Overview of Tablestore features - Tablestore - Alibaba Cloud Documentation Center

Tablestore is a table-based, low-cost serverless storage service that can be used to store large amounts of structured data in scenarios such as billing, instant messaging (IM), Internet of Things (IoT), Internet of Vehicles (IoV), risk control, and intelligent recommendation. You can use Tablestore to query and retrieve online data within milliseconds and analyze data from multiple dimensions.

Terms

The following table describes the terms that are frequently used in Tablestore.

Term	Description
region	Regions are physical data centers that are distributed around the world. Tablestore is deployed across multiple Alibaba Cloud regions. You can select a region to use Tablestore based on your business requirements. For more information, see Regions.
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.
instance	An instance is a logical entity that is used to manage and use tables in Tablestore. Each instance is equivalent to a database. Tablestore manages access from applications and measures resources at the instance level. For more information, see Instances.
endpoint	An endpoint is a connection URL that is used to access a Tablestore instance. To perform operations on tables and data in a Tablestore instance, you must specify the endpoint of the Tablestore instance. For more information, see Endpoints.
time to live (TTL)	TTL is used to manage the lifecycle of data stored in Tablestore data tables. Tablestore automatically deletes data whose TTL is expired. This helps you reduce 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 a variety of data storage models. You can choose a data storage model based on your business requirements. The following table describes the data storage models that are provided by Tablestore.

Model	Description
Wide Column model	The Wide Column 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, time to live (TTL), auto-increment primary key column, conditional update, local transaction, atomic counter, and filter.
TimeSeries model	The TimeSeries model is designed based on the characteristics of time series data. This model is suitable for scenarios, such as IoT device monitoring, and can be used to store data that is collected by devices and the monitoring data of machines. The TimeSeries model supports automatic indexing of time series metadata and time series query based on various composite conditions.
Timeline model	The Timeline 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 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.

Features

This section describes the features provided by each data storage model.

Wide Column model

Feature	Description
Operations on data tables	You can list all data tables in an instance, create a data table, query and update the configurations of a data table, and delete a data table.
Basic data operations	You can call the PutRow, GetRow, UpdateRow, and DeleteRow operations to perform operations on a single row or call the BatchWriteRow, BatchGetRow, and GetRange operations to perform operations on multiple rows. You can read and write data in a table by calling an operation to perform operations on a single row or multiple rows in the table.
Data versions and TTL	You can use the max versions and TTL features to manage the lifecycle of your data to optimize storage efficiency and reduce costs.
Configure an auto-increment primary key column	You can specify a primary key column that is not the partition key as the auto-increment primary key column. If you write data to a table that contains an auto-increment primary key column, you do not need to specify values for the auto-increment primary key column. Tablestore automatically generates values for the auto-increment primary key column. The values generated for the auto-increment primary key column are unique and increase monotonically within a partition.
Conditional updates	If you use conditional update, data in the table can be updated only when the conditions are met. If the conditions are not met, the update fails.
Local transactions	You can create a local transaction for data that shares the same partition key value. After you read or write data within a local transaction, you can commit or abort the local transaction.
Use the atomic counter feature	This feature allows you to implement an atomic counter on a column, and provides real-time statistic data for online applications such as the number of page views (PVs) on various topics.
Configure a filter	Filters sort results on the server side. Only results that meet the filter conditions are returned. This feature effectively reduces the volume of transferred data and shortens the response time.
Secondary index	You can create one or more index tables for a data table and perform queries by using the primary key columns of the index tables. Secondary indexes are classified into global secondary indexes and local secondary indexes. When you use a global secondary index, Tablestore automatically synchronizes data from the indexed columns and primary key columns of a data table to the columns of an index table in asynchronous mode. The synchronization latency can be measured in milliseconds. When you use a local secondary index, Tablestore automatically synchronizes data from the indexed columns and primary key columns of a data table to the columns of an index table in synchronous mode. You can immediately query the data from the index table after the data is written to the data table.
Search index	Search indexes are used for multi-dimensional data queries and statistical analysis in big data scenarios based on inverted indexes and column stores. Search indexes support various query methods, including query based on non-primary key columns, full-text search, prefix query, fuzzy query, Boolean query, nested query, and geo query. Search indexes also support parallel scan and multiple aggregation operations. You can perform aggregation operations to obtain the maximum and minimum values, count and distinct count of rows, sums, and averages, and group results by specific conditions.
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 analytics on data in Tablestore in an efficient manner. You can also use indexes in combination with the SQL query feature to optimize queries.
Tunnel Service	Tunnel Service provides tunnels that are used to export and consume data in full, incremental, and differential modes. After you create a tunnel, you can use it to consume historical and incremental data of a table.
Data security	By default, you can access Tablestore by using the Tablestore console or over the classic network or VPC endpoint. You can bind a VPC to a Tablestore instance and change the network types of the instance to allow access to the instance only over the VPC. This enhances access security. For more information, see Network security management. Tablestore provides the data encryption feature to encrypt data when the data is written to a data table. This ensures the security of table data. When you create a data table, you can configure table encryption. For more information, see Data encryption.
Connect Tablestore to big data platforms	You can manage and use Tablestore by connecting Tablestore to big data platforms, such as DataWorks and Data Management (DMS).
Data delivery	Tablestore uses data delivery to deliver full or incremental data to Object Storage Service (OSS) in real time. This feature is ideal for building data lakes as it enables Tablestore to store historical data in OSS at lower costs while Tablestore implements offline or quasi-real-time analysis of larger amounts of data.
Data visualization	Tablestore supports two data visualization tools: DataV and Grafana. These tools can be used to visualize data in Tablestore.
Monitoring and alerting	You can view monitoring information about Tablestore resources to monitor resource usage. You can also configure alert rules for important metrics of the resources. This way, you can receive notifications of metric anomalies and troubleshoot the errors at the earliest opportunity.
Backup and restoration	You can use Cloud Backup to back up data in Tablestore instances on a regular basis and restore the lost or damaged data at the earliest opportunity. You can use Cloud Backup to back up full and incremental data. Cloud Backup supports the data redundancy mechanism to increase the data reliability of storage vaults.
HBase support	Tablestore HBase Client can be used to access Tablestore by using Java applications built on open source HBase APIs.
Audit log	You can use the audit log feature to query and analyze the logs of operations performed on tables and indexes in a Tablestore instance.

TimeSeries model

Feature	Description
Operations on time series tables	You can list all time series tables in an instance, create a time series table, query and update the configurations of a time series table, and delete a time series table.
Operations on time series data	You can write time series data into a time series table in batches. After data is written to the time series table, you can specify time series identifiers to query the data in the time series within the specified time range.
Time series operations	You can retrieve a time series in a time series table by using composite conditions. After the time series is retrieved, you can call Tablestore API operations to further query data in the time series.
TTL of time series data	You can configure the time to live (TTL) for time series metadata and data in a time series table to manage time series data in an efficient manner and reduce storage usage and costs.
Analytical store	Analytical stores are low-cost storage engines designed and optimized by Tablestore for time series scenarios. Analytical stores support efficient data compression and storage and provide powerful query and analysis capabilities, which makes analytical stores ideal for large-scale data analysis and query tasks.
Specify custom time series identifiers and custom data fields as the primary key columns	By default, the time series identifiers of time series in the TimeSeries model consist of the metric name, data source, and tags. You can specify custom time series identifiers based on your business requirements. In a time series table, a row of time series data is uniquely identified by the time series identifiers and timestamp. You can specify custom data fields as the primary key columns to store multiple rows with the same time series identifiers and timestamp but different time series data.
Lastpoint index	You can use a Lastpoint index to quickly retrieve data of the latest point in time in time series in a time series table.
Use SQL to query time series data	You can use SQL statements to query time series data. In an SQL statement, you can filter time series by specifying metadata conditions and aggregate data based on different dimensions. In addition, you can query only the metadata of time series in SQL. This way, you can manage the metadata of time series by using SQL.
Data security	By default, you can access Tablestore by using the Tablestore console or over the classic network or VPC endpoint. You can bind a VPC to a Tablestore instance and change the network types of the instance to allow access to the instance only over the VPC. This enhances access security. For more information, see Network security management.
Connect Tablestore to big data platforms	You can manage and use Tablestore by connecting Tablestore to big data platforms, such as DataWorks and Data Management (DMS).
Connect Tablestore to Grafana	After you add a Tablestore data source to Grafana, Grafana displays real-time data on a dashboard.
Monitoring and alerting	You can view monitoring information about Tablestore resources to monitor the resource usage. You can also configure alert rules for important metrics of the resources. This way, you can receive notifications of metric anomalies and troubleshoot the errors at the earliest opportunity.
Audit log	You can use the audit log feature to query and analyze the logs of operations performed on tables and indexes in a Tablestore instance.

Timeline model

Feature	Description
Operations on tables	You can create or delete a Meta table and its indexes. You can create or delete a Timeline table and its indexes.
Meta management	You can call operations such as Insert, Delete, Update, Read, and Search to manage Meta data.
Timeline management	You can call the operations for fuzzy query and Boolean query to manage Timeline data.
Queue management	The Queue instance manages a message queue that corresponds to an identifier of a TimelineStore. This instance provides operations such as Store, StoreAsync, BatchStore, Delete, Update, UpdateAsync, Get, and Scan.