Tablestore is a table-based cost-effective serverless storage service that can be used to store large 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), IoT, Internet of Vehicles (IoV), risk control, and intelligent recommendation. Tablestore provides a deeply optimized all-in-one storage solution for IoT applications.
The following table introduces the terms that are frequently used in Tablestore.
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). A CU is the smallest billing unit for read or write operations. For more information, see Read and write throughput.
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.
An endpoint is the connection URL used to access a Tablestore instance. To perform operations on the tables and data in a Tablestore instance, you must specify the endpoint of the instance. For more information, see Endpoints.
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. This helps 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.
This model is similar to the Google Cloud Bigtable and HBase models, and can be used in multiple scenarios such as metadata and big data storage. 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 Wide Column model.
This model is designed to store time series data generated from multiple 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 TimeSeries model.
This model is designed to store message data and is suitable for storing message data generated from IM applications and feed streams. This model can meet the specific requirements of messaging processes, 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 Timeline model.
The following table describes the methods for using Tablestore.
Alibaba Cloud provides a user-friendly web-based console for Tablestore. For more information, log on to the Tablestore console.
SDKs are provided for popular programming languages, such as Java, Go, Python, Node.js, .NET, and PHP. For more information, see SDK overview.
Tablestore allows you to perform operations by running 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 operations on tables in the Wide Column model or 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, Hive, Hadoop MapReduce, Function Compute, Realtime Compute for Apache Flink, and Tablestore SQL query. You can select tools to further process and analyze data based on your business requirements.
You can use the MaxCompute client to create an external table and use the external table to access Tablestore data.
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
You can use Hive or Hadoop MapReduce to access a Tablestore table.
You can use Function Compute to perform real-time computing on the incremental data in Tablestore.
Realtime Compute for Apache Flink
You can use Realtime Compute for Apache Flink to access source tables, dimension tables, or result tables in Tablestore to compute or analyze the data of your big data applications.
Tablestore SQL query
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 analysis 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).
You can use Tablestore Sink Connector to batch import data from Apache Kafka to a data table or time series table in Tablestore.
You can synchronize data from one table to another table in Tablestore by using Tunnel Service, DataWorks, or DataX.
You can use DataWorks to export full or incremental data from Tablestore to MaxCompute.
You can use DataWorks to export full or incremental data from Tablestore to OSS.
You can use the CLI or DataX to download data in Tablestore to a local file. You can also use DataWorks to synchronize data in Tablestore to OSS and download data from OSS to a local file.
To configure user permissions, you can use Resource Access Management (RAM) to grant custom permissions to different users. For more information, see Configure user permissions.
To ensure data storage security and network access security, you can encrypt tables or associate a virtual private cloud (VPC) with your Tablestore instance to allow access only over the VPC. For more information, see Data encryption and Network security management.
To prevent important data from being accidentally deleted, you can use Hybrid Backup Recovery (HBR) to back up important data on a regular basis. For more information, see Back up Tablestore data.
To configure alert notifications for monitoring metrics, you can use CloudMonitor. For more information, see Monitoring and alerting.
To visualize data such as displaying data in charts, you can use DataV or Grafana. For more information, see Data visualization tools.