Introduction
TDengine is a high-performance, scalable time-series database with SQL support. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides caching, stream processing, data subscription and other functionalities to reduce the complexity and cost of development and operation.
This section introduces the major features, competitive advantages, typical use-cases and benchmarks to help you get a high level overview of TDengine.
Major Features
The major features are listed below:
- While TDengine supports using SQL to insert, it also supports Schemaless writing just like NoSQL databases. TDengine also supports standard protocols like InfluxDB LINE，OpenTSDB Telnet, OpenTSDB JSON among others.
- TDengine supports seamless integration with third-party data collection agents like Telegraf，Prometheus，StatsD，collectd，icinga2, TCollector, EMQX, HiveMQ. These agents can write data into TDengine with simple configuration and without a single line of code.
- Support for all kinds of queries, including aggregation, nested query, downsampling, interpolation and others.
- Support for user defined functions.
- Support for caching. TDengine always saves the last data point in cache, so Redis is not needed in some scenarios.
- Support for continuous query.
- Support for data subscription with the capability to specify filter conditions.
- Support for cluster, with the capability of increasing processing power by adding more nodes. High availability is supported by replication.
- Provides an interactive command-line interface for management, maintenance and ad-hoc queries.
- Provides many ways to import and export data.
- Provides monitoring on running instances of TDengine.
- Provides connectors for C/C++, Java, Python, Go, Rust, Node.js and other programming languages.
- Provides a REST API.
- Supports seamless integration with Grafana for visualization.
- Supports seamless integration with Google Data Studio.
For more details on features, please read through the entire documentation.
Competitive Advantages
Time-series data is structured, not transactional, and is rarely deleted or updated. TDengine makes full use of these characteristics of time series data to build its own innovative storage engine and computing engine to differentiate itself from other time series databases, with the following advantages.
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High Performance: With an innovatively designed and purpose-built storage engine, TDengine outperforms other time series databases in data ingestion and querying while significantly reducing storage costs and compute costs.
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Scalable: TDengine provides out-of-box scalability and high-availability through its native distributed design. Nodes can be added through simple configuration to achieve greater data processing power. In addition, this feature is open source.
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SQL Support: TDengine uses SQL as the query language, thereby reducing learning and migration costs, while adding SQL extensions to better handle time-series. Keeping NoSQL developers in mind, TDengine also supports convenient and flexible, schemaless data ingestion.
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All in One: TDengine has built-in caching, stream processing and data subscription functions. It is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software in some scenarios. It makes the system architecture much simpler, cost-effective and easier to maintain.
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Seamless Integration: Without a single line of code, TDengine provide seamless, configurable integration with third-party tools such as Telegraf, Grafana, EMQX, Prometheus, StatsD, collectd, etc. More third-party tools are being integrated.
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Zero Management: Installation and cluster setup can be done in seconds. Data partitioning and sharding are executed automatically. TDengine’s running status can be monitored via Grafana or other DevOps tools.
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Zero Learning Costs: With SQL as the query language and support for ubiquitous tools like Python, Java, C/C++, Go, Rust, and Node.js connectors, and a REST API, there are zero learning costs.
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Interactive Console: TDengine provides convenient console access to the database, through a CLI, to run ad hoc queries, maintain the database, or manage the cluster, without any programming.
With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced. 1: With its superior performance, the computing and storage resources are reduced significantly 2: With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly 3: With its simple architecture and zero management, the operation and maintenance costs are reduced.
Technical Ecosystem
This is how TDengine would be situated, in a typical time-series data processing platform:
On the left-hand side, there are data collection agents like OPC-UA, MQTT, Telegraf and Kafka. On the right-hand side, visualization/BI tools, HMI, Python/R, and IoT Apps can be connected. TDengine itself provides an interactive command-line interface and a web interface for management and maintenance.
Typical Use Cases
As a high-performance, scalable and SQL supported time-series database, TDengine's typical use case include but are not limited to IoT, Industrial Internet, Connected Vehicles, IT operation and maintenance, energy, financial markets and other fields. TDengine is a purpose-built database optimized for the characteristics of time series data. As such, it cannot be used to process data from web crawlers, social media, e-commerce, ERP, CRM and so on. More generally TDengine is not a suitable storage engine for non-time-series data. This section makes a more detailed analysis of the applicable scenarios.
Characteristics and Requirements of Data Sources
| Data Source Characteristics and Requirements | Not Applicable | Might Be Applicable | Very Applicable | Description |
|---|---|---|---|---|
| A massive amount of total data | √ | TDengine provides excellent scale-out functions in terms of capacity, and has a storage structure with matching high compression ratio to achieve the best storage efficiency in the industry. | ||
| Data input velocity is extremely high | √ | TDengine's performance is much higher than that of other similar products. It can continuously process larger amounts of input data in the same hardware environment, and provides a performance evaluation tool that can easily run in the user environment. | ||
| A huge number of data sources | √ | TDengine is optimized specifically for a huge number of data sources. It is especially suitable for efficiently ingesting, writing and querying data from billions of data sources. |
System Architecture Requirements
| System Architecture Requirements | Not Applicable | Might Be Applicable | Very Applicable | Description |
|---|---|---|---|---|
| A simple and reliable system architecture | √ | TDengine's system architecture is very simple and reliable, with its own message queue, cache, stream computing, monitoring and other functions. There is no need to integrate any additional third-party products. | ||
| Fault-tolerance and high-reliability | √ | TDengine has cluster functions to automatically provide high-reliability and high-availability functions such as fault tolerance and disaster recovery. | ||
| Standardization support | √ | TDengine supports standard SQL and provides SQL extensions for time-series data analysis. |
System Function Requirements
| System Function Requirements | Not Applicable | Might Be Applicable | Very Applicable | Description |
|---|---|---|---|---|
| Complete data processing algorithms built-in | √ | While TDengine implements various general data processing algorithms, industry specific algorithms and special types of processing will need to be implemented at the application level. | ||
| A large number of crosstab queries | √ | This type of processing is better handled by general purpose relational database systems but TDengine can work in concert with relational database systems to provide more complete solutions. |
System Performance Requirements
| System Performance Requirements | Not Applicable | Might Be Applicable | Very Applicable | Description |
|---|---|---|---|---|
| Very large total processing capacity | √ | TDengine’s cluster functions can easily improve processing capacity via multi-server coordination. | ||
| Extremely high-speed data processing | √ | TDengine’s storage and data processing are optimized for IoT, and can process data many times faster than similar products. | ||
| Extremely fast processing of high resolution data | √ | TDengine has achieved the same or better performance than other relational and NoSQL data processing systems. |
System Maintenance Requirements
| System Maintenance Requirements | Not Applicable | Might Be Applicable | Very Applicable | Description |
|---|---|---|---|---|
| Native high-reliability | √ | TDengine has a very robust, reliable and easily configurable system architecture to simplify routine operation. Human errors and accidents are eliminated to the greatest extent, with a streamlined experience for operators. | ||
| Minimize learning and maintenance costs | √ | In addition to being easily configurable, standard SQL support and the Taos shell for ad hoc queries makes maintenance simpler, allows reuse and reduces learning costs. | ||
| Abundant talent supply | √ | Given the above, and given the extensive training and professional services provided by TDengine, it is easy to migrate from existing solutions or create a new and lasting solution based on TDengine. |
Comparison with other databases
- Writing Performance Comparison of TDengine and InfluxDB
- Query Performance Comparison of TDengine and InfluxDB
- TDengine vs OpenTSDB
- TDengine vs Cassandra
- TDengine vs InfluxDB
If you want to learn some basics about time-series databases, please check here.
