Inside TDengine

This chapter briefly outlines some of the internal designs of TDengine.

📄️ Architecture

TDengine architecture design, including clusters, storage, caching and persistence, data backup, multi-level storage, and more.

📄️ Storage Engine

TDengine Storage Engine

📄️ Query Engine

As a high-performance time-series big data platform, TDengine’s query and computation capabilities are core components. The platform offers rich query processing features, including not only standard aggregate queries but also advanced functionalities like time-series window queries and statistical aggregation. These query tasks require close collaboration between taosc, vnode, qnode, and mnode. In a complex supertable aggregation query scenario, multiple vnodes and qnodes may need to work together to handle the query and computation tasks. For definitions and introductions to vnode, qnode, and mnode, please refer to Architecture.

📄️ Data Subscription Engine

Data subscription, as a core function of TDengine, provides users with the ability to flexibly acquire the data they need. By understanding its internal principles, users can utilize this feature more effectively to meet various real-time data processing and monitoring needs.

📄️ Stream Processing Engine

Stream Computing Architecture

📄️ Data Compression

Data compression is a technology that reorganizes and processes data using specific algorithms without losing effective information, aiming to reduce the storage space occupied by data and improve data transmission efficiency. TDengine employs this technology during the storage and transmission of data to optimize the use of storage resources and accelerate data exchange speed.