Announcing TDengine 3.0

A new, innovative open-source product for managing massive time-series data environments

At TDengine, we are always looking for opportunities to modernize to meet our customers’ demands for highly efficient, easy-to-use products in a cloud environment. With over 19,000 GitHub stars, our high-performance, cloud-native, simplified solution is the choice of developers in more than 50 countries worldwide. We help them to solve common problems, including high-cardinality and time-series data challenges, with a unique architecture that can support billions of data points. Despite our success, we always aim to bring best-in-class solutions to the market.

We have observed that large-scale IoT deployments are generating an abundance of data, so today, we are excited to announce TDengine 3.0, an innovative open-source platform that scales and simplifies the deployment and management of sizeable time-series data environments and efficiently ingests petabytes of data from IoT sensors and collectors. TDengine 3.0 is designed to help developers handle modern time-series operations relying on the cloud.

Major Features:

  • High Performance on Time-Series Data, up to 5x the speed of other time-series databases (TSDB) and 10x the read/write performance of general-purpose databases.
  • Kubernetes and Serverless Container Support, providing a fully distributed architecture that decouples compute and storage resources for dynamic scaling. TDengine can operate on public, private, or hybrid clouds.
  • Built-In Message Queue tailored for time-series data in IoT architectures. This fast and efficient message queue simplifies data ingestion from different sources.
  • Stream Processing with sliding time windows and standard SQL syntax for traditional continuous queries and event-driven stream computing.
  • High Scale with a cluster that can have billions of time-series data points while starting up a cluster in less than a minute, eliminating high-cardinality issues common in growing IoT and other environments with large numbers of endpoints.
  • Cache Storage of New Data, eliminating the need to integrate with a separate caching solution for high-speed queries of time-series data.
  • Simple Time-Series Data Analytics provides SQL query support and merges with popular analytics and observability tools, including Grafana, Google Data Studio, and Prometheus. Innovations like super tables, storage and compute separation, data partitioning by time interval, and pre-computation make it easy to access and analyze data efficiently.

Join the TDengine Community

TDengine 3.0 is immediately available on GitHub.