TDengine is an open-source huge knowledge platform designed and optimized for Internet of Things (IoT), Connected Vehicles, and Industrial IoT. Besides the 10x quicker time-series database, it supplies caching, stream computing, message queuing and different functionalities to scale back the complexity and prices of growth and operations.
- 10x Faster on Insert/Query Speeds: Through the progressive design on storage, on a single-core machine, over 20K requests may be processed, thousands and thousands of knowledge factors may be ingested, and over 10 million knowledge factors may be retrieved in a second. It is 10 occasions quicker than different databases.
- 1/5 Hardware/Cloud Service Costs: Compared with typical huge knowledge options, lower than 1/5 of computing sources are required. Via column-based storage and tuned compression algorithms for various knowledge sorts, lower than 1/10 of space for storing is required.
- Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing options collectively, it’s not essential to combine Kafka/Redis/HBase/Spark or different software program. It makes the system structure a lot easier and extra sturdy.
- Powerful Data Analysis: Whether it’s 10 years or one minute in the past, knowledge may be queried simply by specifying the time vary. Data may be aggregated over time, a number of time streams or each. Ad Hoc queries or analyses may be executed through TDengine shell, Python, R or Matlab.
- Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and different instruments may be built-in with TDengine with no line of code. MQTT, OPC, Hadoop, Spark, and lots of others shall be built-in quickly.
- Zero Management, No Learning Curve: It takes solely seconds to obtain, set up, and run it efficiently; there are not any different dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.