowledge for a better understanding and application of the table management features: -* [Timeseries Data Model](../Background-knowledge/Navigating_Time_Series_Data.md): Understand the basic concepts and characteristics of time series data to establish a foundation for data modeling. +* [Timeseries Data Model](../Background-knowledge/Navigating_Time_Series_Data_timecho.md): Understand the basic concepts and characteristics of time series data to establish a foundation for data modeling. * [Modeling Scheme Design](../Background-knowledge/Data-Model-and-Terminology_timecho.md): Master the IoTDB time series model and its applicable scenarios to provide a design basis for table management. ## 1. Table Management @@ -274,7 +274,7 @@ ALTER TABLE (IF EXISTS)? tableName=qualifiedName ADD COLUMN (IF NOT EXISTS)? col | COMMENT ON TABLE tableName=qualifiedName IS 'table_comment'; | COMMENT ON COLUMN tableName.column IS 'column_comment'; #changeColumndatatype; -| ALTER TABLE (IF EXISTS)? tableName=qualifiedName ALTER COLUMN (IF EXISTS)? column=identifier SET DATA TYPE new_type=type; +| ALTER TABLE (IF EXISTS)? tableName=qualifiedName ALTER COLUMN (IF EXISTS)? column=identifier SET DATA TYPE new_type=type; ``` **Note:：** @@ -313,4 +313,43 @@ DROP TABLE (IF EXISTS)? ```SQL DROP TABLE table1; DROP TABLE database1.table1; -``` \ No newline at end of file +``` + +## 1.7 Metadata Query +Under the table model, the **total number of measurement points** equals the sum of measurement points of all tables. Currently, the number of measurement points in a single table can be calculated with the formula: +**Measurement points per single table = Number of devices × Number of field columns**. +Support for directly querying measurement points under the table model via SQL statements will be available in future updates. Please stay tuned. + +Take `table1` in the [sample data](../Reference/Sample-Data.md) as an example. + +In the organizational structure of this sample, there are three tag columns (`region`, `plant_id`, `device_id`) and four field columns (`temperature`, `humidity`, `status`, `arrival_time`). + +A unique device is identified by the combination of all tag columns. Each unique combination of `region` + `plant_id` + `device_id` represents an independent device. + +The sample data defines 2 regions: Beijing and Shanghai. Details are as follows: +- **Beijing**: 1 factory with ID 1001 + - 2 devices under this factory: IDs 100 and 101 +- **Shanghai**: 2 factories with IDs 3001 and 3002 + - Factory 3001: 2 devices (IDs 100, 101) + - Factory 3002: 2 devices (IDs 100, 101) + +In total, there are 6 unique tag combinations in the table, corresponding to 6 independent devices. + +### Complete Calculation Example for Single-Table Measurement Points +1. Query the number of devices +```sql +IoTDB:database1> count devices from table1 ++--------------+ +|count(devices)| ++--------------+ +| 6| ++--------------+ +Total line number = 1 +It costs 0.019s +``` + +2. Calculate the total measurement points of the single table +- Number of devices: 6 +- Number of field columns: 4 +- Total measurement points of the table: **6 × 4 = 24** + diff --git a/src/UserGuide/Master/Table/QuickStart/QuickStart_apache.md b/src/UserGuide/Master/Table/QuickStart/QuickStart_apache.md index fe45e3abe..fa5d03643 100644 --- a/src/UserGuide/Master/Table/QuickStart/QuickStart_apache.md +++ b/src/UserGuide/Master/Table/QuickStart/QuickStart_apache.md @@ -45,7 +45,7 @@ This guide will assist you in quickly installing and deploying IoTDB. You can qu 1. Database Modeling Design: Database modeling is a crucial step in creating a database system, involving the design of data structures and relationships to ensure that the organization of data meets the needs of specific applications. The following documents will help you quickly understand IoTDB's modeling design: - - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data.md) + - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data_apache.md) - Introduction to Modeling Design:[Data Model and Terminology](../Background-knowledge/Data-Model-and-Terminology_apache.md) diff --git a/src/UserGuide/Master/Table/QuickStart/QuickStart_timecho.md b/src/UserGuide/Master/Table/QuickStart/QuickStart_timecho.md index 4aa34486e..6bbd2c07a 100644 --- a/src/UserGuide/Master/Table/QuickStart/QuickStart_timecho.md +++ b/src/UserGuide/Master/Table/QuickStart/QuickStart_timecho.md @@ -52,7 +52,7 @@ This guide will assist you in quickly installing and deploying IoTDB. You can qu 1. Database Modeling Design: Database modeling is a crucial step in creating a database system, involving the design of data structures and relationships to ensure that the organization of data meets the needs of specific applications. The following documents will help you quickly understand IoTDB's modeling design: - - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data.md) + - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) - Introduction to Modeling Design:[Data Model and Terminology](../Background-knowledge/Data-Model-and-Terminology_timecho.md) diff --git a/src/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_apache.md b/src/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_apache.md index b97da4240..ef9510cfe 100644 --- a/src/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_apache.md +++ b/src/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_apache.md @@ -25,7 +25,7 @@ This section introduces how to transform time series data application scenarios ## 1. Time Series Data Mode -Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data.md) +Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data_apache.md) ## 2. Tree-Table Twin Mode in IoTDB diff --git a/src/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_timecho.md b/src/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_timecho.md index 51421369b..6b6e2018d 100644 --- a/src/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_timecho.md +++ b/src/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_timecho.md @@ -25,7 +25,7 @@ This section introduces how to transform time series data application scenarios ## 1. Time Series Data Mode -Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data.md) +Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) ## 2. Tree-Table Twin Mode in IoTDB diff --git a/src/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md b/src/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md index 8ae94a763..a8839b142 100644 --- a/src/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md +++ b/src/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md @@ -1,3 +1,6 @@ +--- +redirectTo: Navigating_Time_Series_Data_apache.html +--- -# Timeseries Data Model - -## 1. What Is Time Series Data？ - -In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries. - -![](/img/time-series-data-en-01.png) - -Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram. - -![](/img/time-series-data-en-02.png) - -The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors. - -## 2. Key Concepts of Time Series Data -The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment. - -![](/img/time-series-data-en-04.png) - -### 2.1 Data Point - -- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc. -- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point. - -![](/img/time-series-data-en-03.png) - -### 2.2 Measurement Points - -- Definition: It is a time series formed by multiple data points arranged in increments according to timestamps. Usually, a measuring point represents a collection point and can regularly collect physical quantities of the environment it is located in. -- Also known as: physical quantity, time series, timeline, semaphore, indicator, measurement value, etc -- Example： - - Electricity scenario: current, voltage - - Energy scenario: wind speed, rotational speed - - Vehicle networking scenarios: fuel consumption, vehicle speed, longitude, dimensions - - Factory scenario: temperature, humidity - -### 2.3 Device - -- Definition: Corresponding to a physical device in an actual scene, usually a collection of measurement points, identified by one to multiple labels -- Example： - - Vehicle networking scenario: Vehicles identified by vehicle identification code (VIN) - - Factory scenario: robotic arm, unique ID identification generated by IoT platform - - Energy scenario: Wind turbines, identified by region, station, line, model, instance, etc - - Monitoring scenario: CPU, identified by machine room, rack, Hostname, device type, etc \ No newline at end of file +--> \ No newline at end of file diff --git a/src/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_apache.md b/src/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_apache.md new file mode 100644 index 000000000..8ae94a763 --- /dev/null +++ b/src/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_apache.md @@ -0,0 +1,64 @@ + +# Timeseries Data Model + +## 1. What Is Time Series Data？ + +In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries. + +![](/img/time-series-data-en-01.png) + +Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram. + +![](/img/time-series-data-en-02.png) + +The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors. + +## 2. Key Concepts of Time Series Data +The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment. + +![](/img/time-series-data-en-04.png) + +### 2.1 Data Point + +- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc. +- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point. + +![](/img/time-series-data-en-03.png) + +### 2.2 Measurement Points + +- Definition: It is a time series formed by multiple data points arranged in increments according to timestamps. Usually, a measuring point represents a collection point and can regularly collect physical quantities of the environment it is located in. +- Also known as: physical quantity, time series, timeline, semaphore, indicator, measurement value, etc +- Example： + - Electricity scenario: current, voltage + - Energy scenario: wind speed, rotational speed + - Vehicle networking scenarios: fuel consumption, vehicle speed, longitude, dimensions + - Factory scenario: temperature, humidity + +### 2.3 Device + +- Definition: Corresponding to a physical device in an actual scene, usually a collection of measurement points, identified by one to multiple labels +- Example： + - Vehicle networking scenario: Vehicles identified by vehicle identification code (VIN) + - Factory scenario: robotic arm, unique ID identification generated by IoT platform + - Energy scenario: Wind turbines, identified by region, station, line, model, instance, etc + - Monitoring scenario: CPU, identified by machine room, rack, Hostname, device type, etc \ No newline at end of file diff --git a/src/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_timecho.md b/src/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_timecho.md new file mode 100644 index 000000000..dc29b26d4 --- /dev/null +++ b/src/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_timecho.md @@ -0,0 +1,65 @@ + +# Timeseries Data Model + +## 1. What Is Time Series Data？ + +In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries. + +![](/img/time-series-data-en-01.png) + +Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram. + +![](/img/time-series-data-en-02.png) + +The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors. + +## 2. Key Concepts of Time Series Data +The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment. + +![](/img/time-series-data-en-04.png) + +### 2.1 Data Point + +- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc. +- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point. + +![](/img/time-series-data-en-03.png) + +### 2.2 Measurement Points + +- Definition: It is a time series formed by multiple data points arranged in increments according to timestamps. Usually, a measuring point represents a collection point and can regularly collect physical quantities of the environment it is located in. +- Also known as: physical quantity, time series, timeline, semaphore, indicator, measurement value, etc +- Example： + - Electricity scenario: current, voltage + - Energy scenario: wind speed, rotational speed + - Vehicle networking scenarios: fuel consumption, vehicle speed, longitude, dimensions + - Factory scenario: temperature, humidity +- In the tree model, the total number of measurement points equals the number of leaf nodes under the entire path pattern. For detailed statistics methods, refer to [Count Timeseries](../Basic-Concept/Operate-Metadata_timecho.md#_2-7-count-timeseries) + +### 2.3 Device + +- Definition: Corresponding to a physical device in an actual scene, usually a collection of measurement points, identified by one to multiple labels +- Example： + - Vehicle networking scenario: Vehicles identified by vehicle identification code (VIN) + - Factory scenario: robotic arm, unique ID identification generated by IoT platform + - Energy scenario: Wind turbines, identified by region, station, line, model, instance, etc + - Monitoring scenario: CPU, identified by machine room, rack, Hostname, device type, etc \ No newline at end of file diff --git a/src/UserGuide/Master/Tree/QuickStart/QuickStart_apache.md b/src/UserGuide/Master/Tree/QuickStart/QuickStart_apache.md index 59f0d33b0..ec7bee5ff 100644 --- a/src/UserGuide/Master/Tree/QuickStart/QuickStart_apache.md +++ b/src/UserGuide/Master/Tree/QuickStart/QuickStart_apache.md @@ -47,7 +47,7 @@ This guide will assist you in quickly installing and deploying IoTDB. You can qu 1. Database Modeling Design: Database modeling is a crucial step in creating a database system, involving the design of data structures and relationships to ensure that the organization of data meets the needs of specific applications. The following documents will help you quickly understand IoTDB's modeling design: - - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data.md) + - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data_apache.md) - Introduction to Modeling Design:[Data Model and Terminology](../Background-knowledge/Data-Model-and-Terminology_apache.md) diff --git a/src/UserGuide/Master/Tree/QuickStart/QuickStart_timecho.md b/src/UserGuide/Master/Tree/QuickStart/QuickStart_timecho.md index ab5818efe..cb58bf5f5 100644 --- a/src/UserGuide/Master/Tree/QuickStart/QuickStart_timecho.md +++ b/src/UserGuide/Master/Tree/QuickStart/QuickStart_timecho.md @@ -54,7 +54,7 @@ This guide will assist you in quickly installing and deploying IoTDB. You can qu 1. Database Modeling Design: Database modeling is a crucial step in creating a database system, involving the design of data structures and relationships to ensure that the organization of data meets the needs of specific applications. The following documents will help you quickly understand IoTDB's modeling design: - - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data.md) + - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) - Introduction to Modeling Design:[Data Model and Terminology](../Background-knowledge/Data-Model-and-Terminology_timecho.md) diff --git a/src/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_apache.md b/src/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_apache.md index cfa0674d6..0d2124dc2 100644 --- a/src/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_apache.md +++ b/src/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_apache.md @@ -25,7 +25,7 @@ This section introduces how to transform time series data application scenarios ## 1. Time Series Data Mode -Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data.md) +Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data_apache.md) ## 2. Tree-Table Twin Mode in IoTDB diff --git a/src/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_timecho.md b/src/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_timecho.md index 0afd66d24..88537b320 100644 --- a/src/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_timecho.md +++ b/src/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_timecho.md @@ -25,7 +25,7 @@ This section introduces how to transform time series data application scenarios ## 1. Time Series Data Mode -Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data.md) +Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) ## 2. Tree-Table Twin Mode in IoTDB diff --git a/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md b/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md index a2308a55a..a8839b142 100644 --- a/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md +++ b/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md @@ -1,3 +1,6 @@ +--- +redirectTo: Navigating_Time_Series_Data_apache.html +--- -# Timeseries Data Model - -## 1. What is Time Series Data? - -In today's interconnected world, industries such as the Internet of Things (IoT) and manufacturing are undergoing rapid digital transformation. Sensors are widely deployed on various devices to collect real-time operational data. For example: - -- **Motors** record voltage and current. -- **Wind Turbines** track blade speed, angular velocity, and power output. -- **Vehicles** capture GPS coordinates, speed, and fuel consumption. -- **Bridges** monitor vibration frequency, deflection, and displacement. - -Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**. - -![](/img/time-series-data-en-01.png) - -Each data collection point is referred to as a **measurement point** (also known as a physical quantity, time series, signal, metric, or measurement value). As time progresses, new data is continuously recorded for each measurement point, forming a **time series**. In tabular form, a time series consists of two columns: **timestamp** and **value**. When visualized, a time series appears as a trend chart over time, resembling an "electrocardiogram" of a device. - -![](/img/time-series-data-en-02.png) - -Given the vast amount of time-series data generated by sensors, structuring this data effectively is essential for digital transformation across industries. Therefore, time-series data modeling is primarily centered around **devices** and **sensors**. - -## 2. Key Concepts in Time Series Data - -Several fundamental concepts define time-series data: - -| **Device** | Also known as an entity or equipment, a device is a real-world object that generates time-series data. In IoTDB, a device serves as a logical grouping of multiple time series. A device could be a physical machine, a measuring instrument, or a collection of sensors. Examples include:
- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.
- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.
- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).
- Monitoring systems: A CPU, identified by attributes such as data center, rack, hostname, and device type. | -| ------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| **FIELD** | Also referred to as a physical quantity, signal, metric, or status point, a field represents a specific measurable property recorded by a sensor. Each field corresponds to a measurement point that periodically captures environmental data. Examples include:
- Energy and power: Current, voltage, wind speed, rotational speed.
- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.
- Manufacturing: Temperature, humidity. | -| **Data Point** | A data point consists of a timestamp and a value. The timestamp is typically stored as a long integer, while the value can be of various data types such as BOOLEAN, FLOAT, or INT32.
In tabular format, a data point corresponds to a single row in a time-series dataset, while in graphical representation, it is a single point on a time-series chart.
| -| **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.
For example, if a temperature sensor records data once per second, its sampling frequency is 1Hz (1 sample per second). | -| **TTL** | TTL (Time-to-Live) defines the retention period of stored data. Once the TTL expires, the data is automatically deleted.
IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:
- Manage disk space efficiently, preventing storage overflow.
- Maintain high query performance.
- Reduce memory resource consumption. | \ No newline at end of file +--> \ No newline at end of file diff --git a/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_apache.md b/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_apache.md new file mode 100644 index 000000000..a2308a55a --- /dev/null +++ b/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_apache.md @@ -0,0 +1,51 @@ + +# Timeseries Data Model + +## 1. What is Time Series Data? + +In today's interconnected world, industries such as the Internet of Things (IoT) and manufacturing are undergoing rapid digital transformation. Sensors are widely deployed on various devices to collect real-time operational data. For example: + +- **Motors** record voltage and current. +- **Wind Turbines** track blade speed, angular velocity, and power output. +- **Vehicles** capture GPS coordinates, speed, and fuel consumption. +- **Bridges** monitor vibration frequency, deflection, and displacement. + +Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**. + +![](/img/time-series-data-en-01.png) + +Each data collection point is referred to as a **measurement point** (also known as a physical quantity, time series, signal, metric, or measurement value). As time progresses, new data is continuously recorded for each measurement point, forming a **time series**. In tabular form, a time series consists of two columns: **timestamp** and **value**. When visualized, a time series appears as a trend chart over time, resembling an "electrocardiogram" of a device. + +![](/img/time-series-data-en-02.png) + +Given the vast amount of time-series data generated by sensors, structuring this data effectively is essential for digital transformation across industries. Therefore, time-series data modeling is primarily centered around **devices** and **sensors**. + +## 2. Key Concepts in Time Series Data + +Several fundamental concepts define time-series data: + +| **Device** | Also known as an entity or equipment, a device is a real-world object that generates time-series data. In IoTDB, a device serves as a logical grouping of multiple time series. A device could be a physical machine, a measuring instrument, or a collection of sensors. Examples include:
- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.
- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.
- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).
- Monitoring systems: A CPU, identified by attributes such as data center, rack, hostname, and device type. | +| ------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **FIELD** | Also referred to as a physical quantity, signal, metric, or status point, a field represents a specific measurable property recorded by a sensor. Each field corresponds to a measurement point that periodically captures environmental data. Examples include:
- Energy and power: Current, voltage, wind speed, rotational speed.
- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.
- Manufacturing: Temperature, humidity. | +| **Data Point** | A data point consists of a timestamp and a value. The timestamp is typically stored as a long integer, while the value can be of various data types such as BOOLEAN, FLOAT, or INT32.
In tabular format, a data point corresponds to a single row in a time-series dataset, while in graphical representation, it is a single point on a time-series chart.
| +| **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.
For example, if a temperature sensor records data once per second, its sampling frequency is 1Hz (1 sample per second). | +| **TTL** | TTL (Time-to-Live) defines the retention period of stored data. Once the TTL expires, the data is automatically deleted.
IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:
- Manage disk space efficiently, preventing storage overflow.
- Maintain high query performance.
- Reduce memory resource consumption. | \ No newline at end of file diff --git a/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md b/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md new file mode 100644 index 000000000..c60e112d5 --- /dev/null +++ b/src/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md @@ -0,0 +1,51 @@ + +# Timeseries Data Model + +## 1. What is Time Series Data? + +In today's interconnected world, industries such as the Internet of Things (IoT) and manufacturing are undergoing rapid digital transformation. Sensors are widely deployed on various devices to collect real-time operational data. For example: + +- **Motors** record voltage and current. +- **Wind Turbines** track blade speed, angular velocity, and power output. +- **Vehicles** capture GPS coordinates, speed, and fuel consumption. +- **Bridges** monitor vibration frequency, deflection, and displacement. + +Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**. + +![](/img/time-series-data-en-01.png) + +Each data collection point is referred to as a **measurement point** (also known as a physical quantity, time series, signal, metric, or measurement value). As time progresses, new data is continuously recorded for each measurement point, forming a **time series**. In tabular form, a time series consists of two columns: **timestamp** and **value**. When visualized, a time series appears as a trend chart over time, resembling an "electrocardiogram" of a device. + +![](/img/time-series-data-en-02.png) + +Given the vast amount of time-series data generated by sensors, structuring this data effectively is essential for digital transformation across industries. Therefore, time-series data modeling is primarily centered around **devices** and **sensors**. + +## 2. Key Concepts in Time Series Data + +Several fundamental concepts define time-series data: + +| **Device** | Also known as an entity or equipment, a device is a real-world object that generates time-series data. In IoTDB, a device serves as a logical grouping of multiple time series. A device could be a physical machine, a measuring instrument, or a collection of sensors. Examples include:
- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.
- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.
- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).
- Monitoring systems: A CPU, identified by attributes such as data center, rack, hostname, and device type. | +| ------------------------------- |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **FIELD** | Also referred to as a physical quantity, signal, metric, or status point, a field represents a specific measurable property recorded by a sensor. Each field corresponds to a measurement point that periodically captures environmental data. Examples include:
- Energy and power: Current, voltage, wind speed, rotational speed.
- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.
- Manufacturing: Temperature, humidity.
Under the **table model**, the total number of **measurement points** is the sum of measurement points of all tables (measurement points per table = number of devices × number of field columns). For detailed statistics methods, please refer to [Metadata Query](../Basic-Concept/Table-Management_timecho.md#_1-7-metadata-query) | +| **Data Point** | A data point consists of a timestamp and a value. The timestamp is typically stored as a long integer, while the value can be of various data types such as BOOLEAN, FLOAT, or INT32.
In tabular format, a data point corresponds to a single row in a time-series dataset, while in graphical representation, it is a single point on a time-series chart.
| +| **Frequency** | The sampling frequency determines how often a sensor records data within a given timeframe.
For example, if a temperature sensor records data once per second, its sampling frequency is 1Hz (1 sample per second). | +| **TTL** | TTL (Time-to-Live) defines the retention period of stored data. Once the TTL expires, the data is automatically deleted.
IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:
- Manage disk space efficiently, preventing storage overflow.
- Maintain high query performance.
- Reduce memory resource consumption. | \ No newline at end of file diff --git a/src/UserGuide/latest-Table/Basic-Concept/Table-Management_apache.md b/src/UserGuide/latest-Table/Basic-Concept/Table-Management_apache.md index 6fad824ae..d02429708 100644 --- a/src/UserGuide/latest-Table/Basic-Concept/Table-Management_apache.md +++ b/src/UserGuide/latest-Table/Basic-Concept/Table-Management_apache.md @@ -22,7 +22,7 @@ # Table Management Before starting to use the table management functionality, we recommend familiarizing yourself with the following related background knowledge for a better understanding and application of the table management features: -* [Timeseries Data Model](../Background-knowledge/Navigating_Time_Series_Data.md): Understand the basic concepts and characteristics of time series data to establish a foundation for data modeling. +* [Timeseries Data Model](../Background-knowledge/Navigating_Time_Series_Data_apache.md): Understand the basic concepts and characteristics of time series data to establish a foundation for data modeling. * [Modeling Scheme Design](../Background-knowledge/Data-Model-and-Terminology_apache.md): Master the IoTDB time series model and its applicable scenarios to provide a design basis for table management. ## 1. Table Management diff --git a/src/UserGuide/latest-Table/Basic-Concept/Table-Management_timecho.md b/src/UserGuide/latest-Table/Basic-Concept/Table-Management_timecho.md index 239bca13e..668521cea 100644 --- a/src/UserGuide/latest-Table/Basic-Concept/Table-Management_timecho.md +++ b/src/UserGuide/latest-Table/Basic-Concept/Table-Management_timecho.md @@ -22,7 +22,7 @@ # Table Management Before starting to use the table management functionality, we recommend familiarizing yourself with the following related background knowledge for a better understanding and application of the table management features: -* [Timeseries Data Model](../Background-knowledge/Navigating_Time_Series_Data.md): Understand the basic concepts and characteristics of time series data to establish a foundation for data modeling. +* [Timeseries Data Model](../Background-knowledge/Navigating_Time_Series_Data_timecho.md): Understand the basic concepts and characteristics of time series data to establish a foundation for data modeling. * [Modeling Scheme Design](../Background-knowledge/Data-Model-and-Terminology_timecho.md): Master the IoTDB time series model and its applicable scenarios to provide a design basis for table management. ## 1. Table Management @@ -313,4 +313,43 @@ DROP TABLE (IF EXISTS)? ```SQL DROP TABLE table1; DROP TABLE database1.table1; -``` \ No newline at end of file +``` + +## 1.7 Metadata Query +Under the table model, the **total number of measurement points** equals the sum of measurement points of all tables. Currently, the number of measurement points in a single table can be calculated with the formula: +**Measurement points per single table = Number of devices × Number of field columns**. +Support for directly querying measurement points under the table model via SQL statements will be available in future updates. Please stay tuned. + +Take `table1` in the [sample data](../Reference/Sample-Data.md) as an example. + +In the organizational structure of this sample, there are three tag columns (`region`, `plant_id`, `device_id`) and four field columns (`temperature`, `humidity`, `status`, `arrival_time`). + +A unique device is identified by the combination of all tag columns. Each unique combination of `region` + `plant_id` + `device_id` represents an independent device. + +The sample data defines 2 regions: Beijing and Shanghai. Details are as follows: +- **Beijing**: 1 factory with ID 1001 + - 2 devices under this factory: IDs 100 and 101 +- **Shanghai**: 2 factories with IDs 3001 and 3002 + - Factory 3001: 2 devices (IDs 100, 101) + - Factory 3002: 2 devices (IDs 100, 101) + +In total, there are 6 unique tag combinations in the table, corresponding to 6 independent devices. + +### Complete Calculation Example for Single-Table Measurement Points +1. Query the number of devices +```sql +IoTDB:database1> count devices from table1 ++--------------+ +|count(devices)| ++--------------+ +| 6| ++--------------+ +Total line number = 1 +It costs 0.019s +``` + +2. Calculate the total measurement points of the single table +- Number of devices: 6 +- Number of field columns: 4 +- Total measurement points of the table: **6 × 4 = 24** + diff --git a/src/UserGuide/latest-Table/QuickStart/QuickStart_apache.md b/src/UserGuide/latest-Table/QuickStart/QuickStart_apache.md index fe45e3abe..fa5d03643 100644 --- a/src/UserGuide/latest-Table/QuickStart/QuickStart_apache.md +++ b/src/UserGuide/latest-Table/QuickStart/QuickStart_apache.md @@ -45,7 +45,7 @@ This guide will assist you in quickly installing and deploying IoTDB. You can qu 1. Database Modeling Design: Database modeling is a crucial step in creating a database system, involving the design of data structures and relationships to ensure that the organization of data meets the needs of specific applications. The following documents will help you quickly understand IoTDB's modeling design: - - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data.md) + - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data_apache.md) - Introduction to Modeling Design:[Data Model and Terminology](../Background-knowledge/Data-Model-and-Terminology_apache.md) diff --git a/src/UserGuide/latest-Table/QuickStart/QuickStart_timecho.md b/src/UserGuide/latest-Table/QuickStart/QuickStart_timecho.md index 4aa34486e..6bbd2c07a 100644 --- a/src/UserGuide/latest-Table/QuickStart/QuickStart_timecho.md +++ b/src/UserGuide/latest-Table/QuickStart/QuickStart_timecho.md @@ -52,7 +52,7 @@ This guide will assist you in quickly installing and deploying IoTDB. You can qu 1. Database Modeling Design: Database modeling is a crucial step in creating a database system, involving the design of data structures and relationships to ensure that the organization of data meets the needs of specific applications. The following documents will help you quickly understand IoTDB's modeling design: - - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data.md) + - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) - Introduction to Modeling Design:[Data Model and Terminology](../Background-knowledge/Data-Model-and-Terminology_timecho.md) diff --git a/src/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_apache.md b/src/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_apache.md index b97da4240..ef9510cfe 100644 --- a/src/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_apache.md +++ b/src/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_apache.md @@ -25,7 +25,7 @@ This section introduces how to transform time series data application scenarios ## 1. Time Series Data Mode -Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data.md) +Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data_apache.md) ## 2. Tree-Table Twin Mode in IoTDB diff --git a/src/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_timecho.md b/src/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_timecho.md index 51421369b..6b6e2018d 100644 --- a/src/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_timecho.md +++ b/src/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_timecho.md @@ -25,7 +25,7 @@ This section introduces how to transform time series data application scenarios ## 1. Time Series Data Mode -Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data.md) +Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) ## 2. Tree-Table Twin Mode in IoTDB diff --git a/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md b/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md index 8ae94a763..a8839b142 100644 --- a/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md +++ b/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md @@ -1,3 +1,6 @@ +--- +redirectTo: Navigating_Time_Series_Data_apache.html +--- -# Timeseries Data Model - -## 1. What Is Time Series Data？ - -In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries. - -![](/img/time-series-data-en-01.png) - -Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram. - -![](/img/time-series-data-en-02.png) - -The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors. - -## 2. Key Concepts of Time Series Data -The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment. - -![](/img/time-series-data-en-04.png) - -### 2.1 Data Point - -- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc. -- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point. - -![](/img/time-series-data-en-03.png) - -### 2.2 Measurement Points - -- Definition: It is a time series formed by multiple data points arranged in increments according to timestamps. Usually, a measuring point represents a collection point and can regularly collect physical quantities of the environment it is located in. -- Also known as: physical quantity, time series, timeline, semaphore, indicator, measurement value, etc -- Example： - - Electricity scenario: current, voltage - - Energy scenario: wind speed, rotational speed - - Vehicle networking scenarios: fuel consumption, vehicle speed, longitude, dimensions - - Factory scenario: temperature, humidity - -### 2.3 Device - -- Definition: Corresponding to a physical device in an actual scene, usually a collection of measurement points, identified by one to multiple labels -- Example： - - Vehicle networking scenario: Vehicles identified by vehicle identification code (VIN) - - Factory scenario: robotic arm, unique ID identification generated by IoT platform - - Energy scenario: Wind turbines, identified by region, station, line, model, instance, etc - - Monitoring scenario: CPU, identified by machine room, rack, Hostname, device type, etc \ No newline at end of file +--> \ No newline at end of file diff --git a/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_apache.md b/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_apache.md new file mode 100644 index 000000000..8ae94a763 --- /dev/null +++ b/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_apache.md @@ -0,0 +1,64 @@ + +# Timeseries Data Model + +## 1. What Is Time Series Data？ + +In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries. + +![](/img/time-series-data-en-01.png) + +Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram. + +![](/img/time-series-data-en-02.png) + +The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors. + +## 2. Key Concepts of Time Series Data +The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment. + +![](/img/time-series-data-en-04.png) + +### 2.1 Data Point + +- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc. +- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point. + +![](/img/time-series-data-en-03.png) + +### 2.2 Measurement Points + +- Definition: It is a time series formed by multiple data points arranged in increments according to timestamps. Usually, a measuring point represents a collection point and can regularly collect physical quantities of the environment it is located in. +- Also known as: physical quantity, time series, timeline, semaphore, indicator, measurement value, etc +- Example： + - Electricity scenario: current, voltage + - Energy scenario: wind speed, rotational speed + - Vehicle networking scenarios: fuel consumption, vehicle speed, longitude, dimensions + - Factory scenario: temperature, humidity + +### 2.3 Device + +- Definition: Corresponding to a physical device in an actual scene, usually a collection of measurement points, identified by one to multiple labels +- Example： + - Vehicle networking scenario: Vehicles identified by vehicle identification code (VIN) + - Factory scenario: robotic arm, unique ID identification generated by IoT platform + - Energy scenario: Wind turbines, identified by region, station, line, model, instance, etc + - Monitoring scenario: CPU, identified by machine room, rack, Hostname, device type, etc \ No newline at end of file diff --git a/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_timecho.md b/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_timecho.md new file mode 100644 index 000000000..dc29b26d4 --- /dev/null +++ b/src/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_timecho.md @@ -0,0 +1,65 @@ + +# Timeseries Data Model + +## 1. What Is Time Series Data？ + +In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries. + +![](/img/time-series-data-en-01.png) + +Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram. + +![](/img/time-series-data-en-02.png) + +The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors. + +## 2. Key Concepts of Time Series Data +The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment. + +![](/img/time-series-data-en-04.png) + +### 2.1 Data Point + +- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc. +- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point. + +![](/img/time-series-data-en-03.png) + +### 2.2 Measurement Points + +- Definition: It is a time series formed by multiple data points arranged in increments according to timestamps. Usually, a measuring point represents a collection point and can regularly collect physical quantities of the environment it is located in. +- Also known as: physical quantity, time series, timeline, semaphore, indicator, measurement value, etc +- Example： + - Electricity scenario: current, voltage + - Energy scenario: wind speed, rotational speed + - Vehicle networking scenarios: fuel consumption, vehicle speed, longitude, dimensions + - Factory scenario: temperature, humidity +- In the tree model, the total number of measurement points equals the number of leaf nodes under the entire path pattern. For detailed statistics methods, refer to [Count Timeseries](../Basic-Concept/Operate-Metadata_timecho.md#_2-7-count-timeseries) + +### 2.3 Device + +- Definition: Corresponding to a physical device in an actual scene, usually a collection of measurement points, identified by one to multiple labels +- Example： + - Vehicle networking scenario: Vehicles identified by vehicle identification code (VIN) + - Factory scenario: robotic arm, unique ID identification generated by IoT platform + - Energy scenario: Wind turbines, identified by region, station, line, model, instance, etc + - Monitoring scenario: CPU, identified by machine room, rack, Hostname, device type, etc \ No newline at end of file diff --git a/src/UserGuide/latest/QuickStart/QuickStart_apache.md b/src/UserGuide/latest/QuickStart/QuickStart_apache.md index 59f0d33b0..ec7bee5ff 100644 --- a/src/UserGuide/latest/QuickStart/QuickStart_apache.md +++ b/src/UserGuide/latest/QuickStart/QuickStart_apache.md @@ -47,7 +47,7 @@ This guide will assist you in quickly installing and deploying IoTDB. You can qu 1. Database Modeling Design: Database modeling is a crucial step in creating a database system, involving the design of data structures and relationships to ensure that the organization of data meets the needs of specific applications. The following documents will help you quickly understand IoTDB's modeling design: - - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data.md) + - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data_apache.md) - Introduction to Modeling Design:[Data Model and Terminology](../Background-knowledge/Data-Model-and-Terminology_apache.md) diff --git a/src/UserGuide/latest/QuickStart/QuickStart_timecho.md b/src/UserGuide/latest/QuickStart/QuickStart_timecho.md index ab5818efe..cb58bf5f5 100644 --- a/src/UserGuide/latest/QuickStart/QuickStart_timecho.md +++ b/src/UserGuide/latest/QuickStart/QuickStart_timecho.md @@ -54,7 +54,7 @@ This guide will assist you in quickly installing and deploying IoTDB. You can qu 1. Database Modeling Design: Database modeling is a crucial step in creating a database system, involving the design of data structures and relationships to ensure that the organization of data meets the needs of specific applications. The following documents will help you quickly understand IoTDB's modeling design: - - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data.md) + - Introduction to Time Series Concepts: [Navigating Time Series Data](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) - Introduction to Modeling Design:[Data Model and Terminology](../Background-knowledge/Data-Model-and-Terminology_timecho.md) diff --git a/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_apache.md b/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_apache.md index cbd5260cf..c951dcf63 100644 --- a/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_apache.md +++ b/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_apache.md @@ -25,7 +25,7 @@ ## 1. 时序数据模型 -在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) +在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_apache.md) ## 2. IoTDB 的树表孪生模型 @@ -240,8 +240,6 @@ IoTDB 提供了树转表功能，如下图所示： 该功能支持通过创建表视图的方式，将已存在的树模型数据转化为表视图，进而通过表视图进行查询，实现了对同一份数据的树模型和表模型协同处理。更详细的功能介绍可参考[树转表视图](../User-Manual/Tree-to-Table_apache.md)，需要注意的是：​**创建树转表视图的 SQL 语句只允许在表模型下执行**​。 - - ## 3. 应用场景 应用场景主要包括两类： diff --git a/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_timecho.md b/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_timecho.md index 97e1ec59f..4bd0c054a 100644 --- a/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_timecho.md +++ b/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_timecho.md @@ -25,7 +25,7 @@ ## 1. 时序数据模型 -在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) +在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) ## 2. IoTDB 的树表孪生模型 diff --git a/src/zh/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md b/src/zh/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md index f965eb055..a8839b142 100644 --- a/src/zh/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md +++ b/src/zh/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md @@ -1,3 +1,6 @@ +--- +redirectTo: Navigating_Time_Series_Data_apache.html +--- -# 时序数据模型 - -## 1. 什么叫时序数据？ - -万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 - -![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) - - -通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 - -![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) - -传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 - -## 2. 时序数据中的关键概念有哪些？ - -时序数据中主要涉及的概念如下。 - -| **设备（Device）** | 也称为实体、装备等，是实际场景中拥有物理量的设备或装置。在 IoTDB 中，实体是管理一组时间序列的集合，可以是一个物理设备、测量装置、传感器集合等。如：能源场景：风机，由区域、场站、线路、机型、实例等标识工厂场景：机械臂，由物联网平台生成的唯一 ID 标识车联网场景：车辆，由车辆识别代码 VIN 标识监控场景：CPU，由机房、机架、Hostname、设备类型等标识 | -| ------------------------------- | ------------------------------------------------------------ | -| **测点（FIELD）** | 也称物理量、信号量、指标、点位、工况等，是在实际场景中检测装置记录的测量信息。通常一个物理量代表一个采集点位，能够定期采集所在环境的物理量。如：能源电力场景：电流、电压、风速、转速车联网场景：油量、车速、经度、维度工厂场景：温度、湿度 | -| **数据点（Data Point）** | 由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。如下图表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
| -| **采集频率（Frequency）** | 指物理量在一定时间内产生数据的次数。例如，一个温度传感器可能每秒钟采集一次温度数据，那么它的采集频率就是1Hz（赫兹），即每秒1次。 | -| **数据保存时间（TTL）** | TTL 指定表中数据的保存时间，超过 TTL 的数据将自动删除。IoTDB 支持对不同的表设定不同的数据存活时间，便于 IoTDB 定期、自动地删除一定时间之前的数据。合理使用 TTL 可以控制 IoTDB 占用的总磁盘空间，避免磁盘写满等异常，并维持较高的查询性能和减少内存资源占用。 | \ No newline at end of file +--> \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_apache.md b/src/zh/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_apache.md new file mode 100644 index 000000000..98464575d --- /dev/null +++ b/src/zh/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_apache.md @@ -0,0 +1,45 @@ + +# 时序数据模型 + +## 1. 什么叫时序数据？ + +万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 + +![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) + + +通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 + +![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) + +传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 + +## 2. 时序数据中的关键概念有哪些？ + +时序数据中主要涉及的概念如下。 + +| **设备（Device）** | 也称为实体、装备等，是实际场景中拥有物理量的设备或装置。在 IoTDB 中，实体是管理一组时间序列的集合，可以是一个物理设备、测量装置、传感器集合等。如：能源场景：风机，由区域、场站、线路、机型、实例等标识工厂场景：机械臂，由物联网平台生成的唯一 ID 标识车联网场景：车辆，由车辆识别代码 VIN 标识监控场景：CPU，由机房、机架、Hostname、设备类型等标识 | +| ------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **测点（FIELD）** | 也称物理量、信号量、指标、点位、工况等，是在实际场景中检测装置记录的测量信息。通常一个物理量代表一个采集点位，能够定期采集所在环境的物理量。如：能源电力场景：电流、电压、风速、转速车联网场景：油量、车速、经度、维度工厂场景：温度、湿度 | +| **数据点（Data Point）** | 由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。如下图表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
| +| **采集频率（Frequency）** | 指物理量在一定时间内产生数据的次数。例如，一个温度传感器可能每秒钟采集一次温度数据，那么它的采集频率就是1Hz（赫兹），即每秒1次。 | +| **数据保存时间（TTL）** | TTL 指定表中数据的保存时间，超过 TTL 的数据将自动删除。IoTDB 支持对不同的表设定不同的数据存活时间，便于 IoTDB 定期、自动地删除一定时间之前的数据。合理使用 TTL 可以控制 IoTDB 占用的总磁盘空间，避免磁盘写满等异常，并维持较高的查询性能和减少内存资源占用。 | \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md b/src/zh/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md new file mode 100644 index 000000000..0f7011af7 --- /dev/null +++ b/src/zh/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md @@ -0,0 +1,45 @@ + +# 时序数据模型 + +## 1. 什么叫时序数据？ + +万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 + +![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) + + +通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 + +![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) + +传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 + +## 2. 时序数据中的关键概念有哪些？ + +时序数据中主要涉及的概念如下。 + +| **设备（Device）** | 也称为实体、装备等，是实际场景中拥有物理量的设备或装置。在 IoTDB 中，实体是管理一组时间序列的集合，可以是一个物理设备、测量装置、传感器集合等。如：能源场景：风机，由区域、场站、线路、机型、实例等标识工厂场景：机械臂，由物联网平台生成的唯一 ID 标识车联网场景：车辆，由车辆识别代码 VIN 标识监控场景：CPU，由机房、机架、Hostname、设备类型等标识 | +| ------------------------------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **测点（FIELD）** | 也称物理量、信号量、指标、点位、工况等，是在实际场景中检测装置记录的测量信息。通常一个物理量代表一个采集点位，能够定期采集所在环境的物理量。如：能源电力场景：电流、电压、风速、转速车联网场景：油量、车速、经度、维度工厂场景：温度、湿度
_表模型下**测点数量**等于所有表的测点数之和（每张表的测点数 = device 数量 * field 的列数），具体统计方法可参考_[元数据查询](../Basic-Concept/Table-Management_timecho.md#_1-7-元数据查询) | +| **数据点（Data Point）** | 由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。如下图表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
| +| **采集频率（Frequency）** | 指物理量在一定时间内产生数据的次数。例如，一个温度传感器可能每秒钟采集一次温度数据，那么它的采集频率就是1Hz（赫兹），即每秒1次。 | +| **数据保存时间（TTL）** | TTL 指定表中数据的保存时间，超过 TTL 的数据将自动删除。IoTDB 支持对不同的表设定不同的数据存活时间，便于 IoTDB 定期、自动地删除一定时间之前的数据。合理使用 TTL 可以控制 IoTDB 占用的总磁盘空间，避免磁盘写满等异常，并维持较高的查询性能和减少内存资源占用。 | \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Table/Basic-Concept/Table-Management_apache.md b/src/zh/UserGuide/Master/Table/Basic-Concept/Table-Management_apache.md index 41d46a331..7d8a9a5b3 100644 --- a/src/zh/UserGuide/Master/Table/Basic-Concept/Table-Management_apache.md +++ b/src/zh/UserGuide/Master/Table/Basic-Concept/Table-Management_apache.md @@ -22,7 +22,7 @@ # 表管理 在开始使用表管理功能前，推荐您先了解以下相关预备知识，以便更好地理解和应用表管理功能： -* [时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md)：了解时序数据的基本概念与特点，帮助建立建模基础。 +* [时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_apache.md)：了解时序数据的基本概念与特点，帮助建立建模基础。 * [建模方案设计](../Background-knowledge/Data-Model-and-Terminology_apache.md)：掌握 IoTDB 时序模型及适用场景，为表管理提供设计基础。 ## 1. 表管理 diff --git a/src/zh/UserGuide/Master/Table/Basic-Concept/Table-Management_timecho.md b/src/zh/UserGuide/Master/Table/Basic-Concept/Table-Management_timecho.md index 2f6220131..776f72c75 100644 --- a/src/zh/UserGuide/Master/Table/Basic-Concept/Table-Management_timecho.md +++ b/src/zh/UserGuide/Master/Table/Basic-Concept/Table-Management_timecho.md @@ -22,7 +22,7 @@ # 表管理 在开始使用表管理功能前，推荐您先了解以下相关预备知识，以便更好地理解和应用表管理功能： -* [时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md)：了解时序数据的基本概念与特点，帮助建立建模基础。 +* [时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_timecho.md)：了解时序数据的基本概念与特点，帮助建立建模基础。 * [建模方案设计](../Background-knowledge/Data-Model-and-Terminology_timecho.md)：掌握 IoTDB 时序模型及适用场景，为表管理提供设计基础。 ## 1. 表管理 @@ -323,4 +323,46 @@ DROP TABLE (IF EXISTS)? ```SQL DROP TABLE table1; DROP TABLE database1.table1; -``` \ No newline at end of file +``` + + +### 1.7 元数据查询 + +表模型下**测点数量**等于所有表的测点数之和，目前单表测点数可通过公式：**单表测点数 = device 数量 × field 列的数量** 计算得出，后续会支持通过 SQL 语句直接查询表模型下测点数，敬请期待。 + +以[示例数据](../Reference/Sample-Data.md) 中的表 table1 为例。 + +在该示例组织架构中：共包含三个 tag 列（region 为区域，plant_id 为工厂，device_id 为机器）和四个 field 列（temperature 为温度，humidity 为湿度，status 为状态，arrival_time 为到达时间）。 + +device 的唯一标识由全部 tag 列组合而成，只要 region（区域）+ plant_id（工厂）+ device_id（机器）的组合不重复，就代表一个独立设备。 + +示例数据一共定义了 2 个区域，分别为：北京、上海。其中 + +* 北京区域：包含 1 个工厂，工厂编号 1001； + * 该工厂下共有 2 台设备，设备编号分别为 100、101； +* 上海区域：包含 2 个工厂，工厂编号分别为 3001、3002； + * 工厂 3001 下包含 2 台设备：100、101； + * 工厂 3002 下包含 2 台设备：100、101。 + +综上，整个表一共存在 6 组唯一 tag 组合，对应 6 个独立设备。 + +**单表测点数完整计算示例：** + +1. 查询 device 数量 + +```sql +IoTDB:database1> count devices from table1 ++--------------+ +|count(devices)| ++--------------+ +| 6| ++--------------+ +Total line number = 1 +It costs 0.019s +``` + +2. 计算单表测点数量 +- device 数量：6 +- field 列数：4 +- 单表测点总数：6 × 4 = 24 + diff --git a/src/zh/UserGuide/Master/Table/QuickStart/QuickStart_apache.md b/src/zh/UserGuide/Master/Table/QuickStart/QuickStart_apache.md index bc48bde3f..ed73269c0 100644 --- a/src/zh/UserGuide/Master/Table/QuickStart/QuickStart_apache.md +++ b/src/zh/UserGuide/Master/Table/QuickStart/QuickStart_apache.md @@ -45,7 +45,7 @@ 1. 数据库建模设计：数据库建模是创建数据库系统的重要步骤，它涉及到设计数据的结构和关系，以确保数据的组织方式能够满足特定应用的需求，下面的文档将会帮助您快速了解 IoTDB 的建模设计： - - 时序概念介绍：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) + - 时序概念介绍：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_apache.md) - 建模设计介绍：[建模方案设计](../Background-knowledge/Data-Model-and-Terminology_apache.md) diff --git a/src/zh/UserGuide/Master/Table/QuickStart/QuickStart_timecho.md b/src/zh/UserGuide/Master/Table/QuickStart/QuickStart_timecho.md index a0ab2e59c..89ef3eef6 100644 --- a/src/zh/UserGuide/Master/Table/QuickStart/QuickStart_timecho.md +++ b/src/zh/UserGuide/Master/Table/QuickStart/QuickStart_timecho.md @@ -52,7 +52,7 @@ 1. 数据库建模设计：数据库建模是创建数据库系统的重要步骤，它涉及到设计数据的结构和关系，以确保数据的组织方式能够满足特定应用的需求，下面的文档将会帮助您快速了解 IoTDB 的建模设计： - - 时序概念介绍：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) + - 时序概念介绍：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) - 建模设计介绍：[建模方案设计](../Background-knowledge/Data-Model-and-Terminology_timecho.md) diff --git a/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_apache.md b/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_apache.md index 92f3abacc..fb63d7fe8 100644 --- a/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_apache.md +++ b/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_apache.md @@ -25,7 +25,7 @@ ## 1. 时序数据模型 -在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) +在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_apache.md) ## 2. IoTDB 的树表孪生模型 diff --git a/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_timecho.md b/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_timecho.md index 541f0d39b..c56987874 100644 --- a/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_timecho.md +++ b/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology_timecho.md @@ -25,7 +25,7 @@ ## 1. 时序数据模型 -在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) +在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) ## 2. IoTDB 的树表孪生模型 diff --git a/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md b/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md index 9a32e217c..a8839b142 100644 --- a/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md +++ b/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md @@ -1,3 +1,6 @@ +--- +redirectTo: Navigating_Time_Series_Data_apache.html +--- -# 时序数据模型 - -## 1. 什么叫时序数据？ - -万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 - -![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) - - - -通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 - -![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) - -传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 - -## 2. 时序数据中的关键概念有哪些？ - -时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。 - -![](/img/%E7%99%BD%E6%9D%BF.png) - -### 2.1 数据点 - -- 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。 -- 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。 - -![](/img/%E6%95%B0%E6%8D%AE%E7%82%B9.png) - -### 2.2 测点 - -- 定义：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。 -- 又名：物理量、时间序列、时间线、信号量、指标、测量值等 -- 示例： - - 电力场景：电流、电压 - - 能源场景：风速、转速 - - 车联网场景：油量、车速、经度、维度 - - 工厂场景：温度、湿度 - -### 2.3 设备 - -- 定义：对应一个实际场景中的物理设备，通常是一组测点的集合，由一到多个标签定位标识 -- 示例 - - 车联网场景：车辆，由车辆识别代码 VIN 标识 - - 工厂场景：机械臂，由物联网平台生成的唯一 ID 标识 - - 能源场景：风机，由区域、场站、线路、机型、实例等标识 - - 监控场景：CPU，由机房、机架、Hostname、设备类型等标识 \ No newline at end of file +--> \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_apache.md b/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_apache.md new file mode 100644 index 000000000..457306372 --- /dev/null +++ b/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_apache.md @@ -0,0 +1,68 @@ + +# 时序数据模型 + +## 1. 什么叫时序数据？ + +万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 + +![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) + + + +通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 + +![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) + +传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 + +## 2. 时序数据中的关键概念有哪些？ + +时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。 + +![](/img/%E7%99%BD%E6%9D%BF.png) + +### 2.1 数据点 + +- 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。 +- 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。 + +![](/img/%E6%95%B0%E6%8D%AE%E7%82%B9.png) + +### 2.2 测点 + +- 定义：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。 +- 又名：物理量、时间序列、时间线、信号量、指标、测量值等 +- 示例： + - 电力场景：电流、电压 + - 能源场景：风速、转速 + - 车联网场景：油量、车速、经度、维度 + - 工厂场景：温度、湿度 + + +### 2.3 设备 + +- 定义：对应一个实际场景中的物理设备，通常是一组测点的集合，由一到多个标签定位标识 +- 示例 + - 车联网场景：车辆，由车辆识别代码 VIN 标识 + - 工厂场景：机械臂，由物联网平台生成的唯一 ID 标识 + - 能源场景：风机，由区域、场站、线路、机型、实例等标识 + - 监控场景：CPU，由机房、机架、Hostname、设备类型等标识 \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_timecho.md b/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_timecho.md new file mode 100644 index 000000000..f537b0e63 --- /dev/null +++ b/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data_timecho.md @@ -0,0 +1,70 @@ + +# 时序数据模型 + +## 1. 什么叫时序数据？ + +万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 + +![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) + + + +通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 + +![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) + +传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 + +## 2. 时序数据中的关键概念有哪些？ + +时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。 + +![](/img/%E7%99%BD%E6%9D%BF.png) + +### 2.1 数据点 + +- 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。 +- 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。 + +![](/img/%E6%95%B0%E6%8D%AE%E7%82%B9.png) + +### 2.2 测点 + +- 定义：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。 +- 又名：物理量、时间序列、时间线、信号量、指标、测量值等 +- 示例： + - 电力场景：电流、电压 + - 能源场景：风速、转速 + - 车联网场景：油量、车速、经度、维度 + - 工厂场景：温度、湿度 + +- _树模型下**测点数量**等于整个路径模式下叶子节点的数量，具体统计方法可参考_[统计时间序列总数](../Basic-Concept/Operate-Metadata_timecho.md#_2-7-统计时间序列总数) + + +### 2.3 设备 + +- 定义：对应一个实际场景中的物理设备，通常是一组测点的集合，由一到多个标签定位标识 +- 示例 + - 车联网场景：车辆，由车辆识别代码 VIN 标识 + - 工厂场景：机械臂，由物联网平台生成的唯一 ID 标识 + - 能源场景：风机，由区域、场站、线路、机型、实例等标识 + - 监控场景：CPU，由机房、机架、Hostname、设备类型等标识 \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Tree/QuickStart/QuickStart_apache.md b/src/zh/UserGuide/Master/Tree/QuickStart/QuickStart_apache.md index 70048929a..4f2d8b947 100644 --- a/src/zh/UserGuide/Master/Tree/QuickStart/QuickStart_apache.md +++ b/src/zh/UserGuide/Master/Tree/QuickStart/QuickStart_apache.md @@ -45,7 +45,7 @@ 1. 数据库建模设计：数据库建模是创建数据库系统的重要步骤，它涉及到设计数据的结构和关系，以确保数据的组织方式能够满足特定应用的需求，下面的文档将会帮助您快速了解 IoTDB 的建模设计： - - 时序概念介绍：[走进时序数据](../Background-knowledge/Navigating_Time_Series_Data.md) + - 时序概念介绍：[走进时序数据](../Background-knowledge/Navigating_Time_Series_Data_apache.md) - 建模设计介绍：[数据模型介绍](../Background-knowledge/Data-Model-and-Terminology_apache.md) diff --git a/src/zh/UserGuide/Master/Tree/QuickStart/QuickStart_timecho.md b/src/zh/UserGuide/Master/Tree/QuickStart/QuickStart_timecho.md index bbae53593..8445cc339 100644 --- a/src/zh/UserGuide/Master/Tree/QuickStart/QuickStart_timecho.md +++ b/src/zh/UserGuide/Master/Tree/QuickStart/QuickStart_timecho.md @@ -53,7 +53,7 @@ 1. 数据库建模设计：数据库建模是创建数据库系统的重要步骤，它涉及到设计数据的结构和关系，以确保数据的组织方式能够满足特定应用的需求，下面的文档将会帮助您快速了解 IoTDB 的建模设计： - - 时序概念介绍：[走进时序数据](../Background-knowledge/Navigating_Time_Series_Data.md) + - 时序概念介绍：[走进时序数据](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) - 建模设计介绍：[数据模型介绍](../Background-knowledge/Data-Model-and-Terminology_timecho.md) diff --git a/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_apache.md b/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_apache.md index c2e14d462..c951dcf63 100644 --- a/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_apache.md +++ b/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_apache.md @@ -25,7 +25,7 @@ ## 1. 时序数据模型 -在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) +在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_apache.md) ## 2. IoTDB 的树表孪生模型 diff --git a/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_timecho.md b/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_timecho.md index 97e1ec59f..4bd0c054a 100644 --- a/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_timecho.md +++ b/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology_timecho.md @@ -25,7 +25,7 @@ ## 1. 时序数据模型 -在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) +在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) ## 2. IoTDB 的树表孪生模型 diff --git a/src/zh/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md b/src/zh/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md index f965eb055..a8839b142 100644 --- a/src/zh/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md +++ b/src/zh/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data.md @@ -1,3 +1,6 @@ +--- +redirectTo: Navigating_Time_Series_Data_apache.html +--- -# 时序数据模型 - -## 1. 什么叫时序数据？ - -万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 - -![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) - - -通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 - -![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) - -传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 - -## 2. 时序数据中的关键概念有哪些？ - -时序数据中主要涉及的概念如下。 - -| **设备（Device）** | 也称为实体、装备等，是实际场景中拥有物理量的设备或装置。在 IoTDB 中，实体是管理一组时间序列的集合，可以是一个物理设备、测量装置、传感器集合等。如：能源场景：风机，由区域、场站、线路、机型、实例等标识工厂场景：机械臂，由物联网平台生成的唯一 ID 标识车联网场景：车辆，由车辆识别代码 VIN 标识监控场景：CPU，由机房、机架、Hostname、设备类型等标识 | -| ------------------------------- | ------------------------------------------------------------ | -| **测点（FIELD）** | 也称物理量、信号量、指标、点位、工况等，是在实际场景中检测装置记录的测量信息。通常一个物理量代表一个采集点位，能够定期采集所在环境的物理量。如：能源电力场景：电流、电压、风速、转速车联网场景：油量、车速、经度、维度工厂场景：温度、湿度 | -| **数据点（Data Point）** | 由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。如下图表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
| -| **采集频率（Frequency）** | 指物理量在一定时间内产生数据的次数。例如，一个温度传感器可能每秒钟采集一次温度数据，那么它的采集频率就是1Hz（赫兹），即每秒1次。 | -| **数据保存时间（TTL）** | TTL 指定表中数据的保存时间，超过 TTL 的数据将自动删除。IoTDB 支持对不同的表设定不同的数据存活时间，便于 IoTDB 定期、自动地删除一定时间之前的数据。合理使用 TTL 可以控制 IoTDB 占用的总磁盘空间，避免磁盘写满等异常，并维持较高的查询性能和减少内存资源占用。 | \ No newline at end of file +--> \ No newline at end of file diff --git a/src/zh/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_apache.md b/src/zh/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_apache.md new file mode 100644 index 000000000..98464575d --- /dev/null +++ b/src/zh/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_apache.md @@ -0,0 +1,45 @@ + +# 时序数据模型 + +## 1. 什么叫时序数据？ + +万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 + +![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) + + +通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 + +![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) + +传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 + +## 2. 时序数据中的关键概念有哪些？ + +时序数据中主要涉及的概念如下。 + +| **设备（Device）** | 也称为实体、装备等，是实际场景中拥有物理量的设备或装置。在 IoTDB 中，实体是管理一组时间序列的集合，可以是一个物理设备、测量装置、传感器集合等。如：能源场景：风机，由区域、场站、线路、机型、实例等标识工厂场景：机械臂，由物联网平台生成的唯一 ID 标识车联网场景：车辆，由车辆识别代码 VIN 标识监控场景：CPU，由机房、机架、Hostname、设备类型等标识 | +| ------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **测点（FIELD）** | 也称物理量、信号量、指标、点位、工况等，是在实际场景中检测装置记录的测量信息。通常一个物理量代表一个采集点位，能够定期采集所在环境的物理量。如：能源电力场景：电流、电压、风速、转速车联网场景：油量、车速、经度、维度工厂场景：温度、湿度 | +| **数据点（Data Point）** | 由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。如下图表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
| +| **采集频率（Frequency）** | 指物理量在一定时间内产生数据的次数。例如，一个温度传感器可能每秒钟采集一次温度数据，那么它的采集频率就是1Hz（赫兹），即每秒1次。 | +| **数据保存时间（TTL）** | TTL 指定表中数据的保存时间，超过 TTL 的数据将自动删除。IoTDB 支持对不同的表设定不同的数据存活时间，便于 IoTDB 定期、自动地删除一定时间之前的数据。合理使用 TTL 可以控制 IoTDB 占用的总磁盘空间，避免磁盘写满等异常，并维持较高的查询性能和减少内存资源占用。 | \ No newline at end of file diff --git a/src/zh/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md b/src/zh/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md new file mode 100644 index 000000000..0f7011af7 --- /dev/null +++ b/src/zh/UserGuide/latest-Table/Background-knowledge/Navigating_Time_Series_Data_timecho.md @@ -0,0 +1,45 @@ + +# 时序数据模型 + +## 1. 什么叫时序数据？ + +万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 + +![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) + + +通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 + +![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) + +传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 + +## 2. 时序数据中的关键概念有哪些？ + +时序数据中主要涉及的概念如下。 + +| **设备（Device）** | 也称为实体、装备等，是实际场景中拥有物理量的设备或装置。在 IoTDB 中，实体是管理一组时间序列的集合，可以是一个物理设备、测量装置、传感器集合等。如：能源场景：风机，由区域、场站、线路、机型、实例等标识工厂场景：机械臂，由物联网平台生成的唯一 ID 标识车联网场景：车辆，由车辆识别代码 VIN 标识监控场景：CPU，由机房、机架、Hostname、设备类型等标识 | +| ------------------------------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **测点（FIELD）** | 也称物理量、信号量、指标、点位、工况等，是在实际场景中检测装置记录的测量信息。通常一个物理量代表一个采集点位，能够定期采集所在环境的物理量。如：能源电力场景：电流、电压、风速、转速车联网场景：油量、车速、经度、维度工厂场景：温度、湿度
_表模型下**测点数量**等于所有表的测点数之和（每张表的测点数 = device 数量 * field 的列数），具体统计方法可参考_[元数据查询](../Basic-Concept/Table-Management_timecho.md#_1-7-元数据查询) | +| **数据点（Data Point）** | 由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。如下图表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
| +| **采集频率（Frequency）** | 指物理量在一定时间内产生数据的次数。例如，一个温度传感器可能每秒钟采集一次温度数据，那么它的采集频率就是1Hz（赫兹），即每秒1次。 | +| **数据保存时间（TTL）** | TTL 指定表中数据的保存时间，超过 TTL 的数据将自动删除。IoTDB 支持对不同的表设定不同的数据存活时间，便于 IoTDB 定期、自动地删除一定时间之前的数据。合理使用 TTL 可以控制 IoTDB 占用的总磁盘空间，避免磁盘写满等异常，并维持较高的查询性能和减少内存资源占用。 | \ No newline at end of file diff --git a/src/zh/UserGuide/latest-Table/Basic-Concept/Table-Management_apache.md b/src/zh/UserGuide/latest-Table/Basic-Concept/Table-Management_apache.md index 41d46a331..7d8a9a5b3 100644 --- a/src/zh/UserGuide/latest-Table/Basic-Concept/Table-Management_apache.md +++ b/src/zh/UserGuide/latest-Table/Basic-Concept/Table-Management_apache.md @@ -22,7 +22,7 @@ # 表管理 在开始使用表管理功能前，推荐您先了解以下相关预备知识，以便更好地理解和应用表管理功能： -* [时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md)：了解时序数据的基本概念与特点，帮助建立建模基础。 +* [时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_apache.md)：了解时序数据的基本概念与特点，帮助建立建模基础。 * [建模方案设计](../Background-knowledge/Data-Model-and-Terminology_apache.md)：掌握 IoTDB 时序模型及适用场景，为表管理提供设计基础。 ## 1. 表管理 diff --git a/src/zh/UserGuide/latest-Table/Basic-Concept/Table-Management_timecho.md b/src/zh/UserGuide/latest-Table/Basic-Concept/Table-Management_timecho.md index 2f6220131..776f72c75 100644 --- a/src/zh/UserGuide/latest-Table/Basic-Concept/Table-Management_timecho.md +++ b/src/zh/UserGuide/latest-Table/Basic-Concept/Table-Management_timecho.md @@ -22,7 +22,7 @@ # 表管理 在开始使用表管理功能前，推荐您先了解以下相关预备知识，以便更好地理解和应用表管理功能： -* [时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md)：了解时序数据的基本概念与特点，帮助建立建模基础。 +* [时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_timecho.md)：了解时序数据的基本概念与特点，帮助建立建模基础。 * [建模方案设计](../Background-knowledge/Data-Model-and-Terminology_timecho.md)：掌握 IoTDB 时序模型及适用场景，为表管理提供设计基础。 ## 1. 表管理 @@ -323,4 +323,46 @@ DROP TABLE (IF EXISTS)? ```SQL DROP TABLE table1; DROP TABLE database1.table1; -``` \ No newline at end of file +``` + + +### 1.7 元数据查询 + +表模型下**测点数量**等于所有表的测点数之和，目前单表测点数可通过公式：**单表测点数 = device 数量 × field 列的数量** 计算得出，后续会支持通过 SQL 语句直接查询表模型下测点数，敬请期待。 + +以[示例数据](../Reference/Sample-Data.md) 中的表 table1 为例。 + +在该示例组织架构中：共包含三个 tag 列（region 为区域，plant_id 为工厂，device_id 为机器）和四个 field 列（temperature 为温度，humidity 为湿度，status 为状态，arrival_time 为到达时间）。 + +device 的唯一标识由全部 tag 列组合而成，只要 region（区域）+ plant_id（工厂）+ device_id（机器）的组合不重复，就代表一个独立设备。 + +示例数据一共定义了 2 个区域，分别为：北京、上海。其中 + +* 北京区域：包含 1 个工厂，工厂编号 1001； + * 该工厂下共有 2 台设备，设备编号分别为 100、101； +* 上海区域：包含 2 个工厂，工厂编号分别为 3001、3002； + * 工厂 3001 下包含 2 台设备：100、101； + * 工厂 3002 下包含 2 台设备：100、101。 + +综上，整个表一共存在 6 组唯一 tag 组合，对应 6 个独立设备。 + +**单表测点数完整计算示例：** + +1. 查询 device 数量 + +```sql +IoTDB:database1> count devices from table1 ++--------------+ +|count(devices)| ++--------------+ +| 6| ++--------------+ +Total line number = 1 +It costs 0.019s +``` + +2. 计算单表测点数量 +- device 数量：6 +- field 列数：4 +- 单表测点总数：6 × 4 = 24 + diff --git a/src/zh/UserGuide/latest-Table/QuickStart/QuickStart_apache.md b/src/zh/UserGuide/latest-Table/QuickStart/QuickStart_apache.md index bc48bde3f..ed73269c0 100644 --- a/src/zh/UserGuide/latest-Table/QuickStart/QuickStart_apache.md +++ b/src/zh/UserGuide/latest-Table/QuickStart/QuickStart_apache.md @@ -45,7 +45,7 @@ 1. 数据库建模设计：数据库建模是创建数据库系统的重要步骤，它涉及到设计数据的结构和关系，以确保数据的组织方式能够满足特定应用的需求，下面的文档将会帮助您快速了解 IoTDB 的建模设计： - - 时序概念介绍：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) + - 时序概念介绍：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_apache.md) - 建模设计介绍：[建模方案设计](../Background-knowledge/Data-Model-and-Terminology_apache.md) diff --git a/src/zh/UserGuide/latest-Table/QuickStart/QuickStart_timecho.md b/src/zh/UserGuide/latest-Table/QuickStart/QuickStart_timecho.md index a0ab2e59c..89ef3eef6 100644 --- a/src/zh/UserGuide/latest-Table/QuickStart/QuickStart_timecho.md +++ b/src/zh/UserGuide/latest-Table/QuickStart/QuickStart_timecho.md @@ -52,7 +52,7 @@ 1. 数据库建模设计：数据库建模是创建数据库系统的重要步骤，它涉及到设计数据的结构和关系，以确保数据的组织方式能够满足特定应用的需求，下面的文档将会帮助您快速了解 IoTDB 的建模设计： - - 时序概念介绍：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) + - 时序概念介绍：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) - 建模设计介绍：[建模方案设计](../Background-knowledge/Data-Model-and-Terminology_timecho.md) diff --git a/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_apache.md b/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_apache.md index 92f3abacc..fb63d7fe8 100644 --- a/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_apache.md +++ b/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_apache.md @@ -25,7 +25,7 @@ ## 1. 时序数据模型 -在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) +在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_apache.md) ## 2. IoTDB 的树表孪生模型 diff --git a/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_timecho.md b/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_timecho.md index 541f0d39b..c56987874 100644 --- a/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_timecho.md +++ b/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology_timecho.md @@ -25,7 +25,7 @@ ## 1. 时序数据模型 -在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data.md) +在构建IoTDB建模方案前，需要先了解时序数据和时序数据模型，详细内容见此页面：[时序数据模型](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) ## 2. IoTDB 的树表孪生模型 diff --git a/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md b/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md index 9a32e217c..a8839b142 100644 --- a/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md +++ b/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md @@ -1,3 +1,6 @@ +--- +redirectTo: Navigating_Time_Series_Data_apache.html +--- -# 时序数据模型 - -## 1. 什么叫时序数据？ - -万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 - -![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) - - - -通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 - -![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) - -传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 - -## 2. 时序数据中的关键概念有哪些？ - -时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。 - -![](/img/%E7%99%BD%E6%9D%BF.png) - -### 2.1 数据点 - -- 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。 -- 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。 - -![](/img/%E6%95%B0%E6%8D%AE%E7%82%B9.png) - -### 2.2 测点 - -- 定义：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。 -- 又名：物理量、时间序列、时间线、信号量、指标、测量值等 -- 示例： - - 电力场景：电流、电压 - - 能源场景：风速、转速 - - 车联网场景：油量、车速、经度、维度 - - 工厂场景：温度、湿度 - -### 2.3 设备 - -- 定义：对应一个实际场景中的物理设备，通常是一组测点的集合，由一到多个标签定位标识 -- 示例 - - 车联网场景：车辆，由车辆识别代码 VIN 标识 - - 工厂场景：机械臂，由物联网平台生成的唯一 ID 标识 - - 能源场景：风机，由区域、场站、线路、机型、实例等标识 - - 监控场景：CPU，由机房、机架、Hostname、设备类型等标识 \ No newline at end of file +--> \ No newline at end of file diff --git a/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_apache.md b/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_apache.md new file mode 100644 index 000000000..457306372 --- /dev/null +++ b/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_apache.md @@ -0,0 +1,68 @@ + +# 时序数据模型 + +## 1. 什么叫时序数据？ + +万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 + +![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) + + + +通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 + +![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) + +传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 + +## 2. 时序数据中的关键概念有哪些？ + +时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。 + +![](/img/%E7%99%BD%E6%9D%BF.png) + +### 2.1 数据点 + +- 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。 +- 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。 + +![](/img/%E6%95%B0%E6%8D%AE%E7%82%B9.png) + +### 2.2 测点 + +- 定义：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。 +- 又名：物理量、时间序列、时间线、信号量、指标、测量值等 +- 示例： + - 电力场景：电流、电压 + - 能源场景：风速、转速 + - 车联网场景：油量、车速、经度、维度 + - 工厂场景：温度、湿度 + + +### 2.3 设备 + +- 定义：对应一个实际场景中的物理设备，通常是一组测点的集合，由一到多个标签定位标识 +- 示例 + - 车联网场景：车辆，由车辆识别代码 VIN 标识 + - 工厂场景：机械臂，由物联网平台生成的唯一 ID 标识 + - 能源场景：风机，由区域、场站、线路、机型、实例等标识 + - 监控场景：CPU，由机房、机架、Hostname、设备类型等标识 \ No newline at end of file diff --git a/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_timecho.md b/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_timecho.md new file mode 100644 index 000000000..f537b0e63 --- /dev/null +++ b/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data_timecho.md @@ -0,0 +1,70 @@ + +# 时序数据模型 + +## 1. 什么叫时序数据？ + +万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。 + +![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png) + + + +通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。 + +![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png) + +传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。 + +## 2. 时序数据中的关键概念有哪些？ + +时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。 + +![](/img/%E7%99%BD%E6%9D%BF.png) + +### 2.1 数据点 + +- 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。 +- 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。 + +![](/img/%E6%95%B0%E6%8D%AE%E7%82%B9.png) + +### 2.2 测点 + +- 定义：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。 +- 又名：物理量、时间序列、时间线、信号量、指标、测量值等 +- 示例： + - 电力场景：电流、电压 + - 能源场景：风速、转速 + - 车联网场景：油量、车速、经度、维度 + - 工厂场景：温度、湿度 + +- _树模型下**测点数量**等于整个路径模式下叶子节点的数量，具体统计方法可参考_[统计时间序列总数](../Basic-Concept/Operate-Metadata_timecho.md#_2-7-统计时间序列总数) + + +### 2.3 设备 + +- 定义：对应一个实际场景中的物理设备，通常是一组测点的集合，由一到多个标签定位标识 +- 示例 + - 车联网场景：车辆，由车辆识别代码 VIN 标识 + - 工厂场景：机械臂，由物联网平台生成的唯一 ID 标识 + - 能源场景：风机，由区域、场站、线路、机型、实例等标识 + - 监控场景：CPU，由机房、机架、Hostname、设备类型等标识 \ No newline at end of file diff --git a/src/zh/UserGuide/latest/QuickStart/QuickStart_apache.md b/src/zh/UserGuide/latest/QuickStart/QuickStart_apache.md index 70048929a..4f2d8b947 100644 --- a/src/zh/UserGuide/latest/QuickStart/QuickStart_apache.md +++ b/src/zh/UserGuide/latest/QuickStart/QuickStart_apache.md @@ -45,7 +45,7 @@ 1. 数据库建模设计：数据库建模是创建数据库系统的重要步骤，它涉及到设计数据的结构和关系，以确保数据的组织方式能够满足特定应用的需求，下面的文档将会帮助您快速了解 IoTDB 的建模设计： - - 时序概念介绍：[走进时序数据](../Background-knowledge/Navigating_Time_Series_Data.md) + - 时序概念介绍：[走进时序数据](../Background-knowledge/Navigating_Time_Series_Data_apache.md) - 建模设计介绍：[数据模型介绍](../Background-knowledge/Data-Model-and-Terminology_apache.md) diff --git a/src/zh/UserGuide/latest/QuickStart/QuickStart_timecho.md b/src/zh/UserGuide/latest/QuickStart/QuickStart_timecho.md index bbae53593..8445cc339 100644 --- a/src/zh/UserGuide/latest/QuickStart/QuickStart_timecho.md +++ b/src/zh/UserGuide/latest/QuickStart/QuickStart_timecho.md @@ -53,7 +53,7 @@ 1. 数据库建模设计：数据库建模是创建数据库系统的重要步骤，它涉及到设计数据的结构和关系，以确保数据的组织方式能够满足特定应用的需求，下面的文档将会帮助您快速了解 IoTDB 的建模设计： - - 时序概念介绍：[走进时序数据](../Background-knowledge/Navigating_Time_Series_Data.md) + - 时序概念介绍：[走进时序数据](../Background-knowledge/Navigating_Time_Series_Data_timecho.md) - 建模设计介绍：[数据模型介绍](../Background-knowledge/Data-Model-and-Terminology_timecho.md)