Traditional manual control methods in industrial settings often result in inefficiencies like continuous device operation, poor parameter settings, and redundant energy supply, leading to significant energy waste. While lean management can help, the real solution lies in adopting data-driven digital tools to maximize data value, cut costs, boost productivity, and enhance quality.
An industrial AI company has developed a cloud smart control platform to provide energy management and optimization in high-energy-use areas like air compression and cooling stations while enabling predictive maintenance through data insights. The platform’s modular design allows flexible deployment for specific needs or comprehensive coverage of energy types such as water, electricity, gas, and cooling. Currently, it supports over 1,600 industrial enterprises, handling around 100 GB of IoT data daily with a multi-tenant model.
According to the company’s R&D lead, the platform’s data storage must be decoupled from specific business scenarios and support dynamic field definitions. In auxiliary energy settings, the diverse range of equipment and brands generates over 100 GB of IoT data daily, posing significant challenges for data management and processing. The core requirements are threefold:
- Parameter codes for similar devices can vary, with some parameters being specific to certain models. The system must dynamically store and analyze these unique parameters while allowing for the seamless integration of new device types by adapting the data structure as needed. This ensures all device data can be fully captured and integrated into the existing system.
- With daily data volumes exceeding 100 GB, the system must deliver response times within 200 milliseconds, requiring exceptional real-time query performance and high availability.
- Clients need flexibility for both public and private cloud deployments. The database must support small-scale private setups as well as large-scale cloud clusters, ensuring consistent development and operational efficiency across different environments.
After testing databases like OpenTSDB, HBase, InfluxDB, and others, the company selected TDengine for its superior performance, stability, and data compression capabilities.
High-Concurrency Data Query Performance
“Each tenant generates and processes large volumes of business metrics, using both streaming and batch modes to write data to the time-series database. While basic calculations are handled within the database, more complex computations are performed in the business system’s memory. This demands high performance, efficiency, and stability from the database to handle complex data ingestion and querying requirements.”
The company tested the performance of TDengine 3.2.3.0 against InfluxDB OSS v.1.8 and v.2.7 on a single machine with an 8-core CPU and 32 GB of memory to evaluate the databases’ capabilities.
| Query Granularity | Time Range | TDengine | InfluxDB 2.7.6 | InfluxDB 1.8 | |||
|---|---|---|---|---|---|---|---|
| Query Details | 7 days | 1790 | 1728 | 106 | 90 | 137 | 145 |
| 3m Downsampling first | 7 days | 24 | 21 | 113 | 114 | 93 | 90 |
| 1h Downsampling first | 7 days | 5 | 4 | 12 | 14 | 25 | 30 |
| 1d Downsampling first | 7 days | 3 | 3 | 6 | 7 | 20 | 24 |
| 3m Downsampling first | 6 months | 589 | 608 | 3680 | 3183 | 1784 | 1903 |
| 1h Downsampling first | 6 months | 37 | 36 | 233 | 217 | 152 | 208 |
| 1d Downsampling first | 6 months | 13 | 14 | 84 | 84 | 100 | 97 |
| 3m Downsampling diff | 7 days | 23 | 19 | 124 | 115 | 92 | 89 |
| 1h Downsampling diff | 7 days | 3 | 3 | 12 | 14 | 29 | 28 |
| 1d Downsampling diff | 7 days | 3 | 3 | 10 | 6 | 23 | 27 |
| 3m Downsampling diff | 6 months | 405 | 407 | 3426 | 3229 | 1811 | 1869 |
| 1h Downsampling diff | 6 months | 32 | 32 | 255 | 219 | 203 | 185 |
| 1d Downsampling diff | 6 months | 11 | 11 | 85 | 64 | 100 | 98 |
Overall, TDengine outperformed InfluxDB OSS 1.8 and 2.7 in most aggregation queries, delivering 3 to 10 times faster performance and meeting business requirements. Compared to InfluxDB, it reduced response times for complex queries from seconds to milliseconds, significantly improving report performance and enhancing the user experience.
Cloud and Private Deployment Consistency
TDengine’s design supports both single-machine performance and cluster deployment, even in resource-constrained environments, enabling consistent architecture for both public and private cloud deployments.
Efficient Handling of Large Volumes of Metrics and Queries
In typical use cases at the company, IoT data undergoes multi-dimensional processing to generate a wide range of metrics tailored to different business needs. These include electricity and cost calculations, carbon and coal conversions, equipment runtime statistics, utilization analysis, single-device energy efficiency evaluations, air-compression energy ratios, central air-conditioning COP, energy consumption per product unit, productivity per monetary value, pressure and flow forecasts, and energy-saving rate calculations.
Some metrics are stored directly through stream-batch processing, while others require additional processing during queries. To handle these scenarios, the company developed custom built-in functions to address diverse business needs. These demands pushed the time-series database’s write and query efficiency to its limits, and after thorough validation, TDengine delivered excellent performance in both data ingestion and querying, fully meeting business requirements.
