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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.