Programmer Xiao Meng details the design and implementation of a smart agriculture platform that uses a WeChat mini‑program for remote hardware control, a web‑based dashboard for real‑time monitoring, and a full stack of Spring Boot, Vue, UniApp, MQTT and TDengine technologies.
This guide explains how to configure Spring Boot applications to use multiple coexisting DataSource beans—such as MySQL and TDengine—without runtime switching, covering bean definitions, MyBatis mapper scanning, transaction manager setup, custom @Transactional annotations, and common pitfalls, enabling clean separation of business, log, and time‑series data.
The article reviews the rapid emergence of high‑performance domestic databases such as OceanBase, TiDB, MaxCompute and TDEngine, outlines their development history, compares strengths and weaknesses, discusses suitable scenarios for TiDB, and forecasts future trends for China’s database ecosystem, while providing a curated list of further readings.
This guide walks you through TDengine setup for IoT, covering password changes, login syntax, database and table creation, super‑table design, tag management, and practical insertion examples, all with clear command‑line snippets.
The TDengine Developer Conference in Beijing unveiled the open‑source, cloud‑native TDengine 3.0, detailing its revolutionary architecture that tackles high‑cardinality challenges, introduces RAFT‑based distribution, and showcases real‑world IoT and IT‑operations case studies where enterprises dramatically improved performance and reduced costs.
DeWu chose the open‑source time‑series database TDengine to store billions of daily Sentinel flow‑control metrics, using a super‑table design with per‑resource tables, a three‑node cluster, Druid/MyBatis pooling, and raw‑SQL writes, achieving 10 ms batch write latency, sub‑millisecond queries, and 90 % compression.
This article presents a detailed case study of using TDengine as the core time‑series database for a private‑cloud energy‑management platform, covering project background, overall architecture, connector selection, Java RESTful implementation, performance testing, deployment strategy, and future technical expectations.
This article examines the challenges of selecting an IoT big‑data platform, compares traditional real‑time databases, Hadoop‑based solutions, and modern time‑series databases such as TDengine, InfluxDB and ClickHouse, and provides practical case studies and criteria for making an informed choice.
This article explains TDengine’s architecture, including its management, data, and client modules, virtual node design, write process, and detailed storage file structures, highlighting how its innovative design optimizes resource usage and performance for IoT and other big‑data applications.
