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. TDengine differentiates itself from other TSDBs with the following advantages.
-**High Performance**: 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**: 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**: 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.
-**All in One**：将数据库、消息队列、缓存、流式计算等功能融合一起，应用无需再集成 Kafka/Redis/HBase/Spark 等软件，大幅降低应用开发和维护成本。
-**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 Cost**: With SQL as the query language, support for ubiquitous tools like Python, Java, C/C++, Go, Rust, Node.js connectors, there is zero learning cost.
-**无缝集成**：不用一行代码，即可与 Telegraf, Grafana, EMQX, Prometheus, StatsD, collectd, Matlab, R 等第三方工具无缝集成。
-**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-seriess 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 cost/migration cost is reduced significantly; 3: with its simple architecture and zero management, the operation and maintainence cost is reduced.
采用 TDengine，可将典型的物联网、车联网、工业互联网大数据平台的总拥有成本大幅降低。表现在几个方面，1：由于其超强性能，它能将系统需要的计算资源和存储资源大幅降低；2：因为采用SQL接口，能与众多第三放软件无缝集成，学习迁移成本大幅下降；3：因为其All In One的特性，系统复杂度降低，能降研发成本；4：因为运维维护简单，运营维护成本能大幅降低。
In the time-series data processing platform, TDengine stands in a role like this diagram below:
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 market and other fields. But you shall note that TDengine is a purpose-built database and does tons of optimization based on 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.
### 数据源特点和需求
### Characteristics and Requirements of Data Sources
从数据源角度，设计人员可以从下面几个角度分析 TDengine 在目标应用系统里面的适用性。
From the perspective of data sources, designers can analyze the applicability of TDengine in target application systems as follows.
| 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. |
| 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 also provides extensions specifically to analyze time-series data. |
| 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. |
| 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 fine-grained data | | | √ | TDengine has achieved the same or better performance than other relational and NoSQL data processing systems. |
| 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.|
## TDengine 与其他数据库的对比测试
## Benchmark comparision between TDengine and other databases
