Tablestore function introduction overview - - Alibaba Cloud Documentation Center

Tablestore is a serverless table storage service designed for massive structured data. It supports scenarios such as massive bills, instant messaging (IM) messages, Internet of Things (IoT), Internet of vehicles, risk control, and recommendations. Tablestore provides low-cost storage for large data volumes, online data queries and retrieval with millisecond latency, and flexible data analytics.

Term	Description
Region	A physical data center where Tablestore is deployed. Regions and zones.
Read/write throughput	Measured in read capacity units (RCUs) and write capacity units (WCUs). A CU is the smallest billing unit for read and write operations. Read/write throughput.
Instance	An entity for managing and using Tablestore, equivalent to a database. Access control and resource metering operate at the instance level. See Instances.
Endpoint	Each instance has an endpoint that applications must specify when performing operations on tables and data. See Endpoints.
Data lifecycle	Time to live (TTL) is a property of a data table that specifies the data retention period in seconds. Tablestore clears expired data in the background, reducing storage costs. See Data versions and TTL.

Data storage models

Tablestore provides multiple data storage models. You can select a model based on your business needs. The following table describes these Tablestore models.

Model	Description
Wide table model	A model similar to Bigtable and HBase. It can be used in various scenarios, such as metadata and big data. It supports features such as data versions, lifecycle, auto-increment primary key columns, conditional updates, local transactions, atomic counters, and filters.
Time series model	A model designed for the characteristics of time series data. It can be used in scenarios such as IoT device monitoring, device data collection, and machine monitoring data. It supports features such as automatic creation of time series metadata indexes and rich time series query capabilities.
Message model	A model designed for message data scenarios. It can be used in message scenarios such as IM and feed streams. It meets the requirements for message ordering, massive message storage, and real-time synchronization in message scenarios. It also supports full-text search and multi-dimensional composite queries.

Features

Each Tablestore model supports a different set of features.

Wide table model

Features	Description
Data table operations	Lets you list all data tables in an instance, create a data table, query the configuration of a data table, update the configuration of a data table, and delete a data table.
Basic data operations	Tablestore provides single-row data operation interfaces such as PutRow, GetRow, UpdateRow, and DeleteRow, and multi-row data operation interfaces such as BatchWriteRow, BatchGetRow, and GetRange. You can use these interfaces to read and write data in a table.
Data versions and lifecycle	Use data versions and time to live (TTL) to effectively manage data, reduce data storage usage, and lower storage costs.
Auto-increment primary key column	After you set a non-partition primary key column as an auto-increment column, you do not need to specify a value for the column when you write data. Tablestore automatically generates a value for the auto-increment column. The value is unique and strictly incremental at the partition key level.
Conditional update	Updates data in a table only if a specified condition is met. If the condition is not met, the update fails.
Local transaction	Create a local transaction for a data range within a partition key value. After you perform read and write operations on the data in the local transaction, you can commit or discard the local transaction as needed.
Atomic counter	Use a column as an atomic counter and perform atomic counting operations on the column. This can be used to provide real-time statistics for online applications, such as counting the page views (PVs) of a post.
Filter	Filters the read results on the server-side and determines which rows to return based on the filter conditions. Because only the rows that meet the conditions are returned, this can effectively reduce the amount of data transferred over the network and decrease the response time in most scenarios.
Secondary index	Create one or more index tables and use the primary key columns of the index tables for queries. This extends the primary key query capability of the data table to different columns. Secondary indexes include global secondary indexes and local secondary indexes. Global secondary index: Asynchronously and automatically synchronizes data from the indexed columns and primary key columns of a data table to an index table. The synchronization latency is typically at the millisecond level. Local secondary index: Synchronously and automatically synchronizes data from the indexed columns and primary key columns of a data table to an index table. After data is written to the data table, you can query the data from the index table.
Search index	Based on inverted indexes and columnar storage, search indexes can solve complex query problems for big data. This includes features such as non-primary key column queries, full-text search, prefix queries, fuzzy queries, multi-conditional composite queries, nested queries, geo queries, statistical aggregation (max, min, count, sum, avg, distinct_count, and group_by), and parallel scans.
SQL query	The SQL query feature provides a unified access interface for multiple data engines. Using this feature, you can perform complex queries and efficient analysis on data in Tablestore. When you use SQL to query data, you can also use indexes to optimize queries.
Tunnel Service	Tablestore provides three types of distributed real-time data consumption channels: incremental, full, and incremental plus full. These channels let you consume and process both historical and newly added data in a table.
Data security	By default, Tablestore can be accessed only through classic network domain names, VPC domain names, or the console. You can bind a VPC to an instance and change the instance network type to use Tablestore resources in a VPC, ensuring secure network access. For more information, see Network security management. To ensure the security of table data, Tablestore provides the encryption at rest feature. You can configure data table encryption when you create a data table.
Big data integration	Tablestore can be integrated with big data platforms such as DataWorks and DMS for management and use.
Data lake delivery	Tablestore data lake delivery can perform full backups or deliver data in real time to OSS for storage in a data lake. This meets the needs for lower-cost historical data storage and larger-scale offline and near-real-time data analytics.
Data visualization	You can integrate with data visualization tools such as DataV or Grafana to visualize data in Tablestore.
Monitoring and Alerts	View the monitoring information of Tablestore resources to understand resource usage. Set alert rules for important monitoring metrics to be promptly notified of anomalies and handle them quickly.
Backup and recovery	Use Cloud Backup to periodically back up data in Tablestore instances and restore it promptly if data is lost or damaged. Cloud Backup supports full and incremental data backups. It also supports a data redundancy mechanism to improve the data reliability of vaults.
HBase support	Java applications that use open source HBase APIs can directly access the Tablestore service through the Tablestore HBase Client.
Audit log	Using the audit log feature, you can query and analyze logs of operations related to tables and indexes in a Tablestore instance.

Time series model

Feature	Description
Time series table operations	Lets you list all time series tables in an instance, create a time series table, query the configuration of a time series table, update its configuration, and delete it.
Time series data operations	Write time series data to a time series table in batches. After the data is written, you can specify a time series identifier to query the data of a time series within a specific timestamp range.
Time series retrieval	Retrieve time series in a time series table. The retrieval supports a combination of multiple conditions. After a time series is retrieved, you can call an API to further query the data in the time series.
Time series data lifecycle	Tablestore provides separate lifecycles for time series metadata and time series table data to manage the corresponding data. You can use the time to live (TTL) feature to effectively manage time series data, reduce data storage usage, and lower storage costs.
Analytical Store	Analytical store is a low-cost storage engine that Tablestore has optimized for time series scenarios. It features efficient data compression and storage capabilities and powerful query and analysis functions, making it ideal for handling large-scale data analytics tasks.
Custom time series identifiers and data fields as primary keys	In a time series model, a time series identifier is composed of a measure name, data source, and tags by default. Customize the identifier as needed. A time series identifier and a time point uniquely identify each row in a time series table. To store rows with the same identifier and time point but different time series data, add another data field to the primary key.
Lastpoint index	The Lastpoint index provides the ability to quickly retrieve the data at the latest timestamp of a time series. When you need to get the time series data at the latest timestamp for each time series, you can use the Lastpoint index.
SQL query and analysis	Time series tables support queries using SQL. In SQL, you can filter time series by specifying metadata conditions and perform aggregate operations on data across different dimensions. In addition, SQL supports querying only the metadata of time series, which facilitates metadata management of time series through SQL.
Data security	By default, Tablestore can be accessed only through classic network domain names, VPC domain names, or the console. You can bind a VPC to an instance and change the instance network type to use Tablestore resources in a VPC, ensuring secure network access. For more information, see Network security management.
Big data integration	Tablestore can be integrated with big data platforms such as DataWorks and DMS for management and use.
Integrate with Grafana	After Tablestore data is connected to Grafana, Grafana can generate dashboards based on the table data to display the data in real time to users.
Monitoring and Alerts	View the monitoring information of Tablestore resources to understand resource usage. Set alert rules for important monitoring metrics to be promptly notified of anomalies and handle them quickly.
Audit log	Using the audit log feature, you can query and analyze logs of operations related to tables and indexes in a Tablestore instance.

Message model

Features	Description
Table operations	Create or delete meta tables and their indexes. Create or delete timeline tables and their indexes.
Meta management	Meta management provides interfaces for adding, deleting, modifying, reading single rows, and performing multi-conditional composite queries.
Timeline management	Timeline management provides interfaces for fuzzy message queries and multi-conditional composite queries.
Queue management	A queue is a management instance for a message queue that corresponds to an identifier in a single data store. It primarily provides interfaces for synchronous writes, asynchronous writes, batch writes, deletions, synchronous modifications, asynchronous modifications, single-row reads, and range reads.