IoT TSDB Comparison: InfluxDB vs. TDengine

Haojun Liao
Haojun Liao

This article compares two popular time-series database (TSDB) products – InfluxDB vs. TDengine – to help you determine which is right for your use case. With industries ranging from IoT to manufacturing generating and collecting a constantly increasing amount of time-series data, the growth of the time series DBMS market over the past five years has not come as a surprise. This popularity has resulted in a large number of time series DB solutions coming on the market, sometimes making it difficult to choose the best time series database for a certain business scenario.

InfluxDB is one of the earliest of the new generation of open-source time-series databases and as such is one of the most commonly found in existing deployments. Its large community and documentation resources are major benefits, but it can suffer from slow performance in some conditions, such as when data sets have high cardinality. TDengine is also an open-source time-series database that differentiates itself with high performance, a distributed cloud-native architecture, and built-in caching, data subscription, and stream processing that simplify the overall system design.

The following table compares the basic information of InfluxDB vs. TDengine.

Basic Info: InfluxDB vs. TDengine

Initial release20132017
Main development languageGoC
Main query languageFlux (proprietary)Standard SQL
Operating systemsLinux, macOS, and WindowsLinux, macOS, and Windows

Performance Comparison

Performance is one of the main factors by which time-series databases are compared. Higher performance means more than just faster operations – it also reduces the total cost of ownership for your time-series data operations as fewer hardware resources are required.

According to a performance comparison of InfluxDB vs. TDengine, conducted based on the open-source TSBS framework, TDengine significantly outperforms InfluxDB in all key areas – data ingestion rate, query response time, and disk space usage – while using fewer server-side resources.

As shown in the figures, TDengine provided superior ingestion performance in all scenarios, writing the TSBS data 1.8x to 16.1x faster than InfluxDB. In terms of disk space usage, TDengine and InfluxDB required approximately the same amount of disk space to store the TSBS data for the smaller scenarios. However, in the largest scenario of 10 million devices, InfluxDB occupied 2.8x more disk space.

TDengine also queried the TSBS data much faster than InfluxDB, both in simple and complex scenarios. In the two 4,000-device test scenarios shown above, TDengine responded between 6.5x and 132x faster than InfluxDB.


InfluxDB and TDengine both have rich feature sets and are great choices for processing time-series data. The best choice for your particular scenario may depend on your specific requirements, such as whether SQL support is necessary. TDengine also has significant advantages in terms of performance, giving it the edge in speed- or cost-sensitive use cases.

If you would like to know more about how TDengine can support your enterprise time-series data processing needs, contact us to speak with an account representative.

  • Haojun Liao
    Haojun Liao

    Haojun Liao is Co-Founder & Query Engine Architect at TDengine and is responsible for the development of query processing component of the product. He has a Ph.D. in Computer Applied Technology from the Institute of Computing Technology (Chinese Academy of Sciences) and is focusing on time series data/spatial data analysis and processing.