--- title: Introduction toc_max_heading_level: 2 --- TDengine is a high-performance, scalable [time-series database](https://tdengine.com/tsdb) with SQL support. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides [caching](/develop/cache), [stream processing](/develop/continuous-query), [data subscription](/develop/subscribe) 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: 1. While TDengine supports [using SQL to insert](/develop/insert-data/sql-writing), it also supports [Schemaless writing](/reference/schemaless/) just like NoSQL databases. TDengine also supports standard protocols like [InfluxDB LINE](/develop/insert-data/influxdb-line)，[OpenTSDB Telnet](/develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](/develop/insert-data/opentsdb-json) among others. 2. TDengine supports seamless integration with third-party data collection agents like [Telegraf](/third-party/telegraf)，[Prometheus](/third-party/prometheus)，[StatsD](/third-party/statsd)，[collectd](/third-party/collectd)，[icinga2](/third-party/icinga2), [TCollector](/third-party/tcollector), [EMQX](/third-party/emq-broker), [HiveMQ](/third-party/hive-mq-broker). These agents can write data into TDengine with simple configuration and without a single line of code. 3. Support for [all kinds of queries](/develop/query-data), including aggregation, nested query, downsampling, interpolation and others. 4. Support for [user defined functions](/develop/udf). 5. Support for [caching](/develop/cache). TDengine always saves the last data point in cache, so Redis is not needed in some scenarios. 6. Support for [continuous query](../develop/continuous-query). 7. Support for [data subscription](../develop/subscribe) with the capability to specify filter conditions. 8. Support for [cluster](../cluster/), with the capability of increasing processing power by adding more nodes. High availability is supported by replication. 9. Provides an interactive [command-line interface](/reference/taos-shell) for management, maintenance and ad-hoc queries. 10. Provides many ways to [import](/operation/import) and [export](/operation/export) data. 11. Provides [monitoring](/operation/monitor) on running instances of TDengine. 12. Provides [connectors](/reference/connector/) for [C/C++](/reference/connector/cpp), [Java](/reference/connector/java), [Python](/reference/connector/python), [Go](/reference/connector/go), [Rust](/reference/connector/rust), [Node.js](/reference/connector/node) and other programming languages. 13. Provides a [REST API](/reference/rest-api/). 14. Supports seamless integration with [Grafana](/third-party/grafana) for visualization. 15. 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](https://tdengine.com/2019/07/09/86.html) to build its own innovative storage engine and computing engine to differentiate itself from other time series databases, with the following advantages. - **[High Performance](https://tdengine.com/fast)**: 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. - **[Scalable](https://tdengine.com/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. - **[SQL Support](https://tdengine.com/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. - **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. - **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. - **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. - **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. - **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: ![TDengine Database Technical Ecosystem ](eco_system.webp)
Figure 1. TDengine Technical Ecosystem
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 ](https://tdengine.com/2022/02/23/4975.html) - [Query Performance Comparison of TDengine and InfluxDB](https://tdengine.com/2022/02/24/5120.html) - [TDengine vs InfluxDB、OpenTSDB、Cassandra、MySQL、ClickHouse](https://www.tdengine.com/downloads/TDengine_Testing_Report_en.pdf) - [TDengine vs OpenTSDB](https://tdengine.com/2019/09/12/710.html) - [TDengine vs Cassandra](https://tdengine.com/2019/09/12/708.html) - [TDengine vs InfluxDB](https://tdengine.com/2019/09/12/706.html) If you want to learn some basics about time-series databases, please check [here](https://tdengine.com/tsdb).