Method and terminal for analyzing time series data through Dores data warehouse
By configuring timing database connections and data conversion in the Doris data warehouse, the problem of excessive data warehouse resource occupation and low frequency of timing database usage is solved, efficient timing data analysis is achieved, and the operation process is simplified.
Patent Information
- Application Number
- CN202411281351.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-23
- Publication Date
- 2025-02-07
AI Technical Summary
In the prior art, data warehouses are used to analyze all business needs, including downsampling analysis that timing databases are good at, resulting in excessive data warehouse resource usage, and the frequency of timing databases is not high and inefficient.
By performing connection configuration and data conversion configuration of the timing database in the Doris data warehouse front-end module directory, an interactive connection between the Doris data warehouse and the timing database is established, so that Doris can query the IoT timing data in the timing database and use the calculation of the timing database The engine performs analysis.
This enables technicians to call the timing database by simply operating the Doris data warehouse to perform downsampling analysis, which improves the efficiency of data storage analysis, reduces the difficulty of Doris resource occupancy and writing complex SQL.
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Figure CN119396895A_ABST
Abstract
Description
[0001] This case is a divisional application based on the invention patent with application date of July 23, 2024, application number 202410988360.0, and name “A method and terminal for time series data analysis” as the parent case. Technical Field
[0002] The present invention relates to the technical field of data management and analysis, and in particular to a method and a terminal for performing time series data analysis through a Doris data warehouse. Background Art
[0003] In the new energy industry, IoT time series data reported by many devices are received every moment. For example, charging pile equipment will continuously report IoT data such as current, voltage, and power.
[0004] Based on business needs, these IoT data need to be stored and analyzed. At present, data storage and analysis are generally divided into two dimensions. One dimension is the storage and analysis of time series data. For storage, we use the time series database TDengine, which mainly displays the real-time data reported by the device and retrieves the downsampled data in the set time period; the other dimension is the storage and analysis of the data warehouse. For storage, we use the Doris data warehouse based on the OLAP engine to realize the analysis of real-time data and offline data to meet the needs of reports and monitoring large screens.
[0005] In the current production environment, the focus of demand analysis is mostly on reports and analysis monitoring screens, resulting in a heavy workload for data warehouse analysis. This has led to many data analysis technicians using data warehouses to analyze all business needs for convenience, including downsampling analysis, which time series databases are good at. This has resulted in data warehouses occupying more resources, while data databases that are more suitable for time series data analysis are not used frequently.
[0006] Therefore, how to improve the efficiency of data storage and analysis while ensuring ease of use. Summary of the invention
[0007] The technical problem to be solved by the present invention is: to provide a method and a terminal for time series data analysis through a Doris data warehouse, which improves the efficiency of data storage and analysis while satisfying the convenience of use.
[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:
[0009] A method for analyzing time series data, comprising the steps of:
[0010] S1. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database;
[0011] S2. Query the IoT time series data in the time series database through the Doris data warehouse, and analyze the time series data through the computing engine of the time series database to obtain analysis result data;
[0012] S3. Write the analysis result data back to the Doris data warehouse.
[0013] A method for analyzing time series data using a Doris data warehouse comprises the following steps:
[0014] S1. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database;
[0015] Step S1 includes the steps of:
[0016] S11. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database;
[0017] The connection configuration includes adding a time series database type in the JdbcResource class, and adding formatting parsing for JDBC in the JdbcTable class and the OdbcTable class;
[0018] S12. When the Doris data warehouse is started, it scans and reads the time series database native startup package that has been copied to the dependency package directory in advance, and specifies the time series database type when creating the catalog;
[0019] S13. Doris data warehouse matches JdbcTable and OdbcTable downward according to the reconstructed JdbcResource, creates a connection with the time series database, and performs data interaction based on the data conversion configuration;
[0020] S2. Query the IoT time series data in the time series database through the Doris data warehouse, and analyze the time series data through the computing engine of the time series database to obtain analysis result data;
[0021] S3. Write the analysis result data back to the native table of the Doris data warehouse through Doris's cross-catalog insert.
[0022] In order to solve the above technical problems, another technical solution adopted by the present invention is:
[0023] A terminal for time series data analysis includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:
[0024] S1. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database;
[0025] S2. Query the IoT time series data in the time series database through the Doris data warehouse, and analyze the time series data through the computing engine of the time series database to obtain analysis result data;
[0026] S3. Write the analysis result data back to the Doris data warehouse.
[0027] A terminal for performing time series data analysis through a Doris data warehouse comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:
[0028] S1. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database;
[0029] Step S1 includes the steps of:
[0030] S11. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database;
[0031] The connection configuration includes adding a time series database type in the JdbcResource class, and adding formatting parsing for JDBC in the JdbcTable class and the OdbcTable class;
[0032] S12. When the Doris data warehouse is started, it scans and reads the time series database native startup package that has been copied to the dependency package directory in advance, and specifies the time series database type when creating the catalog;
[0033] S13. Doris data warehouse matches JdbcTable and OdbcTable downward according to the reconstructed JdbcResource, creates a connection with the time series database, and performs data interaction based on the data conversion configuration;
[0034] S2. Query the IoT time series data in the time series database through the Doris data warehouse, and analyze the time series data through the computing engine of the time series database to obtain analysis result data;
[0035] S3. Write the analysis result data back to the native table of the Doris data warehouse through Doris's cross-catalog insert.
[0036] The beneficial effects of the present invention are as follows: a method and terminal for analyzing time series data of the present invention, under the directory of the Doris data warehouse front-end module, performs connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database, so that the Doris data warehouse can query the IoT time series data in the time series database and use the computing engine of the time series database to perform time series data analysis, so that technicians can call the time series database to perform the downsampling analysis it is good at only by operating the Doris data warehouse, thereby improving the efficiency of data storage and analysis while satisfying the convenience of use. BRIEF DESCRIPTION OF THE DRAWINGS
[0037] Figure 1 A flowchart of a method for analyzing time series data according to an embodiment of the present invention;
[0038] Figure 2 A structural diagram of a terminal for time series data analysis according to an embodiment of the present invention;
[0039] Description of labels:
[0040] 1. A terminal for time series data analysis; 2. A processor; 3. A memory. DETAILED DESCRIPTION
[0041] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in conjunction with the implementation modes and the accompanying drawings.
[0042] Please refer to Figure 1 , a method for analyzing time series data, comprising the steps of:
[0043] S1. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database;
[0044] S2. Query the IoT time series data in the time series database through the Doris data warehouse, and analyze the time series data through the computing engine of the time series database to obtain analysis result data;
[0045] S3. Write the analysis result data back to the Doris data warehouse.
[0046] From the above description, it can be seen that the beneficial effects of the present invention are: a method for analyzing time series data of the present invention, under the directory of the Doris data warehouse front-end module, performs connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database, so that the Doris data warehouse can query the IoT time series data in the time series database and use the computing engine of the time series database to perform time series data analysis, so that technicians can call the time series database to perform the downsampling analysis it is good at only by operating the Doris data warehouse, thereby improving the efficiency of data storage and analysis while meeting the convenience of use.
[0047] Further, step S1 includes the steps of:
[0048] S11. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database;
[0049] The connection configuration includes adding a time series database type in the JdbcResource class, and adding formatting parsing for JDBC in the JdbcTable class and the OdbcTable class;
[0050] S12. When the Doris data warehouse is started, it scans and reads the time series database native startup package that has been copied to the dependency package directory in advance, and specifies the time series database type when creating the catalog;
[0051] S13. Doris data warehouse matches JdbcTable and OdbcTable downward according to the reconstructed JdbcResource, creates a connection with the time series database, and performs data interaction based on the data conversion configuration.
[0052] From the above description, it can be seen that through the above steps, a data interaction connection between the Doris data warehouse and the time series database is established.
[0053] Furthermore, the connection configuration for the time series database in step S11 includes the following steps:
[0054] S111. In the TOdbcTableType enumeration class in the thirft directory, add the return enumeration of the time series database to the findByValue method;
[0055] S112. In the catalog directory, add a time series database type to the JdbcResource class, judge the JDBC prefix match in the database parsing method parseDbType, and add formatting parsing for JDBC in the JdbcTable class and the OdbcTable class.
[0056] From the above description, we can know that through the above steps, support for time series databases has been added to the JDBC resource definition, including adding a new database type and judging this type in the parsing method to ensure that the JDBC type of the time series database can be correctly identified when parsing the connection. At the same time, the parsing of the jdbc format has been added to the table definition, and the table information of the time series database, including table name, column name, data type and other information, can be correctly parsed according to the jdbc connection format.
[0057] Furthermore, the data conversion configuration for the time series database in step S11 includes the following steps:
[0058] S113. Modify the JdbcClient class in the external directory to configure data matching conversion.
[0059] From the above description, we can see that considering that the data types of Doris and the time series database may be different, for example, in the database, the type of a field is a string type in Doris, but an integer type in the time series database. Therefore, the machine data conversion configuration is configured so that when performing add, delete, modify, and query operations, the data is converted according to the data type so that the data can be stored and processed correctly.
[0060] Please refer to Figure 2 , a terminal for time series data analysis, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:
[0061] S1. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database;
[0062] S2. Query the IoT time series data in the time series database through the Doris data warehouse, and analyze the time series data through the computing engine of the time series database to obtain analysis result data;
[0063] S3. Write the analysis result data back to the Doris data warehouse.
[0064] From the above description, it can be seen that the beneficial effects of the present invention are: a terminal for time series data analysis of the present invention, under the directory of the Doris data warehouse front-end module, performs connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database, so that the Doris data warehouse can query the Internet of Things time series data in the time series database and use the computing engine of the time series database to perform time series data analysis, so that technicians can call the time series database to perform the downsampling analysis they are good at only by operating the Doris data warehouse, thereby improving the efficiency of data storage and analysis while meeting the convenience of use.
[0065] Further, step S1 includes the steps of:
[0066] S11. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database;
[0067] The connection configuration includes adding a time series database type in the JdbcResource class, and adding formatting parsing for JDBC in the JdbcTable class and the OdbcTable class;
[0068] S12. When the Doris data warehouse is started, it scans and reads the time series database native startup package that has been copied to the dependency package directory in advance, and specifies the time series database type when creating the catalog;
[0069] S13. Doris data warehouse matches JdbcTable and OdbcTable downward according to the reconstructed JdbcResource, creates a connection with the time series database, and performs data interaction based on the data conversion configuration.
[0070] From the above description, it can be seen that through the above steps, a data interaction connection between the Doris data warehouse and the time series database is established.
[0071] Furthermore, the connection configuration for the time series database in step S11 includes the following steps:
[0072] S111. In the TOdbcTableType enumeration class in the thirft directory, add the return enumeration of the time series database to the findByValue method;
[0073] S112. In the catalog directory, add a time series database type to the JdbcResource class, judge the JDBC prefix match in the database parsing method parseDbType, and add formatting parsing for JDBC in the JdbcTable class and the OdbcTable class.
[0074] From the above description, we can know that through the above steps, support for time series databases has been added to the JDBC resource definition, including adding a new database type and judging this type in the parsing method to ensure that the JDBC type of the time series database can be correctly identified when parsing the connection. At the same time, the parsing of the jdbc format has been added to the table definition, and the table information of the time series database, including table name, column name, data type and other information, can be correctly parsed according to the jdbc connection format.
[0075] Furthermore, the data conversion configuration for the time series database in step S11 includes the following steps:
[0076] S113. Modify the JdbcClient class in the external directory to configure data matching conversion.
[0077] From the above description, we can see that considering that the data types of Doris and the time series database may be different, for example, in the database, the type of a field is a string type in Doris, but an integer type in the time series database. Therefore, the machine data conversion configuration is configured so that when performing add, delete, modify, and query operations, the data is converted according to the data type so that the data can be stored and processed correctly.
[0078] A time series data analysis method and terminal of the present invention are suitable for scenarios where data processing and analysis are performed simultaneously through a Doris data warehouse and a time series database.
[0079] Please refer to Figure 1 , Embodiment 1 of the present invention is:
[0080] A method for analyzing time series data, comprising the steps of:
[0081] S1. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database;
[0082] S2. Query the IoT time series data in the time series database through the Doris data warehouse, and analyze the time series data through the computing engine of the time series database to obtain analysis result data.
[0083] In this embodiment, the time series database TDengine is taken as an example. After successfully creating a connection with TDengine, you can view all TDengine tables in Doris by executing switch tdengine (at this time, you can use the native add, delete, modify and query methods in taos-jdbc). For example, you can query the data in TDengine by select (selectlast(xx)from tdengine.iot_ops.charge_chavalue interval(10m)), and you can calculate and analyze it through TDengine's own computing engine.
[0084] S3. Write the analysis result data back to the Doris data warehouse.
[0085] In this embodiment, the data is written back to Doris's native table through Doris's cross-catalog insert (insert into doris.dwd.table).
[0086] Through the above steps, the time series database is used as the ODS layer (source data layer) of the data warehouse, and some requirements suitable for time series database analysis such as downsampling are calculated in the time series database, and the calculation results are written back to the data warehouse, replacing the downsampling algorithm that was originally implemented by the data warehouse with a large amount of resources. This not only improves the computing efficiency, but also reduces the difficulty of writing complex SQL analysis.
[0087] Embodiment 2 of the present invention is:
[0088] A method for analyzing time series data, which differs from the first embodiment in that the present embodiment provides specific instructions on how to perform connection configuration and data conversion configuration for a time series database to establish an interactive connection between a Doris data warehouse and a time series database, including the following steps:
[0089] S11. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database;
[0090] The connection configuration includes adding a time series database type in the JdbcResource class, and adding formatting parsing for JDBC in the JdbcTable class and the OdbcTable class.
[0091] In this embodiment, the analysis logic of Doris is mainly in its FE module, so it is necessary to perform connection configuration and data conversion configuration of the time series database based on the source code in the three folders thirft / catalog / external of the module.
[0092] thirft: thrift is a cross-language RPC service development framework. Many interfaces for interaction between database servers and clients are defined in this folder.
[0093] Catalog: Provides the definition and instantiation of different databases, supports Doris to connect to other different types of databases through catalog, and with the iteration of Doris version, more and more database types will be supported.
[0094] External: Doris also supports external (external table mode) to connect to other different types of databases. The specific implementation method is different from catalog, but the purpose is the same. However, in later versions, catalog will gradually become the mainstream method.
[0095] In this embodiment, the connection with the time series database TDengine is taken as an example for specific description.
[0096] The connection configuration for the time series database in step S11 includes the following steps:
[0097] S111. In the TOdbcTableType enumeration class in the thirft directory, add the return enumeration of the time series database to the findByValue method.
[0098] First, add the return enumeration TDENGINE(9) of TDENGINE to the findByValue method in the TOdbcTableType enumeration class in the thrift directory.
[0099] When querying data across databases, different databases have different definitions of data fields, so it is necessary to match the data types of the custom newly added databases.
[0100] S112. In the catalog directory, add a time series database type to the JdbcResource class, judge the JDBC prefix match in the database parsing method parseDbType, and add formatting parsing for JDBC in the JdbcTable class and the OdbcTable class.
[0101] Secondly, you need to adjust the three classes in the catalog directory, namely JdbcResource, JdbcTable and OdbcTable.
[0102] JdbcResource: defines the configuration for connecting to the database, including connection type, account password, IP port, and other information.
[0103] JdbcTable: Instantiates the database connection and provides corresponding interfaces such as addition, deletion, modification and query.
[0104] OdbcTable: instantiates the database connection and provides corresponding interfaces such as add, delete, modify and query.
[0105] In this embodiment, the TDengine type jdbc:TAOS-RS is added to the JBDC source (JdbcResource), and then the jdbc prefix match is judged in the database parsing method parseDbType; and then the formatted parsing of jdbc is added to JdbcTable and OdbcTable.
[0106] S113. Modify the JdbcClient class in the external directory to configure data matching conversion.
[0107] Since Doris and TDengine have different storage formats, their data types need to be matched and converted. You need to modify the JdbcClient class under the external file. For example, the following commonly used data types need to be matched and converted: [TIMESTAMP / INT / BIGINT / FLOAT / DOUBLE / SMALLINT / TINYINT / TINYINT / BOOL / NCHAR / VARCHAR / JSON / GEOMETRY / BINARY / VARBINARY].
[0108] S12. When the Doris data warehouse is started, it scans and reads the time series database native startup package that has been copied to the dependency package directory in advance, and specifies the time series database type when creating the catalog.
[0109] Based on the connection configuration in steps S111 to S112 above, we can scan the TDengine native startup package (taos-jdbcdriver-${taos-version}-dist.jar) that we copied to the dependency package directory in advance when Doris starts, read it, and then specify the database type TDengine when creating the catalog.
[0110] S13. Doris data warehouse matches JdbcTable and OdbcTable downward according to the reconstructed JdbcResource, creates a connection with the time series database, and performs data interaction based on the data conversion configuration.
[0111] In this embodiment, when creating a catalog, specify the database type TDengine, and Doris will match down to JdbcTable and OdbcTable through the reconstructed JdbcResource, and successfully create the correct connection. Then you can view all TDengine tables in Doris by executing switch tdengine (at this time, you can use the native add, delete, modify and query methods in taos-jdbc).
[0112] Please refer to Figure 2 , Embodiment 3 of the present invention is:
[0113] A terminal 1 for time series data analysis includes a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps in a method for time series data analysis in the above embodiment one or two are implemented.
[0114] In summary, the present invention provides a method and terminal for time series data analysis, which, under the directory of the Doris data warehouse front-end module, performs connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database, so that the Doris data warehouse can query the IoT time series data in the time series database and use the computing engine of the time series database to perform time series data analysis, so that technicians can call the time series database to perform the downsampling analysis they are good at only by operating the Doris data warehouse, thereby improving the efficiency of data storage and analysis while meeting the convenience of use.
[0115] A method and terminal for analyzing time series data of the present invention:
[0116] 1. Greatly reduced the complexity of SQL writing for downsampling using Doris;
[0117] 2. The analysis speed of downsampling related analysis (daily / monthly / yearly indicator curves) has been greatly improved;
[0118] 3. Reduce the resources occupied by Doris and save server costs;
[0119] 4. Unify all business analysis entrances into a data warehouse, making the technical architecture clearer.
[0120] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.
Claims
1. A method for analyzing time series data using a Doris data warehouse, characterized in that: Includes steps: S1. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database; Step S1 includes the steps of: S11. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database; The connection configuration includes adding a time series database type in the JdbcResource class, and adding formatting parsing for JDBC in the JdbcTable class and the OdbcTable class; S12. When the Doris data warehouse is started, it scans and reads the time series database native startup package that has been copied to the dependency package directory in advance, and specifies the time series database type when creating the catalog; S13. Doris data warehouse matches JdbcTable and OdbcTable downward according to the reconstructed JdbcResource, creates a connection with the time series database, and performs data interaction based on the data conversion configuration; S2. Query the IoT time series data in the time series database through the Doris data warehouse, and analyze the time series data through the computing engine of the time series database to obtain analysis result data; S3. Write the analysis result data back to the native table of the Doris data warehouse through Doris's cross-catalog insert.
2. A method for analyzing time series data using Doris data warehouse according to claim 1, characterized in that: The connection configuration for the time series database in step S11 includes the following steps: S111. In the TOdbcTableType enumeration class in the thirft directory, add the return enumeration of the time series database to the findByValue method; S112. In the catalog directory, add a time series database type to the JdbcResource class, judge the JDBC prefix match in the database parsing method parseDbType, and add formatting parsing for JDBC in the JdbcTable class and the OdbcTable class.
3. The method for analyzing time series data using Doris data warehouse according to claim 1, characterized in that: The data conversion configuration for the time series database in step S11 includes the following steps: S113. Modify the JdbcClient class in the external directory to configure data matching conversion.
4. The method for analyzing time series data using Doris data warehouse according to claim 1, characterized in that: The time series database is the TDengine time series database.
5. A method for analyzing time series data using Doris data warehouse according to claim 4, characterized in that: Step S2 comprises the steps of: Doris executes switch tdengine to view all TDengine tables, queries the data in TDengine through select, and performs calculations and analysis through TDengine's calculation engine.
6. A terminal for performing time series data analysis through a Doris data warehouse, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: S1. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database to establish an interactive connection between the Doris data warehouse and the time series database; Step S1 includes the steps of: S11. In the directory of the Doris data warehouse front-end module, perform connection configuration and data conversion configuration for the time series database; The connection configuration includes adding a time series database type in the JdbcResource class, and adding formatting parsing for JDBC in the JdbcTable class and the OdbcTable class; S12. When the Doris data warehouse is started, it scans and reads the time series database native startup package that has been copied to the dependency package directory in advance, and specifies the time series database type when creating the catalog; S13. Doris data warehouse matches JdbcTable and OdbcTable downward according to the reconstructed JdbcResource, creates a connection with the time series database, and performs data interaction based on the data conversion configuration; S2. Query the IoT time series data in the time series database through the Doris data warehouse, and analyze the time series data through the computing engine of the time series database to obtain analysis result data; S3. Write the analysis result data back to the native table of the Doris data warehouse through Doris's cross-catalog insert.
7. A terminal for performing time series data analysis through a Doris data warehouse according to claim 6, characterized in that: The connection configuration for the time series database in step S11 includes the following steps: S111. In the TOdbcTableType enumeration class in the thirft directory, add the return enumeration of the time series database to the findByValue method; S112. In the catalog directory, add a time series database type to the JdbcResource class, judge the JDBC prefix match in the database parsing method parseDbType, and add formatting parsing for JDBC in the JdbcTable class and the OdbcTable class.
8. A terminal for performing time series data analysis through Doris data warehouse according to claim 6, characterized in that: The data conversion configuration for the time series database in step S11 includes the following steps: S113. Modify the JdbcClient class in the external directory to configure data matching conversion.
9. A terminal for performing time series data analysis through Doris data warehouse according to claim 6, characterized in that: The time series database is the TDengine time series database.
10. A terminal for performing time series data analysis through Doris data warehouse according to claim 9, characterized in that: Step S2 comprises the steps of: Doris executes switch tdengine to view all TDengine tables, queries the data in TDengine through select, and performs calculations and analysis through TDengine's calculation engine.