Table Store is a NoSQL multi-model database service independently developed by Alibaba Cloud. Table Store can store large amounts of structured data and provide query and analysis services. The distributed storage and powerful index-based search engine enable Table Store to store PB-grade data while ensuring 10 million TPS and millisecond-level latency. This document introduces terms, models, and features of Table Store.
The following table describes the terms for Table Store.
|Instance||An instance is an entity used to manage tables and data in Table Store. Each instance is equivalent to a database. Table Store implements access control and resource metering for applications at the instance level.|
|Read/write throughput||The read/write throughput is measured by read/write capacity units (CUs), which is the smallest billing unit for read and write operations.|
|Region||A region is a physical data center of Alibaba Cloud.|
|Endpoint||Each Table Store instance has an endpoint. An endpoint must be specified before any operations can be performed on tables or data in Table Store.|
Table Store provides multiple models that you can apply for as needed. The following table describes the models of Table Store.
|Wide Column model||The Wide Column model is applicable to various scenarios, such as metadata and big data. This model supports multiple functions, including data versions, time to live (TTL), auto-increment of primary key columns, conditional updates, local transactions, atomic counters, and filters.|
|Timeline model||The Timeline model is a data model that can meet special requirements of message data scenarios, such as message order preservation, storage of large numbers of messages, and real-time synchronization. This model also supports full-text queries and bool queries. The model is also suitable for use in scenarios such as instant messaging (IM) and feed streams.|
The following table describes the features of Table Store.
|Auto-increment function of the primary key column||If you set a primary key column as an auto-increment column, you do not need to enter values in this column when writing data in a row. Instead, Table Store automatically generates primary key values. The automatically generated key values are unique within the rows that share the same partition key. These values increase sequentially.|
|Conditional update||A conditional update is implemented only when specified conditions are met.|
|Atomic counters||An atomic counter consists of columns. The atomic counter provides real-time statistics for some online applications, such as calculating the real-time page views (PVs) of a post.|
|Filter||Filters can be used to sort results on the server side. Only results that match the filtering conditions are returned. The feature effectively reduces the volume of transferred data and shortens the response time.|
|Search index||Based on inverted index and columnstore index, search-based index solves the complex query problem in big data scenarios.|
|Global secondary index||Global secondary index can be used to create indexes for attribute columns.|
|Tunnel Service||Tunnel Service provides tunnels that are used to export and consume data in the full, incremental, and differential modes. After creating tunnels, you can consume historical and incremental data exported from a specified table.|
|HBase support||Table Store HBase Client can be used to access Table Store through Java applications built on HBase APIs.|