Product Version Description

Last updated: 2025-04-30 16:33:26

Product Version Description

Last updated: 2025-04-30 16:33:26

To provide you with better services, TencentDB for CTSDB has released version 3.0. Clusters of this version are distributed, high-performance clusters and are compatible with the open-source InfluxDB protocol. It is recommended that you choose TencentDB for CTSDB 3.0 distributed clusters.
Services of TencentDB for CTSDB 1.0 and 2.0 are about to be suspended. For details, see Announcement on Service Suspension of Versions 1.0/2.0 and Migration and Upgrade to Version 3.0.
The feature differences between the two versions are described in the table below.
Feature Category
TencentDB for CTSDB 3.0
TencentDB for CTSDB 1.0 and 2.0 (Services About to Be Suspended)
Compatible protocol
Compatible with the open-source InfluxDB protocol.
Based on the self-developed protocol.
Data model
TencentDB for CTSDB 3.0 is specially designed for time series data. The data model consists of four parts: measurement, tag, field, and timestamp. This model stores data by time to support quick data queries by time range. It also supports aggregating, grouping, and sorting the data.
The data model consists of metric, tag, field, and time. It can store various types of data.
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Query language
InfluxQL can be used as the query language. It is specially designed for time series data and has more advantages in processing time series data. This version is compatible with the efficient data structures, protocol APIs, and ecosystem of InfluxDB.
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Elasticsearch-like syntax is used for querying various types of data.
Storage and compression
TencentDB for CTSDB 3.0 selects appropriate data compression algorithms for different types of data to optimize the storage and compression of time series data, such as Delta + RLE + Simple8b, ZigZag + RLE + Simple8b, and ZSTD. This makes the database more efficient in storing and processing a large amount of time series data.
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Some common storage compression algorithms are used to reduce data storage costs.
System architecture
TencentDB for CTSDB 3.0 is based on a cloud-native compute-storage separation architecture. Resources in the data request layer and storage layer are pooled to ensure elastic scaling of resources.
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The storage-compute integration architecture with the elastic scalability capability is used.
Aggregation analysis
Various aggregation query methods are supported, including commonly used aggregate functions such as min, max, sum, count, mean, and percentile, as well as some transformation functions such as ABS, ACOS, ASIN, ATAN, ATAN2, CEIL, COS, EXP, FLOOR, LOG2, LOG10, SIN, and SQRT.
Commonly used aggregate functions such as avg, min, and max are supported. GROUP BY and other clauses are supported for aggregation analysis.
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Ecological compatibility
Compatible with the rich open-source capabilities of InfluxDB. Some tools and libraries specific for time series data are provided.
Partially compatible with the ELK ecosystem.
Smart Ops
Monitoring metrics in multiple dimensions, such as latency, network, read/write performance, and resource utilization rate, are provided to assist in database Ops and optimization, thus ensuring smooth business operation.
Monitoring metrics such as resource utilization rate are provided to ensure business operation.
For links to documents of TencentDB for CTSDB 1.0, 2.0, and 3.0, see the table below.
Product Name
Product Document
TencentDB for CTSDB 3.0
For product overview, see Overview.
For instance purchase, product pricing, renewal, and payment overdue, see Purchase Guide.
For instance creation and usage, see  Creating Database Instance and Using Database Instance.
For instance management and Ops monitoring in the console, see Viewing Instance.
TencentDB for CTSDB 1.0 and 2.0
For renewal and payment overdue, see Purchase Guide.
For instance connection, see Connecting to Instance.
For instance management and Ops monitoring in the console, see Basic Operations.

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Product Name	Product Document
TencentDB for CTSDB 3.0	For product overview, see Overview. For instance purchase, product pricing, renewal, and payment overdue, see Purchase Guide. For instance creation and usage, see Creating Database Instance and Using Database Instance. For instance management and Ops monitoring in the console, see Viewing Instance.
TencentDB for CTSDB 1.0 and 2.0	For renewal and payment overdue, see Purchase Guide. For instance connection, see Connecting to Instance. For instance management and Ops monitoring in the console, see Basic Operations.

Feature Category	TencentDB for CTSDB 3.0	TencentDB for CTSDB 1.0 and 2.0 (Services About to Be Suspended)
Compatible protocol	Compatible with the open-source InfluxDB protocol.	Based on the self-developed protocol.
Data model	TencentDB for CTSDB 3.0 is specially designed for time series data. The data model consists of four parts: measurement, tag, field, and timestamp. This model stores data by time to support quick data queries by time range. It also supports aggregating, grouping, and sorting the data.	The data model consists of metric, tag, field, and time. It can store various types of data.
Query language	InfluxQL can be used as the query language. It is specially designed for time series data and has more advantages in processing time series data. This version is compatible with the efficient data structures, protocol APIs, and ecosystem of InfluxDB.	Elasticsearch-like syntax is used for querying various types of data.
Storage and compression	TencentDB for CTSDB 3.0 selects appropriate data compression algorithms for different types of data to optimize the storage and compression of time series data, such as Delta + RLE + Simple8b, ZigZag + RLE + Simple8b, and ZSTD. This makes the database more efficient in storing and processing a large amount of time series data.	Some common storage compression algorithms are used to reduce data storage costs.
System architecture	TencentDB for CTSDB 3.0 is based on a cloud-native compute-storage separation architecture. Resources in the data request layer and storage layer are pooled to ensure elastic scaling of resources.	The storage-compute integration architecture with the elastic scalability capability is used.
Aggregation analysis	Various aggregation query methods are supported, including commonly used aggregate functions such as min, max, sum, count, mean, and percentile, as well as some transformation functions such as ABS, ACOS, ASIN, ATAN, ATAN2, CEIL, COS, EXP, FLOOR, LOG2, LOG10, SIN, and SQRT.	Commonly used aggregate functions such as avg, min, and max are supported. GROUP BY and other clauses are supported for aggregation analysis.
Ecological compatibility	Compatible with the rich open-source capabilities of InfluxDB. Some tools and libraries specific for time series data are provided.	Partially compatible with the ELK ecosystem.
Smart Ops	Monitoring metrics in multiple dimensions, such as latency, network, read/write performance, and resource utilization rate, are provided to assist in database Ops and optimization, thus ensuring smooth business operation.	Monitoring metrics such as resource utilization rate are provided to ensure business operation.

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