--- title: Introduction toc_max_heading_level: 2 --- 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](/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, suited scenarios and benchmarks to help you get a high level picture for TDengine. ## Major Features The major features are listed below: 1. Besides [using SQL to insert](/develop/insert-data/sql-writing)，it supports [Schemaless writing](/reference/schemaless/)，and it supports [InfluxDB LINE](/develop/insert-data/influxdb-line)，[OpenTSDB Telnet](/develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](/develop/insert-data/opentsdb-json) and other protocols. 2. Support for 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). Without a line of code, those agents can write data points into TDengine just by configuration. 3. Support for [all kinds of queries](/develop/query-data), including aggregation, nested query, downsampling, interpolation, etc. 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 interactive [command-line interface](/reference/taos-shell) for management, maintenance and ad-hoc query. 10. Provides many ways to [import](/operation/import) and [export](/operation/export) data. 11. Provides [monitoring](/operation/monitor) on TDengine running instances. 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 the seamless integration with [Grafana](/third-party/grafana) for visualization. 15. Supports seamless integration with Google Data Studio. For more detail on features, please read through the whole documentation. ## Competitive Advantages TDengine makes full use of [the characteristics of time series data](https://tdengine.com/2019/07/09/86.html), such as structured, no transaction, rarely delete or update, etc., and builds 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)**: TDengine outperforms other time series databases in data ingestion and querying while significantly reducing storage cost and compute costs, with an innovatively designed and purpose-built storage engine. - **[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 handle time-series data better, and supporting 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, there are zero learning costs. - **Interactive Console**: TDengine provides convenient console access to the database to run ad hoc queries, maintain the database, or manage the cluster without any programming. With TDengine, the total cost of ownership of time-series data platform can be greatly reduced. Because 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 In the time-series data processing platform, TDengine stands in a role like this diagram below: ![TDengine Technical Ecosystem ](eco_system.png)
Figure 1. TDengine Technical Ecosystem
On the left side, there are data collection agents like OPC-UA, MQTT, Telegraf and Kafka. On the right side, visualization/BI tools, HMI, Python/R, and IoT Apps can be connected. TDengine itself provides interactive command-line interface and web interface for management and maintenance. ## Suited Scenarios As a high-performance, scalable and SQL supported time-series database, TDengine's typical application scenarios 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, it cannot be used to process data from web crawlers, social media, e-commerce, ERP, CRM, etc. 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)