This topic describes the terms that are used in AnalyticDB for MySQL.
Regions are geographical locations where Alibaba Cloud data centers are deployed. Regions are typically named after the cities where the data centers are deployed. For example, the data centers of the Australia (Sydney) region reside in Sydney.
The zone in which you want to deploy your resources. Each region has multiple isolated locations known as zones, which have their own independent power supply and network. For example, the China (Qingdao) region has two zones, which are Qingdao Zone B and Qingdao Zone C. Resources that are deployed within the same zone share the same network, and therefore have minimal latency between each other. This means that services deployed within the same zone have faster communication speeds, which translates into more efficient business operations.
A group of databases. You can create multiple databases in a cluster. Databases in different clusters are isolated from each other.
A basic unit in AnalyticDB for MySQL clusters. You must specify the node specifications when you create a cluster. The node specifications include the CPU, memory, and storage capacity. For more information, see Editions.
elastic I/O unit (EIU)
Elastic I/O resources of AnalyticDB for MySQL in elastic mode for Cluster Edition are bundled into units and sold as EIUs. You can purchase EIUs when you create a cluster or when you want to scale up storage resources. For more information, see Use EIUs to scale up storage resources.
A basic unit in AnalyticDB for MySQL. A database is logically composed of partitions and physically composed of nodes.
AnalyticDB for MySQL supports the following types of accounts:
Alibaba Cloud account: the account that is used to create and manage clusters. For example, you can use an Alibaba Cloud account to log on to the AnalyticDB for MySQL console, change the billing method of a cluster from pay-as-you-go to subscription, scale up clusters, and delete clusters.
RAM user: the account that is used to create and manage clusters within the specified permissions that are granted by an Alibaba Cloud account.
Database account: the account that is granted a specific set of permissions to manage databases. For example, you can use a database account to create or delete databases, connect to databases, and create or delete tables. Database account names within the same cluster must be unique.
Service account: the account that is used by the Alibaba Cloud technical support personnel to access your AnalyticDB for MySQL cluster. To obtain technical support from Alibaba Cloud, you must grant permissions to the service account of your AnalyticDB for MySQL cluster. When the validity period of the authorization ends, the granted permissions are automatically revoked.
AnalyticDB for MySQL supports two types of tables: partitioned tables and dimension tables.
A table that is used to store metric values of business data. A partitioned table is also known as a standard table.
AnalyticDB for MySQL distributes data across nodes based on a distribution key. Then, each node splits a data file into different files based on a partition key. To import incremental business data, you can specify both a distribution key and a partition key to synchronize incremental data when you create a partitioned table. For more information, see CREATE TABLE.
A collection of service feature descriptions. Each node has a copy of dimension tables. Typically, dimension tables contain small volumes of data and are not frequently changed.
AnalyticDB compute unit (ACU)
Computing and storage resources of AnalyticDB for MySQL Data Lakehouse Edition (V3.0) are bundled into units and sold as ACUs. Each ACU is equivalent to 1 core and 4 GB memory.
A vector is a mathematical representation of entities or applications. Vectors can be used to represent the relationships between entities in a vector space. The distance between two entities in the vector space represents their similarity. Examples: height, age, gender, and region. AnalyticDB for MySQL supports feature vectors only in the fixed-length array format. The following data types are supported:
A method for fast search and matching in a feature vector dataset.
An index of a specific type.
A custom function of a specific type. Each distance calculation formula corresponds to a custom function. Example:
k-nearest neighbor (KNN)
An algorithm that is used to find k points in a feature vector dataset that are the nearest to a query point.
radius nearest neighbor (RNN)
An algorithm that is used to find all points in a feature vector dataset that are within a specific radius of a query point.