@@ -11,19 +11,23 @@ We are hiring, check [here](https://www.taosdata.com/en/careers/)
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@@ -11,19 +11,23 @@ We are hiring, check [here](https://www.taosdata.com/en/careers/)
# What is TDengine？
# What is TDengine？
TDengine is an open-sourced big data platform under [GNU AGPL v3.0](http://www.gnu.org/licenses/agpl-3.0.html), designed and optimized for the Internet of Things (IoT), Connected Cars, Industrial IoT, and IT Infrastructure and Application Monitoring. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and cost of development and operation.
TDengine is a high-performance, scalable time-series database with SQL support. Its code including cluster feature is open souece under [GNU AGPL v3.0](http://www.gnu.org/licenses/agpl-3.0.html). Besides the database, it provides caching, stream processing, data data subscription and other functionalities to reduce the complexity and cost of development and operation. It can be widely applied to Internet of Things (IoT), Connected Vehicles, Industrial IoT, DevOps, energy, finance and many other scenarios. It differentiates itself from other TSDBs with the following advanatages.
-**10x Faster on Insert/Query Speeds**: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.
-**High Peroformance**: 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.
-**1/5 Hardware/Cloud Service Costs**: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.
-**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.
-**Full Stack for Time-Series Data**: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust.
-**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.
-**Powerful Data Analysis**: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.
-**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 and easy to maintain.
-**Seamless Integration with Other Tools**: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.
-**Seamless Integration**: Without a single line of code, TDengine provide seamless integration with third-party tools such as Telegraf, Grafana, EMQ X, Prometheus, StatsD, collectd, etc. More will be integrated.
-**Zero Management**: Installation and cluster setup can be done in seconds. Data partitioning and sharding are done automatically. TDengine’s running status can be monitored via Grafana or other DevOps tools.
-**Zero Management, No Learning Curve**: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.
-**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.
-**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.
