Inside TDengine 3.0

Jeff Tao
Jeff Tao
Share on LinkedIn

TDengine is an open-source, high-performance, cloud-native time-series database (TSDB) management system. In addition to the time-series database, TDengine includes built-in caching, data subscription, and stream processing components, making it a complete, simplified solution for time-series data. With TDengine as your time-series data processing solution, you can reduce the complexity of your systems and lower operating costs. It can be used in several fields, including IoT, IIoT, connected cars, DevOps, and fintech.

Competitive Advantages

The innovative design of the storage and compute components of TDengine, purpose-built to take full advantage of the characteristics of time-series data, offers the following advantages over competing products:

  • High performance: TDengine performs queries and writes several times faster than any other time-series database management system. Compared with general-purpose databases, it queries and writes data ten times faster while using only a tenth of the storage resources.
  • Simplified solution: Message queuing, caching, and stream processing are built into TDengine, eliminating the need to deploy Kafka, Redis, HBase, and Spark for your time-series applications.
  • Cloud-native: The natively distributed design of TDengine makes the best use of cloud resources and provides elasticity, resilience, and observability. You can deploy TDengine in Kubernetes or on a public, private, or hybrid cloud.
  • Open source: The core components of TDengine, including clustering, are open-source software. As of August 1, 2022, 135,900 instances were running worldwide, and the project on GitHub had received 18,700 stars and was forked 4,400 times.
  • Ease of use: TDengine has no external dependencies and can be installed and configured in seconds. It provides a REST API and connectors in various programming languages to seamlessly integrate with third-party tools. Its command-line interface can be used for ad hoc queries and management and includes various maintenance tools.
  • Easy data analytics: TDengine supports standard SQL with time-series-specific extensions. Supertables, compute-storage separation, data sharding and partitioning, pre-computation, and user-defined functions give TDengine an advantage in analyzing data.

With TDengine, you can significantly reduce your total cost of operation compared with other big data platforms for typical IoT, IIoT, and connected car scenarios. TDengine reduces costs in the following ways:

  1. Its high performance reduces the amount of computing and storage resources required.
  2. Its SQL support enables seamless integration with many existing tools and reduces the learning curve.
  3. Its integrated components form a simplified solution that reduces system complexity and development overhead.
  4. Its simple design is easy and cost-effective to maintain.

Key Technical Features

The key features of TDengine are described as follows:

  1. High write speed of structured data in standard SQL or schemaless data over the InfluxDB line protocol, OpenTSDB telnet protocol, and OpenTSDB JSON protocol
  2. Easy integration with data collection software, including Telegraf, Prometheus, StatsD, collectd, icinga2, TCollector, EMQ, and HiveMQ
  3. Aggregate queries, nested queries, downsampling, and interpolation
  4. User-defined functions
  5. Caching the latest entries in each table, eliminating the need to integrate with Redis
  6. Stream processing
  7. Data subscription with support for filtering conditions
  8. Clustering, enabling horizontal scaling through node addition and high reliability through data replication
  9. Command-line interface for automated monitoring of clusters and system status as well as ad hoc queries
  10. Self-monitoring with TDinsight
  11. Connectors for C, C++, Java, Python, Go, Rust, and Node.js
  12. REST API
  13. Seamless integration with data visualization tools such as Grafana and Google Data Studio


The role of TDengine in a time-series data platform is shown in the following figure.

The components on the left side of the figure are data collectors and message queues. These components are constantly writing data into TDengine. On the right side of the figure are visualization, business intelligence, configuration tools, and applications. The command-line and graphical user interfaces for TDengine are shown at the bottom of the figure.

For more technical information, see Architecture.

Join the TDengine Community

  • To download TDengine Community Edition 3.0 for free, see the download page.
  • To view the source code, visit our GitHub repository.
  • For information about TDengine Enterprise Edition or other business inquiries, contact us.
  • Jeff Tao
    Jeff Tao

    With over three decades of hands-on experience in software development, Jeff has had the privilege of spearheading numerous ventures and initiatives in the tech realm. His passion for open source, technology, and innovation has been the driving force behind his journey.

    As one of the core developers of TDengine, he is deeply committed to pushing the boundaries of time series data platforms. His mission is crystal clear: to architect a high performance, scalable solution in this space and make it accessible, valuable and affordable for everyone, from individual developers and startups to industry giants.