This topic describes the table-level features provided by Tablestore SDK for Python.
Data table management
Operation | Description |
You can create a data table to store data. | |
You can enable data at rest encryption (DARE) by configuring data encryption settings when you create a data table. | |
You can update the configuration information of a table, such as the time to live (TTL) and max versions. | |
You can query the names of all tables in an instance. | |
You can query the configurations of a table. | |
You can delete a table. |
Data table features
Feature | Description | Scenario |
You can specify a primary key column that is not the partition key as the auto-increment primary key column of a data table. If you write data to a table that contains an auto-increment primary key column, you do not need to specify values for the auto-increment primary key column. Tablestore automatically generates values for the auto-increment primary key column. Values generated for the auto-increment primary key column are unique and increase monotonically within a partition. | This feature is suitable for scenarios that require a unique identifier for each object, such as item IDs on e-commerce websites, user IDs on large websites, post IDs in forums, and message IDs in chat tools. | |
If you use conditional update, data in a table can be updated only when the conditions are met. Otherwise, the update fails and an error message is returned. | This feature is suitable for scenarios in which you want to specify data update conditions to update the data of a highly concurrent application. | |
After you enable local transaction for a data table, you can create a local transaction based on a partition key value. You can read and write data in the local transaction and then commit or discard the local transaction based on your business requirements. | This feature is suitable for scenarios in which you want to perform atomic operations to read or write one or more rows of data. | |
The atomic counter feature allows you to specify a column as an atomic counter and perform atomic counter operations on the column. | This feature is suitable for scenarios in which you want to collect real-time statistical data for online applications, such as the number of page views (PVs) on various posts. | |
After you configure a filter, query results are filtered on the server side based on the filter conditions. Only rows of data that meet the filter conditions are returned. | This feature is suitable for scenarios in which you want to return query results that meet specific conditions. |