The IoT Platform's offline data storage feature allows you to monitor the basic status of your devices and the Thing Specification Language data they report. Offline data encompasses platform system tables, time series tables, snapshot tables, and custom storage tables. This topic outlines the fundamental features and applications of various storage table types.
Prerequisites
Data must be backed up. For detailed procedures, see data backup.
Limits
These items are available only for Standard and Exclusive Enterprise Edition instances in the China (Shanghai), China (Beijing), China (Shenzhen), Singapore, and US (Virginia) regions.
Scenarios
Beyond data review, storage tables in data storage support the following functionalities:
As a query object for SQL analysis.
As a data source for data API.
Platform system table
The platform system table is designed to store essential information about products, devices, and device groups in a structured format. It includes details such as device creation times and group relationships, simplifying the analysis and management of device data.
It comprises product tables, device tables, device group tables, device group relationship tables, device tag tables, and device location data tables. For an in-depth look, see view platform system table.
Time series table and snapshot table
Utilize the time series and snapshot tables to examine the Thing Specification Language properties and event data that devices report, considering both product and dimensions.
The product storage table is limited to reported properties and event data. For further details, see view product storage table.
To learn more about Thing Specification Language, refer to what is Thing Specification Language.
Custom storage table
Custom storage tables can be created to store data processed by the Data Parsing or SQL Analysis features.
For insights into custom storage tables, see create and view custom storage table.
Furthermore, custom storage tables are applicable in SQL analysis:
New custom storage tables without data can be utilized as result storage for SQL analysis.
Existing custom storage tables with data can function as query objects in SQL analysis.
For additional information, consult SQL analysis.