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Tablestore:Instance

Last Updated:Apr 19, 2024

An instance is a logical entity used in Tablestore to manage tables. Each instance is equivalent to a database. Tablestore implements application access control and resource measurement at the instance level. After Tablestore is activated, you can create an instance in the Tablestore console and then create and manage tables in this instance.

Tablestore allows you to create up to 10 instances in each Alibaba Cloud account and up to 64 tables in each instance. Tables include data tables, secondary index tables, and time series tables. If you want to create more instances or tables, submit a ticket.

实例

Instance types

Tablestore supports two instance types: high-performance instance and capacity instance. Both instance types support petabytes of data for a single table, but differ in costs and applicable scenarios. The following table compares the two instance types.

Important
  • After you create an instance, you cannot change the type of the instance. Proceed with caution.

    If you want to change the type of an instance, you can migrate data from the instance to another instance. You can use tools such as DataWorks to migrate data. For more information, see Synchronize data from one table to another table in Tablestore.

  • You are charged for high-performance storage usage, reserved read throughput, and metered read throughput when you use the search index feature regardless of the instance type. For more information, see Billable items of search indexes.

  • When you use the TimeSeries model, the following rules apply regardless of the instance type: You are charged for the metered read and write throughput that is consumed by read and write operations on data in time series based on the pricing for the throughput that is consumed by read and write operations on common data in capacity instances. You are charged for the metered read and write throughput that is consumed by read and write operations on metadata of time series based on the pricing for the throughput that is consumed by read and write operations on common data in high-performance instances. You are charged for the storage usage of metadata of time series based on the pricing for the storage usage of common data in high-performance instances. For more information, see Billable items of the TimeSeries model.

Instance type

Scenario

Billable item

Read performance

Write performance

Concurrency

High-performance instance

This instance type is suitable for scenarios that require high read and write performance and high concurrency, such as gaming, financial risk control, social networking applications, and recommendation systems.

  • Reserved read throughput or reserved write throughput

  • Metered read throughput or metered write throughput

  • High-performance storage

High

High

High

Capacity instance

This instance type is suitable for business that is cost-sensitive but does not have high requirements on read performance, such as business that involves log monitoring data, Internet of Vehicles (IoV) data, device data, time series data, logistics data, and public opinion monitoring data.

  • Metered read throughput or metered write throughput

  • Capacity storage

Medium

High

Medium

Instance naming conventions

The name of an instance must be unique within a region. The name can be the same across different regions. The following items describe the naming conventions:

  • The name can contain only letters, digits, and hyphens (-).

  • The name must start with a letter.

  • The name cannot end with a hyphen (-).

  • The name is case-insensitive.

  • The name must be 3 to 16 characters in length.

  • The name cannot contain the following words: ali, ay, ots, taobao, and admin.

Instance types that are supported in each region

Region

High-performance Instance

Capacity instance

China (Hangzhou)

Supported

Supported

China East 1 Finance

Supported

Not supported

China (Shanghai)

Supported

Supported

China East 2 Finance

Not supported

Supported

China (Qingdao)

Not supported

Supported

China (Beijing)

Supported

Supported

China (Zhangjiakou)

Supported

Supported

China (Hohhot)

Not supported

Supported

China (Ulanqab)

Supported

Not supported

China (Shenzhen)

Supported

Supported

China (Guangzhou)

Supported

Not supported

China (Chengdu)

Supported

Supported

China (Hong Kong)

Supported

Supported

Singapore

Supported

Not supported

Australia (Sydney)

Not supported

Supported

Malaysia (Kuala Lumpur)

Supported

Supported

Indonesia (Jakarta)

Supported

Supported

Japan (Tokyo)

Not supported

Supported

Germany (Frankfurt)

Supported

Supported

UK (London)

Supported

Supported

US (Silicon Valley)

Supported

Not supported

US (Virginia)

Supported

Supported

India (Mumbai)

Supported

Supported

UAE (Dubai)

Not supported

Supported

Philippines (Manila)

Supported

Not supported

Thailand (Bangkok)

Supported

Not supported

SAU (Riyadh)

Supported

Not supported