High-Performance, Scalable
Time-Series Database with SQL support
Enables efficient, real-time data ingestion, processing and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. TDengine can be widely applied to IoT, Industrial Internet, Connected Vehicles, DevOps, Energy , Finance and many other use-cases.
High-Performance
TDengine’s innovative design and purpose-built storage engine outperforms other time-series databases for data ingestion, querying and data compression while significantly reducing storage and computing costs.
Scalable
TDengine’s native distributed architecture powers out-of-the-box scalability and high availability. Nodes can be added through simple configuration to achieve greater data processing power. In addition, this feature is open source.
SQL support
TDengine uses SQL as the query language, thereby reducing learning and migration costs, while adding SQL extensions to handle time-series data better, and supporting convenient and flexible schemaless data ingestion.
Why developers choose TDengine?
High-Performance, Scalable, SQL support
Open Source
TDengine’s core modules, including cluster feature, are all available under open source licenses. It has 17.8k stars on GitHub and an active developer community.
All in One
With built-in caching, stream processing and data subscription functions, TDengine can work as a big data platform in many scenarios to reduce complexity and cost.
Zero Management
Installation and cluster setup can be done in seconds. TDengine’s running status can be monitored via Grafana or other DevOps tools.
Zero Learning Cost
With SQL as the query language, support for ubiquitous tools like Python, Java, C/C++, Go, Rust, Node.js connectors, there is zero learning cost.
Seamless Integration
Without a single line of code, TDengine provide seamless integration with third-party tools such as Telegraf, Grafana, EMQ X, Prometheus, StatsD, collectd, etc.
Interactive Console
TDengine provides convenient console access to the database to run ad hoc queries, maintain the database, or manage the cluster without any programming.
Use Scenarios
Internet of Things
smart hardware, connected equipment, smart homes, smart cities, smart water
Industrial Internet
smart manufacturing, process industry, production digitization, digital twin
DevOps
IDC, host, virtual machine, network device, container, micro service monitoring
Connected Vehicles
TBox data, battery management system, autonomous driving, fleet management
Energy
clean energy, power distribution, smart meters, energy management
Finance
stock, securities and crypto market data
Selected Blogs
- Intel® Edge Insights for Industrial supports TDengine
Intel Edge Insights for Industrial can now use TDengine as its time-series database management system, offering increased performance for industrial time-series analysis. - TDengine connector published on Google Data Studio
With the TDengine connector for Google Data Studio, you can leverage the reporting and visualization features of Data Studio for your data stored in TDengine. - Official Grafana plug-in for TDengine releasedWith the official TDengine plug-in, a joint effort of the Grafana and TDengine teams, you can now add TDengine as a data source for Grafana with a single command.
- TDengine and Tremor solution for system monitoring and alerting
See how you can create an effective alerting system for high-volume metrics by leveraging TDengine and Tremor. - 58.com’s Seasoned DBAs choose TDengine For A High-performance & Scalable IoT Platform
In the driving learning and testing business, it is necessary to store and analyze data generated by sensors in learning vehicles and correlate them to driving instructors. This is a typical time series data scenario. The developers at 58.com were … Read more - From 1 hour in MySQL to 10 s with TDengine in a Connected Industrial Vehicles Platform
iLink, is the industrial vehicle networking application of LiuGong which used MySQL as the database engine. But processing speed was extremely slow and the database was prone to downtime, due to unreasonably complex queries at the application layer and very … Read more
