You can use Tablestore SDK for Java to perform various operations on time series tables, time series data, analytical stores, and Lastpoint indexes in the TimeSeries model.
Tablestore TimeSeries model is designed based on the characteristics of time series data and is suitable for scenarios such as IoT device monitoring and can be used to store the data that is collected by devices and the monitoring data of machines. For more information, see TimeSeries model.
Operations on time series tables
The following table describes the management operations that you can perform on time series tables.
Operation | Description |
Create a time series table to store time series data. | |
Modify the configurations, such as the time to live (TTL) of data in a time series table. After you modify the TTL of data in a time series table, Tablestore automatically and asynchronously deletes the data whose retention period exceeds the TTL. | |
Query the names of all time series tables in an instance. | |
Query the information about a time series table, such as the TTL configuration. | |
Delete a time series table. |
Time series operations
The following table describes the time series operations.
Feature | Description |
You can retrieve time series information based on specific conditions, such as metric name and data source information. | |
You can update the time series metadata of one or more time series at the same time. | |
You can delete time series metadata of one or more time series at the same time. |
Operations on time series data
The following table describes the operations that you can perform on time series data.
Operation | Description |
You can write one or more rows of time series data to a time series table at the same time. | |
You can query the time series data that meets the specified conditions in a time series. |
Analytical store operations
The analytical store feature is primarily used for long-term storage and analysis of time series data. The following table describes the management operations that you can perform on analytical stores.
After time series data is written to a time series table, if you want to query and analyze time series data in a time series by using an analytical store, you can create a mapping table for the analytical store and then execute SELECT statements to query time series data. For more information, see SQL query examples.
Operation | Description |
You can create an analytical store for an existing time series table. | |
You can update the time to live (TTL) configuration of a time series analytical store. After the TTL of an analytical store is updated, Tablestore automatically deletes data whose retention period exceeds the TTL value from the analytical store in an asynchronous manner. | |
You can query information of an analytical store, including time to live (TTL) configuration, data synchronization options, data synchronization status, and data storage usage. | |
You can delete an analytical store that is created for a time series table. |
Lastpoint index operations
You can use a Lastpoint index to quickly retrieve data of the latest point in time in time series in a time series table. The following table describes the management operations that you can perform on Lastpoint indexes.
Operation | Description |
You can create a Lastpoint index for a time series table. | |
You can query data in a Lastpoint index. | |
You can use a search index that is created for a Lastpoint index to retrieve data in the Lastpoint index. Search indexes can be used to accelerate data retrieval for Lastpoint indexes and provide multi-dimensional query and statistical analysis capabilities. | |
You can delete a Lastpoint index that is created for a time series table. |