TDengine Documentation

TDengine is a highly efficient platform to store, query, and analyze time-series data. It is specially designed and optimized for IoT, Internet of Vehicles, Industrial IoT, IT Infrastructure and Application Monitoring, etc. It works like a relational database, such as MySQL, but you are strongly encouraged to read through the following documentation before you experience it, especially the Data Modeling sections. In addition to this document, you should also download and read the technology white paper. For the older TDengine version 1.6 documentation, please click here.

TDengine Introduction

Getting Started

Overall Architecture

Data Modeling

Efficient Data Ingestion

Efficient Data Querying

  • Major Features: support various standard query functions, setting filter conditions, and querying per time segment
  • Multi-table Aggregation: use STable and set tag filter conditions to perform efficient aggregation
  • Downsampling: aggregate data in successive time windows, support interpolation

TAOS SQL

Advanced Features

  • Continuous Query: Based on sliding windows, the data stream is automatically queried and calculated at regular intervals
  • Data Publisher/Subscriber: subscribe to the newly arrived data like a typical messaging system
  • Cache: the newly arrived data of each device/table will always be cached
  • Alarm Monitoring: automatically monitor out-of-threshold data, and actively push it based-on configuration rules

Connector

  • C/C++ Connector: primary method to connect to TDengine server through libtaos client library
  • Java Connector(JDBC): driver for connecting to the server from Java applications using the JDBC API
  • Python Connector: driver for connecting to TDengine server from Python applications
  • RESTful Connector: a simple way to interact with TDengine via HTTP
  • Go Connector: driver for connecting to TDengine server from Go applications
  • Node.js Connector: driver for connecting to TDengine server from Node.js applications
  • C# Connector: driver for connecting to TDengine server from C# applications
  • Windows Client: compile your own Windows client, which is required by various connectors on the Windows environment
  • Rust Connector: A taosc/RESTful API based TDengine client for Rust

Components and Tools

Connections with Other Tools

  • Grafana: query the data saved in TDengine and provide visualization
  • MATLAB: access data stored in TDengine server via JDBC configured within MATLAB
  • R: access data stored in TDengine server via JDBC configured within R
  • IDEA Database: use TDengine visually through IDEA Database Management Tool

Installation and Management of TDengine Cluster

TDengine Operation and Maintenance

Performance: TDengine vs Others

More on IoT Big Data

FAQ