@@ -15,7 +15,8 @@ One of the modules of TDengine is the time-series database. However, in addition
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@@ -15,7 +15,8 @@ One of the modules of TDengine is the time-series database. However, in addition
With TDengine, the total cost of ownership of typical IoT, Internet of Vehicles, and Industrial Internet Big Data platforms can be greatly reduced. However, since it makes full use of the characteristics of IoT time-series data, TDengine cannot be used to process general data from web crawlers, microblogs, WeChat, e-commerce, ERP, CRM, and other sources.
With TDengine, the total cost of ownership of typical IoT, Internet of Vehicles, and Industrial Internet Big Data platforms can be greatly reduced. However, since it makes full use of the characteristics of IoT time-series data, TDengine cannot be used to process general data from web crawlers, microblogs, WeChat, e-commerce, ERP, CRM, and other sources.
A complete TDengine system runs on one or more physical nodes. Logically, it includes data node (dnode), TDEngine application driver (TAOSC) and application (app). There are one or more data nodes in the system, which form a cluster. The application interacts with the TDengine cluster through TAOSC's API. The following is a brief introduction to each logical unit.
A complete TDengine system runs on one or more physical nodes. Logically, it includes data node (dnode), TDEngine application driver (TAOSC) and application (app). There are one or more data nodes in the system, which form a cluster. The application interacts with the TDengine cluster through TAOSC's API. The following is a brief introduction to each logical unit.
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@@ -197,7 +197,7 @@ A complete TDengine system runs on one or more physical nodes. Logically, it inc
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@@ -197,7 +197,7 @@ A complete TDengine system runs on one or more physical nodes. Logically, it inc
To explain the relationship between vnode, mnode, TAOSC and application and their respective roles, the following is an analysis of a typical data writing process.
To explain the relationship between vnode, mnode, TAOSC and application and their respective roles, the following is an analysis of a typical data writing process.
<center> Figure 2: Typical process of TDengine </center>
<center> Figure 2: Typical process of TDengine </center>
1. Application initiates a request to insert data through JDBC, ODBC, or other APIs.
1. Application initiates a request to insert data through JDBC, ODBC, or other APIs.
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@@ -266,7 +266,7 @@ If a database has N replicas, thus a virtual node group has N virtual nodes, but
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@@ -266,7 +266,7 @@ If a database has N replicas, thus a virtual node group has N virtual nodes, but
<center> Figure 4: TDengine Slave Writing Process </center>
<center> Figure 4: TDengine Slave Writing Process </center>
1. Slave vnode receives a data insertion request forwarded by Master vnode;
1. Slave vnode receives a data insertion request forwarded by Master vnode;
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@@ -412,7 +412,7 @@ For the data collected by device D1001, the number of records per hour is counte
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@@ -412,7 +412,7 @@ For the data collected by device D1001, the number of records per hour is counte
TDengine creates a separate table for each data collection point, but in practical applications, it is often necessary to aggregate data from different data collection points. In order to perform aggregation operations efficiently, TDengine introduces the concept of STable. STable is used to represent a specific type of data collection point. It is a table set containing multiple tables. The schema of each table in the set is the same, but each table has its own static tag. The tags can be multiple and be added, deleted and modified at any time. Applications can aggregate or statistically operate all or a subset of tables under a STABLE by specifying tag filters, thus greatly simplifying the development of applications. The process is shown in the following figure:
TDengine creates a separate table for each data collection point, but in practical applications, it is often necessary to aggregate data from different data collection points. In order to perform aggregation operations efficiently, TDengine introduces the concept of STable. STable is used to represent a specific type of data collection point. It is a table set containing multiple tables. The schema of each table in the set is the same, but each table has its own static tag. The tags can be multiple and be added, deleted and modified at any time. Applications can aggregate or statistically operate all or a subset of tables under a STABLE by specifying tag filters, thus greatly simplifying the development of applications. The process is shown in the following figure:
<center> Figure 5: Diagram of multi-table aggregation query </center>
<center> Figure 5: Diagram of multi-table aggregation query </center>
The taos-jdbcdriver is implemented in two forms: JDBC-JNI and JDBC-RESTful (supported from taos-jdbcdriver-2.0.18). JDBC-JNI is implemented by calling the local methods of libtaos.so (or taos.dll) on the client, while JDBC-RESTful encapsulates the RESTful interface implementation internally.
The taos-jdbcdriver is implemented in two forms: JDBC-JNI and JDBC-RESTful (supported from taos-jdbcdriver-2.0.18). JDBC-JNI is implemented by calling the local methods of libtaos.so (or taos.dll) on the client, while JDBC-RESTful encapsulates the RESTful interface implementation internally.
TDengine provides many connectors for development, including C/C++, JAVA, Python, RESTful, Go, Node.JS, etc.
TDengine provides many connectors for development, including C/C++, JAVA, Python, RESTful, Go, Node.JS, etc.
At present, TDengine connectors support a wide range of platforms, including hardware platforms such as X64/X86/ARM64/ARM32/MIPS/Alpha, and development environments such as Linux/Win64/Win32. The comparison matrix is as follows:
At present, TDengine connectors support a wide range of platforms, including hardware platforms such as X64/X86/ARM64/ARM32/MIPS/Alpha, and development environments such as Linux/Win64/Win32. The comparison matrix is as follows:
You can log in the Grafana server (username/password:admin/admin) through localhost:3000, and add data sources through `Configuration -> Data Sources` on the left panel, as shown in the following figure:
You can log in the Grafana server (username/password:admin/admin) through localhost:3000, and add data sources through `Configuration -> Data Sources` on the left panel, as shown in the following figure:
- Host: IP address of any server in TDengine cluster and port number of TDengine RESTful interface (6041), default [http://localhost:6041](http://localhost:6041/)
- Host: IP address of any server in TDengine cluster and port number of TDengine RESTful interface (6041), default [http://localhost:6041](http://localhost:6041/)
- User: TDengine username.
- User: TDengine username.
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@@ -42,13 +42,13 @@ Enter the data source configuration page and modify the corresponding configurat
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@@ -42,13 +42,13 @@ Enter the data source configuration page and modify the corresponding configurat
Click `Save & Test` to test. Success will be prompted as follows:
Click `Save & Test` to test. Success will be prompted as follows:
As shown in the figure above, select the TDengine data source in Query, and enter the corresponding sql in the query box below to query. Details are as follows:
As shown in the figure above, select the TDengine data source in Query, and enter the corresponding sql in the query box below to query. Details are as follows:
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@@ -58,7 +58,7 @@ As shown in the figure above, select the TDengine data source in Query, and ente
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@@ -58,7 +58,7 @@ As shown in the figure above, select the TDengine data source in Query, and ente
According to the default prompt, query the average system memory usage at the specified interval of the server where the current TDengine deployed in as follows:
According to the default prompt, query the average system memory usage at the specified interval of the server where the current TDengine deployed in as follows:
> Please refer to Grafana [documents](https://grafana.com/docs/) for how to use Grafana to create the corresponding monitoring interface and for more about Grafana usage.
> Please refer to Grafana [documents](https://grafana.com/docs/) for how to use Grafana to create the corresponding monitoring interface and for more about Grafana usage.
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@@ -68,11 +68,11 @@ A `tdengine-grafana.json` importable dashboard is provided under the Grafana plu
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@@ -68,11 +68,11 @@ A `tdengine-grafana.json` importable dashboard is provided under the Grafana plu
Click the `Import` button on the left panel and upload the `tdengine-grafana.json` file:
Click the `Import` button on the left panel and upload the `tdengine-grafana.json` file:
tdSql.execute("create table if not exists tb1 using stb(t0,t1,t2,t3,t4,t5,t6,t7,t8) tags (1,127,32767,2147483647,9223372036854775807,11.123450279,22.123456789,'aaa','aaa')")
tdSql.execute("insert into tb1 (ts,value) values (1600000000000, \"aaa\")")
