Introduction to TDengine

This page lists articles that provide an introduction to TDengine, an open-source, cloud-native time series database (TSDB) optimized for Internet of Things (IoT), connected cars, and Industrial IoT scenarios.

  • High-Performance Time-Series Database: TDengine is a high-performance time-series database featuring a novel storage engine that makes full use of the characteristics of time-series data. This engine significantly improves the speed of querying and ingesting data, as well as the compression ratio of the data it stores. With only a single core, TDengine can process over 20,000 requests, insert millions of data points, and read more than 10 million data points per second. It is at least 10 times faster than general databases, and its data compression ratio is at least 5 times higher.
  • Simplified Time-Series Data Solution: To simplify system design and reduce operation costs, TDengine integrates its time-series database with built-in caching, stream processing, and data subscription components that take full advantage of the characteristics of time-series data. This integrated design goes beyond efficient storage and analysis of time-series data to provide a comprehensive and simplified solution for time-series data processing.
  • Cloud-Native Time-Series Database: With a cloud-native time-series database, you can quickly spin up infrastructure to prototype, develop, test, and deliver new applications and features, shortening the time to market while reducing costs through flexible payment models such as pay-as-you-go. TDengine provides the five elements – scalability, elasticity, resiliency, observability, and automation – required of a true cloud-native time-series database. These characteristics are all built on a distributed system architecture designed for the cloud.
  • Open-Source Time-Series Database: TDengine is proud to offer the Community Edition as an open-source time-series database that provides exactly the same core functionality and performance of the Enterprise Edition. The TDengine project is very active, having over 12,000 pull requests and 69,000 commits on GitHub since going open-source in 2019, with half of those pull requests coming in the past year.
  • Time-Series Data Analytics Made Easy: Through supertables, storage and compute separation, data partitioning by time interval, precomputation, and other means, TDengine provides powerful and easy data analytics capabilities.
  • Easy Time-Series Data Platform: For data administrators, TDengine significantly reduces the effort to deploy and manage your data. For developers, TDengine provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, TDengine gives easy data access.
  • SQL Time-Series Database: The TDengine Team chose SQL as the query language since it is the most popular query language and is familiar to many programmers. Its ubiquity reduces the learning curve and reduces application migration costs. At the same time, TDengine has extended the standard SQL syntax to facilitate analysis and processing of time series data.
  • Reduce Database TCO by 50%: With its high performance, SQL support, integrated components, and flexible pricing options, TDengine can reduce your total cost of operations (TCO) for data processing by 50%.