{"id":14101,"date":"2024-07-03T15:05:00","date_gmt":"2024-07-03T22:05:00","guid":{"rendered":"https:\/\/tdengine.com\/?p=14101"},"modified":"2025-11-03T08:03:59","modified_gmt":"2025-11-03T16:03:59","slug":"iot-performance-comparison-influxdb-and-timescaledb-vs-tdengine","status":"publish","type":"post","link":"https:\/\/tdengine.com\/iot-performance-comparison-influxdb-and-timescaledb-vs-tdengine\/","title":{"rendered":"IoT Performance Comparison: InfluxDB and TimescaleDB vs. TDengine"},"content":{"rendered":"\n

The performance of your TSDB doesn\u2019t just impact your ability to ingest, store, and analyze large amounts of data; it directly affects your total cost of ownership<\/a> (TCO). Better ingestion rates, query response times and compression ratios mean your system consumes fewer resources to process the same amount of data. To demonstrate TDengine’s robust performance<\/a>, we evaluated the platform against two key players in this space – InfluxDB and TimescaleDB – in an IoT scenario.<\/p>\n\n\n\n

Performance Comparison<\/h2>\n\n\n\n

Ingestion Performance<\/h3>\n\n\n\n

Time series databases need to ingest massive amounts of data, and TDengine achieves the fastest ingestion speeds across all TSBS scenarios, ranging from 1.04 to 16 times the speed of the other products.<\/p>\n\n\n\n

\"\"<\/a>\n
TDengine ingests data significantly faster than TimescaleDB or InfluxDB<\/figcaption>\n<\/figure>\n\n\n\n

Resource Consumption<\/h3>\n\n\n\n

In addition, TDengine uses less processing power than InfluxDB or TimescaleDB<\/em> to ingest the datasets. At its peak, InfluxDB’s CPU usage even reaches 100% during the ingestion process, while TDengine remains under 17%. Although TimescaleDB used a similar amount of CPU resources to TDengine, it spent far more time to compress and order the data after writing it to the database.<\/p>\n\n\n\n

\"\"<\/a>\n
TDengine has low CPU usage, while InfluxDB demands significant processing resources<\/figcaption>\n<\/figure>\n\n\n\n

Query Performance<\/h3>\n\n\n\n

As performance can differ based on a number of factors, the TSBS framework covers a wide range of query types. TDengine provided the fastest query response across all scenarios<\/em>, confirming that organizations dependent on real-time analytics are best served with this purpose-built platform.<\/p>\n\n\n\n

\"\"<\/a>\n
For simpler queries, TDengine’s response time was 1.03 to 80.5 times faster than InfluxDB and TimescaleDB<\/figcaption>\n<\/figure>\n\n\n\n

More complex queries allowed TDengine to show off its processing power, reaching 87.1 times the performance of TimescaleDB in the long-daily-sessions<\/em> scenario and 132 times the performance of InfluxDB in the stationary-trucks<\/em> scenario. This demonstrates that TDengine is best prepared to handle the most performance-intensive queries without slowing down. <\/p>\n\n\n\n

\"\"<\/a>\n
TDengine processes queries significantly faster than InfluxDB or TimescaleDB, especially for complex scenarios<\/figcaption>\n<\/figure>\n\n\n\n

Disk Storage<\/h3>\n\n\n\n

In smaller-scale scenarios, all three database products took up a similar amount of disk space. When the datasets increased to one million or ten million devices, however, the benefits of TDengine’s storage design and architecture came into play; for large-scale datasets, TDengine uses less than half the storage resources that InfluxDB requires<\/em>.<\/p>\n\n\n\n

TimescaleDB had a significantly higher disk footprint in the two largest scenarios. The clearest example was in the ten million device scenario, where data processed by TimescaleDB occupied more than 12 times the disk space used by TDengine<\/em>.<\/p>\n\n\n\n

\"\"<\/a>\n
When processing data from 10 million devices, TDengine consumes 12 times less disk space than TimescaleDB and 2.8 times less than InfluxDB<\/figcaption>\n<\/figure>\n\n\n\n

Conclusion<\/h2>\n\n\n\n

Across all key test metrics for ingestion, compression, and querying, TDengine clearly emerges as the highest-performing time series database<\/em>.<\/p>\n\n\n\n