In this test, TDengine is compared with OpenTSDB in the terms of writing throughput, query throughput, aggregation query response time and on-disk compression. The results demonstrates that TDengine outperforms OpenTSDBwith 25x greater write throughput, 32x larger query throughput, 1000x faster in aggregation query (1000x when grouping by tags and 40x when grouping by time) while using 5x less disk space.
In this test, TDengine is compared with Cassandra in the terms of writing throughput, data ingestion, aggregation query response time and on-disk compression. The results demonstrates that TDengine outperforms Cassandra with 20x greater write throughput, 17x larger data ingestion, 4000x faster in aggregation query (2500x when grouping by tags and 119x when grouping by time) while using 26.7x less disk space.
In this test, TDengine is compared with InfluxDB in the terms of writing throughput, query throughput, aggregation query response time and on-disk compression. The result demonstrates that TDengine outperforms InfluxDB with 5x greater write throughput, 35x larger query throughput, 140x faster in aggregation query (250x when grouping by tags and 12x when grouping by time) while using 2.1x less disk space.
TDengine is a light, high-efficient, single-node open-source and IOT-oriented data processing engine. As a data engine designed for IOT, TDengine has huge advantages in writing, querying, storage, etc. In this article, we will talk about the architecture and storage design of TDengine to help users to fully understand it.
A stable real-time monitoring system is an absolute necessity for a robust and efficient application system. For huge monitoring data volumes, the bottleneck of the whole system’s performance is usually in the data persistence layer.
In the era of the Internet of Things (IoT), the real-time data generated by connected vehicles enhance user experiences in car rental and fleet management business, drive innovative business model like usage-based insurance, and build the foundation of autonomous driving and vehicle-to-everything (V2X) paradigm. Time series data processing platforms like TDengine are purposely built for time series data and much more cost-efficient in structured IoT data processing.
Besides being a time-series database, TDengine provides caching, message queuing, and stream computing functionalities. It is a full stack for time-series data processing, so you it is not necessary to integrate with other big data tools.
TDengine is designed and optimized for IoT. It reduces the computing and storage resources significantly, and it reduce the complexity of development and operation significantly too. Also, it has many other cool features, plus, it is open sourced
General big data platform can handle all types of data, but it faces technology challenges when it handles the huge amount of IoT data. It’s not efficient, not cost effective either. We can utilize the IoT data characteristics to roll out a highly efficient solution
Data generated by connected devices has special characteristics compared with typical Internet Applications. If we utilize these characteristics, we can design a new data processing tool to improve the data processing efficiency significantly