Tablestore is a table-based, low-cost serverless storage service optimized for storing huge volumes of structured data. Tablestore allows you to query and retrieve online data within milliseconds and perform multi-dimensional analysis on stored data. Tablestore is suitable for scenarios such as billing, instant messaging (IM), Internet of Things (IoT), Internet of Vehicles (IoV), risk control, and intelligent recommendation. Tablestore provides a deeply optimized one-stop storage solution for IoT applications.
The following table lists concepts that are frequently used in Tablestore.
|Region||Regions are physical data centers distributed around the world. Tablestore is deployed across multiple Alibaba Cloud regions. You can select a region based on your business requirements. For more information, see Region.|
|Read or write throughput||The read or write throughput is measured by read or write capacity units (CUs), which is the smallest billing unit for read or write operations. For more information, see Read/write throughput.|
|Instance||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. For more information, see Instance.|
|Endpoint||Endpoints are the connection URLs for Tablestore instances. To perform operations on your Tablestore instances, you must specify the endpoint of the instance. For more information, see Endpoint.|
|Time To Live (TTL)||TTL is used to manage the lifecycle of data stored in Tablestore. Tablestore automatically clears data when the TTL of data expires, helping you manage storage space and save storage costs. The TTL of data is specified in seconds. For more information, see Data versions and TTL.|
Data storage models
Tablestore provides three data storage models: the Wide Column model, the TimeSeries model, and the Timeline model. You can select a model based on your business requirements. Different models support different features. For more information, see Features.
|Wide Column||This model is similar to the Bigtable and HBase models, and can be used to store data such as metadata or data for big data applications. The Wide Column model supports features such as max versions, TTL, auto-increment primary key column, conditional update, local transaction, atomic counter, and filter. For more information, see Overview.|
|TimeSeries||This model is optimized for storing time series data, and can be used to store data generated from scenarios such as IoT device monitoring, device data collection, and machine monitoring. The TimeSeries model supports automatic indexing of time series metadata and time series query by various composite conditions. For more information, see Overview.|
|Timeline||This model is optimized for storing message data, and is suitable for storing message data generated from IM applications and feed streams. This model can meet the specific requirements of message data, such as message order preservation, storage of large numbers of messages, and real-time synchronization. This model also supports full-text search and Boolean query. For more information, see Overview.|
How to use
You can use Tablestore in one of the following methods.
|Tablestore console||Alibaba Cloud provides a user-friendly web-based console for Tablestore. For more information, log on to the Tablestore console.|
|Software development kit (SDK)||SDKs are provided for popular programming languages, such as Java, Go, Python, Node.js, .NET, and PHP. For more information, see SDK overview.|
|Tablestore command-line interface (CLI)||Supports operating Tablestore with simple commands. For more information, see Start the Tablestore CLI and configure access information.|
You can use the Tablestore console or Tablestore CLI to try out Tablestore in the Wide Column model or time series table operations in the TimeSeries model. For more information, see Use Tablestore.
Computing and analysis
Tablestore is compatible with a wide range of tools, such as MaxCompute, Spark, Data Lake Analytics (DLA), Hive, Hadoop MapReduce, and Function Compute, to help you further process and analyze data.
|MaxCompute||Use MaxCompute to access Tablestore||You can use the MaxCompute client to create an external table and use the external table to access Tablestore data.|
|Spark||Overview||You can use Spark to perform complex computing and analysis on Tablestore data that is accessed by using E-MapReduce (EMR) SQL or DataFrame.|
|Hive or Hadoop MapReduce (MR)||Tutorial||You can use Hive or Hadoop MapReduce to access a Tablestore table.|
|Function Compute||Use Function Compute||You can use Function Compute to perform real-time computing on the incremental data in Tablestore.|
|Flink||Create a Tablestore dimension table||You can use Flink to access dimension tables or result tables in Tablestore to compute or analyze the data of your big data applications.|
|SQL query||Overview||The SQL query feature provides a unified access interface for multiple data engines. You can use the SQL query feature to perform complex queries and analytics on data in Tablestore in an efficient manner.|
Data migration and synchronization
You can seamlessly migrate or synchronize data from third-party systems to Tablestore. You can also synchronize data from Tablestore to other Alibaba Cloud services, such as Object Storage Service (OSS).
|Data import||Overview||You can use Tablestore Sink Connector to batch import data from Apache Kafka to a data table or time series table in Tablestore.|
|Synchronize data from one table to another table||You can synchronize data from one table to another table by using Tunnel Service, DataWorks, or DataX.|
|Data export||Export full data in script mode||You can use DataWorks to export full or incremental data from Tablestore to MaxCompute.|
|Overview||You can use DataWorks to export full or incremental data from Tablestore to OSS.|