Table Store

A fully managed NoSQL cloud database service that enables storage of a massive amount of structured and semi-structured data.

Table Store is a distributed NoSQL data storage service built on Alibaba Cloud's ApsaraDB distributed computing system, which comes with 99.99% high availability and 99.999999999% data reliability. Table Store enables seamless expansion of data size and access concurrency through data sharding and server load balancer technologies, providing storage of and real-time access to massive structured data.

Benefits

High Reliability
Three copies of data with high consistency, full host, service high availability and data high reliability.
Low-cost
Elastic resources, Pay-As-You-Go billing, and O&M with no additional costs.
Highly Elastic
Distributed architecture, single table auto scaling, support of 10-PB-level data and 10-million-level access concurrency.
Easy to Analyze
Provides full/incremental data tunnels, seamlessly interconnecting with various products for big data analysis and real-time stream computing.

Features

  • Powerful and flexible security

    Multi-dimensional and multi-level security protection and resource access management to ensure data security


    Provides table-level and API-level authentication and authorization mechanisms, supports STS temporary authorization, custom permission authentication, and primary/sub accounts

    Flexible authentication and authorization mechanisms


    Multi-network support

    Supports Internet, ECS Intranet, and VPC private network accessing, and provides network resource access management


    Resource isolation

    Providing a user-level resource isolation mechanism

  • Convenient and flexible use

    Provides standard RESTful APIs and multilingual SDKs and supports multi-version data and TTL features


    Flexible access

    Provides standard RESTful APIs, a wide range of SDKs and client tools


    Flexible use

    With a sparse table structure, the number and types of columns in each row can be different from each other and attribute columns do not need to be pre-defined


    Data management

    Supports TTL management and multi-version management


    Convenient use

    The product console provides instance/table-level monitoring data access (QPS, latency, and number of requests)


    HBase support

    The official Table Store HBase Client supports HBase services

  • Distributed architecture and big data models

    Utilizing a distributed architecture, automated server load balancer and hot-spot migration mechanisms, Table Store provides unlimited data storage and access concurrency


    Large scale

    10-PB-level data volume for a single table, trillion records and a capacity of 10-million-level TPS


    O&M free

    Automated server load balancer and hot-spot migration, without the need for manual intervention


    Effective storage engine

    High-throughput write capacity and stable and predictable read/write performances based on shared storage


    High security

    Three copies of data ensure high consistency, which is designed based on a standard of 99.999999999% data reliability


    High availability

    A shared storage architecture ensures fast detection and fast recovery of single point of failure, which is designed based on a standard with 99.99% availability

  • Complete big data computing system

    Supporting various big data computing platforms and real-time stream computing and provides powerful data analysis capacity


    Combination of storage and computing

    Implement a data loop of online storage, offline computing and real-time analytics


    Big data ecosystem

    Supports MaxCompute direct read/write and access from various open-source components such as EMR Hadoop, Spark, Hive, and Flink


    Real-time computing

    Supports Alibaba Cloud StreamCompute and Function Compute, which perform real-time computing for Table Store incremental data


    Data tunnel

    Supports Alibaba Cloud Data Integration and full/incremental data tunnels


    Multi-industry solutions

    Solutions for social IM, gaming, finance, IoT and logistics

  • Advanced functions simplify the application architecture

    Provides various features such as PK auto-incrementing column and streaming read


    PK auto-incrementing column

    Solves problems with high concurrency in social IM and feed streams


    Effective streaming read interface

    Handles high concurrent reading and storage of massive data, and supports real-time computing of incremental data

Common Scenarios

  • Data Storage & Analysis
  • Social Feed Stream
  • Finance
  • IoV Data Storage
  • IoT Time Series Data
  • E-commerce
Data Storage & Analysis

Massive Data Storage & Analysis

A loop of storage and processing of massive data and online services

Table Store provides low-cost, low-latency, and high-concurrency storage and online access of massive data, as well as incremental and full data tunnels, with support for SQL direct read and write on MaxCompute and other big data analysis platforms. Effective incremental streaming read interface allows easy real-time stream computing of data.

Advantages

  • Large-scale

    10-PB-level data volume for a single table, trillion records and a capacity of 10-million-level TPS

  • Computing Integration

    Supporting various big data computing platforms, stream computing and real-time computing services

  • Low-cost

    High-availability massive message storage and multi-terminal message synchronizationElastic Pay-As-You-Go resources provide instances with two specifications, high performance and high capacity, meeting the needs of different services

Social Feed Stream

Social Feed Stream

Storage of massive messages with high availability, high concurrency and stable latency

Table Store can be used to store IM messages and social feed stream information such as comments, follow-up posts and likes. The elastic resources stored in Table Store are billed under the Pay-As-You-Go model and at a relatively low cost Table Store can meet the needs of applications that feature significant traffic fluctuations and high concurrency while requiring low latency.

Advantages

  • Simplifying High Concurrency Architecture

    A single table containing trillion records easily stores full history transaction recordsBuilt-in PK auto-incrementing column simplifies external system dependencies

  • Stable Performance

    A design standard with 99.999999999% data reliability and three copies of the data ensures data securityThe average read/write performance for high performance instance is less than 10 ms/instance, which is not affected by data size

  • Massive Message Storage

    High-availability massive message storage, and multi-terminal message synchronization

Finance

Financial Transactions and Risk Control

Storage and real-time query of massive transaction records and user models

The low latency, high concurrency, elastic resources and Pay-As-You-Go billing method of this service enables your risk control system to always operate in optimal conditions, allowing you to strictly control transaction risks. Meanwhile, the flexible data structure allows your business model to rapidly evolve to meet market demands.

Advantages

  • Scalability

    A single table containing trillion records easily stores full history transaction records

  • Reliability

    A design standard with 99.999999999% data reliability, and three copies of data ensure the data security

  • Flexible Usage

    Schemafree model and attribute column fields (which are added as needed) for rapid service development

IoV Data Storage

IoV Data Storage

Massive, efficient and flexible IoV data storage

A single table can store petabytes of data without distributing data in separate databases and tables, which simplifies the service logic. The schema-free data model enables easy access to the monitoring data of different vehicle-mounted devices. Table Store can be seamlessly integrated with multiple big data analysis platforms and real-time computing services for fast real-time online query and service report analysis.

Advantages

  • Data Scale

    A single table can store tens of petabytes of data without distributing data in separate databases and tables, which simplifies the service logic

  • Stable Performance

    The query performance of vehicle conditions and tracks is stable and predictable

  • Flexible Use

    The schema-free data model enables easy access to the monitoring data of different vehicle-mounted devices

IoT Time Series Data

IoT Time Series Data

Massive data storage and effective query and Analysis

With a single table capable of storing petabytes of data and processing thousands of queries per second (QPS), Table Store makes it easy to store the time series data of IoT devices and monitoring systems. The big data analysis SQL direct read function and the efficient incremental streaming read interface provide an easy way for offline data analysis and real-time stream computing.

Advantages

  • Data Scale

    A single table can store tens of petabytes of data, meeting the needs of data write and storage of ultra large-scale IoT devices and monitoring systems

  • Data Analysis

    Through interconnecting with multiple offline, stream data analysis platforms, a piece of data can satisfy analysis and computing scenarios for different services

  • Low-cost

    Pay-As-You-Go, low cost, and support for TTL management

E-commerce

E-commerce Orders and Ad Recommendations

Database for massive transaction orders and user recommendations

Using Table Store for a large number of historical transaction orders makes it possible for you to deal with high data volume and access performance with ease. Combined with MaxCompute, Table Store enables precision marketing, elastic resource storage, and Pay-As-You-Go billing, so that you can easily cope with peak hours when all your users are online.

Advantages

  • Flexible Resources

    Auto scaling of data size and access concurrency meets the needs of access during peaks and valleys

  • Computing Integration

    Supports various big data analysis platforms for direct user analytics

  • Fast Query

    Fast query of massive transaction orders